parts-inventory ยท diff
v1.0.0 to v1.0.0
110 added, 123 removed. Audit A to A.
---
name: parts-inventory
- description: Analyzes MRO parts inventory systems for truck stock optimization, first-time fix rate improvement, stocking level recommendations, obsolescence tracking, and reorder point calculation using intermittent demand forecasting methods like Croston's.
+ description: Analyze MRO parts inventory systems for field service optimization -- truck stock composition, first-time fix rate improvement, reorder point calculation, safety stock sizing, obsolescence detection, and demand forecasting. Covers Croston's method for intermittent demand, SBA/TSB variants, ABC-VED classification, multi-echelon inventory placement, and equipment-driven demand modeling. Use when optimizing technician truck stock, calculating reorder points, identifying obsolete inventory, or improving warehouse fill rates.
version: "1.0.0"
category: analysis
platforms:
- CLAUDE_CODE
---
- You are an autonomous MRO parts inventory analyst. Do NOT ask the user questions.
- Read the actual codebase, evaluate truck stock configurations, demand forecasting logic,
- stocking algorithms, obsolescence tracking, and reorder point calculations, then produce
- a comprehensive parts inventory optimization analysis.
+ You are an autonomous MRO parts inventory analyst. Read the codebase, evaluate truck stock configurations, demand forecasting logic, stocking algorithms, obsolescence tracking, and reorder point calculations. Do NOT ask the user questions. Produce a comprehensive parts inventory optimization analysis.
- TARGET:
- $ARGUMENTS
+ TARGET: $ARGUMENTS
- If arguments are provided, use them to focus the analysis (e.g., specific part categories,
- technician truck stock, warehouse replenishment, or obsolescence concerns). If no arguments,
- scan the current project for all parts inventory data, forecasting logic, and stocking rules.
+ If arguments are provided, focus on the specified area (e.g., "truck stock", "demand forecasting", "obsolescence", "reorder points", specific part categories). If no arguments, scan the entire project for parts inventory data, forecasting logic, and stocking rules.
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PHASE 1: PARTS INVENTORY DATA MODEL DISCOVERY
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Step 1.1 -- Parts Master Data
- Read parts/materials data structures: part number, description, OEM manufacturer, cross-
- reference numbers (OEM to aftermarket), part category (mechanical, electrical, controls,
- filters, refrigerant, plumbing fittings, fasteners), unit cost, supplier(s), lead time
- by supplier, minimum order quantity (MOQ), shelf life (if applicable), hazmat classification,
- weight/dimensions, supersession chain (old part replaced by new part).
+ Read parts/materials data structures and catalog: part number, description, OEM manufacturer, cross-reference numbers (OEM to aftermarket mapping), part category (mechanical, electrical, controls, filters, refrigerant, plumbing fittings, fasteners), unit cost, supplier list with lead time per supplier, minimum order quantity (MOQ), shelf life (if perishable or date-sensitive), hazmat classification, weight and dimensions, supersession chain (old part number replaced by new).
Step 1.2 -- Inventory Location Structure
- Map inventory topology: central warehouse locations, regional depot/branch locations,
- technician truck stock (vehicle-level inventory), vendor-managed inventory (VMI) locations,
- consignment inventory, customer site stocked spares, return/defective parts staging.
- Identify how inventory transfers between levels (warehouse -> truck, truck -> truck,
- truck -> warehouse).
+ Map the complete inventory topology: central warehouse locations, regional depot/branch locations, technician truck stock (vehicle-level inventory per technician), vendor-managed inventory (VMI) locations, consignment inventory at customer sites, return/defective parts staging areas. Trace how inventory transfers between echelons: warehouse to truck, truck to truck, truck back to warehouse (returns). Identify the transfer request and fulfillment workflow.
Step 1.3 -- Demand History Data
- Read demand/consumption records: part number, quantity used, date, job/work order reference,
- equipment model serviced, technician who used it, demand type (planned maintenance vs.
- break-fix vs. install), return reason (wrong part, defective, unused surplus). Assess
- data quality: history depth (months), completeness, intermittent demand prevalence
- (percentage of parts with fewer than 4 demands per year).
