Prioritize MRO storeroom items based on criticality and effect

By Doug Wallace, CPIM, Life Cycle Engineering

Although there’s no direct correlation between the two, an ABC analysis on MRO parts is similar to criticality rankings that reliability engineers develop for plant equipment. Criticality rankings define an asset’s relative priority in terms of its effect on plant safety, productivity, efficiency, or other criteria. An ABC analysis determines the relative priority of MRO storeroom items based on their criticality to the support of operations and their potential effect on plant inventory and investment.

Completing the ABC analysis involves three key activities: prioritization, stratification, and optimization. The results of the analysis provide valuable information that can be used to determine how each part should be monitored and managed from the perspectives of procurement, warehousing, and strategic inventory management.

Prioritization

The first part of the analysis consists of rank ordering MRO storeroom parts based on a set of predefined criteria. There are several different schools of thought on the best approach to doing this. It’s important to understand the advantages and disadvantages of each before deciding which method is best suited to your particular situation.
Unlike a dependent-demand manufacturing operation with a master schedule and complete bills of material, material requirements in the MRO world are largely unpredictable. The inherent assumption in ABC analysis for MRO parts is that, at least to some degree, the past is a fairly reliable predictor of the future. Investment brokers are quick to remind us that’s not the case, but in lieu of accurate material forecast data, there’s little option. That brings up several key points to consider.

First, it’s not advisable to prioritize items by on-hand inventory value or any other variable that can change significantly from day to day, depending on issues, receipts, or other transactions that take place in the storeroom. Doing so would produce dramatically different results, depending on the day you took the data snapshot.

Second, from a warehouse management perspective, there are definite benefits to analyzing inventory by activity level - number of issues or quantity of parts issued. However, using this method to prioritize inventory might negate some of the more important benefits of the ABC analysis as discussed below. If activity data is going to be used for such things as determining the most efficient stocking location for parts, the required data should be readily available from outside of the ABC process.

Third, for a newly established storeroom, or where no historical data is available, it might be useful to prioritize items initially on the basis of unit cost until you develop a sufficient base of historical data.

Given these considerations, the recommended criterion for prioritization is annualized dollar value of usage. This method combines the effects of unit cost and activity level, and it provides a more stable data set obtained from a horizon of historical transaction information. Calculate the average annual usage for each item, extend it at the unit cost, and then list the parts in order from highest to lowest. Several caveats apply to this approach.

First, it’s important to understand that usage is based on net quantity issued - total issues minus any returns to stock. Don’t factor in inventory adjustments, scrap, or other transactions that affect the perpetual inventory balance. Not having in place proper practices that ensure timely and accurate recording of transactions when parts are issued or returned distorts the quality of the usage data and the prioritization will be degraded.
Second, it’s necessary to capture an appropriate horizon of historical data to get a reasonable representation of past usage levels. For example, in a leading-edge manufacturing environment, where product life cycles are short, a six-month horizon might be sufficient to get a fairly accurate estimate of usage.

Because in many MRO environments parts might turn over only once every few years, it’s best to use the longest possible horizon, assuming the quality of the data remain intact. Typically, a minimum of one year’s history is required, but if available, three years’ worth of data is probably sufficient.

Regardless of the time horizon, the third thing to consider is where each part is with respect to its life cycle. Parts in the early stage of their life cycles might have little historical issue data available. In this case, the calculated annualized usage likely will be understated compared to actual future requirements. On the other hand, later in the life cycle, parts are likely to have historical issue data that are more heavily weighted toward the earlier part of the horizon because of declining demand over that timeframe.

In this case, the calculated annualized usage likely will be overstated, compared to actual future requirements. Finally, as equipment and associated spare parts come to the very end of their life cycles, there might be a sudden spike in demand as a result of a “last-time buy.” This also can skew the calculated annualized usage.

These considerations, although important to the ultimate quality of the completed analysis, can be factored in during the optimization step and don’t have to be addressed during the prioritization part of the process.

Stratification

Once prioritized, subject the parts to a modified Pareto analysis - commonly referred to as the 80/20 rule - to stratify them into categories. Typically three categories are used: A, B, and C, hence the name “ABC analysis.”

The A items are the most critical. They require tight inventory controls, frequent review of demand forecasts and usage rates, highly accurate part data, and frequent cycle counts to verify perpetual inventory balance accuracy. Typically, these comprise 5% to 10% of the total item count and the top 70% to 85% of the total annual usage dollar value.
Next, B items are of lesser criticality. They require nominal inventory controls, occasional reviews of demand forecasts and usage rates, reasonably accurate part data, and less frequent but regular cycle counting. They typically comprise the next 15% to 25% of the total item count and the next 10% to 20% of the total annual dollar value of usage.

