| name | operations-manager |
| description | Expert operations management covering process optimization, operational efficiency, resource management, and continuous improvement. Use when designing workflows, auditing operational maturity, building capacity plans, evaluating vendors, running Lean Six Sigma DMAIC projects, or optimizing cost-per-unit metrics.
|
| license | MIT + Commons Clause |
| metadata | {"version":"1.0.0","author":"borghei","category":"hr-operations","updated":"2026-03-31T00:00:00.000Z","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.
Workflow
- 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.
- Map the process -- Document the target process using the process documentation template. Identify every decision point, handoff, and system dependency.
- 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.
- Analyze gaps -- Run root-cause analysis (5 Whys or fishbone). Quantify the gap between baseline and target for each KPI.
- Design improvement -- Propose changes using DMAIC or PDCA. Include a pilot scope, rollback criteria, and expected ROI.
- 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
# 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
- Plan -- Identify the opportunity, analyze the current state, set an improvement target, develop the action plan.
- Do -- Implement on a small scale, document observations, collect data.
- Check -- Compare results to the target. If gap remains, perform root-cause analysis.
- Act -- If successful, standardize and scale. If not, return to Plan with new hypotheses.
Reference Materials
references/process_design.md - Process design principlesreferences/lean_operations.md - Lean methodologyreferences/vendor_management.md - Vendor management guidereferences/cost_optimization.md - Cost reduction strategies
Scripts
python scripts/process_mapper.py --file process_steps.csv
python scripts/process_mapper.py --file process_steps.csv --json
python scripts/capacity_planner.py --file resources.csv --forecast demand.csv
python scripts/capacity_planner.py --file resources.csv --forecast demand.csv --json
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 |