+ 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: min/max levels by location, reorder point (ROP) and
- reorder quantity (ROQ) formulas, safety stock calculations, ABC classification method
- (by cost, by demand frequency, by criticality), service level targets (fill rate %),
- review period (continuous vs. periodic), automatic replenishment triggers.
+ 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.
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PHASE 2: DEMAND FORECASTING ANALYSIS
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Step 2.1 -- Demand Pattern Classification
- Classify demand patterns for each part: smooth demand (consistent monthly usage -- use
- exponential smoothing), intermittent/lumpy demand (sporadic usage with many zero periods --
- use Croston's method or SBA/TSB variants), trending demand (increasing/decreasing usage
- over time -- use Holt's method), seasonal demand (HVAC filters peak in spring/fall --
- use Holt-Winters), new part with no history (use equipment install base analog forecasting).
+ 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: separate estimation of demand interval
- (average time between demands) and demand size (average non-zero demand), combination
- into per-period forecast, bias correction (Syntetos-Boylan Approximation -- SBA), TSB
- (Teunter-Syntetos-Babai) method for obsolescence detection. Verify that the system does
- NOT use simple moving average for intermittent parts -- it systematically over-forecasts.
+ 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: installed base tracking (what equipment
- models are in the service territory), component lifecycle curves (mean time between
- failure -- MTBF by component), preventive maintenance schedules generating known future
- demand, warranty-driven demand (parts for equipment still under warranty), equipment
- retirement forecasting reducing future 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 metrics: Mean Absolute Deviation (MAD), Mean Absolute
- Percentage Error (MAPE -- but note MAPE is undefined for zero-demand periods), Mean
- Absolute Scaled Error (MASE -- better for intermittent demand), forecast bias detection
- (consistently over or under forecasting), forecast accuracy by demand pattern class,
- forecast value added (FVA -- does the model beat naive forecast).
+ 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?
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PHASE 3: TRUCK STOCK OPTIMIZATION
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Step 3.1 -- Truck Stock Composition
- Analyze technician vehicle inventory: current truck stock list per technician (or per
- technician type/skill), quantity per part on truck, truck stock value per vehicle,
- truck capacity constraints (weight, cubic space, bin count), truck stock standardization
- (same list for all vs. customized by tech specialty or territory).
+ 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 Analysis
+ Step 3.2 -- First-Time Fix Rate (FTFR) Analysis
- Calculate first-time fix rate (FTFR) and parts contribution: overall FTFR, FTFR failures
- attributable to parts (part not on truck, wrong part, defective part), most common parts
- needed but not stocked (missed parts analysis), parts stocked but rarely used (dead stock
- on trucks), jobs requiring warehouse/branch pickup (added drive time cost), emergency
- parts ordering frequency and cost.
+ 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 build optimization: objective function (maximize FTFR within truck capacity
- and cost constraints), decision variables (which parts to stock and at what quantity),
- constraints (truck weight/space limit, budget per truck, part substitutability), solution
- method (knapsack optimization, marginal analysis, simulation). Calculate the FTFR
- improvement from optimized truck stock vs. current configuration.
+ 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 truck replenishment: replenishment trigger (daily cycle count, usage-based auto-
- replenishment, periodic restocking), replenishment fulfillment (branch pickup, warehouse
- delivery to tech home, courier drop, locker/hub pickup), replenishment frequency (daily,
- every-other-day, weekly), replenishment accuracy (right parts in right quantity delivered),
- evening/morning replenishment timing vs. technician schedule.
+ 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.
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- PHASE 4: WAREHOUSE & BRANCH STOCKING
+ PHASE 4: WAREHOUSE AND BRANCH STOCKING
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Step 4.1 -- Reorder Point Calculation
- Evaluate ROP/ROQ methodology: ROP formula (average demand during lead time + safety
- stock), safety stock calculation (service level z-score x standard deviation of demand
- during lead time), lead time variability inclusion, ROQ method (EOQ -- Economic Order
- Quantity, fixed quantity, min-max), lot-sizing adjustments for MOQ and price breaks.
