{
"cells": [
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"# In this example notebook, we forecast 7 days in advance, for an item with:\n",
"# - 1-day shelf life\n",
"# - periodic daily replenishment\n",
"# - Training data between 2017-05-24 and 2017-11-02\n",
"# - 4 Forecasts are generated between 2017-11-03 and 2017-11-30, every 7 days\n",
"# - The demand that can be satisfied with the replenishment plan over the test period (2017-11-03 to 2017-11-30) is then assessed vs. actual performance\n",
"\n",
"# We see the following benefits from using WasteNot, versus using an unbuffered demand predictor:\n",
"# Improvement in Service Levels (91% -> 98% of demand satisfied)\n",
"# Increase in Profit ($117K -> $122K, an improvement of $5K over 28 days, or c.+4%)\n",
"\n",
"###### Input your access_token here!######\n",
"access_token = 'xxxxxxxxxxxxx'\n",
"infile = 'https://raw.githubusercontent.com/bluedotthinking/wastenot-documentation/master/example_data/bdt_example_input.csv'\n",
"\n",
"import requests\n",
"import numpy as np\n",
"requests.packages.urllib3.disable_warnings() \n",
"import json\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.dates as mdates\n",
"\n",
"def generate_payload(input_df, input_dict):\n",
" r = json.dumps(input_dict)\n",
" return r\n",
"\n",
"def import_data(infile):\n",
"# Import the time-series representing your historical demand - example CSV provided here:\n",
" input_df = pd.read_csv(infile)\n",
" return input_df\n",
"\n",
"def generate_forecast(access_token, payload):\n",
" # Calling the WasteNot API service with the payload, check the response text\n",
" url = \"https://api.bluedotthinking.com/forecast\"\n",
" headers = {\n",
" 'Content-Type': 'application/json',\n",
" 'access_token': access_token\n",
" }\n",
"\n",
" response = requests.request(\"POST\", url, headers=headers, data=payload, verify=True)\n",
" print ('API response')\n",
" print (response.text)\n",
" print ('-----')\n",
" return response\n",
"\n",
"# Plotting:\n",
"# a) Historical demand for the last 30 days, \n",
"# b) Units to be replenished using the replenishment_df - this includes the buffer\n",
"def visualise_results(input_df, json_data):\n",
"\n",
" input_df['timestamp'] = pd.to_datetime(input_df['timestamp']) \n",
"\n",
" # Load the output payload into pandas dataframes, converting the timestamp columns into datetimes for easier plotting\n",
" replenishment_df = pd.DataFrame(data={'timestamp':json_data['replenishment_timestamp'],\n",
" 'replenished_units':json_data['replenishment_units']\n",
" })\n",
" replenishment_df['timestamp'] = pd.to_datetime(replenishment_df['timestamp'])\n",
"\n",
" forecast_df = pd.DataFrame(data={'timestamp': json_data['forecast_timestamp'],\n",
" 'predicted_demand_units': json_data['predicted_demand_units'],\n",
" 'optimised_demand_units': json_data['optimised_demand_units'],\n",
" })\n",
" forecast_df['timestamp'] = pd.to_datetime(forecast_df['timestamp'])\n",
" plt.rc('font', size=12)\n",
" fig, ax = plt.subplots(figsize=(20, 6))\n",
"\n",
" # Specify how our lines should look\n",
" ax.plot(input_df.timestamp[-30:], input_df.demand_units[-30:], color='black', \n",
" linestyle='-', label='Historical Data: Demand Units')\n",
" ax.plot(forecast_df.timestamp, forecast_df.predicted_demand_units, color='blue', \n",
" marker=\"o\", linestyle='--', label='Forecast: Predicted Demand Units')\n",
" ax.bar(replenishment_df.timestamp, replenishment_df.replenished_units, color='green', \n",
" # marker=\"o\",linestyle='--', \n",
" label='Optimised Forecast: Buffered Units to Deliver')\n",
"\n",
" # For a less busy graph, only show the beginning of each week (Monday) as a major label\n",
" ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MO))\n",
" ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\n",
" ax.xaxis.set_minor_locator(mdates.DayLocator())\n",
"\n",
" ax.set_xlabel('Datetime')\n",
" ax.set_ylabel('Units')\n",
" ax.set_title('Historical & Optimised Forecast Demand using WasteNot')\n",
"\n",
" ax.grid(True)\n",
" ax.legend(loc='upper left');\n",
"\n",
" # Visualising the tables containing the delivered units (optimised), by day\n",
" print ('Replenishment Schedule & Amounts')\n",
" print (replenishment_df)\n",
" print ('-----')\n",
" # Visualising the tables containing the forecasted demand units (unoptimised and optimised), by day\n",
" print ('Forecast Dates & Amounts')\n",
" print (forecast_df)\n",
" print ('-----')\n"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" timestamp demand_units\n",
"0 2017-05-24T00:00:00 559\n",
"1 2017-05-25T00:00:00 1407\n",
"2 2017-05-26T00:00:00 1727\n",
"3 2017-05-27T00:00:00 930\n",
"4 2017-05-28T00:00:00 642\n",
".. ... ...\n",
"186 2017-11-26T00:00:00 1033\n",
"187 2017-11-27T00:00:00 1166\n",
"188 2017-11-28T00:00:00 1078\n",
"189 2017-11-29T00:00:00 724\n",
"190 2017-11-30T00:00:00 568\n",
"\n",
"[191 rows x 2 columns]\n",
"0 2017-11-02T00:00:00\n",
"API response\n",
"{\"status\":\"success\",\"forecast_timestamp\":[\"2017-11-03\",\"2017-11-04\",\"2017-11-05\",\"2017-11-06\",\"2017-11-07\",\"2017-11-08\",\"2017-11-09\"],\"predicted_demand_units\":[1794.0,1085.0,757.0,970.0,1071.0,1060.0,1255.0],\"optimised_demand_units\":[2070.0,1252.0,874.0,1119.0,1236.0,1223.0,1449.0],\"replenishment_timestamp\":[\"2017-11-03\",\"2017-11-04\",\"2017-11-05\",\"2017-11-06\",\"2017-11-07\",\"2017-11-08\",\"2017-11-09\"],\"replenishment_units\":[2070.0,1252.0,874.0,1119.0,1236.0,1223.0,1449.0]}\n",
"-----\n",
"1 2017-11-09T00:00:00\n",
"API response\n",
"{\"status\":\"success\",\"forecast_timestamp\":[\"2017-11-10\",\"2017-11-11\",\"2017-11-12\",\"2017-11-13\",\"2017-11-14\",\"2017-11-15\",\"2017-11-16\"],\"predicted_demand_units\":[1787.0,1080.0,758.0,966.0,1054.0,1055.0,1258.0],\"optimised_demand_units\":[2057.0,1243.0,872.0,1112.0,1213.0,1214.0,1448.0],\"replenishment_timestamp\":[\"2017-11-10\",\"2017-11-11\",\"2017-11-12\",\"2017-11-13\",\"2017-11-14\",\"2017-11-15\",\"2017-11-16\"],\"replenishment_units\":[2057.0,1243.0,872.0,1112.0,1213.0,1214.0,1448.0]}\n",
"-----\n",
"2 2017-11-16T00:00:00\n",
"API response\n",
"{\"status\":\"success\",\"forecast_timestamp\":[\"2017-11-17\",\"2017-11-18\",\"2017-11-19\",\"2017-11-20\",\"2017-11-21\",\"2017-11-22\",\"2017-11-23\"],\"predicted_demand_units\":[1797.0,1099.0,783.0,992.0,1074.0,1074.0,1271.0],\"optimised_demand_units\":[2074.0,1268.0,903.0,1145.0,1240.0,1240.0,1467.0],\"replenishment_timestamp\":[\"2017-11-17\",\"2017-11-18\",\"2017-11-19\",\"2017-11-20\",\"2017-11-21\",\"2017-11-22\",\"2017-11-23\"],\"replenishment_units\":[2074.0,1268.0,903.0,1145.0,1240.0,1240.0,1467.0]}\n",
"-----\n",
"3 2017-11-23T00:00:00\n",
"API response\n",
"{\"status\":\"success\",\"forecast_timestamp\":[\"2017-11-24\",\"2017-11-25\",\"2017-11-26\",\"2017-11-27\",\"2017-11-28\",\"2017-11-29\",\"2017-11-30\"],\"predicted_demand_units\":[1806.0,1089.0,803.0,1006.0,1074.0,1086.0,1282.0],\"optimised_demand_units\":[2081.0,1255.0,926.0,1159.0,1238.0,1251.0,1477.0],\"replenishment_timestamp\":[\"2017-11-24\",\"2017-11-25\",\"2017-11-26\",\"2017-11-27\",\"2017-11-28\",\"2017-11-29\",\"2017-11-30\"],\"replenishment_units\":[2081.0,1255.0,926.0,1159.0,1238.0,1251.0,1477.0]}\n",
"-----\n"
]
}
],
"source": [
"input_df = pd.read_csv(infile)\n",
"\n",
"input_df = input_df.loc[(input_df['timestamp']<='2017-12-01')\n",
" ]\n",
"print (input_df)\n",
"\n",
"replenishment_df_list = []\n",
"forecast_df_list = []\n",
"\n",
"n_sims = 4\n",
"\n",
"for n in range(n_sims):\n",
