SAP Community - Internet of Things

Stronger together: SAP Industry 4.0 Ecosystem Summit SEA Edition

2022-09-20T15:39:57+02:00

Industry 4.0 is more than a decade old. Companies are working on concepts or even already on projects to digitize their production process, optimize the supply chain and make processes in the factories intelligent. By creating concepts and working on projects it become clearer: Industry 4.0 needs a strong ecosystem to help making it real. SAP’s Industry 4.0 strategy hinges upon the power of its ecosystem that ranges from system Integration partners, industrial machinery manufacturers, network providers to the next generation technology providers.

SAP has built a strong ecosystem that helps joint customers to realize Industry 4.0 projects. Nurturing a vibrant ecosystem of diverse partners requires significant effort. To create an atmosphere to exchange, network and innovate, SAP started the Industry 4.0 Ecosystem Summits, a series of events where SAP and partners from the ecosystem inspire other partners by presenting joint projects and initiatives. It is an initiative to multiply the power of these relationships.

While the European and Japanese summits went virtual, South-East Asia (sometimes referred to as ASEAN) was to be home to the first in-person summits.

This is a region with tremendous potential! According to a McKinsey Study in 2022, the potential economic impact of Industry 4.0 in ASEAN is supposed to be between $ 200-600 billion annually by 2025. The top impacted areas are Manufacturing Operations, Predictive Maintenance, and Inventory Optimization. These areas also happen to be the top areas for SAP’s Industry 4.0 focussed solutions as well.
“Strength lies in differences, not in similarities.” these words by Stephen Covey clearly articulate the theme of the South East Asia Industry 4.0 Summits. Instead of choosing one theme or a single Industry 4.0 pillar, we decided to showcase the breadth of SAP’s Industry 4.0 Ecosystem.

Graham Conlon at the Singapore Edition

The South-East-Asian Industry 4.0 Ecosystem Summits on August 2nd and 4th were opened by keynotes from Graham Conlon, Vice President, Head of Digital Supply Chain APJ, and Ashish Pujari, Global Head of Industry 4.0 Ecosystem. They highlighted how the Industry 4.0 definition has broadened across the entire supply chain and how Industry 4.0 delivers value and in many instances makes the business case for an S/4HANA transformation. Hence, it is an essential part of the RISE conversations. Both also underlined why partners play here a vital role.

Ashish Pujari at the Bangkok Edition

Both events were supported by a set of divers partners from the region. HCL belongs to the Global System Integrators category that work with SAP from multiple angles. Within Industry 4.0, they are working with us on the Design & Manufacturing areas. Sachin Vasantha Prabhu, BTP Solutions Architect at HCL, presented a smart warehousing solution built on SAP’s Business Technology platform.
This presentation was followed by a talk from Krishnan Ramaswamy, Domain Consultant and SAP Digital Manufacturing - Presales Lead at Systema Asia about Systema's approach how to work with the market on Digital manufacturing and why SAP plays an important role for Systema. Systema is a globally acting System integrator and one of the most important partners within SAP's Digital Manufacturing Portfolio.
From the next generation category Jonathan Tan, Managing Director at UnaBiz, joined the summits. Unabiz calls themselves the first massive IoT Service provider. UnaBiz is part of the sap.io Program which supports startups with an SAP related focus to become successful in the market and Jonathan talked about many successful customer projects in Asia.
Last but not least, Teamviewer demonstrated how their integrated industry cloud solution supports blue collar workers to do their jobs efficient and handsfree supported by Augmented Reality. Tushar Joshi (presenter at the Singapore Edition), and Kanit Sarindu (presenter at the Bangkok Edition), Enterprise Account Managers at TeamViewer talked about TeamViewer’s strategy and vision accompanied by a great live demo with Govindu Kakinada, Enterprise Solutions Engineer.

At the end, Malachy Martin, Global Director – Center of Excellence Digital Supply Chain at SAP, joined an Expert Panel with all Partners to give insights how Industry 4.0 evolves with the support of different kinds of partners.

The overall feedback was very positive. The audience (75 registrants and around 50 attendees) liked the variety of presentations and different ways of partnering with SAP. Further highlights in the Singapore Edition were an “Industry 4.0-themed tour” of the SAP Singapore Innovation Centre and the award ceremony for Deloitte South-East Asia for delivering a regional project transforming field service management with SAP’s Industry 4.0 solutions. The networking rounds after the events were used for fruitful discussions between the partners and SAP colleagues.

Pavneet Bedi, Global Digital Supply Chain Partner Director, Partner Ecosystem Success, who is closely working together with strategic partners like HCL, organized and supported the events with his strong network by finding the right speakers and defining the right storyline for the events.

Please share feedback or thoughts in a comment.

For further information you can visit the website where you can also get insights about the virtual European and virtual Japanese Edition 2022. SAP | SAP Industry 4.Now Ecosystem Summit SEA

Automated Yard Processes using TinyML – 1 of 2

2022-09-28T16:08:45+02:00

Introduction (Business Case)

This blog is part one of a two-part series presenting the automation of the yard process and is a joint work together with 1chenhong . Part one will be on the project use case of this demo as well as how the different architectures tie in together.

Part two is reserved for the technical deep dive of the Machine Learning (ML) solution that is deployed in the microcontroller itself.

The topic was done in collaboration with Arduino's PRO business division who supported in providing the Arduino Portenta H7 microcontroller.

How is the current situation like?

A typical day in a yard involves the inflow and outflow of trucks carrying goods; managing this is a highly challenging affair as each truck would have different shipments and would be bound for different locations. For a typical yard, this could involve hundreds of trucks thus having a yard management software would greatly simplify this process. This is where SAP Yard Logistics comes into the picture.

At present, in-coming truck drivers are required to key in the license plate number when they arrive at the yard. The gate agent or yard manager then retrieves the tasks associated with it and informs the drivers - with constant communication between the agent and driver as each task is completed.

How will it improve the Business Case?

To further streamline this process, one way would be through an Automatic License Plate Recognition (ALPR) of the incoming truck. The machine learning solution that automates this process helps to reduce human error and streamline the gate-in processes. The detected license plate is passed to the Yard Logistics software which retrieves the task associated with the truck (through its license plate) & the driver is presented the tasks for the day.

At each stage, the driver checks-in at the respective locations which automatically triggers the completion of the task in Yard Logistics. The result is a seamless check-in experience without the need for manual intervention by the gate agent.

For a yard that handles multiple truck movements daily, this results in significant man-hours saved as the yard manager can focus on other tasks whilst at the same time still being kept updated on the progress as each task is completed. The driver too benefits from a streamlined check-in process that is about 6x faster.

This project is set-up with the aim of being an innovation showcase to be displayed in the Singapore Experience Centre.

Why Microcontrollers?

Presently, most Artificial Intelligence (AI) processing is done in the cloud, with most models being extremely resource-intensive even on the latest graphics cards. The increasingly interconnectedness for individuals as well as businesses have driven the demand for AI edge devices. AI edge devices shipments have been forecasted to hit 2.6 billion by 2025, up from 161 million in 2018 (Dipert B., 2018).

The growth in diversity of AI use cases have also driven up the demand such as in logistics or supply chain. Edge devices - such as microcontrollers while not as powerful as cloud solutions confer many benefits such as privacy, latency, bandwidth, & cost.

These are crucial as real-time inferencing is made possible without having the need to send the data to the cloud for processing which increases latency. The low cost of microcontrollers also enables the businesses to utilize them at multiple key areas - such as gates without having the need to procure costly laptops and associated set-ups such as other hardware.

While this project is on ALPR, intelligent cameras - such as having Computer Vision solutions deployed on microcontrollers with a camera - can also handle a myriad of other tasks such as tracking of vehicles or monitoring sensitive areas for possible intrusion.

Process Flow

ALPR - synonymous with Automatic Number Plate Recognition (ANPR) - is not a new technology and has been in existence since 1976 (History of ANPR). Some popular uses commercially would be in detecting traffic violations.

What sets this project apart from existing solutions would be in the deployment of the ALPR solution into a microcontroller – specifically the Arduino Portenta H7 + Vision Shield.

Whilst there exists a plethora of available ALPR technologies from Haar Cascades to more powerful object detection models such as Warped Planar Object Detection Network (WPOD-NET) – a powerful CNN that is able to detect license plates even if they are highly distorted due to oblique angles (Silva & Jung, 2018) – these models are tuned for high performance with a large network size thereby requiring high memory & computation power which makes their deployment unfeasible on microcontrollers which inherently have smaller power & memory.

That does not invalidate the possibility of ALPR model on the Arduino Portenta H7 however as it is one of the most powerful microcontrollers available having a dual core processor – a Cortex M7 at 480MHz and a Cortex M4 at 240MHz and is able to run amongst others, Micropython as well as Tensorflow Lite which is vital in this process and whose use is covered in depth in Part two.

Figure 1 shows the process flow:

Figure 1:Process Flow

The entry gate is monitored by the Arduino Portenta H7 and upon arrival of the truck, it detects the license plate & runs the Computer Vision algorithm

The license plate is shown as QR code on the screen at the gate

Driver scans this QR code with SAP AppGyver which retrieves the relevant yard tasks for the vehicle

On SAP AppGyver, the driver is shown an overview of all the tasks to be performed & proceeds to 1st destination

Upon arrival, task is executed & driver scans a static QR code that is placed at destination upon completion of task

This scanning auto completes the task in SAP Yard Logistics (no other action required from driver or yard manager)

Process repeats till all tasks are completed & driver then leaves yard

Solution Architecture

Figure 2:Solution Architecture

Figure 2 above depicts a high-level overview of how the different products interact with each other.

As the truck enters the yard, the Arduino Portenta H7 detects the license plate, pre-processes & runs its Machine Learning algorithm on the device itself.

This detected license plate is then passed to the backend server which generates a QR code of the license plate.

The driver is presented with this dynamic QR code on a screen and uses SAP AppGyver to scan the QR code. In doing so, SAP AppGyver retrieves the yard tasks assigned to the driver by communicating with the webservice (based on license plate).

The retrieved yard tasks are displayed in SAP AppGyver presenting the driver with an overview of the location to head to. As mentioned in the process flow, the driver then uses SAP AppGyver to check-in upon completion of each task which marks it as completed in SAP Yard Logistics.

At the end of all the assigned tasks, the driver is then able to simply check-out of the yard.

Technical dive on steps mentioned in Architecture Flow

The ML model comprises two parts - the digit recognition for the license plate as well as the dangerous goods classification. Given the technical deep dive for both these parts, they shall be covered in a separate blog post here

ABAP Webservice

Figure 3:3D View of Truck Unit in SAP Yard Logistics

The SAP Yard Logistics (YL) software allows yard managers to assign and monitor the yard tasks of a truck unit (TU), as seen in Figure 3 above. To verify whether an incoming truck is authorised to enter the yard, the yard manager needs to check in SAP YL if an inbound truck has an assigned yard order for that day. This step can be automated with webservices. For example, the Arduino Portenta H7 reads and detects the license plate number of an inbound truck and sends the license plate number to SAP YL and check if a yard order for the day has an associated license plate number. If yes, the truck is authorised to enter the yard and the driver receives instructions on further steps. Otherwise, the truck is deemed unauthorised.

The authentication of license plate numbers’ yard access through existing yard orders can be automated through web services. In addition, web services also automate other important processes such as data retrieval (to communicate instructions to driver) as well as yard task job status update.

In total, four web services were written using Advanced Business Application Programming (ABAP):

given license plate number, output yard order number

given yard order number, output list of yard task numbers

given yard task number, output destination within yard

given yard task number, update job status of yard task number when completed.

Eclipse IDE and SAP GUI were used to develop these web services.

For example, on the SAP GUI, search se37 for function modules, and search /SAPYL/* for related SAP YL function modules.

As an example, Figure 4 below shows the function module /SAPYL/BAPI_YO_GET_LIST which takes in the input parameters YARD_NO = ‘YARD’, DOC_CAT =’YO’, and YARD_ORDER_NO is the yard order number and outputs the associated yard task numbers.

Figure 4:Snippet of Function Module

Icon for component name in SAP GUI

To develop the function module querying logic, in the SAP GUI, in the Import and Tables tabs, highlight the associated type field of a parameter of interest, and click the icon (as shown above) in the top bar to get the component names associated in ABAP.

For example, for Import parameters, it reveals that the component names are /SAPYL/E_YARD_NO, /SAPYL/E_DOCUMENT_CATEGORY, and /SAPYL/E_YO_NO respectively for YARD_NO, DOC_CAT, and YARD_ORDER_NO as shown in Figure 5 below.

Figure 5: Component Names for Import Parameters

For table types, such as the output docflow table, highlight the associated type field of docflow and click on the icon. Next, in the top bar, highlight structure field, then click on icon again. Following which, deselect all choices and select Table Types, as shown in Figure 6 below. The table type name can be found. For this example, the output docflow name is /SAPYL/T_BAPI_YARD_ORDER_DFLOW.

Figure 6: Table type names

Once the function module querying logic has been understood, and the associated ABAP component names have been identified, the web services are constructed in the eclipse IDE. In eclipse IDE, for each web service, the data definitions, service definitions, service bindings, and classes are defined. As an example, a portion of the second web service (get yard task numbers) class definition is shown here in Figure 7 below.

Figure 7: Snippet of class definition for second web service call

From Figure 7, it is apparent that the component names found from the GUI were used. In addition, the function module querying logic follows from the GUI. This was how the web services were developed.

Backend Server

After the Arduino Portenta H7 has detected the license plate number, it checks with SAP Yard Logistics if the incoming truck is authorised to enter the yard. Using the developed web services, it matches the license plate number with the existing yard order numbers.

Suppose an associated yard order has been found and the incoming truck is authorised to enter. Further suppose that the associated yard task numbers and destination locations data have been retrieved through the web services. The goal now is to communicate these sets of instructions to the truck driver. Note that each incoming truck is unique and so the instructions for each truck driver are unique and different.

Therefore, it is important to generate a dynamic QR code that dynamically varies the sets of instructions given. To do so, the Arduino Portenta H7 first sends the detected license plate number to a backend server. Next, on-site at the check-in point, a computer retrieves the license plate number from the backend server and use the web services to generate the unique instructions. Given the relative ease of use in set-up and implementation, for this part of the Yard Logistics demo, the backend server chosen was Gmail.

The Micropython umail package (Shawwwn, 2018) which allows for communication between Arduino Portenta H7 and Gmail was downloaded and installed.

Next, a Gmail account was created and an App password (Broadley C., 2022) was set to allow the Arduino Portenta H7 to login into the Gmail account. This sets the backend server up.

When the Arduino Portenta H7 detects the license plate, it logs in to the Gmail account and sends the detected license plate number to said account.

Dynamic QR

On the gate-in on-site monitor display, the local computer constantly polls the backend Gmail account server. If an email message has been received recently, it deems that a truck unit is inbound and wants to receive its unique instructions via QR code. If no such recent message has been received, the monitor shows a blank screen.

It is easy to implement recency check by comparing the Arduino Portenta H7 email send date time and the local computer date time. For example, if the time & dates are standardized – taking into time differences – the check is executed. If the time, in terms of seconds are within 3 minutes or 180 seconds of each other, the received email is deemed to have been recent; this triggers the retrieval of the email containing the license plate number and a QR code is generated from this.

The subsequent steps for showing and confirming the yard tasks are handled by SAP AppGyver and are elaborated on in the subsequent section.

Figure 8 shows how the on-site monitor screen would look like if an authorized (i.e., expected) license plate number was found.

Figure 8: Actual view of the generated dynamic QR

For the on-site monitor to constantly poll the account, a turboflask package was used. The guide written by Grinberg (2021) has been invaluable for the development of the dynamic flask server. With some simple tweaks to the html code and some Python scripting, a local image was changed to blank image if no recent license plate number was detected and changed to the generated QR code if a recent license plate number was detected. The html script then calls the changed-and-stored image from a folder and auto-refreshes. In this way, the on-site monitor was able to dynamically generate the dynamic QR code for unique driver instructions.

During this process, it was discovered that the Arudino Portenta H7’s clock time zone is by default PDT -7, while Singapore’s local time is GMT+8, a time difference of 15 hours. A Python code to standardise the time difference was written and implemented as follows.