Of course, C items have the least effect in terms of warehouse activity and financials, and therefore require minimal inventory controls. In fact, depending on the nature of the items, they might be good candidates for free-bin stores. Analysis of demand forecasts and usage rates on C items is sometimes waived in favor of placing infrequent orders, often in large quantities, to maintain plenty of stock on hand. Such C items typically comprise 65% to 80% of the total item count and the last 5% to 10% of the total annual dollar value of usage. Because of low usage, any dead or inactive inventory normally will fall into the C category.

There’s no set rule for establishing the cutoffs between categories. In fact, many CMMS/EAM systems allow the user to define the cutoffs. Generally, a good starting point is to define A items as those that represent the top 80% of total annual usage based on the prioritized list, B items as the next 15%, and C items as the last 5%.

Optimization

If the process was as simple as described above, it would have limited value. Like many other critical work processes, an effective ABC analysis requires just that – analysis of the results.

Because of the effect ABC classifications have on other inventory management processes described below, and the relative importance of A items, it’s desirable to have the A category represent as much of the total annualized usage as possible, as long as the number of items is manageable.

Achieving this balance requires an a priori understanding as to what each classification represents in terms of required activity levels in other material management processes. If necessary and appropriate, adjust the cutoff for the A category upward or downward and rerun the stratification to obtain a better item distribution. However, it’s generally a good idea to limit the A category to no more than 85% of total annual usage, and no less than 75%.

Before you can consider the analysis complete, it might be beneficial to review the results with other stakeholders. Maintenance managers, operations managers, engineers, and technicians might provide further insight into future demand for particular items that would warrant modifying the ABC classification. This isn’t intended to be an opportunity to rethink the entire analysis or subvert its intent. Instead, it’s an opportunity for subject matter experts to provide valuable input, and the feedback should be thoughtful, but provided quickly.

There are several ways the process can be improved. Start by eliminating the effect of dead inventory. Transactions in the horizon for any inactive or obsolete items can result in overstating the true priority of so-called dead inventory and artificially forcing other active items to a lower priority or classification. Reviewing inactive and obsolete inventory on a periodic basis ensures that any items flagged as dead and awaiting disposition have no effect on the ABC classifications. Exclude any such items from the prioritization process, or at least have their usage forced to zero.

Next, identify critical spares. In many MRO operations, there are storeroom parts designated as critical spares. These often will be expensive, sometimes one-of-a kind items, with long lead times. Because of the potential implications of an equipment failure that requires these parts, it’s imperative to keep them in stock or readily available to minimize production disruption.

From a criticality standpoint, they generally would be thought of as A-type items, and if there’s any usage in the horizon, they will almost always end up as A items through the prioritization and stratification process. However, because often they have extremely low usage, if any, there’s a distinct possibility that they can end up at or near the bottom of the prioritized list and therefore end up in the C category during the stratification process. This poses a potential problem if critical spares might not get the attention they require.

One solution to this is to establish a fourth category (D) to designate critical spares. In this case, the critical spares are identified up front and segregated from the rest of the inventory items before the prioritization process begins. Only the remaining, non-critical items are prioritized and stratified into A, B, and C classifications.

Procurement and warehouse applications

The results of an ABC analysis extend into a number of other inventory control and management processes. An example is when reviewing stocking levels. As with financial investments, past results don’t guarantee future performance. However, A items generally will have greater effect on projected investment and purchasing spending and, therefore, should be managed more aggressively in terms of minimum and maximum inventory levels.

Consider obsolescence review. By definition, inactive items fall to the bottom of the prioritized list. Therefore, the bottom of the C category is the best place to start when performing a periodic obsolescence review.

Next is cycle counting. The higher the usage, the more activity an item is likely to have, hence the greater likelihood that multiple transactions will result in inventory errors. Therefore, to ensure accurate record balances, higher-priority items are cycle-counted more frequently. Generally A items are counted once every quarter, B items once every six months, and C items once every 12 months.

You also can identify items for potential consignment or vendor stocking. Because A items tend to have a greater effect on investment, they would be the best candidates to investigate for alternative stocking arrangements that can reduce investment liability and associated carrying costs.

Lastly, you can examine turnover ratios and associated inventory goals. By definition, A items have greater usage than B or C items, and should have a greater turnover ratio. When establishing investment and turnover metrics, segregate inventory data by ABC classification, with different targets for each category.

To make the most effective use of ABC classifications, complete the analysis on an annual basis, and more often as necessary if there are significant changes in the MRO activity level.

Doug Wallace, CPIM, is a Materials Management Subject Matter Expert at Life Cycle Engineering. Contact him at dwallace@LCE.com and 843.810.7619.

© Life Cycle Engineering

For More Information

843.744.7110 | info@LCE.com