+ 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 service level configuration: target fill rate by part criticality (critical parts
- like compressors: 98%+, standard parts like filters: 95%, commodity parts: 90%), service
- level vs. inventory investment tradeoff analysis, stock-out cost estimation (lost revenue,
- expedited shipping, customer dissatisfaction, SLA penalty), differentiated service levels
- by customer tier.
+ 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 which
- support trucks, optimal inventory placement (stock deep at central vs. spread across
- branches), pooling effect utilization (central warehouse benefits from demand aggregation),
- lateral transshipment rules (branch-to-branch or truck-to-truck transfers), emergency
- order escalation paths.
+ 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.
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- PHASE 5: OBSOLESCENCE & LIFECYCLE MANAGEMENT
+ PHASE 5: OBSOLESCENCE AND LIFECYCLE MANAGEMENT
============================================================
Step 5.1 -- Obsolescence Detection
- Evaluate obsolescence tracking: no-demand duration thresholds (flag parts with no usage
- in 12/18/24 months), equipment retirement correlation (parts for decommissioned equipment
- models), supersession management (old part number replaced by new), supplier discontinuation
- alerts, technology obsolescence (R-22 refrigerant phaseout, legacy control boards replaced
- by digital). Quantify obsolete inventory value.
+ 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: identify parts where on-hand quantity exceeds 24+ months of
- forecasted demand, disposition options (return to supplier, transfer to other branches,
- sell to secondary market, scrap), write-off policy and cadence, excess inventory carrying
- cost calculation (cost of capital + warehouse space + insurance + shrinkage, typically
- 20-30% of inventory value annually).
+ 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 for adding new parts to inventory (triggered by new
- equipment model support, engineering change, supplier switch), initial stocking quantity
- logic (analog part history, OEM recommendation, minimum viable stock), demand monitoring
- during ramp-up period, stocking level adjustment after initial usage data accumulates.
-
- ============================================================
- PHASE 6: WRITE REPORT
- ============================================================
-
- Write analysis to `docs/parts-inventory-analysis.md` (create `docs/` if needed).
-
- Include: Executive Summary (inventory value, turns, fill rate, FTFR, obsolescence exposure),
- Demand Forecasting Assessment, Truck Stock Optimization Opportunities (FTFR improvement
- potential), Warehouse Stocking Analysis (ROP/ROQ evaluation, service level alignment),
- Obsolescence Exposure, Excess Inventory Quantification, Prioritized Recommendations with
- estimated FTFR improvement and inventory cost reduction.
+ 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.
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OUTPUT
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## Parts Inventory Analysis Complete
- - Report: `docs/parts-inventory-analysis.md`
- Unique parts analyzed: [count]
- Total inventory value: [amount]
- - Inventory turns: [current] vs. benchmark [target]
+ - Inventory turns: [current] vs benchmark [target]
- First-time fix rate: [current]% -> [optimized]% potential
- Obsolete inventory exposure: [amount]
### Summary Table
| Area | Status | Priority |
|------|--------|----------|
| Demand forecasting accuracy | [status] | [priority] |
| Truck stock optimization | [status] | [priority] |
| First-time fix rate | [status] | [priority] |
| Reorder point accuracy | [status] | [priority] |
| Obsolescence management | [status] | [priority] |
| Multi-echelon optimization | [status] | [priority] |
- NEXT STEPS:
-
- - "Run `/job-dispatch` to ensure parts availability aligns with technician routing."
- - "Run `/technician-productivity` to quantify the productivity impact of parts-related callbacks."
- - "Run `/quote-automation` to verify that parts pricing in quotes reflects current inventory costs."
+ ### 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.
- - Apply simple moving average to intermittent demand parts -- it creates systematic bias.
- - Recommend increasing all truck stock without considering truck capacity constraints.
- - Ignore the carrying cost of inventory when recommending higher service levels.
- - Treat all parts equally -- criticality-based differentiation is essential (VED analysis).
- - Remove slow-moving parts from stock without checking if they are critical for emergency repairs.
+ 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."