# next, we compute the email message time against current time

    current_year=int(str(dt.datetime.now())[0:4])



    leap_years=[2020,2024,2028,2032,2036,2040,2044,2048,2052,2056,2060,2064,2068,2072]



    #dictionary that tells the maximum days in a month as value1, and which is the 'next month' as value2 in tuple (v1,v2)

    if current_year in leap_years:

        month_dic ={'Jan':(31, 'Feb') , 'Feb':(29,'Mar'),'Mar':(31,'Apr'), 'Apr':(30,'May'), 'May':(31,'Jun'), 'Jun':(30,'Jul')

                ,'Jul':(31,'Aug'), 'Aug':(31,'Sep'),'Sep':(30,'Oct'),'Oct':(31,'Nov'),'Nov':(30,'Dec'),'Dec':(31,'Jan')}

    else:

        month_dic ={'Jan':(31, 'Feb') , 'Feb':(28,'Mar'),'Mar':(31,'Apr'), 'Apr':(30,'May'), 'May':(31,'Jun'), 'Jun':(30,'Jul')

                ,'Jul':(31,'Aug'), 'Aug':(31,'Sep'),'Sep':(30,'Oct'),'Oct':(31,'Nov'),'Nov':(30,'Dec'),'Dec':(31,'Jan')}

    LOCAL_TIMEZONE = dt.datetime.now(dt.timezone.utc).astimezone()

    temp=str(int(datetime[4][0:2])+int(str(LOCAL_TIMEZONE)[-6:][0:3])- int(datetime[5][0:3]))



    if len(temp)==1:

        temp='0'+temp

    #we have generalized the timezones issue. it should work in all countries now. there is no hardcoding. it even updates the minutes



    temp_min=str(int(datetime[4][3:5])+ int(str(LOCAL_TIMEZONE)[-6:][0]+str(LOCAL_TIMEZONE)[-6:][4:6] ) - int(datetime[5][0]+datetime[5][3:5]))

    if len(temp_min)==1:

        temp_min='0'+temp_min

        

    if int(temp_min)>60:

        temp_min=str(int(temp_min)-60)

        temp=str(int(temp)+1)

        if len(temp_min)==1:

            temp_min='0'+temp_min

        

        

    if int(temp)>24:

        temp = str(int(temp)-24)

        if len(temp)==1:

            temp='0'+temp

        

        month_days_lim = int(month_dic[datetime[2]][0])

        

        temp_day = int(datetime[1])+1

        if temp_day > month_days_lim:

            new_day = temp_day-month_days_lim

            new_month = month_dic[datetime[2]][1]

            

            datetime[1]=str(new_day)

            datetime[2]=new_month

            datetime[4]=temp+':'+temp_min+datetime[4][5:] #update the time here, including minutes

            if new_month =='Jan': #need increment year by 1

                new_year = str(int(datetime[3])+1)

                datetime[3]=new_year

        else: 

            datetime[1]=str(temp_day)

            datetime[4]=temp+':'+temp_min+datetime[4][5:] #update the time here, including minutes

    else:

        datetime[4]=temp+':'+temp_min+datetime[4][5:] #update the time here, including minutes



    email_year = datetime[3]

    email_month = datetime[2]

    email_day = datetime[1]

    email_time= datetime[4]

    month_conversion={'01':'Jan','02':'Feb','03':'Mar','04':'Apr','05':'May','06':'Jun','07':'Jul','08':'Aug','09':'Sep','10':'Oct','11':'Nov','12':'Dec'}

    now = dt1.now()

    now_year=str(now)[0:4]

    now_month = month_conversion[str(now)[5:7]]

    now_day=str(now)[8:10]

    now_time=str(now)[11:19] 

    #*********************************************************************

    # finally, we do the comparison and output dynamic QR code if email message was recent



    if now_year == email_year and now_month == email_month and now_day == email_day:

        #check for time are they within 3 minutes

        #convert hours to minutes, minutes to seconds, perform the deduction if within tolerance range, accept as recent 

        email_time_in_secs = int(email_time[0:2])*60*60+int(email_time[3:5])*60+int(email_time[6:8])

        now_time_in_secs = int(now_time[0:2])*60*60+int(now_time[3:5])*60+int(now_time[6:8])

        time_difference = abs(email_time_in_secs - now_time_in_secs)

        if time_difference<=180: #if the messages are wthin 3 minutes tolerance, accept as recent message and display QR code

            print('return boolean true and generate detected lp number on qr code save in local folder')

            generate_qr_code(detected_LP)



            #open checkinqr code and upload it as master image for flask html to read

            current_directory = os.getcwd()

            img_path = current_directory+'\\image_resource\\'.replace('\\', '/')

            img = Image.open(img_path.replace('\\', '/')+'checkin-qrcode.png')

            

            img_path_save = current_directory+'\\static\\'.replace('\\', '/') 

            img.save(img_path_save+'master_image.png')



            #open checkinqr code and upload it as master image for flask html to read

            #current_directory = os.getcwd()

            img_path = current_directory+'\\image_resource\\'.replace('\\', '/')

            img = Image.open(img_path.replace('\\', '/')+str(detected_LP)+'.jpg')

            

            img_path_save = current_directory+'\\static\\'.replace('\\', '/') 

            img.save(img_path_save+'detected_LP_image.png')

            

        else: #if same day but not within 3 minutes then is no

            print('return no and blank')



            #did not receive detected_LP, just open blank png and upload as master image for flask html to read

            current_directory = os.getcwd()

            img_path = current_directory+'\\image_resource\\'.replace('\\', '/')



            img = Image.open(img_path.replace('\\', '/')+'blank.png')

            img_path_save = current_directory+'\\static\\'.replace('\\', '/') 

            img.save(img_path_save+'master_image.png')

            img.save(img_path_save+'detected_LP_image.png')

    else:

        #if not same day, return no

        print('return no and blank')



        #did not receive detected_LP, just open blank png and upload as master image for flask html to read

        current_directory = os.getcwd()

        img_path = current_directory+'\\image_resource\\'.replace('\\', '/')



        img = Image.open(img_path.replace('\\', '/')+'blank.png')

        img_path_save = current_directory+'\\static\\'.replace('\\', '/') 

        img.save(img_path_save+'master_image.png')

        img.save(img_path_save+'detected_LP_image.png')

SAP AppGyver

Why SAP AppGyver?

Whilst the web services allow for interaction without navigating the SAP Yard Logistics page itself, there is a need for the driver to interact with the webservice to retrieve as well as execute the assigned tasks.

SAP AppGyver bridges this gap between the driver and the SAP Yard Logistics software. By presenting the task in an easy-to-read page, the driver is able to get all the information required & execute the tasks accordingly with SAP AppGyver handling the calls to the relevant web service.

Set-up in SAP AppGyver

The app itself is set to all be on one page so that the user is able to see all the required information without having to navigate pages. To prevent cluttering and overloading of information, the components were set to appear/disappear accordingly when the task is done.

This is done by toggling the app variables; animate component was also installed from the marketplace to enhance the app flow

A snippet of this section is shown in Figure 9 below:

Figure 9: SAP AppGyver Animate Component Logic

Sequence of Flow

The following screenshots depicts the flow for the driver check-in to the yard:

Once the license plate is detected & the QR code is displayed on the screen for the driver, the following actions are performed.

Initial screen that is presented upon start-up of the App

Figure 10: Start-up screen

Upon scanning QR code of license plate at the gate, the Driver is presented with an overview of tasks for the day

Figure 11: Screen seen upon successful scan of dynamic QR

Driver heads to DOOR-1001 & task is executed. Upon scanning QR code that is stationed there, the driver is prompted with a confirmation screen

Figure 12: Confirmation prompt to confirm yard task

Upon clicking ‘confirm’, the task is completed and the button to scan for the next task at PARK-0006 is shown. In SAP Yard Logistics, the yard task associated with DOOR-1001 is automatically confirmed with no input needed from Yard Manager.

Figure 13: Successful completion of yard task

Process repeats for PARK-0006 and once all tasks are done, Driver is then able to leave the yard

Video of End-to-End demo

The video below incorporates all the information above and demonstrates how the toy truck, upon arrival has its license plate detected and with the usage of SAP AppGyver, the driver is able to go about completing the tasks at the locations specified. Each completion is automatically confirmed in SAP Yard Logistics.

In essence, the video shows the toy truck arriving and the license plate detection algorithm being run. After which, the dynamic QR is displayed and the video transitions between SAP AppGyver as well as the auto-confirmation happening on SAP Yard Logistics.

As there is no sound in the video, there is text inserted at various points.

Conclusion

To summarize, we have created machine learning models which are converted to TFLite and deployed in the Arduino Portenta H7. The ML solution detects & sends this information to a server which generates a QR code for the driver to scan.

Through the use of SAP AppGyver, the driver is able to scan the QR code and retrieve the lists of tasks with the check-in & confirmation of the relevant tasks handled by SAP AppGyver.

By integrating the Arduino Portenta H7, SAP AppGyver, & SAP Yard Logistics together, the gate-in process is further streamlined with the driver having all the information on hand. The Yard Manager too is able to have a real-time update of the process without the need for manual intervention.

The automation by integration of these IoT devices with existing processes also results in faster check-in times of up to 6x. Moreover, given the low-cost of the microcontrollers, this solution is easily scalable, deployable, and redeployable.

If you have any questions or would like to know more about the process, feel free to write in to us for more details. Do feel free to provide feedback in the comments.

References

Broadley, C. (2022, June 16). Gmail is disabling less secure apps: What to do next. WP Mail SMTP. Retrieved September 22, 2022, from https://wpmailsmtp.com/gmail-less-secure-apps/

Diaz, A. M. (n.d.). Transport of Dangerous Goods by road - UNECE. ECA-ECE-ICAP Workshop: UN Road Safety Conventions and Approaches to Preventing Drink Drivin. Retrieved September 21, 2022, from https://unece.org/fileadmin/DAM/trans/doc/2014/wp1/ECE-TRANS-PRESENTATION-2014-1e.pdf

Dipert, B. (2018, September 24). Artificial Intelligence Edge device shipments to reach 2.6 billion units annually by 2025. Edge AI and Vision Alliance. Retrieved August 20, 2022, from https://www.edge-ai-vision.com/2018/09/artificial-intelligence-edge-device-shipments-to-reach-2-6-billion-units-annually-by-2025/

Grinberg, M. (2021, May 17). Dynamically update your flask web pages using turbo-Flask. miguelgrinberg.com. Retrieved September 22, 2022, from https://blog.miguelgrinberg.com/post/dynamically-update-your-flask-web-pages-using-turbo-flask

History of ANPR. ANPR International. (n.d.). Retrieved September 19, 2022, from http://www.anpr-international.com/history-of-anpr/

Shawwwn. (2018, September 18). Shawwwn/uMail: A Lightweight, scalable SMTP client for sending email in MicroPython. GitHub. Retrieved September 22, 2022, from https://github.com/shawwwn/uMail

Credits

gunternissan , vriddhishetty , lsm1401 : For all your invaluable help & advice throughout this entire project. Especially when we were unsure of how to proceed for the subsequent steps

declanlee & minpyemoe_sap : For your guidance in rapidly helping bring me up to speed with SAP AppGyver as well as readily answering the questions I had

yu.ning.wang : For ever so willingly helping with the design aspect for SAP AppGyver. The current iteration would not have been possible without your input & assistance

Automated Yard Processes using TinyML - 2 of 2

2022-09-29T08:48:49+02:00

Introduction

This is part 2 of the blog on automation of the yard process. In part one, we have seen how the different architectures tie together.

In this part, it will be a more in-depth look at the Machine Learning model specifically the digit recognition that is deployed in the microcontroller.

Machine Learning Model

One key aspect of ML models in microcontrollers would be the need to convert to TensorFlow Lite (TFLite). During this process, additional optimization such as quantization can be performed to reduce model size and latency.

License Plate Detection

Given that speed & accuracy are vital in the detection - since we do not want the driver to wait too long for detection which would negate the benefits - the search space for the machine learning algorithm is narrowed.

As this is part of an innovation showcase to be put up in the Singapore Experience Centre, Singapore license plates were used. The license plates pasted on the toy trucks were made to replicate the real-world scenario. As such, the typeface used was Charles Wright and the format of the license plates follow that of actual Prime Movers - Class 5 vehicles which transport goods.

Firstly, the truck license plate is detected with Micropython’s find_rect function. Thereafter, individual characters are extracted from the cropped license plate using the find_blobs function. Optical Character Recognition (OCR) TinyML models were trained in EdgeImpulse development platform, where the individual characters are classified with the machine learning algorithms. In EdgeImpulse platform, the image training and testing performance accuracies were high.

However, in practice, when deployed, it was found that the OCR TinyML models failed to recognise the license plate numbers despite having high model performance in training and testing. The disparity between deployed and train/test performance might be attributed to the following.

For example, the detected image in deployment differs from the train/test images in terms of lighting, angle, and even noise. Frequently, an extracted character was a number but an alphabet was detected or vice versa with a 1-model approach, where the TinyML model tries to predict 36 classes per character (10 numbers and 26 alphabets). Therefore, to assist the deployed models to correctly recognize the license plate numbers, it is necessary to exploit domain knowledge of the license plate number: Where should a number be expected, and where should an alphabet be expected? Consequently, a 2-models approach was developed, where a number recognition TinyML model is run in character slots where numbers are expected, and an alphabet recognition TinyML model is run in character slots where alphabets are expected, thus reducing the search space and the probability of false detection.

The corresponding license plate is of the form ‘xx ####y’ where ‘x’ and ‘y’ denote alphabets and # denotes numbers. From this domain knowledge, the first alphabet is always ‘X’ and the second is either ‘D’ or ‘E’ - current iteration is E but there exists vehicles with the previous iteration ‘D’ hence both are included. #### simply denote the numbers from ‘0000’ to ‘9999’ with leading zeroes accepted. ‘y’ is a checksum which follows an algorithm that is available online. For brevity, this algorithm will not be mentioned here. But having the checksum allows for the final character to be identified based on the checksum algorithm without the need for a detection.

Since the first character is always ‘X’, and the last character ‘y’ is a checksum, it is not necessary to run the machine learning models on all 7 characters. Instead, predictions on the 5 characters are sufficient. Further, since the second character is always ‘D’ or ‘E’ due to domain knowledge rules, it is deemed unnecessary to train a 26-character A-Z OCR character recognition model. Instead, a binary class D-E OCR character recognition TinyML model suffices.

Taken together, when the rectangle shape of the license plate is detected and each character is extracted, two machine learning models are run to read the license plate numbers. First, a D-E prediction on the second character. Secondly, in the third, fourth, fifth-, and sixth-character slots, numbers recognition predictions are performed. For each character slot, TinyML outputs the probabilities of class predictions. For each character slot in the numbers character, the top 3 class predictions are output (for the alphabet prediction, the deployed model works well and only the top 1 class prediction is chosen).

Finally, all possible combinations from the candidate class predictions are assembled. If any of the candidate solution matches the pre-approved master list of license plate number, that candidate solution is deemed to be the detected license plate number and an authorised license plate number is deemed to have been found. The figure below shows this.

Figure 1: Snippet showing solution candidates identified for license plate XD3386L

In Figure 1 above, the 3 rows depict the 3 independent machine learning inferences performed on the license plate number ‘XD3386L’. In each of the number character slot, the 3 numbers show the top 3 class prediction for the character slot. For example, [‘1’, ‘3’, ‘6’] indicates that the TinyML algorithm predictions that the number to be a character 1, 3, or 6. In this example, the third inference correctly recognizes the license plate number since one of the candidate solutions matches the license plate number.

Dangerous Goods

Note: Dangerous Goods code is installed but it is not used in the demo set-up presently with the aim of using it for subsequent processes as the demo gets refined.

Given the nature of Dangerous Goods, there are standardized regulations set forth by the United Nations (UN) to ensure harmonization and which its signatories adhere to. One instance would be affixing the labels clearly on the cargo transport units.

A sample of the possible hazard labels from United Nations Economic Commission for Europe (UNECE) are shown herein Figure 2:

Figure 2: Hazard label samples from UNECE

For the purposes of the demo showcase, 3 classes were chosen to be pasted on the truck. An additional 4th class - No Dangerous Goods - will also be present and simply indicates that the truck is not carrying any such goods.

Figure 3: Chosen hazard labels for Dangerous Goods Classification

Data Collection

These 3 classes were then printed out and pasted to the side of the toy truck. 1500 photos of each class - 3 + 1 background class - were captured directly using the Arduino Portenta H7 + Vision Shield camera component and OpenMV IDE. Figure 4 below shows the actual image as captured by the Arduino Portenta H7.

