Spare Parts Strategy for Maintenance Teams: Price Is Not the Risk
A sound spare parts strategy is maintenance-aligned: you stock critical spares, forecast intermittent demand, secure supplier terms and use your CMMS to minimise total downtime cost. BS EN 62550 ties provisioning to supportability and dependability, and the 80/20 planned-to-unplanned maintenance guidance anchors your targets. Expect lower downtime risk without carrying excess inventory.
TL;DR:
- Score each part by failure likelihood and operational impact, then combine criticality with ABC value classes so inexpensive components that halt production receive appropriate policies.
- Stock a part when expected downtime cost, calculated from failure probability and downtime impact, exceeds carrying cost; otherwise, buy it on demand.
- For intermittent demand, use Croston methods or bootstrap usage history, then convert lead time demand and variability into safety stock and reorder points.
- Use JIT or vendor managed inventory only when demand is stable and suppliers are reliable; otherwise, delivery delays shift risk into downtime.
- Measure effective lead time from requisition through receipt using actual order records, then pilot one asset family and compare availability and cost before expansion.
Table of Contents
- Which spare parts strategy fits your maintenance posture?
- How do you classify and prioritise spares?
- Which stocking policies and inventory models work for spare parts?
- How do you forecast intermittent spare parts demand?
- How should you manage suppliers, lead times and contingencies?
- How do you store spares so they stay serviceable?
- Which tools and KPIs keep the strategy honest?
- How do you run a critical spares review and embed the strategy?
- Why this approach works in practice
- How our service and maintenance support helps you apply this
- FAQ
- Sources
Which spare parts strategy fits your maintenance posture?
No single stocking model suits every site. The right choice depends on how predictable your failures are, how long replacements take and how many sites share parts. Operational research treats spare parts management as a lifecycle problem, with classification, forecasting, optimisation and simulation as the backbone of the decisions, as this operational research review sets out.
- Insurance parts: hold these for items with long lead times, single-source supply or severe failure consequences, where waiting for delivery would stop production.
- Centralised stocking: one store lowers carrying cost and total stock, but lengthens the wait at remote sites.
- Decentralised stocking: parts held at each site shorten response time, at the price of duplicated inventory.
- JIT and vendor-managed inventory: these suit stable demand and reliable suppliers. Without both, they simply move the risk into your downtime.
- Pooling and lateral transshipment: distributed operations can share scarce parts across sites, a theme that recent literature on multi-echelon systems stresses.
How do you classify and prioritise spares?
Classification decides which items earn shelf space and which are bought on demand. Start with criticality: score each part on the likelihood of failure and the impact on operations, then plot the result on a risk matrix. Engineering guidance on spares in storage recommends exactly this risk-matrix approach.
Layer ABC analysis on top. Value-based classes combined with a critical or non-critical flag give you groups of SKUs that share a policy, so a cheap but line-stopping part is never treated like cheap general stock. For your highest-criticality assets, FMECA or RCM shows which failure modes genuinely demand a spare.
A critical spares review, as described by Operations Engineer, can follow this sequence:
- List the parts on each critical asset from the parts master.
- Score failure likelihood and operational impact.
- Apply the expected-cost rule to each high-scoring item.
- Flag insurance parts: long lead time, single source, line-stopping impact.
- Record the stock-or-source decision and a review date.
Pro Tip: Link part criticality to asset criticality, so a low-cost seal on a bottleneck machine is scored by what it stops, not by its price.
Which stocking policies and inventory models work for spare parts?
The decision rule underneath every policy is expected cost. Stock a part when its expected downtime cost, meaning the probability of failure multiplied by the downtime cost, exceeds the carrying cost, as this foundational treatment frames it. Otherwise, procure on demand.
The expected downtime cost is £1,000, so you stock the item. These inputs are illustrative, not market data.
Once an item is in, choose a replenishment policy:
- EOQ: works for steadier, higher-volume consumables where order cost and holding cost trade off cleanly.
- Periodic review (R,S): suits items reviewed on a fixed cycle and topped up to a target level.
- (R,s,S) or (R,s,Q): reorders only when stock falls to a threshold, which avoids many small orders.
- Rationing: reserve stock for urgent breakdown orders and let routine maintenance wait, an approach rationing research links to lower on-hand inventory cost.
Size safety stock around sporadic demand and long lead times, not around averages that hide the gaps between failures.
How do you forecast intermittent spare parts demand?
Spare parts demand is lumpy. Parts are consumed when equipment fails, not at a steady rate, so historical maintenance and usage data matter more than a smooth trend. Standard time-series methods assume continuity and tend to overreact to the rare spikes while underestimating the long zero runs.
- Croston-style methods: model the interval between demands and the demand size separately, which suits slow movers.
- Bootstrapping: resamples your own history to build a lead-time demand distribution without assuming its shape.
