operations-manager

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

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

# 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.

Reference Materials

• references/process_design.md - Process design principles
• references/lean_operations.md - Lean methodology
• references/vendor_management.md - Vendor management guide
• references/cost_optimization.md - Cost reduction strategies

Scripts

# 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