Figure 4: Actual photo as taken from Arduino Portenta H7 + Vision Shield

Model Selection & Training

Google Colab Pro was used as it had a decent GPU for training. The photos were placed into a train folder on drive & an 80-20 train-validation split was done. The method of splitting was via split-folders

!pip install split-folders

Setting directories for Train-Val Split

The images are collated in train directory with each image being in a sub-folder

Figure 5: Each class split into its own sub-folder

An empty output folder is created into which the 80-20 train-val split will be placed

import splitfolders 

train_dir = r'/content/drive/MyDrive/Colab Notebooks/Dangerous goods/Train'

output_dir = r'/content/drive/MyDrive/Colab Notebooks/Dangerous goods/Output'

splitfolders.ratio(train_dir, output=output_dir, seed=5126, ratio=(.8, .2),group_prefix=None, move=False)

 

##re-instantiate new dir for the actual training-test-split 

new_train = r'/content/drive/MyDrive/Colab Notebooks/Dangerous goods/Output/train'

new_val = r'/content/drive/MyDrive/Colab Notebooks/Dangerous goods/Output/val'



train_data_gen = tf.keras.preprocessing.image_dataset_from_directory(new_train, label_mode='categorical', image_size=(IMG_WIDTH,IMG_HEIGHT), batch_size=batch_size, color_mode='grayscale')

val_data_gen = tf.keras.preprocessing.image_dataset_from_directory(new_val, label_mode='categorical', image_size=(IMG_WIDTH,IMG_HEIGHT), batch_size=batch_size, color_mode='grayscale')

Model selection

All images were resized to 96x96 to ensure consistency with the Digit Recognition Model which was trained on Edge Impulse. Since the images are already grayscale, and there was not much complexity with the classes being distinguished by the lines and/or words, a simple model from scratch was written

IMG_WIDTH =  96

IMG_HEIGHT = 96

batch_size = 64





model = Sequential()



# convolutional layer

model.add(Conv2D(32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', input_shape=(IMG_WIDTH,IMG_HEIGHT,1),kernel_constraint=tf.keras.constraints.MaxNorm(1))) # padding='same' so there's no dimensionality reduction especially since our data isn't feature-rich

model.add(MaxPool2D(pool_size=(2,2),strides=2, padding='same')) 



#2nd layer 

model.add(Conv2D(32, kernel_size=(3,3), activation='relu', padding='same',kernel_regularizer='l1'))

model.add(MaxPool2D(pool_size=(2,2),strides=2, padding='same'))



#3rd layer

model.add(Conv2D(16, kernel_size=(3,3), activation='relu', padding='same'))

model.add(MaxPool2D(pool_size=(2,2), strides=2, padding='valid'))

 

#flatten output of conv

model.add(Flatten())



# FCNN layer

model.add(Dense(16, activation='relu'))



#add dropout 

model.add(Dropout(0.3))



# Output layer

model.add(Dense(4,activation='softmax'))

Compiling Model

The following metrics - Categorical Accuracy, Matthews Correlation Coefficient (MCC), Receiver Operating Characteristic Area Under Curve (ROC-AUC), & Precision - were measured with the key metric being categorical accuracy

from tensorflow.keras.optimizers import SGD, Adam

 

opt = Adam(learning_rate=0.0003)



#Compile the model

model.compile(optimizer=opt,

              loss='categorical_crossentropy',

              metrics=[tf.keras.metrics.CategoricalAccuracy(),                       

                       tfa.metrics.MatthewsCorrelationCoefficient(num_classes=4,name='mcc'),

                       tf.keras.metrics.AUC(name='AUC'),

                       tf.keras.metrics.Precision(name='Precision')

                       ])

The model summary is as shown

Figure 6: Model summary

Through the various iterations, it was discovered that not having too many parameters was vital to minimize overfitting. A smaller number would also result in a smaller file size when converted to TF Lite.

Training model

Earlystopping, ReduceLROnPlateau, & ModelCheckpoint callbacks were used. The latter being essential to get the best performing model for conversion.

earlystopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', 

                                                 patience = 15,

                                                 mode = 'min',                                                  restore_best_weights = True,

                                                 verbose = 1)

 

reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor = 'val_loss',

                                                 factor = 0.5,

                                                 patience = 2,

                                                 verbose = 1,

                                                 min_delta = 1e-5,

                                                 mode = 'min')

 

checkpoint_filepath = '/content/drive/MyDrive/Colab Notebooks/Dangerous goods/DG_4classes_96x96_220808__{epoch}.h5' 

checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(

        checkpoint_filepath,

        monitor="val_loss",

        save_best_only=True,

    )



#earlystopping,

callbacks = [ reduce_lr, earlystopping, checkpoint_callback]



##Fitting the model

EPOCHS =150

history = model.fit(train_data_gen,

                    validation_data=val_data_gen,

                    verbose=2,

                    callbacks=callbacks,

                    epochs=EPOCHS)

The best performing model - epoch 93 - ended up with a validation accuracy of 99.92% with a corresponding validation loss of 4.3%

TFLite

The model was then full integer quantized to TFLite for eventual deployment on the microcontroller.

Representative dataset

For full-integer quantization, a representative dataset is required for calibration to be performed. Hence the same code for instantiating training was used with the main difference being a batch size of 1.

train_ds_for_conversion = tf.keras.preprocessing.image_dataset_from_directory(new_train, label_mode='categorical', image_size=(IMG_WIDTH,IMG_HEIGHT), batch_size=1, color_mode='grayscale')

 

def represent_data_gen():

  for image_batch, labels_batch in train_ds_for_conversion:

    yield [image_batch]

Convert to TFLite

#convert the best model to tflite

keras_model = tf.keras.models.load_model('/content/drive/MyDrive/Colab Notebooks/Dangerous goods/DG_4classes_96x96_220808__93.h5')



#Convert to TFLite 

converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)

converter.optimizations = [tf.lite.Optimize.DEFAULT]

converter.experimental_new_converter=True

converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]

converter.representative_dataset = represent_data_gen 



#representative_dataset  

converter.target_spec.supported_types = [tf.int8]

converter.inference_input_type = tf.int8  

converter.inference_output_type = tf.int8  

 

tfmodel = converter.convert()

A simple labels .txt file was manually created with the 4 classes being in order in which they appeared (& were trained) in the code - Class1,Class3,Class7,NoDG. Both files were then placed into the microcontroller, with the quantized TFLite file coming in at 57kB.

The code and the label file were called together through a template made accessible in OpenMV IDE for deploying Edge Impulse trained models. Simple changes in the filenames were all that were required since coding it from scratch using Tensorflow produced same output model file as with using Edge Impulse

Conclusion

In this blog, we have seen how the machine learning model was constructed. By narrowing the search space from domain knowledge of Singapore license plates, we are able to optimise the detection and rapidly increase the detection time.

In addition, the initial 1-model approach performed well in training/testing but did not perform as well in actual deployment. The subsequent 2-model approach – one for numbers and the other for alphabets – performed much better with a decrease in false positives.

We have also seen how with aid of open-source platforms like Edge Impulse, training a quantized model is made more accessible especially if transfer learning is involved. Coding from scratch and quantization is also made accessible through Tensorflow Lite, with the Dangerous Goods model having performed well in addition to coming in at just 57kB.

As popularity in edge AI develops, increased support for quantization will allow for even more powerful models to be deployed in future.

We hope you have enjoyed this series of blog posts. If you have any questions or would like to know more about the process, feel free to write in to us for more details. Do feel free to provide feedback in the comments.

The Next Revolution: Industry 4.0 in the Intelligent Enterprise

2022-10-24T10:04:18+02:00

Industry 4.0 is no longer just about data and analytics, but about connected and automated production. Henry Ford’s mass-manufacturing concepts are on the way out because flexible and highly automated manufacturing are no longer mutually exclusive – no matter whether you manufacture a customer-specific or standardized product.

Ten years ago, the fourth industrial revolution began. The first machines in factories were equipped with sensors and connected to digital dashboards. Data has become new gold: they promised to use analytics to revolutionize the world. Are we still at the beginning of this revolution or at the end?

Today, smart sensors and embedded computing can be found everywhere: in trucks, ships, manufacturing, or warehouses. They not only generate and send information that provides valuable insights into the status and condition of machines and assets but also enable them to control the machine in real-time. What value can the business gain from this?

Let’s look at a number of deployment scenarios for Industry 4.0 in production.

AGILITY FROM CONFIGURABILITY

Which companies benefit from being able to automatically control the entire supply chain through machines and sensors?

“Auto-control” management means saving effort in manual processes along the entire supply chain and realizing the full potential of intelligent machines and sensors.

Businesses in Europe in particular are creating opportunities here – their strength is traditionally more in customer-centric manufacturing, rather than mass production.

However, standard products also benefit from flexibility. The global crisis of supply and logistics poses challenges for every manufacturer.

Only those who dynamically parameterize production to deploy alternative materials and processes at the push of a button will win the global race for capacity and resources.

Innovative manufacturers of configurable products now offer so many variants that it is no longer possible to map them using classic routings and master data concepts. They also benefit from the “auto-control” of production.

But is the configuration of a bill of materials in product development the same as that of a production line? No. The configuration of production maps the flexibility of the machines, materials, and operators of each production line – it is the digital twin of the factory.

Specifically, it includes all possible configurations of operator guidance, machine parameters, work programs, and routings. Henry Ford chose to sacrifice exactly this flexibility for the sake of efficiency and limited himself to producing black cars. This choice poses a dilemma for many companies. Today, it concerns the maintenance of master data for production control and is amplified by the increasing complexity of the machines which is perceived as a challenge on the IT side rather than an opportunity. In this context, a production configuration offers immense advantages.

Figure 1: Agility through “remote” driven production

FACTORIES OPTIMIZING THEMSELVES

Data can only be the gold of the digital age if it is used to automatically optimize production.

We already find this intelligence in individual machine control, which means that the machine optimizes itself during the production process. This individual optimization of each machine alone cannot exploit the full added value of Industry 4.0.

Only when planning reacts dynamically to real-time data such as product confirmations and the factory is no longer controlled by theoretical values, can the factory reach its full potential.

The same applies to maintenance and product development. Every line of business can be integrated into dynamic feedback loops with manufacturing. As a result, the efficiency of the business increases.

Figure 2: Self-Optimization of Production Planning

CONNECTED MANUFACTURING

Do I just use intelligent sensor systems to fill the kanban and control AGVs, or do I benefit from flexible, automated, and, above all, holistic material flow control in production?

• Manufacturing space is valuable and should not be wasted on storage of materials.

• Flexibility in supplying production lines with material is a prerequisite for agile use of production lines.

• Full transparency of material stocks in production increases planning flexibility.

These are three good reasons to prioritize material flow control and implement Industry 4.0 for end-to-end processes.

Figure 3: The management of material flows varies from company to company. Intralogistics is a priority everywhere.

SAP POP-UP-FACTORY: A PLACE TO GET INSPIRED BY INDUSTRY 4.0

Automation and mechanical engineers, and manufacturers of sensor system technology have had to prove themselves in the Industry 4.0 space over the last decade. Now there are vendors like SAP who continue to drive automation and turn such concepts into reality.

Experience the value of Industry 4.0 in an intelligent enterprise – industry-specific, tangible, and scalable – from small quick wins to big transformations – at the SAP Pop-up Factory in Walldorf.

Have we made you curious? We are only at the beginning of a move towards large-scale automation of end-to-end processes.

Become an SAP intelligent enterprise and join the revolution!

Dear reader, now, you may wonder why I personally care about the industrial revolution. I grew up right next to the Görlitz Rolling Stock Corporation in a family-owned metal workshop and I fell in love with the smell and look of machines. I got a physics degree and then worked in different positions in the automation, electronics and semiconductor industry for 15 years. When I realized that software is key to making factories even more successful, I moved from hardware to software industry. And ever since I really enjoy working with businesses to find new solutions to their challenges.

Why the Metaverse is a Gutenberg Moment for Business

2022-11-07T16:33:05+01:00

Much like Johannes Gutenberg revolutionized printing to bring the first wave of self-education to the masses that helped spark the Renaissance, the metaverse promises to seriously transform business models and the lives of the people behind them. Gartner researchers reported executives have spent over $120 billion in metaverse-related ventures in the first half of 2022 alone. They expected 30% of organizations in the world will have products and services ready for the metaverse by 2026. To be clear, the metaverse is pure vision, predicated on the emergence of what many call Web 3.0, the next iteration of the internet where networks of interconnected real-world, augmented, and virtual experiences fuel a creator economy.

“In the metaverse, people will own everything they create, collaborating across a decentralized universe of experts,” said Martin Wezowski, chief futurist at SAP New Ventures and Technology. “The metaverse is a new platform for value creation from human relationships and community-building. Imagine being on a business call where natural language processing catalogues the major discussion points, and blockchain technology tokenizes them for ownership by the speakers, creating immutable proof of each person’s expertise and value. Business leaders need to rethink strategies and operations to stay relevant in the creator economy.”

Definition of metaverse for business

On a recent episode of the Digital Supply Chain podcast, IDC analyst Jan Burian said that his research firm defined the metaverse as “a highly immersive future environment that blends the physical and digital to drive shared sense of presence, interaction, and continuity across the multiply spheres of work.”

Unlike today’s cloud-based platforms which centralize and silo information between the digital and physical worlds, organizations operating in the metaverse will smoothly navigate between real-world and technology-driven experiences as they develop and sell products and services. For example, sending out a smart contract within and beyond company walls, companies could quickly find qualified workers whose self-sovereign identity proves their expertise. Whether they’re designing a city, airplane, or piece of furniture, people will have the ability to collaborate using all five senses in distributed, virtual networks. Manufacturers could construct digital twins of an entire factory in the metaverse. Lorenzo Veronesi, the other IDC analyst on the Supply Chain podcast, said that companies could enroll student trainees in a virtual, fully immersive factory experience, preparing them for full productivity on day one of employment.

“Metaverse applications are expected to work across the real world, on your mobile device, in augmented reality on your goggles, and in the virtual world wherever you are, through any wearable,” said Wezowski. “Currently there are closed environments in gaming or finance where blockchain technology creates NFTs -- non-fungible tokens that have value. In manufacturing, some companies are using digital twins to capture and analyze sensor-based data from machines. The metaverse will scale distributed, verified data across billions of devices, transactions, and people.”

Contextual workforce learning

Since experts, whether employees, partners, or customers, will be connected in large communities of verified networks in the metaverse, business leaders will need to rethink workforce training and development. For example, the metaverse promises to bring hyper-personalized learning to people, putting today’s so-called intelligent algorithms to shame.

“We’re experimenting with the concept of the ‘digital ME’ that would serve as a digital representation of someone’s unique abilities, experiences, accomplishments, and ambitions,” said Wezowski. “Knowledge Packs, plugged into your digital ME, could augment whatever tasks you’re performing and decisions you’re making. Instead of just translating a different language you’re hearing from a customer, the technology provides a cultural perspective to help you better understand and respond to their concerns in the moment. If protracted budget negotiations get frustrating, the technology offers conversational topics to move the discussion along faster.”

Business metaverse could overcome consumer skepticism

Emergence of the metaverse is five to 10 years out. However, unlike the first generation of the internet that began with consumers, the metaverse could be driven by post-pandemic work anywhere norms. While a Forrester survey found less than 30% of US and UK consumer respondents thought the metaverse would be good for society, one analyst blogged that “Metaverse-style experiences have a better chance of driving near-term value in the workplace….some employees who learn how to use metaverse-style experiences at work will want to use them at home too.”

Indeed, Gartner analysts predicted 25% of people will spend at least one hour per day in the metaverse by 2026 for work, shopping, education, social and/or entertainment. Wezowski advised business leaders to start preparing now.

“In the first phase of the internet, people had to learn technology. The metaverse turns that around as technology will learn from people who are at the center of Web 3.0,” he said. “People could be augmented to achieve their greatest creativity for themselves and the communities around them. Business networks would then act autonomously, fostering decentralized communities of innovation to their highest value. Now is the time to deploy your imagination department to envision your brand’s worth in this future environment.”

Follow me @smgaler

This blog also appeared on SAP BrandVoice on Forbes.

Workplace Trends In The Metaverse: No One Else Can Be You

2022-12-12T13:29:16+01:00

If the metaverse unfolds as expected, competitive office politics could become a relic of the past, as employees who are fully confident in their unique value work together to achieve shared goals with employers. Before dismissing this future as a fantasy, think back to the early 1990s. No one could fully envision how the internet would shake up every industry to its core. Gartner analysts recently predicted that by 2025, 10% of workers will regularly use virtual spaces -- up from 1% in 2022.

Imagine a workplace where competition doesn’t matter

The metaverse could create an open, global talent marketplace so trusted that verified organizations could find and hire the experts they need because everything about those individuals – including their accomplishments, aspirations, and development goals – was documented and readily available with their permission. Worker shortages could disappear because companies could submit tenders for projects to a community of meta creators. Experts would autonomously connect with a company’s opportunities and KPIs, working together to accomplish common goals.

“In the metaverse, employees won’t worry about someone trying to take their job because it will be impossible,” said Martin Wezowski, chief futurist at SAP New Ventures and Technology. “No one else can be you because your worth is verifiably documented, spanning not just your experience and accomplishments, but just as important, your people skills, optimism, and curiosity. Confident employees are more inclined to complete each other and not compete against opposing agendas.”

Metaverse builds a more human-centered workplace

The metaverse will consist of physical, augmented, and virtual worlds. Applied to the workplace of the future, people could easily gain contextual information and make better decisions across a realm of daily responsibilities. Data could include the employee’s personal passions and professional ambitions, along with relevant information from colleagues across business ecosystems, factoring in overall organizational and objectives.

“Employees are not a single function or persona. They are cognitive humans who wear numerous hats in the course of a day,” said Wezowski. “Instead of getting stressed out trying to switch gears and keep up with multi-faceted requests, individuals working in the metaverse could be augmented with the contextual information they need as they perform various tasks, moving smoothly between their roles, whether it’s C-level executive, innovator, manager, project member, trainee, mentor, and more.”

