Parts Inventory Analysis Complete

Step 1.3 -- Demand History Data

Read demand/consumption records and assess data quality: part number, quantity used, date, job/work order reference, equipment model serviced, technician ID, demand type (planned maintenance vs break-fix vs install vs warranty), return reason codes (wrong part, defective, unused surplus). Measure data quality: history depth in months, record completeness, intermittent demand prevalence (percentage of parts with fewer than 4 demands per year).

Step 1.4 -- Current Stocking Rules

Identify existing stocking logic and parameters: min/max levels by location, reorder point (ROP) and reorder quantity (ROQ) formulas, safety stock calculation method, ABC classification method (by cost, by demand frequency, by criticality, or multi-criteria), target service level (fill rate percentage), review period (continuous review vs periodic review), automatic replenishment trigger mechanisms.

============================================================ PHASE 2: DEMAND FORECASTING ANALYSIS

Step 2.1 -- Demand Pattern Classification

Classify demand patterns for each part category:

• Smooth demand (consistent monthly usage): use exponential smoothing or simple moving average.
• Intermittent/lumpy demand (sporadic usage with many zero-demand periods): use Croston's method or SBA/TSB variants.
• Trending demand (increasing or decreasing usage over time): use Holt's double exponential smoothing.
• Seasonal demand (e.g., HVAC filters peaking spring/fall): use Holt-Winters triple exponential smoothing.
• New parts with no history: use analog forecasting from similar parts on similar equipment.

Step 2.2 -- Intermittent Demand Forecasting

Evaluate Croston's method implementation (critical for MRO parts):

• Separate estimation of demand interval (average time between non-zero demands) and demand size (average of non-zero demand quantities).
• Combination into per-period demand forecast.
• Bias correction: Syntetos-Boylan Approximation (SBA) which reduces Croston's upward bias.
• TSB (Teunter-Syntetos-Babai) method for demand that may be trending toward obsolescence.
• VERIFY the system does NOT apply simple moving average to intermittent-demand parts -- SMA systematically over-forecasts for lumpy demand patterns.

Step 2.3 -- Equipment-Driven Demand

Check for equipment-based demand forecasting integration:

• Installed base tracking: what equipment models exist in the service territory.
• Component lifecycle curves: Mean Time Between Failure (MTBF) by component type.
• Preventive maintenance schedules generating deterministic future demand.
• Warranty-driven demand: parts consumed for equipment still under warranty coverage.
• Equipment retirement forecasting: reducing future demand as old models are decommissioned.

Step 2.4 -- Forecast Accuracy Measurement

Evaluate forecast accuracy tracking:

• Mean Absolute Deviation (MAD).
• Mean Absolute Percentage Error (MAPE) -- note MAPE is undefined when actual demand is zero.
• Mean Absolute Scaled Error (MASE) -- preferred metric for intermittent demand.
• Forecast bias detection: is the system consistently over-forecasting or under-forecasting?
• Accuracy segmented by demand pattern class (smooth vs intermittent vs seasonal).
• Forecast Value Added (FVA): does the model outperform a naive forecast baseline?

============================================================ PHASE 3: TRUCK STOCK OPTIMIZATION

Step 3.1 -- Truck Stock Composition

Analyze technician vehicle inventory:

• Current truck stock list per technician or per technician specialty type.
• Quantity per part stocked on each truck.
• Total truck stock value per vehicle.
• Truck capacity constraints: weight limit, cubic space, bin count.
• Truck stock standardization: identical list for all technicians vs customized by specialty or territory demand profile.

Step 3.2 -- First-Time Fix Rate (FTFR) Analysis

Calculate FTFR and isolate the parts contribution:

• Overall FTFR across all job types.
• FTFR failures attributable specifically to parts: part not on truck, wrong part carried, defective part.
• Most frequently needed parts that are NOT stocked on trucks (missed parts analysis).
• Parts stocked but rarely or never used (dead stock consuming truck space).
• Jobs requiring warehouse/branch pickup: count, average added drive time, cost.
• Emergency parts orders: frequency, expedited shipping cost, customer wait time.

Step 3.3 -- Truck Stock Optimization Model

Evaluate or define the optimization approach:

• Objective function: maximize FTFR within truck capacity and cost constraints.
• Decision variables: which parts to stock and at what quantity per truck.
• Constraints: truck weight/space limit, budget per truck, minimum stocking for critical parts, part substitutability.
• Solution method: knapsack optimization, marginal value analysis, or Monte Carlo simulation.
• Calculate projected FTFR improvement from optimized truck stock vs current configuration.

