Machines need spare parts. Any business operating significant quantities of machinery will carry of a stock of replacement components. Those parts fulfill a variety of purposes. They are there to replace items such as bearings, seals, and filters that wear out during normal operations, and they need to be ready for planned upgrades and overhaul activities. They act as insurance, allowing maintenance teams to fix breakdowns faster than the lead times required to secure replacements from an external supplier.
Ensuring that the organization has the right number of the right spare parts in its inventory can be a continual source of tension, however. Maintenance and operations teams want to maximize availability to reduce the risk of unplanned downtime and lost production that results from missing parts. Finance staff members want to minimize the amount of valuable capital tied up on the shelves, and they worry about obsolescence costs associated with parts bought for equipment that’s no longer in use.
Both sides are right. Research by SKF has shown that optimizing spare-parts management can reduce inventory budgets and holding costs by 15% to 20%, while simultaneously cutting stockouts (and resulting lost production) by 30% to 50%.
But how does a company achieve those optimal inventories? The trick lies in a better understanding of the organization’s assets, of the spare parts they require, and of the nature of demand for those parts.
Some parts are all the same
Most organizations maintain a register of their assets together with a database of the spare parts required to support those assets. Often, these parts databases have been developed organically over time, leading to inconsistency and duplication. For example, parts for two versions of the same asset may be listed in different ways on the database, and simple standard parts like belts or switches may appear on the database under different names. This duplication matters because it reduces inventory efficiency. A company may order additional versions of the same part because the database doesn’t show that they already have an appropriate item in their inventory.
Eliminating this waste requires database standardization. The best companies use a common architecture for assets in their database and standard maintenance bill-of-materials (BOM) structures and catalogue descriptions for the spare parts associated with those assets. It’s common for companies to be able to reduce the number of items in their spare-parts inventories by 10% to 15% just by eliminating duplicate or obsolete items.
Once the current required parts are known, a company needs to decide how many of each part it should keep in inventory. Getting that right calls for an understanding of the way demand patterns vary, according to the nature of the part, and of the criticality of the asset to which it belongs.
Demand for spare parts falls into three basic categories: consumable spares, operational spares and insurance spares, and the best forecasting strategies for each are very different.
Consumable spares are items such as filters and lubricants. Typically, they are lower-cost objects used in quite large numbers. When companies look at their historical consumption of spares such as these, they will see a record of relatively level demand over time. Setting the optimal inventory level for these parts is a matter of establishing the quantity required to meet the overall average level of demand, plus an appropriate safety stock. That figure can then be refined to account for seasonal variations in demand as production patterns change and updated over the longer term as the company adjusts its asset base.
Operational spares are items like fans and motors for which demand is intermittent and unpredictable. Setting the right inventory level for these parts requires a more-sophisticated statistical approach. By analyzing historical use, companies can gain an understanding both of their average consumption of these parts and consumption variability. They can also build a picture of the mean, minimum, and maximum lifetimes for parts in service.
In practice, the lifetime of operational parts usually follows a “bathtub” curve. Some parts fail early, typically as a result of manufacturing or installation defects. After some time, these early failures tail off and the failure rate falls to a low level of random events. Finally, as parts get older, they begin to wear out, and the failure rate rises again. By applying different statistical distributions to each of these three failure causes, companies can build a picture of the probability of failure of a particular type of part at any point in its life.
They can then establish an appropriate service level for the part in question and set their inventory targets to meet that level, given the probability of failure and lead time required to obtain extra parts from the original supplier. The right service level will depend on the price of the spare and the criticality of the asset. A 10% probability of a stockout for a motor that runs one of six ventilators in a building may not create a significant problem, for example. An identical motor used to run a vital production machine will require a much higher level of availability, however.
The final category, insurance spares, requires a very different approach. These are typically high-value parts for critical assets with very long supply lead times. Analysis of historical consumption is unhelpful in setting inventory levels for such parts, since the company may have consumed few, or none, of the part in the past. Likewise, it is very difficult to make meaningful estimates of the risk of the part failing in service in the future. One powerful way to make decisions about these kinds of part is to use a return-on-investment (ROI) approach to prioritize spend.
To do this, the company calculates the likely financial impact of the part failing with no immediate replacement available by multiplying the cost of lost production by the lead time required to obtain a new part and dividing this figure by the cost of keeping a replacement part in inventory. These ROI calculations allow the company to prioritize its expenditure on insurance parts. It may, for example, decide to stock only parts above a certain a level of ROI, or it may allocate a fixed budget to insurance parts, starting with the highest-ROI items and working down the list until the budget is exhausted.
A basis for continuous improvement
Getting spare parts inventory levels right usually delivers significant improvements in both cost and asset availability. For the best companies, that’s only the start, however. Industry leaders monitor part consumption on an ongoing basis to identify exceptions that may indicate an underlying issue with assets or operating practices. If a particular machine starts to experience an unexpectedly high number of early-life bearing failures, that might suggest an issue with improper assembly or lubrication. Spotting these trends in parts consumption allows the company to launch root-cause analysis efforts to understand and rectify the source of the problem.
A similar approach can be used to prioritize reliability improvement efforts. If a particular machine or category of parts is contributing disproportionately to consumable or operational part costs, the company may choose to launch a kaizen (improvement) effort to tackle that machine’s performance and reliability. Alternatively, it may consider installing condition monitoring technology to support the early identification of problems or investing in alternative technologies that offer greater reliability.
These efforts lead to a virtuous circle. As machine reliability improves and spares consumption falls, companies can alter their inventory parameters accordingly, freeing up further capital and reducing carrying costs. And as overall use patterns change, it becomes easier to spot the remaining outliers, helping focus future improvement efforts.