Autonomous workplaces go beyond automation

In the first digital age, automation freed up people from tedious rote tasks. The metaverse would make this kind of rudimentary automation look like child’s play. Instead of static processes that are pre-programmed for completion in a one-dimensional physical space, the metaverse could provide all of the immediate information employees needs at the exact moment they need it in virtual, augmented, and/or physical reality.

“These packages of personalized knowledge would speak to workers where they are with a dynamic, autonomous flow of information that moves between that person’s worlds, without overwhelming them,” said Wezowski. “Employees would grasp new knowledge and apply it faster because learning in the metaverse could be fully immersive, using all five senses. People would build stronger, trusted relationships because multi-sensory interactions in the metaverse would be more authentic and personalized.”

Tapping the value of people

In the decentralized metaverse, people will not only remain an organization’s most valuable asset, but their individual skills could yield even greater value for themselves and the company. For example, alerts about pending machine part failures prevent downtime on the factory floor. What if machine intelligence could autonomously source engineering designs from the larger market ecosystem to prevent that problem in the first place?

“Creative decision-making could be the new value for organizations as the highest output that humans do better than any machines,” said Wezowski. “We would want to be not the best, but our optimal self in each situation, able to apply our unique human ingenuity, augmented by technology, to reach our objectives. This alters the employee/employer relationship to more of a win-win. Humans and machine intelligence will operate in empathic symbiosis, nurturing the core value of every company, which is the people.”

Innovators with bold thinking leap ahead

Much as digitalization upended last century business norms, the autonomous nature of the metaverse could fundamentally reshape our definition of meaningful work.

“Let’s say that engineers in Asia design a part that’s 3D-printed in Mexico, distributed to factories in the US for robotized assembly, and sold in target markets based on precise market demand forecasts,” said Wezowski. “With the autonomous flow of highly contextualized information, people could be prompted to step in and make their most valuable contribution. They could transact business based on values they choose, such as money or mentoring. When employees focus on companies and projects that help them grow in new ways, it's beyond business transformation. This is human transformation.”

Follow me @smgaler

This blog also appeared on SAP BrandVoice on Forbes.

IoT 3.0

2023-01-03T15:40:33+01:00

For many years I have been sending IoT data into central databases starting with Connect a Lego Mindstorms NXT to the HCP Internet of Things Services via a Raspberry Pi over Bluetooth, followed by Send your Raspberry Pi sensor data to SAP Vora via Apache Kafka managed by the SAP Data Hub, and Send your Raspberry Pi sensor data to SAP Vora via Eclipse Paho MQTT managed by the SAP Data Hub.

That had advantages in that I could leverage central application logic to Display your Lego Mindstorms sensor data on a Fiori Overview Page and deploy it to the HCP Portal Fiori Launchpad or Analyse your Raspberry Pi I2C sensor data with the HANA Cloud Platform, predictive services.

The architecture had been looking like this:

IoT data sent to a central instance and retrieved back from there.

With the emergence of Web 3.0 another option presented itself:

A decentralized distributed ledger holds the IoT data with the former central instance becoming an equal member.

For this blog I leverage Duino IoT that is a feature of Duino-Coin, a hybrid crypto currency.

This time, my device is an ESP32:

It mines Duino-Coin combining it with my IoT data:

Core 1 received a correct job with size of 87 bytes

Core 0 found a correct hash (2.62s)

Core 0 retrieved job feedback: GOOD, hashrate: 21.39kH/s, share #6062

Core 0 asking for a new job for user: architectSAP

AHT readings: 20.22*C, 47.76% rH

Core 0 received a correct job with size of 87 bytes

Core 1 found a correct hash (4.63s)

Core 1 retrieved job feedback: GOOD, hashrate: 21.13kH/s, share #6069

Core 1 asking for a new job for user: architectSAP

AHT readings: 20.22*C, 47.78% rH

Core 1 received a correct job with size of 87 bytes

Core 0 found a correct hash (5.54s)

As a result, my IoT data is stored with the blockchain and for example accessible through the Doino-Coin Web Wallet:

In my opinion, the potential for distributed ledgers for IoT is enormous especially also by reducing integration challenges and risks.

Building an End-To-End Machine Learning Solution for Product Inspection Centered On SAP Kyma

2023-01-31T16:44:03+01:00

Overview and Motivation

Over the last few months, Sanraj Mitra, Shetty Vriddhi and myself have been working on a product inspection solution combining SAP solutions with external technologies. Product inspection is a key part of quality assurance where goods are assessed for compliance with the company’s standards. Due to the wide range of checklists for each product, this often is a complex procedure. By automating the simpler tasks in this process and handing these responsibilities off to a computer, we would be able to lighten the workload of inspectors and speed up the duration of the inspection.In this use case, we will be simulating product inspection of beer cartons. Using a camera and a computer vision model, we will be able to count the number of beer bottles in a passing carton to ascertain whether the carton passes or fails inspection.

Scenario

Imagine a conveyor belt transporting beer cartons in a factory. By mounting a camera at a vantage point along the conveyor belt, we can record the cartons as they pass by. This live feed from the camera can be used as input to a machine learning model to count the number of beer bottles in each carton.

Application frontpage

Solution Architecture and Process

Solution Architecture

Although this is an end-to-end solution, the star of the show is of course the object detection. Let's have a closer look at its process flow.

Process Flow for Beer Bottle Detection

In addition to the above process flow, an SAC dashboard and SAP Build Apps mobile application are provided for end users to review the data collected for decision making.

In Detail

In this section we will go into more details about each component of the architecture.

ESP32-CAM

The first step was to find a suitable camera to record the beer cartons as they passed by. The requirements here was the camera needed to have Wi-Fi connectivity and be programmable. We were looking to create a WebSocket connection between the camera and the serving endpoint due to the quicker speed and low overhead per message, thus we needed to be able to program the camera to do so. The ESP32-CAM is a relatively affordable microcontroller with a camera mounted that fit the bill.

When powered, the ESP32-CAM will run a continuous loop to take a picture and send it through a WebSocket connection it establishes with the Flask app in SAP Kyma. We chose not to deploy the model on the ESP32-CAM but that is also a viable option for those who require machine learning on the edge, albeit with possible reduction in the model’s effectiveness.

YOLOv4 Bottle Detector Model

Next, we trained an object detection model to analyze the beer carton images from the microcontroller. To create our bottle detector model, we opted to train a YOLOv4 model on our bottle dataset. This is a process called transfer learning where we leverage the existing knowledge of a pre-built model to solve a new problem. We created a suitable dataset by using the ESP32-CAM to take images of the beer carton from the top. The areas within the image containing beer bottles are then labelled. This process is repeated with different number of bottles in the carton, with the carton at different positions.

Training Data Image Example

Training Data Labels Example

Once trained, the model’s weights are saved and are placed in the same Docker container as the Flask application. Using the trained model, we are able to detect how many bottles are in each picture frame, and where they are. In this way, we can label the original image with green bounding boxes wherever a bottle is detected, for easy reviewing of the model's performance.

Beer Carton Enters Camera View

Beer Carton Exits Camera View

Flask Web App

The web app allows us to provide an endpoint to receive the image from the ESP32-CAM. It also serves the YOLOv4 model and saves the inference results to our HANA Cloud database. In addition, the frontend provides users with the option to view a live feed of the incoming frames from the ESP32-CAM to ensure that the camera and model are operating as expected, in real-time.

The web app is containerized using Docker and pushed to Docker Hub. SAP Kyma / Kubernetes will then download the image from Docker Hub and use it to build a container to run the application. The Docker repository is linked to Kyma through the deployment yaml file.

Application Frontpage

SAP Cloud Application Programming Model (CAP) and SAP HANA Cloud

As mentioned, we also need a place to save our results. For this we used an SAP HANA Cloud HDI container. To make reading and writing to the database easier, we opted to deploy a CAP application in SAP Kyma to create a service layer. This way, we will be able to use HTTP requests and OData services to send and receive data from the database. The Flask application is able to write to the database through POST requests, while the SAP Build Apps application and SAC dashboard are able to retrieve the latest data through OData service.

Table Schema in HANA Cloud

SAP Analytics Cloud (SAC) and SAP Build Apps

Finally, we need to provide the end users with ways to view the data collected from the application. We demonstrated the utility in two products, SAC and SAP Build Apps in serving different purposes.

The SAC dashboard aims to provide users with a higher-level view across all production metrics. Here users can analyze historic trends and numbers.

SAC Dashboard

Meanwhile, the SAP Build Apps app is targeted towards staff working on the ground to gain quick access to inspection data. Here, users are able to view the batch information for specific dates, with an option to drill down further and double-check on the inspections' bottle counts.

On the Home page, the user can view basic statistics as well as select the batch date to be inspected.

Mobile App Home Page

After selecting the date, a list of all cartons that were manufactured on that date will be returned. The user can thus view which cartons passed or failed the inspection.

Mobile App Batch Inspection Page

Clicking onto any row will direct the user to that specific carton's information page. A labelled image of the carton will be displayed for the user to confirm that the machine learning model's bottle count is accurate. This can also be used to spot for consistent errors in the missing bottles. For example if the middle bottle on the top row is always missing, that could indicate an issue with the machine, rather than just random error.

Mobile App Product Page

Closing

To summarize, we have created an end-to-end product inspection solution revolving around SAP Kyma. While this proof of concept focused on an object counting use case, it can be easily extended to more complex inspection tasks.

Many thanks to Sanraj Mitra, vriddhishetty, lsm1401, gunteralbrecht, and jayadnure for their help and guidance throughout the development of this solution.

Industry 4.0 and the Standard ISA-95: The Pathway to Revolutionizing the Energy Industry

2023-04-05T23:11:38+02:00

The digital revolution of Industry 4.0, powered by big data and digital twins, has opened up new opportunities for the energy industry. By providing closer insight into production processes, digital technologies can enable more efficient, optimized, and automated operations to improve margins, reduce costs and reduce energy consumption. The Global Energy Industry is rapidly incorporating technologies such as the internet of Things (IoT), artificial intelligence (AI), 3D printing, cloud computing, and other cutting-edge innovations to support more efficient, competitive operations.

To ensure smooth and efficient operations, it is necessary that there is industry-wide interoperability and standardization. An excellent example is the Standard ISA-95 (also known as IEC 62264), developed by the International Society of Automation (ISA).

The Standard ISA-95 helps to streamline the collection, storage, and exchange of data to enable more meaningful insights and visibility. It provides the basis for digital data integration, describing the processes and equipment that support and manage digital communications within an enterprise. It differentiates between host sites (target systems such as electric meters and PLCs) and client sites (if available). It allows host sites to effectively collect and process data from other connected sites. It does this by using four levels – each of which have a distinct role in device control, level 0 being the field devices, level 1 being the field controllers (PLCs, PACs, PACs, etc.), level 2 being the supervisory systems, and level 3 being the enterprise system.

The Standard ISA-95 supports a range of communication networks, such as proprietary and open standards like OPC UA. It is already widely implemented, enabling deep data integration, messaging, and services. It makes it easy to connect equipment and subscribe to events to get updates and manage objects and addresses in the asset model. In addition, it allows users to develop enterprise control systems rather than individual control systems.

The traditional ISA-95 production stack is evolving into a digital manufacturing platform.

In short, the Standard ISA-95 is an intelligent, user-friendly platform that integrates different communication protocols, enabling more efficient, cost-effective, and better-informed operations. This gives the Global Energy Industry a cost-effective way to standardize its processes over a vast network and digitalize and secure its operations. This standardized system is essential for the energy industry to realize the promise of Industry 4.0 – enabling the energy industry to revolutionize its business.

SAP Industry 4.0 solutions and technology can bring data-driven processes and operational flexibility to the entire energy business.

https://www.sap.com/products/scm/industry-4-0.html

Streamlining Sensor Data Integration: Enhancing Telematics with AWS and SAP Digital Vehicle Hub

2023-05-30T16:21:15+02:00

Introduction:

In the rapidly evolving world of connected vehicles, leveraging telematic data is crucial for improving operations, enhancing driver safety, and optimizing vehicle performance. Integrating various systems and databases can be complex and time-consuming, but with the right tools and strategies, it becomes an achievable goal. In this blog post, we will explore how the combination of AWS services and SAP Digital Vehicle Hub can simplify the process of enabling telematic data persistence, integrating Otonomo with Amazon Kinesis, and displaying sensor data on SAP's user interface of SAP Digital Vehicle Hub.

Enabling Telematic Data Persistence into an AWS Timeseries Database:

One of the key steps in harnessing the power of telematic data is its persistent storage for analysis and future use. By leveraging Amazon Timeseries Database, you can store and manage vast amounts of sensor data efficiently. This eliminates the need for manual intervention, allowing a seamless and automated transfer of telematic data from vehicles to the database.

Benefits:

Reduced implementation efforts: By leveraging Amazon Timeseries Database, the process of bringing sensor data from vehicles is simplified, reducing implementation efforts significantly.

Streamlined data transfer: The automated integration enables a streamlined and efficient transfer of data from vehicles to the timeseries database.

Cost-effective storage: Amazon Timeseries database offers cost-effective storage solutions, allowing you to handle large volumes of sensor data without breaking the bank.

Continuing Integration of Otonomo with Amazon Kinesis:

Integrating Otonomo, a leading automotive data services platform, with Amazon Kinesis provides a powerful solution for processing and streaming real-time vehicle data. Amazon Kinesis allows you to receive and process massive volumes of sensor data at high speed, ensuring timely and accurate insights.

Benefits:

Enhanced data processing: By integrating Otonomo with Amazon Kinesis, you can efficiently process and analyze vast amounts of sensor data, unlocking valuable insights for your operations.

Real-time analytics: Amazon Kinesis enables real-time data streaming, empowering you to make informed decisions and respond promptly to critical events.

Cost-effective scalability: With Amazon Kinesis, you can scale your data processing capabilities based on demand, ensuring a cost-effective solution that grows with your needs.

Enabling Persistence of Telematic Data Using SAP Digital Vehicle Hub Naming Conventions:

SAP Digital Vehicle Hub is a comprehensive platform for managing vehicle-related data. By leveraging its naming conventions, you can ensure consistent and standardized storage of telematic data in the Timeseries database. This enables easy access and retrieval of sensor data when needed, enhancing the overall functionality of your system.

Benefits:

Standardized data organization: Utilizing SAP Digital Vehicle Hub's naming conventions ensures consistent and organized storage of sensor data, simplifying data retrieval and management.

Seamless integration: The integration of SAP Digital Vehicle Hub with the Timeseries database enables a seamless flow of telematic data, reducing complexity and maintenance efforts.

Improved data accessibility: With standardized naming conventions, sensor data can be easily accessed and displayed across various applications and user interfaces.

Displaying Sensor Data on SAP Digital Vehicle Hub's User Interface:

To fully leverage the power of telematic data, it is crucial to present it in a user-friendly and intuitive manner. By displaying the sensor data from the Amazon Timeseries database directly into the vehicle master of SAP Digital Vehicle Hub, you can provide a comprehensive view of vehicle performance and enable informed decision-making.

Benefits:

Enhanced data visualization: Displaying sensor data on SAP Digital Vehicle Hub's user interface enables intuitive visualization, making it easier for users to interpret and analyze the information.

Improved operational insights: By presenting sensor data within the context of SAP Digital Vehicle Hub, you gain valuable insights into vehicle performance.

Technical Integration details:

Prerequisite: Subscriptions to AWS and Otonomo accounts are available.

The following services are required in AWS for the integration: Kinesis Data Stream, Kinesis Data Analytics, Amazon Timestream

Follow the below steps to set up the integration

Configure the Streaming for fleet to send the data to AWS Kinesis from the Otonomo Dashboard

Configure Streaming for a Fleet in Otonomo Dashboard

Create a data stream in AWS Kinesis

AWS Kinesis data stream overview section

Create a Database and table in Amazon Timestream

Amazon TImeseries tables section

Upload the JAR file provided by SAP Digital Vehicle Hub in S3. For JAR file please reach our us by creating a ticket using component LOD-DMO-DVH (this process could change in the future)

Upload the provided JAR file in S3 bucket

Create a AWS Kinesis Analytics Application with the provided JAR file

Provide the path of the upload JAR file in S3 in configuration of streaming application

Create Runtime properties for the application

Runtime Properties section for streaming application

Once, the application is created you should be able to open the Apache Flink Dashboard and see a running job called “Preprocess and save to Timestream”

Job in Apache Flink Dashboard

Create a technical user in AWS with permission "AmazonTimestreamReadOnlyAccess" using IAM

Create an access key for the technical user

Create a destination in your BTP subaccount: DVH_AWS_IOT.
See: Setting up destination for fetching telematics data from AWS for more information

Create a vehicle using create vehicle API with same vin present in Otonomo fleet and “telematicsDataSource” as “02” (AWS Kinesis Timestream).