" train_df = input_df[:-(7*(n_sims-n))]\n",
" print (n, train_df['timestamp'].max())\n",
" unit_cost =1.\n",
" unit_sale_price=5.\n",
" shelf_life_seconds=86400\n",
" forecast_start_datetime = (pd.to_datetime(train_df['timestamp']).max() + pd.Timedelta(1, unit='d')).strftime(\"%Y-%m-%d\")\n",
" forecast_end_datetime = (pd.to_datetime(train_df['timestamp']).max() + pd.Timedelta(7, unit='d')).strftime(\"%Y-%m-%d\")\n",
" input_dict = {'timestamp':list(train_df['timestamp'].values),\n",
" 'demand':[int(x) for x in train_df['demand_units'].values],\n",
" \"cost\": unit_cost, \"sale_price\": unit_sale_price, \n",
" \"shelf_life_seconds\": shelf_life_seconds,\n",
" \"replenishment_schedule\": 'periodic',\n",
" \"replenishment_dayofweek\":[0,1,2,3,4,5,6],\n",
" \"forecast_start_datetime\":forecast_start_datetime,\n",
" \"forecast_end_datetime\":forecast_end_datetime,\n",
" }\n",
" payload = generate_payload(train_df, input_dict)\n",
" \n",
" # Running forecast\n",
" response = generate_forecast(access_token, payload)\n",
"\n",
" # Loading the response into JSON format\n",
" json_data = json.loads(response.text)\n",
" replenishment_df = pd.DataFrame(data={'timestamp':json_data['replenishment_timestamp'],\n",
" 'replenished_units':json_data['replenishment_units']\n",
" })\n",
" replenishment_df['timestamp'] = pd.to_datetime(replenishment_df['timestamp'])\n",
"\n",
" forecast_df = pd.DataFrame(data={'timestamp': json_data['forecast_timestamp'],\n",
" 'predicted_demand_units': json_data['predicted_demand_units'],\n",
" 'optimised_demand_units': json_data['optimised_demand_units'],\n",
" })\n",
" forecast_df['timestamp'] = pd.to_datetime(forecast_df['timestamp']) \n",
" replenishment_df_list.append(replenishment_df)\n",
" forecast_df_list.append(forecast_df) \n",
"\n",
"final_forecast_df = pd.concat(forecast_df_list)\n",
"final_replenishment_df = pd.concat(replenishment_df_list)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----Actual----\n",
"Actual Demand units: 32529\n",
"\n",
"----Unbuffered----\n",
"Revenue: 149475.0\n",
"Cost: 32186.0\n",
"Profit: 117289.0\n",
"Service Level: 91.90260997878816 %\n",
"Satisfied Units: 29895\n",
"\n",
"----Buffered for Max Profit----\n",
"Revenue: 159550.0\n",
"Cost: 37106.0\n",
"Profit: 122444.0\n",
"Service Level: 98.0970826032156 %\n",
"Satisfied Units: 31910\n",
"\n",
"----Buffered for 100% Service Level----\n",
"Revenue: 162645\n",
"Cost: 46025\n",
"Profit: 116620\n",
"Service Level: 100.0 %\n",
"Satisfied Units: 32529\n",
"\n"
]
}
],
"source": [
"\n",
"\n",
"\n",
"\n",
"final_replenishment_df['timestamp'] = pd.to_datetime(final_replenishment_df['timestamp']) \n",
"final_forecast_df['timestamp'] = pd.to_datetime(final_forecast_df['timestamp']) \n",
"input_df['timestamp'] = pd.to_datetime(input_df['timestamp']) \n",
"\n",
"comparison_df = pd.merge(final_forecast_df, input_df, on='timestamp', how='inner')\n",
"\n",
"unit_cost = 1.\n",
"unit_sale_price = 5.\n",
"\n",
"\n",
"\n",
"comparison_df['unbuffered_satisfied_units'] = np.minimum(comparison_df['predicted_demand_units'], \n",
" comparison_df['demand_units'])\n",
"\n",
"comparison_df['buffered_satisfied_units'] = np.minimum(comparison_df['optimised_demand_units'], \n",
" comparison_df['demand_units'])\n",
"\n",
"human_buffer_multiplier = 1.43\n",
"comparison_df['max_human_buffered_satisfied_units'] = np.minimum(comparison_df['predicted_demand_units']*human_buffer_multiplier, \n",
" comparison_df['demand_units'])\n",
"\n",
"\n",
"\n",
"print ('----Actual----')\n",
"actual_demand_units = comparison_df['demand_units'].sum()\n",
"print ('Actual Demand units:',actual_demand_units)\n",
"print ('')\n",
"print ('----Unbuffered----')\n",
"print ('Revenue:',comparison_df['unbuffered_satisfied_units'].sum()*unit_sale_price)\n",
