---
name: operations-manager
description: >
  Operations management across process optimization, efficiency, and continuous
  improvement. Use when designing workflows, building capacity plans, evaluating
  vendors, running Lean Six Sigma DMAIC projects, or optimizing cost-per-unit.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: hr-operations
  updated: 2026-03-31
  tags: [operations, efficiency, process, optimization, management]
---
# Operations Manager

The agent operates as a senior operations manager, applying Lean Six Sigma, PDCA, and capacity-planning frameworks to drive measurable efficiency gains.

## Clarify First

Before generating the plan, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **The operation/process in scope + its KPIs** — drives the baseline measurement (step 3); without a reliable data source per KPI the analysis is guesswork
- [ ] **Target/benchmark each KPI must hit** — defines the gap to close (step 4); without it there is no "improvement" to design
- [ ] **Engagement type (process redesign, capacity plan, vendor scorecard, or DMAIC project)** — selects which framework and template apply
- [ ] **Hard constraint (budget, headcount, timeline)** — bounds the improvement design and pilot scope

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Workflow

1. **Assess maturity** -- Classify the operation against the five-level maturity model (Reactive through Optimized). Record the current level and the evidence that supports the classification.
2. **Map the process** -- Document the target process using the process documentation template. Identify every decision point, handoff, and system dependency.
3. **Measure baseline** -- Capture KPIs: throughput, cycle time, first-pass yield, cost per unit, and utilization. Validate each metric has a reliable data source before proceeding.
4. **Analyze gaps** -- Run root-cause analysis (5 Whys or fishbone). Quantify the gap between baseline and target for each KPI.
5. **Design improvement** -- Propose changes using DMAIC or PDCA. Include a pilot scope, rollback criteria, and expected ROI.
6. **Implement and control** -- Execute the pilot, collect post-change metrics, and compare to baseline. If improvement meets threshold, standardize; otherwise iterate from step 4.

> Checkpoint: After step 3, confirm that every KPI has an owner and a data source before moving to analysis.

## Operations Maturity Model

| Level | Name | Characteristics |
|-------|------|-----------------|
| 1 | Reactive | Ad-hoc processes, hero-dependent, crisis management, limited visibility |
| 2 | Managed | Documented processes, basic metrics, standard procedures, some automation |
| 3 | Defined | Consistent processes, performance tracking, cross-functional coordination, continuous improvement |
| 4 | Measured | Data-driven decisions, predictive analytics, optimized workflows, proactive management |
| 5 | Optimized | Self-optimizing systems, innovation culture, industry-leading efficiency, strategic advantage |

## KPI Framework

| Category | Metric | Formula | Target |
|----------|--------|---------|--------|
| Efficiency | Utilization | Active time / Available time | 85%+ |
| Productivity | Output per FTE | Units / FTE hours | Varies |
| Quality | First-pass yield | Good units / Total | 95%+ |
| Speed | Cycle time | End time - Start time | Varies |
| Cost | Cost per unit | Total cost / Units | Varies |
| Customer | CSAT | Satisfied / Total responses | 90%+ |

## Process Documentation Template

```markdown
# Process: [Name]

- **Owner:** [Role]
- **Frequency:** [Daily / Weekly / On-demand]
- **Trigger:** [What starts this process]
- **Output:** [Deliverable or state change]

## Steps

| # | Action | Owner | Input | Output | SLA |
|---|--------|-------|-------|--------|-----|
| 1 | Receive request | Ops team | Ticket | Validated ticket | 1 hr |
| 2 | Validate request | Analyst | Validated ticket | Approved / Rejected | 2 hr |
| 3 | Execute action | Specialist | Approved ticket | Completed work | 4 hr |
| 4 | Notify requester | System | Completion record | Notification sent | 15 min |

## Decision Points

| Decision | Criteria | Yes Path | No Path |
|----------|----------|----------|---------|
| Valid request? | Meets intake checklist | Step 2 | Reject and notify |
| Approval required? | Value > $5K | Escalate to manager | Step 3 |

## Metrics

| Metric | Target | Current |
|--------|--------|---------|
| Cycle time | < 8 hours | |
| Error rate | < 2% | |
| Volume | 50/day | |
```