Creating a Vehicle using Postman

Open View/Modify Vehicles App in IAM FLP. Search for the newly created vehicle and open it. Navigate to the “Indicator Chart” section, select the sensor for which you would like to view the data

Indicator Chart section in View/Modify Vehicles App

You should now be able to visualize the telematics data for vehicles present in your system. There also new features panned which should help you in visualize the complex data of the vehicle sensors in an easier way.

Please, share your feedback in the comments. If you have any questions, you can reach out to me or harshachaithanya.kethepalle

Also, make sure to follow the blog, we have exciting new features on the way, they will be shared in upcoming blogs.

Thanks!

How is Nordlaks Leveraging IoT Technology and Cloud Processing to Farm Salmon in a more Sustainable Way?

2023-06-20T16:25:51+02:00

When The Department of Fisheries challenged the industry in 2015 to find new solutions for farming fish in the open seas, Norway based Nordlaks Oppdrett stepped up and won the competition based upon an innovative and sustainable approach.

Reinventing Salmon Production in a Sustainable Way
Family owned Nordlaks Oppdrett AS has been a leader in aquaculture in Norway since the company’s founding in 1989, with comprehensive operations from breeding premium Atlantic salmon to sales and marketing. However, continuing its robust growth depends on adopting new and sustainable production methods, as space within fjords for traditional net-pen farming is limited.

Nordlaks realized that the answer lay in economies of scale and designed a unique sea vessel called Havfarm which is currently the largest floating structure in the world. To put it in perspective, it is almost the length of 4 football fields and is designed to have a lifetime of at least 25 years without the need to bring it into a dry dock for maintenance and repairs.

Mr. Bjarne Johnsen, Project Manager Havfarm at Nordlaks explained: “To keep up with growing global demand and support sustainable farming, we recognized the opportunity to turn to the open seas to expand production. We realized that the answer lay in economies of scale. If we built larger containers, we would no longer be restricted to the calm waters of the fjord and could afford to deploy more robust materials and sophisticated safety measures.”

Leveraging IoT Sensors and Cloud Processing to Empower Operations and Maintenance Processes
It is not only the sheer size of the Havfarm that is impressive, but also the technology that empowers it.

To take salmon aquaculture to a new level, Nordlaks looked to the latest technology to support operations and maintenance processes. They strategically placed IoT sensors on the vessel to monitor, analyze and broadcast motion data in real-time. This data is used to run the digital twin model of the vessel in SAP^® Enterprise Product Development (SAP EPD), a cloud-based application that enables improved and streamlined processes for monitoring of operations and maintenance.

This monitoring system enables Nordlaks to focus inspections and maintenance activities on critical areas of the vessel structure, identified through Digital Twin technology and virtual sensors. It measures the integrity of the structure under adverse conditions to ensure everything from the longevity of the materials to the grip of the mooring. Furthermore, heat maps are used to monitor feed output ensuring that there is no lasting impact on the environment.

The company now has access to historic and near real-time data and actionable insights leading to improved decision-making. They can monitor the balance of the vessel to improve ballast operations. And it has now processes in place to support efficient feed spill monitoring and simplified soil sampling underneath the vessel required to meet strict compliance regulations.

As Mr. Bjarne Johnsen states “Everything we do needs to be sustainable and that means when we leave an area, there should be no trace that we were ever there. He concluded, “SAP Enterprise Product Development helps us monitor the Havfarm vessel infrastructure and environmental impacts on the seabed, which is key to our strategy for sustainable growth”.

Request a free trial and watch an overview video to learn how to enable fast, collaborative product development and lifecycle management with SAP EPD. In addition, you can watch a Nordlaks video.

Events-2-Business Action Framework – Create Plant Maintenance Notification in SAP S/4 HANA

2023-08-03T02:18:50+02:00

Our approach

Our aim is to adopt event-driven architecture, by building an open-source framework on the SAP Business Technology Platform (SAP BTP) which can integrate events generated from non-SAP platforms and solutions into SAP business processes thereby extending the value of existing SAP applications and processes. The events may originate from various sources such as IoT platforms, mobile and web applications, enterprise systems, and industrial production, logistics platforms, and so on. Thus, this framework helps in making business intelligent and interconnected to improve overall process efficiency. The Event-to-Business Framework can run on your own BTP or as SaaS solution provided by NTTDATA Business Solutions.

Figure-1: “Events-to-Business Actions” Framework

Business Scenario

Challenges

Industrial processes in factories, plants, warehouses, and logistics generate critical events from disparate and discrete industrial systems and there is a need to trigger business processes as a response to these events.

The Case

Create a Plant Maintenance Notification for the inhouse service technicians to maintain or repair an equipment, triggered by sensors reacting on changing conditions.

IoT device reports regularly status of equipment, e.g. temperature monitoring of a manufacturing machine and report to MS Azure IoT platform

Rules for dedicated telemetry properties, e.g. abnormal temperature to trigger export of event data (temperature too high)

“Create Plant Maintenance Notification” in SAP S/4 HANA by streaming the event to Event-to-Business Framework, where the streaming event is interpreted and enriched by business rules to create final content for functional transaction.

This action payload triggers the API in the connected SAP business system, here S/4 HANA.

Figure-2: Process Steps

Process steps

Sensor/IoT devices determine conditions

IoT devices with sensors monitor the operation-critical figures of an equipment and transmit them to an IoT platform for further activities. We simulate the case with an IoS App, developed by NTTDATA Business Solutions.

Figure-3: Simulate abnormal temperature increase

Ingest the data in an IoT platform
(here Microsoft Azure IoT Central)

Figure-4: Data Export to SAP Event Mesh

The event data is ingested in a platform, such as an IoT platform. In our scenario we use Microsoft Azure IoT Central. But any other platform, which can export data managed by rules, can be used. We select the records which are relevant for the process with Data Export functionality. In this step we also enrich the data string with content we need in the next steps of the Event-to-Business Framework. The data string is published on to SAP Event Mesh.

Activities in Event-2-Business Framework

Figure-5: UI modeler

As shown in Figure-5, this framework provides an out-of-the-box UI modeler that is intuitive to configure different types of actions like workflow or process execution that can be taken in SAP backend systems as an outcome of the incoming events from external sources via SAP Event Mesh.

Actions

In this scenario with our preconfigured actions, we create a plant maintenance notification in a SAP S/4 HANA system. For flexible configuration we use main actions (for each transaction), which can be chained with pre-action and post-action activities.

Figure-6: Action Categories

Figure-7: Actions to create Plant Maintenance Notification

This framework leverages business rules capability within the SAP Build Process Automation, which lets you automatically choose a business action that should be executed for an incoming event based on the configured decision tables.

For the creation of a Plant Maintenance Notification you need the Main-Action to feed the API with all required fields to execute the transaction. The data to feed the API has its source in the IoT platform (see Enrichment) or is derived from business rules. A Pre-Action fetches the derived data from the business rules, e.g. maintenance plant, notification type. The Post-Action is used for actions after the successful execution of the Main-Action. In this case we use it to set the status and notification number in the device in MS Azure IoT. With that we avoid that the event is executed again after already creating a notification in SAP Plant Maintenance.

Business Rules

Figure-8: Business Rules

The Business Rule triggered by the action, for Plant Maintenance Notification the Pre-Action triggers the Business Rule to determine relevant parameters to feed the payload of the Main-Action.

Using If – Then Rules to determine Notification Type, Maintenance Plant, Asset Location etc. based on Device Location (information from MS Azure IoT Enrichments).

Logging

Figure-9: Error Log

Every transaction processed is recorded in the (error) log. From the log lauchpad you can control the details of the event with its payload. A restart function is on the SAP development roadmap.

Reference Architecture

The architecture is based on leveraging the services of SAP Business Technology Platform which includes event-based integration with SAP Event Mesh, the upcoming event bridging functionality of SAP Integration Suite (Beta), Advanced Event Mesh (to be updated), Business rules capability in SAP Build Process Automation coming from SAP Workflow Management, SAP Destination Service, SAP Private Link service, SAP Connectivity Service and Cloud Connector and a Node.js extension application on the SAP Cloud Foundry runtime.

Figure-10: High-level architecture with SAP S/4HANA on-premise and private cloud

The following steps depict the information flow across systems

(1) Event is triggered from Microsoft Azure and is published on to SAP Event Mesh.

In Event-to-Business-Action framework (extension app),

(2) processor module endpoint is subscribed to SAP Event Mesh to receive the event.

(3) processor module leverages the Business Rules capability of SAP Build Process Automation to derive business action (For example, Purchase Order Requisition creation in SAP S/4HANA system) based on certain characteristics of incoming event.

(4), (5), (6), (7) and (8) processor module triggers the defined action in the SAP S/4HANA system using the SAP Destination Service and SAP Private Link Service.

In case SAP S/4HANA is on-premise and private cloud (refer to Figure-6) – communication with SAP S/4HANA happens via SAP Connectivity Service and Cloud Connector.

(9) and (10) are on the SAP development roadmap. SAP S/4HANA publishes an event (e.g., Plant Maintenance Notification creation) to Microsoft Azure Event Grid via SAP Event Mesh Connectivity Bridge for further processing by Microsoft Azure applications.

Key Advantages of This Architecture

Developers implementing this framework architecture would benefit from the following design considerations.

A flexible and generic framework that can be easily extensible for any Line of Business (LoB) scenario /workflow/process and any source system events.

Event-driven integration architecture with SAP Event Mesh as a central hub, including a bi-directional flow of events (Microsoft Azure to SAP S/4HANA and vice versa). We are also evaluating on leveraging Advanced Event Mesh (to be updated soon).

Enriched with resilient and high availability architectural patterns.

Network security-focused design with SAP Private Link specifically for RISE with SAP customers between SAP BTP on Azure (any region) and SAP S/4HANA on Microsoft Azure

Scenario Setup and Deployment

To get the entire scenario working, there are prerequisites and initial configurations required in SAP Business Technology Platform, Microsoft Azure, and SAP S/4HANA.

Please refer to the published GitHub repository and Discovery Center mission of the initial set-up (see blog).

About Author

Simplifying the IoT Puzzle with SAP Asset Performance Management and Cumulocity IoT

2023-08-16T16:23:24+02:00

Following up from my previous blog on SAP Asset Performance Management (APM) and SAP IoT, I'd like to share some additional thoughts about IoT and APM, because we see such a diverse range of different customer situations. While some customers have started rationalising on a preferred ‘strategic’ IoT platform, others have a range of IoT tools, and many have inherited extensive applications and historians which will remain valuable data sources. In almost all customers we see a mix of these scenarios, and no ‘one platform’ nirvana. Therefore, I’d like to reiterate how important it is for APM to provide robust IoT capabilities as well as a flexible integration framework to simplify this IoT puzzle.

As we see our customers continue to rationalize their IT/OT platforms and shift to a more outcome based approach, our APM IoT strategy will continue to be agnostic and be able to ingest equipment/sensor data from any data source or end point: smart or IoT devices/edge gateways, data historians and data lakes, and other IoT/OT platforms or middleware. Furthermore, APM offers standard integrations with SAP S/4HANA and ECC Plant Maintenance and it can blend maintenance records and inspection data and measuring documents along with sensor timeseries data to provide a complete view of the asset health for condition-based maintenance (CBM) and predictive maintenance (PdM). Some of CBM/PdM connectivity use cases that we have seen in our customer’s project include:

Oil & gas customers with data historians and data lake via REST APIs

Utilities customers with SCADA systems via OPC UA

Transportation customer with connected assets via MQTT and edge

Industrial manufacturing customers with shopfloor systems via Plant Connectivity (PCo)

You might have seen this press release from Software AG announcing that they are with working us on integration with SAP Asset Performance Management. This relationship is a great example of our strategy at work – integration with their market-leading Cumulocity IoT will help our joint, existing and new, customers to simplify their IoT connectivity scenarios and focus on leveraging their IoT data to optimize their maintenance plans and activities.

We also see this relationship opening the door for more innovative use cases such as servitization. As our equipment manufacturing customers start to roll out as-a-service business models, Cumulocity IoT and SAP Asset Performance Management can help ensure the smart equipment/product is always working and optimize the maintenance schedules based on actual usage and health conditions.

Please watch this space and we will have more IoT-related news later this year. To learn more about SAP Asset Performance Management, please visit sap.com/apm and check out my latest APM Overview webinar here.

Events-2-Business Action Framework – Create Service Ticket in SAP CX

2023-08-25T16:28:28+02:00

Companies are driving the digitization of the factory, plants, warehouses, and business networks and creating an ecosystem by linking Information Technology (IT) with Operational Technology (OT). Bridging the gaps between different landscapes and processes with integrated frameworks is key for building a seamless, efficient, bi-directional collaborative ecosystem.

This scenario is built to trigger service tickets in SAP CX automatically for repair or maintenance based on critical events determined by IoT device.

The intention of the initial project was to set-up an event-driven framework based on SAP Business Technology Platform (SAP BTP) to respond to and integrate with events generated from industrial production processes in plants, warehouses, and logistics into enterprise business systems, triggering associated business processes to enhance enterprise operations and enable rapid decision-making. Target of this generic framework is to configure new scenarios rather than developing them individually. With the framework we reduce the implementation time significantly.

This scenario is fully built on the framework without individual development or modification.

See blog initial project shared by PVN PavanKumar (SAP Labs India):

https://blogs.sap.com/2023/01/27/part-1-events-to-business-actions-architecture-an-event-driven-framework-on-sap-btp-to-implement-industry-4.0-scenarios-with-microsoft-azure-services/

See my blog about Events-2-Business Action Framework –
Create Plant Maintenance Notification in SAP S/4 HANA

https://blogs.sap.com/2023/08/03/events-2-business-action-framework-create-plant-maintenance-notification-in-sap-s-4-hana/

Our approach

Our aim is to adopt event-driven architecture, by building an open-source framework on the SAP Business Technology Platform (SAP BTP) which can integrate events generated from non-SAP platforms and solutions into SAP business processes thereby extending the value of existing SAP applications and processes. The events may originate from various sources such as IoT platforms, mobile and web applications, enterprise systems, and industrial production, logistics platforms, and so on. Thus, this framework helps in making business intelligent and interconnected to improve overall process efficiency. The Event-to-Business Framework can run on your own BTP or as SaaS solution provided by NTTDATA Business Solutions.

Figure-1: “Events-to-Business Actions” Framework

Business Scenario

Challenges

The Case

Create Service Ticket for external service technician to repair or maintain equipment

IoT device reports regularly status of equipment, i.e. manufacturing machine to MS Azure IoT platform

Rules for dedicated telemetry property, i.e. oil consmption to trigger export of exception data (oil consumption too high)

Stream event and interpret content enriched by business rules to create functional transaction for
“Create SAP CX Service Ticket“

Trigger posting mechanism in SAP CX

Figure-2: Process Steps

Process steps

Sensor/IoT devices determine conditions

Figure-3: Simulate abnormal temperature increase

Ingest the data in an IoT platform
(here Microsoft Azure IoT Central)

Figure-4: Data Export to SAP Event Mesh

Activities in Event-2-Business Framework

Figure-5: UI modeler

Actions

In this scenario with our preconfigured actions, we create a service ticket SAP CX system. For flexible configuration we use main actions (for each transaction), which can be chained with pre-action and post-action activities.

Figure-6: Action Categories

Figure-7: Actions to create Service Ticket

This framework leverages business rules capability within the SAP Build Process Automation, which lets you automatically choose a business action that should be executed for an incoming event based on the configured decision tables.

For the creation of a Service Ticket you need the Main-Action to feed the API with all required fields to execute the transaction. The data to feed the API has its source in the IoT platform (see Enrichment) or is derived from business rules. A Pre-Action fetches the derived data from the business rules, e.g. service priority, service category. The Post-Action is used for actions after the successful execution of the Main-Action. In this case we use it to set the status and ticket number in the device in MS Azure IoT. With that we avoid that the event is executed again after already creating a service ticket in SAP CX.

Business Rules

Figure-8: Business Rules

The Business Rule triggered by the action, for Service Ticket the Pre-Action triggers the Business Rule to determine relevant parameters to feed the payload of the Main-Action.

Using If – Then Rules to determine Service Priority, Incident Category, Service Category, etc. from Pump Type of Device.

Logging

Figure-9: Error Log

Every transaction processed is recorded in the (error) log. From the log lauchpad you can control the details of the event with its payload. A restart function is on the SAP development roadmap.

Reference Architecture

Figure-10: High-level architecture with SAP CX

Key Advantages of This Architecture

Developers implementing this framework architecture would benefit from the following design considerations.

A flexible and generic framework that can be easily extensible for any Line of Business (LoB) scenario /workflow/process and any source system events.

Event-driven integration architecture with SAP Event Mesh as a central hub, including a bi-directional flow of events (Microsoft Azure to SAP S/4HANA and vice versa). We are also evaluating on leveraging Advanced Event Mesh (to be updated soon).