"print ('Cost:',comparison_df['predicted_demand_units'].sum()*unit_cost)\n",
"print ('Profit:',(comparison_df['unbuffered_satisfied_units'].sum()*unit_sale_price) - (comparison_df['predicted_demand_units'].sum()*unit_cost))\n",
"print ('Service Level:',100*comparison_df['unbuffered_satisfied_units'].sum()/actual_demand_units,'%')\n",
"print ('Satisfied Units:',int(comparison_df['unbuffered_satisfied_units'].sum()))\n",
"print ('')\n",
"\n",
"print ('----Buffered for Max Profit----')\n",
"print ('Revenue:',comparison_df['buffered_satisfied_units'].sum()*unit_sale_price)\n",
"print ('Cost:',comparison_df['optimised_demand_units'].sum()*unit_cost)\n",
"print ('Profit:',(comparison_df['buffered_satisfied_units'].sum()*unit_sale_price) - (comparison_df['optimised_demand_units'].sum()*unit_cost))\n",
"print ('Service Level:',100.*comparison_df['buffered_satisfied_units'].sum()/actual_demand_units,'%')\n",
"print ('Satisfied Units:',int(comparison_df['buffered_satisfied_units'].sum()))\n",
"print ('')\n",
"\n",
"\n",
"print ('----Buffered for 100% Service Level----')\n",
"\n",
"print ('Revenue:',int(comparison_df['max_human_buffered_satisfied_units'].sum()*unit_sale_price))\n",
"print ('Cost:',int(human_buffer_multiplier*comparison_df['predicted_demand_units'].sum()*unit_cost))\n",
"print ('Profit:',(int(comparison_df['max_human_buffered_satisfied_units'].sum()*unit_sale_price)) - int(human_buffer_multiplier*comparison_df['predicted_demand_units'].sum()*unit_cost))\n",
"print ('Service Level:',100*comparison_df['max_human_buffered_satisfied_units'].sum()/actual_demand_units,'%')\n",
"print ('Satisfied Units:',int(comparison_df['max_human_buffered_satisfied_units'].sum()))\n",
"print ('')"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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"
\n",
" \n",
" \n",
" | \n",
" Buffer Multiplier Value | \n",
" Profit | \n",
" Revenue | \n",
" Cost of Goods | \n",
" Service Level % | \n",
"
\n",
" \n",
" \n",
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\n",
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" 1.08 | \n",
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" 156120.00 | \n",
" 34760.88 | \n",
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\n",
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\n",
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" 1.10 | \n",
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" 96.61 | \n",
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\n",
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" 1.11 | \n",
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" 35726.46 | \n",
" 96.89 | \n",
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\n",
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" 158050.60 | \n",
" 36048.32 | \n",
" 97.18 | \n",
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\n",
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\n",
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\n",
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" 1.15 | \n",
" 122405.60 | \n",
" 159419.50 | \n",
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" 98.02 | \n",
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\n",
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" 1.16 | \n",
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\n",
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" | 17 | \n",
" 1.17 | \n",
" 122619.38 | \n",
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" 37657.62 | \n",
" 98.54 | \n",
"
\n",
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" 37979.48 | \n",
" 98.74 | \n",
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\n",
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" 98.99 | \n",