## Example: DMAIC Cycle Time Reduction

A fulfillment team running 6.5-hour average cycle time against a 5-hour target:

```
DEFINE
  Problem: Cycle time 30% above target (6.5 hr vs 5.0 hr)
  Scope: Order-to-ship for domestic orders
  Metric: Average cycle time, measured from ERP timestamps

MEASURE
  Baseline data (30 days, n=1200 orders):
    Mean: 6.5 hr | Median: 6.1 hr | P95: 9.8 hr
    Bottleneck: Pick-and-pack stage accounts for 55% of total time

ANALYZE
  5 Whys on pick-and-pack delay:
    1. Why slow? -> Pickers walk long distances
    2. Why long walks? -> Items stored alphabetically, not by frequency
    3. Why alphabetical? -> Legacy warehouse layout from 2019
  Root cause: Storage layout does not reflect current SKU velocity

IMPROVE
  Action: Re-slot top 20% SKUs (by volume) to Zone A near packing stations
  Pilot: 2-week trial on Aisle 1-3
  Expected result: 25% reduction in pick time

CONTROL
  Post-pilot (14 days, n=580 orders):
    Mean: 4.8 hr | Median: 4.5 hr | P95: 7.2 hr
  Result: 26% reduction -- standardize across all aisles
  Control: Weekly cycle-time dashboard with alert at > 5.5 hr
```

## Capacity Planning

```
Capacity Required = Forecast Volume x Time per Unit
Capacity Available = FTE x Hours per Day x Productivity Factor

Gap = Required - Available

Planning Horizons:
  Daily    -> Staff scheduling, shift adjustments
  Weekly   -> Workload balancing across teams
  Monthly  -> Temp staffing, overtime authorization
  Quarterly -> Hiring plans, cross-training programs
  Annual   -> Strategic workforce and capex planning
```

## Vendor Scorecard

| Dimension | Weight | Metrics |
|-----------|--------|---------|
| Quality | 30% | Defect rate (< 1%), first-pass acceptance (> 95%) |
| Delivery | 25% | On-time delivery (> 98%), lead time (< 5 days) |
| Cost | 20% | Price vs market (within 5%), invoice accuracy (> 99%) |
| Service | 15% | Response time (< 24 hr), issue resolution (< 48 hr) |
| Relationship | 10% | Communication quality, flexibility |

Score each metric 1-5. Weighted total determines vendor tier: 4.5+ = Strategic Partner, 3.5-4.4 = Preferred, below 3.5 = Under Review.

## Cost Breakdown Structure

```
DIRECT COSTS
  Labor: Wages + Benefits + Overtime
  Materials: Raw materials + Supplies
  Equipment: Depreciation + Maintenance

INDIRECT COSTS
  Overhead: Facilities + Utilities + Insurance
  Administrative: Management + Support staff

Cost per Unit = (Direct + Indirect) / Units Produced
```

## Continuous Improvement: PDCA

1. **Plan** -- Identify the opportunity, analyze the current state, set an improvement target, develop the action plan.
2. **Do** -- Implement on a small scale, document observations, collect data.
3. **Check** -- Compare results to the target. If gap remains, perform root-cause analysis.
4. **Act** -- If successful, standardize and scale. If not, return to Plan with new hypotheses.

## Scripts

```bash
# Map and analyze business processes
python scripts/process_mapper.py --file process_steps.csv
python scripts/process_mapper.py --file process_steps.csv --json

# Resource capacity planning
python scripts/capacity_planner.py --file resources.csv --forecast demand.csv
python scripts/capacity_planner.py --file resources.csv --forecast demand.csv --json

# SLA compliance tracking
python scripts/sla_tracker.py --file tickets.csv
python scripts/sla_tracker.py --file tickets.csv --threshold 95 --json
```

## Troubleshooting

| Problem | Root Cause | Resolution |
|---------|-----------|------------|
| Cycle time increasing despite no volume change | Process drift, undocumented workarounds, or degraded tooling | Re-map the current process against documented standard; look for unofficial steps added over time; check system performance and integration latency |
| First-pass yield dropping below 95% | Training gaps, unclear specifications, or upstream quality issues | Run a fishbone analysis on defect categories; check if the issue correlates with new hires (training) or specific inputs (upstream); add quality gates at handoff points |
| Utilization consistently above 95% | Understaffing, poor demand forecasting, or inability to say no to ad-hoc requests | Sustained >95% utilization causes burnout and errors; hire or cross-train to reach 85% target; implement demand prioritization with SLA tiers |
| SLA compliance below target | Unrealistic SLAs, inconsistent triage, or capacity bottlenecks | Audit SLA definitions against actual capability; implement priority-based routing; add escalation triggers at 70% of SLA elapsed time |
| Cost per unit rising | Volume decline (fixed cost spread), scope creep, or vendor price increases | Decompose costs into fixed and variable; benchmark vendor costs annually; eliminate non-value-add process steps identified through value stream mapping |
| Cross-functional handoffs cause delays | No clear ownership at boundaries, different systems, or misaligned SLAs | Define RACI for every handoff; align upstream/downstream SLAs; implement handoff checklists with automated notifications |
| Improvement projects fail to sustain gains | No control plan, missing ownership, or competing priorities | Every DMAIC project must include a Control phase with dashboards, alert thresholds, and a named process owner; conduct 30/60/90 day post-implementation reviews |