Enriched with resilient and high availability architectural patterns.

Network security-focused design with SAP Private Link specifically for RISE with SAP customers between SAP BTP on Azure (any region) and SAP S/4HANA on Microsoft Azure

Scenario Setup and Deployment

About Author

Events-2-Business Framework – Create Shopfloor Confirmation in SAP S/4 HANA

2023-08-29T16:32:25+02:00

Our approach

Figure-1: “Events-to-Business Actions” Framework

Business Scenario

Challenges

The Case

Create Shop Floor Confirmation (here with NTT DATA IoTConnect App)

Simulation of a packing process in shopfloor which needs a worker confirmation

We simulate the recording of
- Packed good parts
- Scrap
- Rework

This leads to a partial confirmation on a shopfloor order

App triggers data record for each case and report to MS Azure IoT platform

Stream each event and interpret content enriched by business rules to create functional transaction for “Shopfloor order confirmation“

Trigger posting mechanism in SAP S/4 HANA

Figure-2: Process Steps

Process steps

Sensor/IoT devices determine conditions

Figure-3: Simulate Shopfloor Confirmation

Ingest the data in an IoT platform
(here Microsoft Azure IoT Central)

Figure-4: Data Export to SAP Event Mesh

Activities in Event-2-Business Framework

Figure-5: UI modeler

Actions

In this scenario with our preconfigured actions, we create a shopfloor confirmations in a SAP S/4 HANA system. For flexible configuration we use main actions (for each transaction), which can be chained with pre-action and post-action activities.

Figure-6: Action Categories

Figure-7: Actions to create Shopfloor Confirmation

This framework leverages business rules capability within the SAP Build Process Automation, which lets you automatically choose a business action that should be executed for an incoming event based on the configured decision tables.

For the creation of a Shopfloor Confirmation you need the Main-Action to feed the API with all required fields to execute the transaction. The data to feed the API has its source in the IoT platform (see Enrichment) or is derived from business rules. A Pre-Action fetches the derived data from the business rules. The Post-Action is used for actions after the successful execution of the Main-Action. In this case we use it to update the production order and confirmation number in the device in MS Azure IoT.

Business Rules

Figure-8: Business Rules

Logging

Figure-9: Error Log

Every transaction processed is recorded in the (error) log. From the log lauchpad you can control the details of the event with its payload. A restart function is on the SAP development roadmap.

Reference Architecture

Figure-10: High-level architecture with SAP S/4HANA on-premise and private cloud

Key Advantages of This Architecture

Developers implementing this framework architecture would benefit from the following design considerations.

A flexible and generic framework that can be easily extensible for any Line of Business (LoB) scenario /workflow/process and any source system events.

Event-driven integration architecture with SAP Event Mesh as a central hub, including a bi-directional flow of events (Microsoft Azure to SAP S/4HANA and vice versa). We are also evaluating on leveraging Advanced Event Mesh (to be updated soon).

Enriched with resilient and high availability architectural patterns.

Network security-focused design with SAP Private Link specifically for RISE with SAP customers between SAP BTP on Azure (any region) and SAP S/4HANA on Microsoft Azure

Scenario Setup and Deployment

About Author

Be Ready: Future-Proofing Asset Management in Your Organization

2023-10-20T17:22:09+02:00

My basement flooded a few weeks ago, and my wife was on a mission to get to the bottom of the issue. And like any good husband, I got out of her way, and let here get to the root of the problem. As it happens, due to unprecedented rain fall over a very short space of time (who says there is no such thing as climate change) the sump pump, the device that protects us against flooding, had failed. Unfortunately, it was not smart enough to alert us to this situation and simply gave up.

And this is a metaphor for the challenges we are seeing from larger disruptions all over the world, from the impacts of environmental, geopolitical, and other types of risks.

Yet, if you work at all in asset management, you’ve probably felt the impact of disruption much more deeply than most. Asset management and field service management are among the operations most hurt by unexpected events as well as environmental, geopolitical, and other types of risk. In fact, when companies aren’t careful, many disruptions to business actually start with an asset failure (like my sump pump).

That’s why asset management is evolving. The future of modern asset management is always on, increasingly intelligent, and now – accessible wherever you are.

Supporting asset management’s role in strong supply chains

As we move to the future, asset management continues to grow in importance. That’s because asset management professionals are responsible for a rapidly growing network of, often very expensive, equipment, and much of this equipment is more complex – and intelligent – than ever before. Advanced technologies such as IOT and artificial intelligence (AI) are increasingly available and play a role in effectively executing business processes. In field service management, there are a greater number of field service operators, too, spread across a larger range of services and supporting more customers.

Whether you’re managing your own assets or providing asset management services to customers, these operations have never been more demanding of your time and attention. However, despite this growing complexity, many businesses continue to depend on the asset management and field service management software designed for the equipment and processes of 20 years ago. This software isn’t prepared to support the demands of modern equipment – or the disruptions of tomorrow.

The latest generation of asset management software connects business processes by closing the loop between asset strategy and maintenance execution, helps companies make better decisions by contextualizing their asset health and performance data through real-time insights, and supports improved collaboration with the ecosystem through network solutions.

Enabling asset management in the moment and on the go

As you look to the future, it’s time to ask yourself one all-important question: how can my assets be always-on, even if my asset management software and professionals aren’t?

Any asset management software will focus on keeping your assets up and running, but the right asset management software will do so by empowering your workers anytime, anywhere.

A brief look back in time

Early 2020 posed an extreme challenge for those businesses that didn’t have the tools or data integration to continue supporting everyday operations off site. You may now offer your workers some asset management tools on mobile, but do they provide all the features workers need to do their jobs and keep your assets running? Are they integrated and serving the data insights workers need to make fast, accurate decisions?

Modern asset management software is designed for a holistic approach to asset management that empowers you to connect, contextualize, and collaborate. Ultimately, it’s one part of a whole business that doesn’t work as intended when it’s siloed away from other operations.

For example, if you’re a manufacturer, your asset management team should be able to collaborate freely with operators and service providers. The impact of disruption is compounded by silos across the business. In field service management, you should be able to integrate front- and back-office processes for effective collaboration across teams – while keeping customers informed.

Expecting the unexpected with predictive analytics

The seasonally high rain fall in my area wasn’t a complete surprise. However, the failure of my asset (the sump-pump) was. I had no way of knowing that it was going to break down. Why? Because it didn’t tell me! I didn’t have the data. Why didn’t I have an app on my phone to alert me when the water level got to a certain point? Or when the part started to fail (which was probably several weeks ago)?

In that same way, the right data and predictive analytics can help your business prepare for what’s next in asset management.

For example, AI-based predictive maintenance is designed to help you determine a piece of equipment’s failure curve based on calculated probabilities. With these insights, you can clearly see the equipment’s remaining useful life and quickly make the best decision for your business. Predictive analytics can also reveal the likelihood of service needs for specific operations or customers by analyzing historical data from previous service requests.

AI enhances your understanding of asset operations and helps you make more-informed decisions, especially when these insights are available on mobile for your workers on the go. Embedding AI into asset management can help you get on the path to achieving your most important asset management goals, such as eliminating unplanned asset downtime and increasing resource utilization. Enterprise asset management (EAM) solutions from SAP include AI-powered capabilities such as these.

EAM solutions from SAP, including the SAP Field Service Management solution, can help you create a more-resilient supply chain, deliver service precision, and revolutionize equipment performance. To learn more about SAP solutions and how they help enable modern, mobile asset management, visit us online.

Also, download the ARC Advisory Group report to learn how asset-intensive companies are rethinking their asset management priorities.

Oh! My wife now has a smart pump, and an app on her mobile phone to tell her exactly how it is performing. What else would you get your wife of 30 years on her wedding anniversary.

AWS and SAP – Joint reference Architecture for IoT scenarios using Amazon Monitron

2023-11-14T14:30:24+01:00

When industrial machinery breaks down, it often results in significant operational downtime. Predictive maintenance can alleviate many issues related to equipment upkeep, but its implementation is not without challenges. The process usually requires the integration of sensors, data infrastructure, data analysis, and machine learning, a blend of software and hardware skills that often leads to lengthy setup periods and delayed return on investment. Therefore, it is vital to find a solution for managing equipment failure that seamlessly integrates with existing business procedures for asset management and plant maintenance.

Amazon Monitron enables the customers to implement a predictive maintenance program and reduce unplanned downtime. SAP customers use SAP Enterprise Asset Management to create maintenance notifications for industrial equipment.

In this blog post, we will walk you through a framework on SAP Business Technology Platform (SAP BTP) with which we can provide an integration framework between Amazon Monitron and SAP S/4HANA Asset Management and thereby automating the process of creating maintenance notifications.

In the upcoming sections of the blog post, we will describe the solution architecture, systems involved, PoC scenario and a quick demo followed by the instructions to deploy the solution from a GitHub repository.

Engagement

The outcome described in this article was facilitated by an engagement between SAP and Amazon Web Services which resulted in the creation and validation of a joint reference architecture use cases and PoC. Teams from PVN Pavan Kumar (SAP Labs India) and Soulat Khan (Amazon Web Services) have participated in this engagement.

Business Scenario

Predictive maintenance plays a crucial role in helping industries avoid downtime by using advanced analytics and machine learning algorithms to predict equipment failures before they occur. By analyzing historical data and real-time sensor readings, it can identify patterns and anomalies that indicate potential issues. This enables proactive maintenance thereby minimizing the impact on production.

In this scenario, abnormal conditions are detected and the events are generated and sent to the Events-to-Business Action Framework which maps it to an associated business action, which in this case, is to create a maintenance notification in SAP S/4HANA Asset Management. The business value of this integration helps industries to improve operational efficiency and ensure uninterrupted production.

Amazon Monitron is an end-to-end system that uses machine learning to detect abnormal conditions in industrial rotating equipment, enabling you to implement a predictive maintenance program and reduce unplanned downtime. It includes sensors to capture vibration and temperature data, a gateway to securely transfer data to AWS, a service that analyzes the data using machine learning (ML), and a companion mobile and desktop app to set up the devices and track the condition of your machinery. The Monitron service can export data to a kinesis stream and S3, making data available for downstream SAP integration. For more information, please check out Amazon Monitron.

Image courtesy : https://aws.amazon.com/pm/monitron

SAP Enterprise Asset Management (EAM) helps organizations to plan, optimize, execute, and track the necessary activities, priorities, skills, materials, tools, and information associated with an asset. Failure to manage and maintain enterprise assets can lead to unplanned downtime, suboptimal asset performance, and supply shortages. Customers also rely on EAM systems to demonstrate compliance with regulatory bodies to preclude liability if a failure occurs. To learn more about SAP EAM solution, refer SAP Enterprise Asset Management

Solution Architecture

The following steps depicts the information flow across systems:

(1) Event is triggered from Amazon Monitron Hardware and sent to Amazon Monitron Software.

(2) and (3) Amazon Kinesis streams the sensor data from Amazon Monitron and lands it into the Amazon S3 bucket.

(4) AWS Lambda is a serverless function, which will orchestrate the process of detecting a stream contains any alerts related to failure or warnings, and then the inference result is passed to SAP Advanced Event Mesh.

(5a) AWS secrets manager is used to store credentials; these are used by the lambda function to provide payload to SAP Advanced Event Mesh.

(5) Event-to-Business-Action framework (extension app) processor module's endpoint is subscribed to SAP Advanced Event Mesh, hence receives this event.

(6) Event-to-Business-Action framework processor module leverages the Decisions capability of SAP Build Process Automation to derive a business action (for example, in this scenario, Plant Maintenance Notification creation in SAP S/4HANA system) based on certain characteristics of incoming event.

(7), (8), (9) (10) and (11) Event-to-Business-Action framework processor module triggers the defined action in the SAP S/4HANA system by using the SAP Destination Service and SAP Private Link Service.

The integration architecture uses SAP “Events-to-Business Actions” architecture which is an event-driven framework on SAP Business Technology Platform (SAP BTP) to respond to and integrate with events generated from industrial production processes in plants, warehouses, and logistics into enterprise business systems, triggering associated business processes to enhance enterprise operations and enable rapid decision-making.

Scenario Setup and Deployment

To get the entire scenario working, there are prerequisites and initial configurations required in SAP Business Technology Platform, Amazon Web Services, and SAP S/4HANA.

Please refer to the published GitHub repository which contains code samples and instructions for implementing this scenario

GitHub Repo

Discovery Center Mission

We have created this reference architecture with accompanying sample code which can be configured and extended as per your requirements. However, it is up to the end user to utilize the code, make modifications and keep it maintained for productive use.

If you are using AWS IoT services for your own sensors, please follow the instructions here:

GitHub Repo

Benefits The business value and technical benefits of this integration are the following:

Detect machine issues before they occur with machine learning (ML) and take action to reduce operational costs.

Start monitoring equipment in minutes with easy installation and automatic, secure analysis through the Amazon Monitron end-to-end system.

Improve system accuracy continuously as Amazon Monitron learns from technician feedback entered in the mobile and web apps.

Network security-focused design with SAP Private Link specifically for RISE with SAP customers between SAP BTP on AWS (any region) and SAP S/4HANA on AWS

Event-driven integration architecture with SAP Advanced Event Mesh as a central hub.

Demo

The industrial equipment in Manufacturing plant is connected to Amazon Monitron that sends an event to SAP Advanced Event Mesh once abnormal condition is detected and the Event to Business Actions framework process the event and enriches it will relevant business context using decision tables from SAP Build Process Automation and creates a maintenance notification in SAP S/4HANA.

You can watch a demo here that showcases the PoC scenario

In Closing

We hope this blog has given you a brief idea on how to integrate events generated from industrial equipment to trigger different business actions/workflows/process in SAP backend systems.

On the SAP side, Many thanks to Uma Anbazhagan from SAP for driving this topic by collaborating with all the stakeholders involved in the engagement, Swati Maste and Ajit Kumar Panda for supporting the development of this solution, Madankumar Pichamuthu for enabling collaboration and finally Anirban Majumdar, PVN Pavan Kumar, Sivakumar N for leadership support for this project.

On the Amazon AWS side, Many thanks to Soulat Khan, Ganesh Suryanarayanan, Sunny Patwari, Renga Sridharan, Aiyappa Machanda, Abhik Ray, Suresh Pulivarthi, Krishnakumar Ramadoss and the SAP APN Partner Innovation team for their support in this project.

To learn more about SAP BTP, see the learning journey on SAP Learning called Discover SAP Business Technology Platform, a great introduction to BTP and the Intelligent Enterprise strategy to see what it’s all about for free.

As part of the global partnership between SAP and AWS, we have jointly developed an openSAP course titled “Build Resilient Applications on SAP BTP with Amazon Web Services

[Registration link] https://open.sap.com/courses/aws1

By joining this free openSAP course, you'll delve into multiple Joint Reference Architecture patterns, gain in-depth knowledge of the architectural patterns and also have the opportunity to enhance your skills through hands-on exercises.

To learn more about Amazon AWS, visit AWS training

For more information about this topic or to ask a question, please contact us at paa_india@sap.com

SAP Business Network Asset Collaboration and Classification Content by ECLASS: FAQs

2024-02-05T08:12:27.272000+01:00

This FAQ blog deals with questions related to the partnership of SAP Business Network Asset Collaboration and the classification content provider ECLASS. Main aspects are how the ECLASS classification is made available in SAP Business Network Asset Collaboration, how customers can get access to the ECLASS classification content in SAP Business Network Asset Collaboration, and what to do if you are missing ECLASS classification content either in SAP Business Network Asset Collaboration or in ECLASS.

General Questions

What is ECLASS?

ECLASS is a cross-industry product-data standard for the classification and unique description of products and services.

For more information on the company and the standard, please refer to the official ECLASS Web pages (https://eclass.eu/).

How does SAP Business Network Asset Collaboration work with ECLASS?

SAP Business Network Asset Collaboration and ECLASS have a partnership, and ECLASS acts as a Content Provider in SAP Business Network Asset Collaboration. This means that ECLASS is a participant in the network and ECLASS classification objects get created by ECLASS in their account and will be shared by ECLASS with their licensees in the network.

Content-Related Questions

Which ECLASS version is available with SAP Business Network Asset Collaboration?

ECLASS versions 10.1 and 12.0 are currently available.

Which ECLASS objects will be available in SAP Business Network Asset Collaboration?

ECLASS classification objects will be loaded as SAP Business Network Asset Collaboration Template objects, i.e., classes, subclasses, attribute groups, nested structures, attributes, and code lists.
Note: In ECLASS version 10.1, you can still see ECLASS model templates. These were used as a temporary workaround to realize the loading of ECLASS aspects when SAP Business Network Asset Collaboration (then SAP Asset Intelligence Network) did not have the nested structures functionality yet.

With ECLASS version 12.0, ECLASS Blocks and Aspects are represented as nested structures in SAP Business Network Asset Collaboration.
The obsolete Model Templates (= representation of Aspects) are still visible in version 10.1 but should not be used anymore.

