Classify spare parts by demand pattern and criticality to select an appropriate forecasting approach.
Engineering & Maintenance
Machine Learning-Based Spare Parts Demand Forecasting for MRO Inventories
Apply machine learning forecasting models to predict spare parts demand, set stocking policies and reduce both stockouts and excess MRO inventory value.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Spare parts demand is difficult to forecast because failures are intermittent, lead times vary by supplier, and criticality differs enormously between a bearing and a control system card. Reorder-point methods based on simple averages either tie up capital in excess stock or leave critical assets exposed to stockouts during a breakdown. This course teaches maintenance planners, inventory analysts and reliability engineers to apply machine learning techniques suited to intermittent and lumpy demand patterns, including Croston's method, bootstrapping approaches and gradient-boosted regression models that incorporate failure history, asset criticality and lead-time variability. Participants build forecasting pipelines from historical CMMS consumption data, validate accuracy against held-out periods, and translate forecast outputs into reorder points, safety stock levels and criticality-based service targets. The course also addresses data quality problems common in MRO datasets, such as inconsistent part numbering and unrecorded cannibalisation, and shows how to combine statistical forecasts with engineering judgement for low-volume critical spares. By the end, participants can select a forecasting method for a given part class, set service levels aligned to criticality, and present inventory investment recommendations a finance function will accept.
Expected Learning Outcomes
Apply intermittent demand methods such as Croston's method to parts with irregular consumption history.
Build regression or gradient-boosted models that incorporate failure history and lead-time variability.
Validate forecast accuracy against held-out historical periods using appropriate error metrics.
Translate forecast output into reorder points, safety stock levels and criticality-based service targets.
Identify and correct data quality issues in CMMS consumption records that distort forecasting accuracy.
Present inventory investment recommendations that balance stockout risk against working capital tied up in stock.
Who Should Attend
Maintenance planners responsible for spare parts stocking and reorder decisions.
Inventory analysts supporting MRO warehouses across multiple plant sites.
Reliability engineers linking failure modes to spare parts criticality classification.
Supply chain professionals managing supplier lead times for maintenance spares.
CMMS data owners responsible for the quality of consumption and stock records.
Finance business partners evaluating maintenance inventory investment decisions.
Course Modules
Select any module to see its sessions and points.
01Understanding Spare Parts Demand Patterns
2 sessions · 8 points
Session 1Classifying Demand and Criticality for Forecasting
- Segment spare parts into smooth, intermittent, erratic and lumpy demand categories using consumption history.
- Combine demand pattern classification with asset criticality ratings to prioritise forecasting effort.
- Identify parts where engineering judgement should override a purely statistical forecast, such as insurance spares.
- Map each part class to a candidate forecasting method suited to its demand and criticality profile.
Session 2Preparing CMMS Data for Forecasting
- Audit CMMS consumption records for inconsistent part numbering, duplicate entries and missing transactions.
- Reconcile cannibalisation and inter-plant transfers that distort true consumption at a single stocking location.
- Structure historical data into time-bucketed demand series suitable for input to forecasting models.
- Document data quality issues found and agree a remediation plan with CMMS administrators.
02Forecasting Methods for Intermittent Demand
2 sessions · 8 points
Session 1Statistical Methods for Lumpy and Intermittent Spares
- Apply Croston's method and its variants to separate demand size from demand interval for intermittent spares.
- Use bootstrapping techniques to simulate demand distributions for low-volume critical parts.
- Compare exponential smoothing against intermittent-demand methods to confirm the better fit for each part class.
- Select forecast horizons appropriate to supplier lead time and typical replenishment cycle.
Session 2Machine Learning Models Incorporating Failure and Lead-Time Data
- Build gradient-boosted regression models that include failure history, asset age and lead-time variability as features.
- Engineer features from maintenance history such as time since last failure and recent work order frequency.
- Tune model hyperparameters and guard against overfitting on limited historical spare parts data.
- Compare machine learning model output against statistical benchmarks on the same validation period.
03Validating Forecasts and Setting Stocking Policies
2 sessions · 8 points
Session 1Measuring Forecast Accuracy for Spare Parts
- Apply error metrics suited to intermittent demand, such as mean absolute scaled error, rather than standard MAPE.
- Backtest forecasts against held-out historical periods to assess model stability over time.
- Identify parts where forecast error remains high and flag them for manual review rather than automated reorder.
- Report forecast accuracy by part class to stakeholders in terms they can act on operationally.
Session 2Setting Reorder Points and Service Level Targets
- Translate demand forecasts and lead-time variability into reorder points using safety stock formulas.
- Set differentiated service level targets by criticality class, protecting critical spares more than consumables.
- Calculate safety stock using demand and lead-time variability rather than a flat percentage buffer.
- Simulate stockout risk under different service level assumptions to support policy decisions.
04Deploying and Sustaining a Forecasting Programme
2 sessions · 8 points
Session 1Building a Repeatable Forecasting Pipeline
- Automate the extraction of consumption data from the CMMS into a repeatable forecasting workflow.
- Schedule periodic re-forecasting so stocking policies reflect recent consumption and lead-time changes.
- Build a review process for exceptions where forecasts and engineering judgement diverge significantly.
- Document the forecasting methodology so it can be maintained after the original analyst moves on.
Session 2Communicating Inventory Decisions to Stakeholders
- Translate forecast-driven stocking recommendations into a business case for inventory investment or reduction.
- Present stockout risk and working capital trade-offs in language finance stakeholders can evaluate.
- Track realised service levels and inventory value against forecast-driven targets after implementation.
- Refine stocking policies iteratively as realised performance data accumulates.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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