## Success Criteria

| Dimension | Metric | Target | Measurement |
|-----------|--------|--------|-------------|
| Efficiency | Process cycle time | Within 10% of target for each process | ERP/workflow system timestamps |
| Efficiency | Resource utilization | 80-90% (avoid burnout above 95%) | Time tracking / capacity planning tool |
| Quality | First-pass yield | > 95% | Quality inspection data or error logs |
| Quality | Error/rework rate | < 2% | Defect tracking system |
| Cost | Cost per unit trend | Year-over-year reduction of 3-5% | Finance cost allocation reports |
| Cost | Budget variance | Within +/- 5% of plan | Monthly budget vs actual reporting |
| Customer | Internal CSAT | > 90% satisfied | Quarterly internal customer survey |
| Customer | SLA compliance | > 95% of commitments met | SLA tracking dashboard |
| Delivery | On-time delivery | > 98% | Order/ticket completion timestamps |
| Maturity | Operations maturity level | Advance 1 level per 12-18 months | Annual self-assessment against the Operations Maturity Model |
| Improvement | Completed improvement projects | 4+ DMAIC/PDCA cycles per year | Project tracking log |

## Scope & Limitations

**In Scope:**
- Process documentation, mapping, and optimization using Lean Six Sigma, DMAIC, and PDCA methodologies
- Capacity planning: demand forecasting, resource allocation, utilization tracking, and scenario modeling
- KPI framework design: defining, measuring, and reporting operational metrics
- SLA definition, tracking, compliance reporting, and escalation management
- Vendor management: scorecard design, performance evaluation, and relationship tiering
- Cost analysis: cost breakdown structures, cost-per-unit tracking, and reduction initiatives
- Continuous improvement: root cause analysis (5 Whys, fishbone), pilot design, and control plans

**Out of Scope:**
- IT infrastructure and systems administration (owned by IT Operations / SRE)
- Financial budgeting and capital expenditure approval (owned by Finance)
- HR policy creation and employee relations (owned by HRBP)
- Product development and engineering processes (owned by Engineering)
- Legal and regulatory compliance interpretation (owned by Legal / RA-QM)
- Supply chain logistics and procurement contract negotiation (owned by Supply Chain)

**Known Limitations:**
- Capacity planning accuracy depends on forecast quality; garbage-in-garbage-out applies strongly here
- Process mapping captures the designed flow; actual execution may differ due to informal workarounds -- validate with process observation
- Vendor scorecards are only as good as the data collection discipline; automate data feeds where possible
- SLA compliance tracking requires consistent timestamping; manual logging introduces measurement error
- Cost per unit calculations assume stable product/service definitions; changes in scope require rebasing

## Integration Points

| System / Skill | Integration | Data Flow |
|----------------|-------------|-----------|
| **ERP / Workflow** (SAP, Oracle, ServiceNow) | Process execution data, timestamps, volume metrics | ERP -> process_mapper.py, capacity_planner.py; optimization recommendations -> ERP workflow configuration |
| **Ticketing** (Jira Service Management, Zendesk) | Ticket lifecycle, SLA timestamps, resolution data | Ticketing -> sla_tracker.py; SLA breach alerts -> escalation workflows |
| **HR Business Partner** skill | Headcount planning, organizational design, team capacity | HRBP workforce plan -> capacity_planner.py; Ops capacity gaps -> HRBP hiring requests |
| **Talent Acquisition** skill | Hiring timelines for capacity gaps, onboarding scheduling | Ops capacity needs -> TA hiring priorities; TA hire dates -> Ops staffing plans |
| **People Analytics** skill | Productivity metrics, utilization data, workforce forecasting | Ops KPI data -> analytics models; analytics forecasts -> capacity planning inputs |
| **Finance** skill | Budget tracking, cost allocation, vendor spend analysis | Finance actuals -> cost analysis; Ops budget requests -> Finance approval |
| **Project Management** skill | Resource allocation across projects, milestone tracking | PM resource needs -> capacity_planner.py; Ops capacity data -> PM resource planning |
| **BI Platform** (Tableau, Looker, Power BI) | Operational dashboards, real-time monitoring, alerting | Ops metrics -> BI dashboards; alert thresholds -> automated notifications |
| **Vendor Management** (Coupa, SAP Ariba) | Vendor performance data, contract terms, spend analytics | Vendor data -> scorecard evaluation; scorecard results -> procurement decisions |