In the beginning the following ECLASS segments will be available in the productive landscape:

– Segment 19 (Information, communication and media technology)

– Segment 23 (Machine element, fixing, mounting)

– Segment 27 (Electric engineering, automation, process control engineering)

– Segment 36 (Machine, apparatus)

– Segment 37 (Industrial piping)

More segments will be made available in upcoming releases. The sequence/priority of additional segments will be decided based on customer requests (see below).

It looks like ECLASS is currently only available in English and German language? Will there be other languages available?

Yes, ECLASS classification is currently available in languages English and German. Further languages supported by ECLASS can be made available for version 12.0 upon request (see below).

What can I do if I need additional ECLASS segments or additional ECLASS languages in the SAP Business Network Asset Collaboration productive landscape?

Please create an incident under component SBN-AIN-ACF-CNT and request the segment or language you need. ECLASS will then upload the requested segment(s) as soon as possible.

Note: You should only create such a segment request for ECLASS if you already have an ECLASS license (see LICENSING section below).

What can I do if I need additional classification content in ECLASS?

Missing content can be requested from ECLASS. Please follow the general process of submitting Change Requests to the ECLASS standard (refer to https://eclass.eu/support/guides/change-request). For questions, please contact directly ECLASS Office: info@eclass.de.

Licensing Questions

Does ECLASS classification content come for free with SAP Business Network Asset Collaboration?

A subset of ECLASS structural elements will be made available for a free Trial in the Test and Demo system. The classification content (= ECLASS segments) which is available as free trial are:

– Segment 19 (Information, communication and media technology)

– Segment 27 (Electric engineering, automation, process control engineering)

– Segment 36 (Machine, apparatus)

– Segment 37 (Industrial piping)

To use this free trial content, please create a connection request with ECLASS e.V. in the Test and Demo system. ECLASS will then trigger the sharing of the free trial content for your account. For productive use – i.e., in SAP Business Network Asset Collaboration Production landscape – you require a dedicated license from ECLASS to be able to model business objects using ECLASS content.

Note: There will be no migration possible from the free trial version to the productive system.

How can I license ECLASS and what are the costs?

Licensing of ECLASS classification content is handled by ECLASS. You can view the available licensing models on the ECLASS Web site (https://eclass.eu/en/eclass-standard/licenses).
For direct inquiries regarding the exact licensing options for your company, please directly contact the ECLASS office (info@eclass.de).

My company already has an ECLASS license. Can I use this for SAP Business Network Asset Collaboration as well?

ECLASS will share their objects with their licensees based on their customer list. So, if you already have an ECLASS license this will also be valid for use of ECLASS in SAP Business Network Asset collaboration.
As an ECLASS licensee, please create a connection request with ECLASS e.V. in the Production system. ECLASS will then trigger the sharing of their content for your account. For any license-related questions (including ECLASS sharing via SAP Business Network Asset Collaboration) you can also contact ECLASS directly (e-mail: info@eclass.de).

I have a Premium account in SAP Business Network Asset Collaboration, and I want my Invitee accounts to create classification content based on ECLASS classification. Will my Invitees need an own ECLASS license for this?

If objects are to be modelled based on ECLASS classification objects a dedicated ECLASS license is required independent of the SAP Business Network Asset Collaboration account type. Therefore, Invitees who model objects in SAP Business Network Asset Collaboration based on ECLASS classification content need an own ECLASS license.

Jowat: Seamless Just-in-Time Operations with SAP Digital Manufacturing to Make Better Decisions

2024-02-23T15:58:30.269000+01:00

Jowat Group is one of the world’s top adhesive manufacturers for industrial use. Tradition and experience from 100 years of corporate development and experience allow it to act as leading company in technology and innovations, in numerous markets and application areas. Jowat SE’s annual production amounts to 100,000 tons of industrial adhesives. Their production is complex and requires high volumes of specialized chemicals and a tremendous amount of power. With raw materials and energy costs skyrocketing, premium manufacturer Jowat looked to get a tighter grip on its inventory management and production processes.

To ensure maximum efficiency, Jowat requires enough raw materials to fulfil orders, while minimizing waste and inventory costs. Moreover, the company needs smooth workflows as any delay in order placement, production, or the delivery of raw materials can have huge cost implications for the company.

As a result, Jowat works hard to ensure that its 24/7 manufacturing processes always run like clockwork.

With demand for industrial adhesives surging, the company was eager to ensure that its production processes could scale to meet increasing order numbers and still deliver optimum efficiency.

Targeting Seamless Digital Workflows

Originally established in 1919, Jowat is no stranger to innovation. Leaders at Jowat identified that the next stage of the company’s evolution would require a radical digital transformation of its production and inventory management processes.

“Our factory workers relied heavily on paper-based processes,” explained Dirk-Alexander Segger, Detmold Plant Manager at Jowat. “For instance, when employees had finished mixing and decanting adhesives, they handwrote notes to track the volume and type of raw materials that they used during the process. Shortly after decanting, workers would then input the information from their notes into our inventory management and manufacturing execution systems—adding to their workload and increasing the risk of errors.”

Tracking materials usage manually created both time-delay, and potential for human errors, as well as discrepancies between the stock amounts recorded in Jowat’s inventory system and the actual volume of raw materials available on the factory floor. Moreover, with information stored across multiple systems and in different formats, plant managers had no clarity to identify process bottlenecks or potential enhancements.

Annette Podleschny-Borchardt, Project Lead at Jowat, commented: “To enhance efficiency, improve transparency, and prepare for growth, we decided it was time to go entirely paperless. Building digital processes would give us a more detailed, up-to-date picture of our stock position, which would help to drive smarter raw materials purchasing decisions as well as production improvements.”

“We aimed to take a more proactive approach to our manufacturing processes. Developing robust, reliable, end-to-end digital workflows was the perfect way to achieve this goal,” explained Christine Künne, Head of IT and Business Process Services at Jowat SE.

Embracing Digital Transformation

To find the best solution to support its digital transformation initiative, Jowat invited multiple technology vendors to outline the strengths of their solutions. To keep customization low, Jowat targeted a solution that it could easily adapt to meet the highly specialized nature of just-in-time chemicals manufacturing.

After reviewing 15 different solutions, Jowat selected SAP Digital Manufacturing (SAP DM). As well as providing a solid foundation for digitalization, SAP Digital Manufacturing also integrates seamlessly with Jowat’s existing SAP solutions, including SAP S/4HANA, SAP Extended Warehouse Management, and SAP Business Technology Platform.

“We were immediately interested in SAP Digital Manufacturing because it is a cloud-native solution,” commented Dirk-Alexander Segger. “Previously, we used on-premise solutions that required us to carry out extensive and disruptive updates. With SAP Digital Manufacturing, we get instant access to the latest features thanks to the quarterly release schedule.”

Jowat also valued SAP’s proactive engagement with the chemicals manufacturing industry. “We really appreciate that SAP is eager to co-innovate with industry leaders like Jowat to develop next-generation manufacturing solutions,” said Annette Podleschny-Borchardt. “Through the SAP Customer Engagement Initiative, we can help expert developers at SAP build smart factory solutions that meet the needs of our sector down to the finest detail.”

Sticking with the Experts

To ensure a smooth implementation of SAP Digital Manufacturing, Jowat relied on support from SAP Services and Support.

“Whenever we hit a stumbling block, the experts helped us work through the issues. For instance, we wanted to develop a dedicated production operation dashboard, which would give plant managers fast access to key production metrics. As this was uncharted territory, it naturally took a lot of time and energy to work through and SAP was behind us every step of the way,” said Dirk-Alexander Segger.

Currently, Jowat is completing the first phase of the SAP Digital Manufacturing implementation at its Detmold factory before expanding the solution across all plants. Christine Künne explained: “With a phased implementation we can ensure that the processes we build at our Detmold plant are tried-and-tested to deliver maximum efficiency. This will give us the best chance of success when we roll out these processes across all our facilities.”

“Consultants from SAP Services and Support brought not only their expertise to the project, but also the willingness to listen to the needs and the challenges of our industry. Because of this, we were able to collaborate extremely effectively,” added Annette Podleschny-Borchardt.

Driving Efficient Manufacturing with High-quality Data

With SAP Digital Manufacturing, Jowat left error-prone, manual processes behind and benefits now from seamless digital workflows with end-to-end transparency. This increases quality and eases work for shop floor operators. The activity confirmation is automatically done in the SAP system enabling real-time inventory insight.

Consequently, Jowat gained the following benefits:

100% digitalized manufacturing process
One source of truth for detailed production information
Time savings of 3 person days per week through automated raw material consumption updates allowing employees focus on higher value activities
100% accurate inventory

Dirk-Alexander Segger said: “Today workers feed consumption information at the point of production. Previously, it would take one full-time employee three days a week to track raw materials consumption. With SAP Digital Manufacturing, we have a real-time, accurate picture of our raw materials usage and needs. This is helping us optimize usage and reduce costs.”

Annette Podleschny-Borchardt added: “Production processes are so much faster now that we have built streamlined digital processes. Before, our paper-based ways of working could add up to five days to production timelines. With SAP Digital Manufacturing, we’ve shaved off this time, allowing us to produce and distribute products to our customers much faster—boosting satisfaction.”

Planning ahead for an AI-driven Future

With reliable, up-to-date information in SAP Digital Manufacturing, plant managers and leaders at Jowat have full visibility across the entire manufacturing cycle. Christine Künne said: “SAP Digital Manufacturing has helped improve the traceability of our products and given us a much more detailed understanding of our manufacturing operations. Ultimately, that helps to enhance planning and provides new insights into potential process improvements.”

Jowat’s plans for SAP Digital Manufacturing go far beyond a global rollout. The company is already exploring the potential to scale and streamline manufacturing processes further with artificial intelligence (AI) and Internet of Things (IoT) technologies.

“AI holds tremendous potential for us,” reflected Annette Podleschny-Borchardt. “We’re excited to explore how it can help us to ensure consistently excellent quality across heavily customized product lines, and how we can enhance production uptime with IoT-driven predictive maintenance. Having an AI-ready solution like SAP Digital Manufacturing at the heart of our operations makes taking these next steps in our innovation journey much easier.”

Summarizing the overall benefits, Christine Künne concluded “With the implementation of SAP Digital Manufacturing through SAP Services and Support, we see ourselves on the way to becoming a transparent factory with a reduction in risks, errors, and process costs thanks to optimized processes.”

Click here to see what benefits other companies have gained including improving quality with AI-assisted visual inspection. Be sure to listen to a webinar how a renowned manufacturer revolutionizes manufacturing with Industry 4.0. Also, read a report from Oxford Economics enabling you to make your supply chain more resilient and sustainable.

IoT - Ultimate Data Cyber Security - with Enterprise Blockchain and SAP BTP 🚀

2024-04-22T11:07:39.454000+02:00

It wasn't that long ago that Cyber Security and Resilience translated to High Availability and Disaster Recovery. Driven by the Sensitivity, Confidentiality, and Availability requirements of the Data, Systems and Applications were built to have the highest possible availability. That requirement came from the times when an entire end to end Business Process ran in a single standalone Application.

Fast forward to today, end to end Business Processes can begin in one Application on one side of the world and pass through Applications somewhere else in the world and finish in Applications in an altogether different location in the world. The beginning of the Business Process can be Sensors and Things at the Edge, producing Data, Events, something has happened and triggered the Business Process.

This creates several problems:

Protect the Originality & Integrity of the Data - When the Thing, the IoT Sensor, let's say critical infrastructure, something like a Base Station in a Telco Network, or a piece of the Electrical Grid for a Utility Company, when the Thing, the IoT Sensor creates the piece of Data, a Record of something that just happened we need to make sure that piece of Data cannot be modified or destroyed and therefore protect the originality and integrity of the Data

Moving the Data from the Edge to the SAP Application in the Cloud or DataCenter and at the same time Protect the Originality & Integrity of the Data - we need to get the Data from the Thing, the IoT Sensor, to the SAP Application in the Cloud or DataCenter and we need to be sure, to have surety that the Data which arrives in the SAP Cloud or DataCenter is exactly the same Data that was created at the Edge at the Thing, the IoT Sensor. If this Data gets changed in any way, we won't be able to trust the Business Processes and Insights which are depending on that Data. And so, in the activity of moving the Data we need to make sure that that piece of Data cannot be modified or destroyed and therefore protect the originality and integrity of the Data

And so, here we are, the biggest threat to Enterprise SAP Applications is no longer the High Availability, the Server crashing, that's all under control and possible to take care of,

The biggest threat to IoT & Enterprise SAP Applications is Cyber Security and Cyber Attacks

A common thought is that a hacker, or cyber attacker wants to modify Data for their own financial benefit, for example change the destination bank account number for invoice payments, yes those threats are still there, but biggest threat is that a hacker or cyber attacker deletes or modifies Master and Transaction Data resulting that the Master and Transactional Data does not have integrity and cannot be used, and if the attacker succeeded to modify the Backup as well, then the Enterprise could be out of business for a long time trying to pick up the pieces and get up and running again.

The biggest threat to IoT & Enterprise SAP Applications is Cyber Attacks rendering the Master and Transaction Data un-trustworthy

And that's where the Enterprise Blockchain comes in, and this blog is going to explain why.

Ok let's go🚀

Welcome to the eighth blog in this series on Enterprise Blockchain and SAP. If you have been following the previous blogs then you'll be familiar with the blog template. We'll begin by talking about and framing the problem, in this case Data Cyber Security for IoT and SAP and then go in to identifying the enabling technology which will have the best capabilities and be the most appropriate to solving the problem all the way through to the reference solution architecture to be able to implement the solution.

The blog is going to break the subject down in to three sections:

Section 1.0: The What is it of IoT & SAP, and Enterprise Blockchain

Section 2.0: The Why is it, of IoT & SAP, and Enterprise Blockchain

Section 3.0: The How is it, of IoT & SAP, and Enterprise Blockchain

tl:dr

If you want to protect the originality and integrity and confidentiality of IoT data, from the Edge to Insights and SAP Business Processes, then the answer is an Enterprise Blockchain Database, where the Enterprise Blockchain Tenants are running on Edge Hosts/Servers and in the SAP BTP Business Technology Platform, enabling Enterprise Blockchain Database to protect the Data from the Edge Hosts/Servers to the SAP BTP.

IoT Internet of Things Edge Data Cyber Security and SAP Asset Performance Management BTP Protected by Enterprise Blockchain - atkrypto.io

[the finer technical details of getting the data from the Enterprise Blockchain to SAP Asset Management and S/4HANA will be clearer possibly after Sapphire Orlando in June. In the mean time there are a number of ways to get the Data in and out of the Enterprise Blockchain running on the SAP BTP Kyma Service]

Enterprise Blockchain is both:

. a Secure Store

. a Secure Communication Channel

Enterprise Blockchain is the Cyber Security for Enterprise IoT Data from the Edge to Insights 🚀

and now.... the long answer...

Section 1.0: The What is it of IoT & SAP, and Enterprise Blockchain

What is IoT Internet of Things ?

History of IoT

In 2021, there were over 10 billion IoT devices in the world, and by 2025, the IDC expects global data generation to exceed 73 zettabytes – which is equal to 73 trillion gigabytes. Although we can’t really quantify digital data in physical terms, we can say that if all that data were converted into 1990s floppy disks – and they were laid out end to end – they could go to the moon and back over 5,000 times.

[Source: What is IoT? The Future of Business | SAP]

What are some IoT applications?

Looking at IoT applications, which are sometimes described as use cases, can help ground the discussion about what IoT is. Broadly, IoT applications occur in one of nine settings.

Human health. Devices can be attached to or inserted inside the human body, including wearable or ingestible devices that monitor or maintain health and wellness, assist in managing diseases such as diabetes, and more.
Home. Homeowners can install devices such as home voice assistants, automated vacuums, or security systems.
Retail environments. Devices can be installed in stores, banks, restaurants, and arenas to facilitate self-checkout, extend in-store offers, or help optimize inventory.
Offices. IoT applications in offices could entail energy management or security for buildings.
Standardized production environments. In such settings, including manufacturing plants, hospitals, or farms, IoT applications often aim to gain operating efficiencies or optimize equipment use and inventory.
Custom production environments. In customized settings like those in mining, construction, or oil and gas exploration and production, IoT applications might be used in predictive maintenance or health and safety efforts.
Vehicles. IoT can help with condition-based maintenance, usage-based design, or presales analytics for cars and trucks, ships, airplanes, and trains.
Cities. IoT applications can be used for adaptive traffic control, smart meters, environmental monitoring, or managing resources.
Outside. In urban environments or other outdoor settings, such as railroad tracks, autonomous vehicles, or flight navigation, IoT applications could involve real-time routing, connected navigation, or shipment tracking.

Other real-world examples abound. IoT solutions are being used in myriad settings: in refrigerators, to help restaurants optimize their food-compliance processes; in fields, to track livestock; in offices, to track how many and how often meeting rooms are used; and beyond.