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" 1.21 | \n",
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" 161210.35 | \n",
" 38945.06 | \n",
" 99.12 | \n",
"
\n",
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" 1.22 | \n",
" 122146.78 | \n",
" 161413.70 | \n",
" 39266.92 | \n",
" 99.24 | \n",
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\n",
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" 99.34 | \n",
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\n",
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" | 24 | \n",
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" 161682.80 | \n",
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" 99.41 | \n",
"
\n",
" \n",
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" 1.25 | \n",
" 121565.00 | \n",
" 161797.50 | \n",
" 40232.50 | \n",
" 99.48 | \n",
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\n",
" \n",
" | 26 | \n",
" 1.26 | \n",
" 121322.44 | \n",
" 161876.80 | \n",
" 40554.36 | \n",
" 99.53 | \n",
"
\n",
" \n",
" | 27 | \n",
" 1.27 | \n",
" 121079.88 | \n",
" 161956.10 | \n",
" 40876.22 | \n",
" 99.58 | \n",
"
\n",
" \n",
" | 28 | \n",
" 1.28 | \n",
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" 162035.40 | \n",
" 41198.08 | \n",
" 99.63 | \n",
"
\n",
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" 1.29 | \n",
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\n",
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" 1.31 | \n",
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" 42163.66 | \n",
" 99.71 | \n",
"
\n",
" \n",
" | 32 | \n",
" 1.32 | \n",
" 119732.28 | \n",
" 162217.80 | \n",
" 42485.52 | \n",
" 99.74 | \n",
"
\n",
" \n",
" | 33 | \n",
" 1.33 | \n",
" 119449.57 | \n",
" 162256.95 | \n",
" 42807.38 | \n",
" 99.76 | \n",
"
\n",
" \n",
" | 34 | \n",
" 1.34 | \n",
" 119166.86 | \n",
" 162296.10 | \n",
" 43129.24 | \n",
" 99.79 | \n",
"
\n",
" \n",
" | 35 | \n",
" 1.35 | \n",
" 118884.15 | \n",
" 162335.25 | \n",
" 43451.10 | \n",
" 99.81 | \n",
"
\n",
" \n",
" | 36 | \n",
" 1.36 | \n",
" 118601.44 | \n",
" 162374.40 | \n",
" 43772.96 | \n",
" 99.83 | \n",
"
\n",
" \n",
" | 37 | \n",
" 1.37 | \n",
" 118318.73 | \n",
" 162413.55 | \n",
" 44094.82 | \n",
" 99.86 | \n",
"
\n",
" \n",
" | 38 | \n",
" 1.38 | \n",
" 118036.02 | \n",
" 162452.70 | \n",
" 44416.68 | \n",
" 99.88 | \n",
"
\n",
" \n",
" | 39 | \n",
" 1.39 | \n",
" 117753.31 | \n",
" 162491.85 | \n",
" 44738.54 | \n",
" 99.91 | \n",
"
\n",
" \n",
" | 40 | \n",
" 1.40 | \n",
" 117470.60 | \n",
" 162531.00 | \n",
" 45060.40 | \n",
" 99.93 | \n",
"
\n",
" \n",
" | 41 | \n",
" 1.41 | \n",
" 117187.89 | \n",
" 162570.15 | \n",
" 45382.26 | \n",
" 99.95 | \n",
"
\n",
" \n",
" | 42 | \n",
" 1.42 | \n",
" 116905.18 | \n",
" 162609.30 | \n",
" 45704.12 | \n",
" 99.98 | \n",
"
\n",
" \n",
" | 43 | \n",
" 1.43 | \n",
" 116619.02 | \n",
" 162645.00 | \n",
" 46025.98 | \n",
" 100.00 | \n",
"
\n",
" \n",
" | 44 | \n",
" 1.44 | \n",
" 116297.16 | \n",
" 162645.00 | \n",
" 46347.84 | \n",
" 100.00 | \n",
"
\n",
" \n",
" | 45 | \n",
" 1.45 | \n",
" 115975.30 | \n",
" 162645.00 | \n",
" 46669.70 | \n",
" 100.00 | \n",
"
\n",
" \n",
" | 46 | \n",
" 1.46 | \n",
" 115653.44 | \n",
" 162645.00 | \n",
" 46991.56 | \n",
" 100.00 | \n",
"
\n",
" \n",
" | 47 | \n",
" 1.47 | \n",
" 115331.58 | \n",
" 162645.00 | \n",
" 47313.42 | \n",
" 100.00 | \n",
"
\n",
" \n",
" | 48 | \n",
" 1.48 | \n",
" 115009.72 | \n",
" 162645.00 | \n",
" 47635.28 | \n",
" 100.00 | \n",
"
\n",
" \n",
" | 49 | \n",
" 1.49 | \n",
" 114687.86 | \n",
" 162645.00 | \n",
" 47957.14 | \n",
" 100.00 | \n",
"
\n",
" \n",
" | 50 | \n",
" 1.50 | \n",
" 114366.00 | \n",
" 162645.00 | \n",
" 48279.00 | \n",
" 100.00 | \n",
"
\n",
" \n",
"
\n",
"