- Moving-average total lead time: IBM’s maintenance stocking guidance recommends this over relying on a supplier’s stated lead time.
- CMMS and procurement data: combine work-order history, asset usage and purchasing records to see actual consumption, not planned consumption.
Forecast output is only useful when it feeds a decision. Convert the forecast lead-time demand and its variability into a safety stock level and a reorder point, then compare the result with the expected-cost rule above. Reviewing forecast error each cycle keeps the reorder points honest.
How should you manage suppliers, lead times and contingencies?
Your effective lead time is longer than the figure on the quote. Include requisition-to-order processing time, supplier delivery and receipt into stock, and track a supplier-weighted average rather than the best case.
- Framework agreements: negotiate agreed lead times and measure performance against them.
- VMI or consignment: worth considering for high-value or long-lead parts, where the supplier holds stock near you. Inventory optimisation guidance lists VMI and JIT among the standard vendor strategies.
- Emergency shipments: pre-agree routes and costs for critical items, so the first call during a breakdown is not a negotiation.
- Lateral transshipment: agree how sites lend critical parts to one another, and who pays.
Pro Tip: Record actual receipt dates against order dates for every critical part, then reset your planning lead times from that measured data regularly.
How do you store spares so they stay serviceable?
A spare that fails on installation is worse than no spare. The preserving-reliability guidance recommends logging storage dates and shelf life and specifying protection and intra-storage maintenance.
- Limited-life items: identify them, log receipt and storage dates, and rotate stock using FIFO where it applies.
- Environment and packaging: control temperature and humidity, and use VCI bags or sealed crates for metal components.
- Intra-storage maintenance: periodically exercise or inspect items such as bearings and motors rather than assuming they are fine.
- Traceability: label and barcode parts, and run pre-installation checks to confirm they are fit for use.
- CMMS integration: schedule storage checks as work orders, so they happen on a review cycle and not by memory.
Which tools and KPIs keep the strategy honest?
Your CMMS or EAM is the data spine. Asset-linked “where-used” records show which machines depend on each part, and integration with purchasing and stores keeps stock and demand visible in one place. IBM’s overview of spare parts management notes that AI is emerging for forecasting and optimisation, and it earns its place mainly where you have enough clean history to learn from.
Track a small set of KPIs:
- Service level or fill rate for critical items
- Inventory turns and days of inventory
- MTTR impact of parts waiting
- Planned versus unplanned maintenance ratio
A practical target is to plan most maintenance, which reduces how much you must stock against surprises.
How do you run a critical spares review and embed the strategy?
Work in a fixed sequence, and pilot before you scale:
- Build the asset register and parts master.
- Classify and prioritise by criticality and ABC.
- Model expected cost and choose stocking rules.
- Secure supplier arrangements and lead-time terms.
- Implement storage rules and CMMS workflows.
- Track KPIs and iterate.
Involve maintenance planners, procurement, stores, suppliers and a senior operations sponsor from the start, because each controls part of the outcome. Pick one small asset family for the pilot, validate your assumptions, and measure availability and cost before rolling out wider. A hierarchical planning framework helps here, since it separates strategic, tactical and operational decisions.
Why this approach works in practice
I favour provisioning that follows maintenance reality over provisioning that follows the stores budget. Expected-cost logic is blunt, but it forces an honest conversation about what an hour of downtime costs. It has trade-offs: your probability estimates will be rough, and some parts will be over- or under-stocked at first. That is why the approach is iterative. Start with the roadmap, plug in your own downtime numbers, and adjust thresholds as the evidence accumulates.
— Trevor
How our service and maintenance support helps you apply this
We install, train your teams on and support our packaging machines, and we can help set up parts provisioning for the equipment you already run with us.
- Installation and training, plus ongoing support and maintenance, through our service team
- Parts and technical help on our support and parts page
Contact us for a spare-parts and service review, or to discuss a tailored maintenance contract.
FAQ
What is a good inventory turnover ratio for spare parts?
There is no universal figure, because turns depend on criticality and lead time. Insurance spares are meant to turn slowly, so judge turns by class and by the service level achieved, not by one site-wide number.
What are spares and consumables?
Spares are replacement components held to restore equipment after failure, often with long lead times. Consumables are items used up routinely, such as lubricants or filters, and usually suit simpler min/max replenishment.
How to improve spare parts availability?
Classify parts by criticality, stock the ones whose expected downtime cost exceeds carrying cost, and measure real lead times. Better supplier terms and CMMS-linked tracking then keep the right items on the shelf.
What is the meaning of spare parts?
Spare parts are components kept in reserve to replace worn or failed ones, so equipment returns to service quickly. BS EN 62550 frames provisioning as part of supportability and dependability.
Sources
- BS EN 62550:2017 | 31 Mar 2017 | BSI Knowledge
- OR in Spare Parts Management: A Review
- PRESERVING THE RELIABILITY OF SPARES AND EQUIPMENT IN STORAGE