Step 3.4 -- Truck Replenishment Process

Assess replenishment operations:

• Replenishment trigger: daily cycle count, usage-based auto-replenishment, periodic restocking schedule.
• Fulfillment method: branch pickup, warehouse delivery to technician home, courier/locker/hub.
• Replenishment frequency: daily, every-other-day, weekly.
• Replenishment accuracy: right parts in right quantity delivered correctly.
• Timing: evening/morning restocking vs technician schedule alignment.

============================================================ PHASE 4: WAREHOUSE AND BRANCH STOCKING

Step 4.1 -- Reorder Point Calculation

Evaluate ROP/ROQ methodology:

• ROP formula: average demand during lead time + safety stock.
• Safety stock: service level z-score multiplied by standard deviation of demand during lead time.
• Lead time variability: is supplier lead time variance included in safety stock calculation?
• ROQ method: Economic Order Quantity (EOQ), fixed quantity, min-max replenishment.
• Lot-sizing adjustments for MOQ and price-break quantities.

Step 4.2 -- Service Level Optimization

Check differentiated service level configuration:

• Target fill rate by part criticality: critical parts (compressors, control boards) at 98%+, standard parts (filters, belts) at 95%, commodity parts (fasteners, fittings) at 90%.
• Service level vs inventory investment tradeoff analysis (diminishing returns curve).
• Stock-out cost estimation: lost revenue from repeat visit, expedited shipping, customer dissatisfaction, SLA penalty.
• Differentiated service levels by customer tier or contract terms.

Step 4.3 -- Multi-Echelon Inventory

Evaluate multi-echelon optimization:

• Central warehouse stocking supports branches, branches support trucks.
• Optimal inventory placement: stock deep at central (pooling benefit) vs spread across branches (proximity benefit).
• Demand aggregation: central warehouse exploits statistical pooling across branches.
• Lateral transshipment rules: branch-to-branch or truck-to-truck emergency transfers.
• Emergency order escalation paths and associated cost tracking.

============================================================ PHASE 5: OBSOLESCENCE AND LIFECYCLE MANAGEMENT

Step 5.1 -- Obsolescence Detection

Evaluate obsolescence tracking mechanisms:

• No-demand duration thresholds: flag parts with zero usage in 12/18/24 months.
• Equipment retirement correlation: parts for decommissioned equipment models flagged automatically.
• Supersession management: old part numbers linked to replacement part numbers.
• Supplier discontinuation alerts integrated into parts master.
• Technology obsolescence: phased-out refrigerants (R-22), legacy control boards replaced by digital.
• Quantify total obsolete inventory value and carrying cost.

Step 5.2 -- Excess Inventory Management

Assess excess inventory identification and disposition:

• Identify parts where on-hand quantity exceeds 24+ months of forecasted demand.
• Disposition options: return to supplier (RMA), transfer to other branches, sell to secondary market, scrap/write-off.
• Write-off policy: frequency, approval authority, accounting treatment.
• Excess inventory carrying cost calculation: cost of capital + warehouse space + insurance + shrinkage (typically 20-30% of inventory value annually).

Step 5.3 -- New Part Introduction

Check new part onboarding process:

• Trigger: new equipment model support, engineering change, supplier switch, customer request.
• Initial stocking quantity logic: analog part history, OEM recommendation, minimum viable stock.
• Demand monitoring during ramp-up period with accelerated review cycle.
• Stocking level adjustment after sufficient usage data accumulates.

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:

• Identify which sections are incomplete or lack evidence
• Re-analyze the deficient areas with expanded search patterns
• Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:

• Flag specific gaps in the output
• Note what data would be needed to complete the analysis

============================================================ OUTPUT

Parts Inventory Analysis Complete

• Unique parts analyzed: [count]
• Total inventory value: [amount]
• Inventory turns: [current] vs benchmark [target]
• First-time fix rate: [current]% -> [optimized]% potential
• Obsolete inventory exposure: [amount]

Summary Table

Prioritized Recommendations

1. {highest-impact recommendation with estimated FTFR or cost improvement}
2. {second recommendation}
3. {third recommendation}

DO NOT:

• Apply simple moving average to intermittent-demand parts -- it creates systematic over-forecasting bias.
• Recommend increasing all truck stock without accounting for truck capacity constraints.
• Ignore inventory carrying cost when recommending higher service levels -- the cost curve is exponential above 95%.
• Treat all parts equally -- VED (Vital, Essential, Desirable) criticality classification is essential.
• Remove slow-moving parts from stock without verifying they are not critical for emergency repairs.
• Write analysis reports to disk -- output findings directly in the response.

NEXT STEPS:

• "Run /job-dispatch to align parts availability with technician routing and scheduling."
• "Run /fleet-maintenance to correlate vehicle maintenance with truck stock replenishment logistics."
• "Run /demand-forecasting to evaluate forecasting models across the broader supply chain."

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /parts-inventory — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.