What is the economic impact of IoT?

The potential value of IoT is large and growing. By 2030, we estimate it could amount to up to $12.5 trillion globally. That includes the value captured by consumers and customers of IoT products and services.

The potential economic value of IoT differs based on settings and usages, with factory settings and human health applications representing outsize shares of this total. Factory settings could generate $1.4 trillion to $3.3 trillion by 2030, or just over a quarter of the total value potential. IoT economic impact in human health settings could reach around 14 percent of the total estimated value.

Another way of looking at IoT’s value is to explore use-case clusters (similar uses adapted to different settings). Some of the most common use cases account for a sizable share of IoT’s potential economic value:

operations optimization, which is basically making the various day-to-day management of assets and people more efficient (41 percent)
health (15 percent)
human productivity (15 percent)
condition-based maintenance (12 percent)

Other clusters include sales enablement, energy management, autonomous vehicles (the fastest-growing cluster), and safety and security.

[Source: What is IoT: The Internet of Things explained | McKinsey]

Like everything else we do in Enterprise IT,

IoT Internet of Things is about Data

What is Enterprise Blockchain ?

Enterprise Blockchain is both:

. a Secure Store

. a Secure Communication Channel

McKinsey & Company, in their December 2023 Featured Insights Publication, gave a beautiful description of what is unique and special about Blockchain, "Blockchain is a secure database shared across a network of participants, where up-to-date information is available to all participants at the same time". If we just pause for a moment and let that sink in, and think about what that means, to Business Processes, to Collaboration, to System Resilience, we start to see what is so special about Blockchain Databases and Distributed Ledger Technology.

In these previous blogs, I made a deep dive in to what Enterprise Blockchain is and why we should be positioning it in our Enterprise Architecture:

Why I love SAP and Blockchain Databases and why you should too🚀

SAP Enterprise Architecture: Positioning Blockchain Database as an Enterprise Technology Standard🚀

SAP Enterprise Architecture: Let the Use Case find the Blockchain 🚀

IoT Internet of Things and SAP - Enterprise Blockchain is the next generation Data Cyber Security Protection - atkrypto.io

and in a nutshell, Enterprise Blockchain is:

. The Digital Transformation of Information Security into Cyber Security

. The Next Generation Data Integrity, Originality, Confidentiality Protection

. Re-imagining Information Security

. Natively, out of the box, due to its special characteristics the strongest, hardest, most resilient Enterprise Database product

To wrap up this section:

. Iot Internet of Things is about Data

. Enterprise Blockchain is about Cyber Security of Data

Section 2.0: The Why is it, of IoT & SAP, and Enterprise Blockchain

So, why does IoT, Internet of Things, in the Enterprise IT, when implemented in conjunction with SAP Applications need Enterprise Blockchain ?

IoT is about Data, and the Data in most cases originates from the Edge and outlying parts of the Network.

The problem is the Cyber Security of getting the Data from the Edge and outlying parts of the Network to the safe zones of the SAP Cloud and yours or SAP's DataCenters.

IoT's biggest risk is that the Data coming from the IoT Devices is not trustworthy. In the same way as asking the wrong person for directions can leave you going all around the houses, if the Data coming from the IoT Devices, has been changed/contaminated/modified/poluted/made unreliable, whether purposefully through malicious acts or cyber attack, or accidentally, the result will be IoT's Data insights which cannot be trusted, and the result of that could be catastrophic. Just imagine not being able to trust the temperature of refrigeration during pharmaceutical and food production !

If we cannot protect and the originality and integrity of IoT Data, secure it, then how can we trust IoT Data ?

As Mckinsey & Company say:

IoT is about the Data

So, if we are going to do IoT and include IoT Data in our Business Processes and Insights, then we need to care for and protect the Data that is coming from IoT Devices.

Imagine, as described in the previous blog, when we let the Use Case find the Enterprise Blockchain, we have a Business Requirement, a Business Demand, to make IoT Data trustable.

When we look in our Enterprise Technology Standards, and we look for the Technology Standard in our Enterprise Portfolio which is positioned to bring the strongest protection to Data, we find the Enterprise Blockchain.

Comparison Enterprise Blockchain Database and Traditional Legacy Database - atkrypto.io

In the previous blogs, we have discussed in detail about the special characteristics of Enterprise Blockchain and just why it natively out of the box protects the integrity of data to a level that legacy database products cannot do, in a nutshell....

IoT is about Data

IoT is about the Data that goes in to the SAP Applications in the Cloud and Data Centers

This means IoT Device Data depends on a Database or a Datastore

What kind of Database do IoT Devices produces ? What capabilities does the Database for the IoT Devices Data need to have ?

1. It must not be possible to modify the Data in the Database which comes from the IoT Devices - the Database needs to be immutable

2. The Data in the Database, the integrity and originality of that Data must be protected to the highest level that is technically possible

3. The Data must be available with the highest availability, the Database must be resilient to attack

When we look in our Enterprise Technology Standards we find 1 Technology Standard in the Enterprise which has those capabilities, and that is..... Enterprise Blockchain

Enterprise Blockchain ticks those three boxes...

Immutable - tick that box

Integrity must be protected to the highest level - tick that box, thanks to the Enterprise Blockchain Hash Mechanism and the Enterprise Blockchain Consensus Mechanism

Highest level of resilience and availability - tick that box thanks to the Distributed and Decentralised nature of the Enterprise Blockchain

This is why, Enterprise Blockchain is the enabler of trustable outcomes from Enterprise IoT Devices' Data.

atkrypto.io what is a blockchain

But there's more than that, IoT Devices can produce a lot of data, and the volumes of data can be big.

And this is why, in this blog we take the Enterprise Blockchain Technology story one level further and we introduce the:

Enterprise Blockchain Wallet

Off-Chain Data Storage

In the Enterprise Blockchain Platforms, the Enterprise Blockchain Wallet is used for Off-Chain storage of big data and in the following paragraphs we will explain why.

What is the Enterprise Blockchain Wallet, and what is Off-Chain Data Storage and why would we use them and why do we need them ?

As we have explained in a previous blog, the Enterprise Blockchain Database, the Distributed Ledger, can be looked at simply as a Database Table (which is replicated and synchronised across multiple Servers) and in principle it stores the Data like this:

Blockchain is a very simple form of database atkrypto.io

This is fine, and suited to what we call Structured Data, and as AWS nicely describe, Structured Data is information like words and numbers. This kind of data is perfectly suited to being stored in an Enterprise Blockchain Database and also a legacy Database. Examples of the data would Names, Addresses, Phone Numbers, Product Information etc.

But, IoT Devices can produce a lot of Data, for example, there could be photographs proving that general waste was tipped at the correct certified location, photographs and in large volumes would be too big to be stored on the Enterprise Blockchain Database itself.

And that's ok, Enterprise Blockchain Platforms are ready for that, and have been designed to store both Structured Data and Data which is in files which are so big that they cannot be stored in the Enterprise Blockchain Database itself, for example the photographs from a Waste Truck's onboard camera proving that waste was responsibly tipped in the correct location and taken at the same time as recording GPS location coordinates proving the location of the Waste Truck.

So, if we can't store the large photographs files in large quantities to the Enterprise Blockchain Database, then how, in an Enterprise Blockchain Platform do we store large files of Data ?

Voila.... bring in the Enterprise Blockchain Platform Wallet. The best Enterprise Blockchain Platform products include what is called the Enterprise Blockchain Platform Wallet, or to make it shorter, the Enterprise Blockchain Wallet.

The Enterprise Blockchain Wallet enables us to store large Data, like large Files safely and securely off the chain, or 'Off-Chain'.

But if we store the large Data files Off-Chain in the Enterprise Blockchain Wallet, then how do we also have them some how on the Enterprise Blockchain Database ?

The way this works is elegant, in any decent Enterprise Blockchain Platform, the Enterprise Blockchain Wallet location is completely configurable, and could be anywhere from SAP HANA Cloud (Data Lake), or for example multiple hyperscaler object stores, such as Amazon S3, OSS (Alicloud Object Storage
Service), SAP HANA Cloud, Data Lake, and Azure Blob Storage.

The configurable Enterprise Blockchain Wallet of the Enterprise Blockchain Platform looks like this:

Enterprise Blockchain Platform - Enterprise Blockchain Wallets - Configurable Enterprise Wallets - atkrypto.io

Ok, so we've got the IoT Data stored in the (configurable) Enterprise Blockchain Wallet, but what about securing the IoT Data ? Obviously the Enterprise Blockchain Wallet storage location has built in security, for example the SAP HANA Cloud, the AWS S3 Buckets, but we need more than the out of the box security of these products, the reason we are using the Enterprise Blockchain Database is because of the amazing security strengths that it natively out of the box has, and so, what about the Enterprise Blockchain Wallet, doesn't the Enterprise Blockchain Platform have some cool super hard way of protecting the data in the Enterprise Blockchain Wallet ?

Well yes it does, this is the magic of Enterprise Blockchain Database 'Off-Chain' storage in the Enterprise Blockchain Wallet. This is so unique to Blockchain Technologies.

What happens is this, when store data in the Enterprise Blockchain Wallet, the Enterprise Blockchain Platform software runs a hash algorithm over the data that we have stored and the data, and the large file gets hashed:

The data or the file in the Enterprise Blockchain Wallet gets hashed, and then, that hash is stored in the Enterprise Blockchain Database.

This means we now have a unique hash of that data or file, and if anybody or anything makes even the tiniest teeniest change to that data or file, next time we run a hash over that data or file the result will be different that the original hash which is safely stored in the Enterprise Blockchain Database and this is how we will know that the data has been changed and we cannot trust the Data and therefore we cannot use it for our Enterprise IoT Data processing and Insights..

On the other hand, if just before we load the data in to the SAP Enterprise Applications, eg SAP Asset Performance Management and SAP S/4HANA, from the Enterprise Blockchain Wallet, if we run a hash over the data and the hash result is the same as we have in the Enterprise Blockchain Database, then we will know we can trust the Data and we can use it in our SAP Applications and we will have trustable IoT Data.

Enterprise Blockchain Wallet Data Hashes Stored in the Enterprise Blockchain Database - atkrypto.io

And this is why, for all of these reasons,

Trustable Enterprise IoT Data depends on Data being stored in the Enterprise Blockchain

But that's not the end of the Why IoT Data needs Enterprise Blockchain.

As we showed at the beginning of the blog in this picture:

IoT Internet of Things Edge Data Cyber Security and SAP Asset Performance Management BTP Protected by Enterprise Blockchain - atkrypto.io

As the picture shows, we have an Enterprise Blockchain Database Tenant installed on a Server Host at the Edge of the Network AND we have an Enterprise Blockchain Data Tenant installed on the SAP BTP Kyma Runtime.

The consequence of this is that we have a distributed Enterprise Blockchain Database table which stretches from the Edge of the Network where the IoT Devices are all the way across the Network to the SAP BTP and DataCenter.

This means we have Enterprise Blockchain Data Protection from the source where the IoT Devices are to the Insights and Business Processes where the SAP Applications are.

We have taken the Enterprise Blockchain to the Data at the source at the IoT Devices instead of taking the IoT Device's Data across all of the Networks to the safety of the SAP BTP and DataCenter. This is because we need to store the Data in the Enterprise Blockchain as close as possible to the source of the Data. The closer the Enterprise Blockchain Tenant is to the source of the Data, the safer the Data will be, it's as simple as that. Enterprise Blockchain is the next generation Cyber Security for IoT Data, and we need to minimise the amount of exposure IoT Data has to previous generation security technologies and approaches.

And this is why we say, Enterprise Blockchain is a Secure Communication Channel, because instead of integrating Applications sending and replicating Data across Networks, we are sharing the Data across the Enterprise Blockchain and the Enterprise Blockchain is the Secure Communication Channel.

IoT Internet of Things and SAP - Enterprise Blockchain is the next generation Data Cyber Security Protection - atkrypto.io

To conclude this section, the Why to, of IoT and Enterprise Blockchain, IoT Data needs to Trustable.

Enterprise Blockchain, due to its native super strong security strength when used as a store of Data enables IoT Data to be Trustable, and the Enterprise Blockchain Software needs be installed as close as possible to the source of the IoT Data, as close as possible to the IoT Devices.

Section 3.0: The How is it, of IoT & SAP, and Enterprise Blockchain

Now that we know why trustable Enterprise IoT needs the Enterprise Blockchain Database to protect the integrity and originality of the Data, how do we implement it today ?

Well that's easy, here are the ingredients and the recipe...

Ingredients, you're going to need:

Data Source(s) eg

IoT Devices which are either REST or MQTT (this could be other protocols and transfer mechanisms depending upon the required Adapters)

SAP and IoT IoT Devices sending Data to MQTT Broker in the Edge Host Instance of the Enterprise Blockchain Platform Database Tenant - atkrypto.io

SAP and IoT IoT Devices sending Data to REST Endpoint in the Edge Host Instance of the Enterprise Blockchain Platform Database Tenant - atkrypto.io

Large Storage for Large Data and the Enterprise Blockchain Wallet

SAP HANA Cloud (Data Lake)

Enterprise Blockchain Platform and specifically one which is capable of running Tenants as close to the Source of the IoT Data as possible at the Edge. We do NOT want to send the IoT Data across the Internet to a Blockchain somewhere in the Cloud, that would defeat the object of the exercise.

These are the basic ingredients, the data from the IoT Devices will be stored either Off-Chain in the SAP HANA Cloud (Data Lake) which will also be the Enterprise Blockchain Platform (configurable) Wallet, or On-Chain in the Enterprise Blockchain Platform Database Ledgers, this Enterprise Blockchain Database Ledger will be running from the Edge to the SAP BTP and DataCenters and then SAP Applications like SAP Asset Performance Management and SAP S/4HANA will be able use the Data in Business Processes and Insights.

And your Technical Reference Architecture will look something like this:

IoT Internet of Things Edge Data Cyber Security and SAP Asset Performance Management BTP Protected by Enterprise Blockchain - atkrypto.io

And that's how you do it.

Wrapping up, conclusions:

. Trustable Enterprise IoT depends on Data being stored in the Enterprise Blockchain at the Edge and in the SAP BTP

Enterprise Blockchain is:

. The Digital Transformation of Information Security to Cyber Security

. The Next Generation Data Integrity, Originality, Confidentiality Protection

. Re-imagining Information Security

. Natively, out of the box, due to its special characteristics the strongest, hardest, most resilient Enterprise Database product

Enterprise Blockchain is both:

. a Secure Store

. a Secure Communication Channel

The configurable Enterprise Blockchain Wallet enables you to store Big Data 'Off-Chain' and the hashes of the IoT Data are stored safely and securely on the Enterprise Blockchain Database.

The good news is, as we discussed in the previous blog, this is no longer hype, we can do all of this today, and now, within the SAP Partner Edge Open EcoSystem there are enabling technology Blockchain Products designed and built by SAP Experts specifically for the needs of SAP Customers to make doing Blockchain and SAP easy, and so you can do SAP and Blockchain, today it's real and there's nothing stopping you.

So what are we waiting for ? Oh yeah, more use cases, ok, that will be the next blog.

What do you think, are the words IoT, Blockchain, Web3, Distributed Ledger Technology, starting to appear in your Company's visions and technology visions ? What use cases are you looking at ? Let's chat about it in the comments.

For now, over and out.

Andy Silvey.

Independent SAP Technical Architect and CEO of atkrypto.io

Author Bio:

Andy Silvey is a 25 years SAP Technology veteran [15 years SAP Basis and 10 years SAP Tech Arch including Tech, Integration, Security, Data from 3.1H to S/4HANA PCE on RISE and the BTP and everything in between, and former SCN Moderator and Mentor alumni].

Andy is also co-Founder of atkrypto inc, an startup whose ambition is to make Blockchain easy for Enterprise.

atkrypto.io's flagship product is the atkrypto Enterprise Blockchain Platform for SAP, and atkrypto.io is a SAP Partner Edge Open EcoSystem Partner.

The atkrypto Enterprise Blockchain Platform for SAP has been designed by SAP Independent Experts for the needs of SAP Customers and to be deployed on the SAP BTP Kyma Runtime Service and leverage native integration to SAP Products.

atkrypto Enterprise Blockchain Platform for SAP has a number of unique qualities, including being the only Blockchain software in the world which has a DataCenter version and a light mobile version which can run on Edge/IoT/Mobile devices and enables data to be written to the Blockchain at the Edge where that same Blockchain is running on a Server in the DataCenter, protecting the integrity and originality of data from the Edge to Insights. Taking Blockchain to the Data at the Edge instead of taking the Data to the Blockchain.

All of this makes atkrypto,io the DePIN Decentralised Physical Infrastructure Network solution for Enterprise.

atkrypto is one of the Next20 startups being featured at TM Forum's DTW Ignite in Copenhagen in June

If you will be at DTW24 come and talk to us about Cyber Security of SAP Data with Enterprise Blockchain.