I’ll bet your maintenance and repair teams would be ok with incurring higher stock out risks one some spare parts if they knew that the inventory reduction savings would be used to spread out the inventory investment more effectively to other parts and boost overall service levels.

I’ll double down that your Finance team, despite always being challenged with lowering costs, would support a healthy inventory increase if they could clearly see that the revenue benefits from increased uptime, fewer expedites, and service level improvements clearly outweighed the additional inventory costs and risk.

A spare parts tradeoff curve will enable service parts planning teams to properly communicate the risks and costs of each inventory decision.  It is mission critical for parts planning and the only way to adjust stocking parameters proactively and accurately for each part.  Without it, planners, for all intents and purposes, are “planning” with blinders on because they won’t be able to communicate the true tradeoffs associated with stocking decisions.

For example, if a proposed increase to the min/max levels of an important commodity group of service parts is recommended, how do you know whether the increase is too high or too low or just right?  How can you fine-tune the change for thousands of spares?  You won’t and you can’t.  Your inventory decision making will rely on reactive, gut feel, and broad-brush decisions causing service levels to suffer and inventory costs to balloon.

So, what exactly is a spare parts tradeoff curve anyway?

It’s a fact-based, numerically driven prediction that details how changes in stocking levels will influence inventory value, holding costs, and service levels.  For each unit change in inventory level there is a cost and a benefit.  The spare parts tradeoff curve identifies these costs and benefits across different stocking levels. It lets planners discover the stock level that best balances the costs and benefits for each individual item.

Here are two simplified examples. In Figure 1, the spare parts tradeoff curve shows how the service level (probability of not stocking out) changes depending on the reorder level.  The higher the reorder level, the lower the stockout risk.  It is critical to know how much service you are gaining given the inventory investment.  Here you may be able to justify that an inventory increase from a reorder point of 35 to 45 is well worth the investment of 10 additional units of stock because service levels jumps from just under 70% to 90%, cutting your stockout risk for the spare part from 30% to 10%!

​

Figure 1: Cost versus Service Level

Figure 2: Service Level versus Size of Inventory

In this example (Figure 2), the tradeoff curve exposes a common problem with spare parts inventory.  Often stock levels are so high that they generate negative returns.  After a certain stocking quantity, each additional unit of stock does not buy more benefit in the form of a higher service level.  Inventory decreases can be justified when it is clear the stock level is well past the point of diminishing returns. An accurate tradeoff curve will expose the point where it is no longer advantageous to add stock.

By leveraging #probabilisticforecasting to drive parts planning, you can communicate these tradeoffs accurately, do so at scale across hundreds of thousands of parts, avoid bad inventory decisions, and balance service levels and costs.  At Smart Software, we specialize in helping spare parts planners, Directors of Materials Management, and financial executives managing MRO, spare parts, and aftermarket parts to understand and exploit these relationships.

Every organization that runs equipment needs spare parts. All of them must cope with issues that are generic no matter what their business. Some of the problems, however, are industry specific. This post discusses one universal problem that manifested in a nuclear plant and one that is especially acute for any electric utility.

The Universal Problem of Data Quality

We often post about the benefits of converting parts usage data into smart inventory management decisions. Advanced probability modeling supports generation of realistic demand scenarios that feed into detailed Monte Carlo simulations that expose the consequences of decisions such as choices of Min and Max governing the replenishment of spares.

However, all that new and shiny analytical tech requires quality data as fuel for the analysis. For some public utilities of all kinds, record keeping is not a strong suit, so the raw material going into analysis can be corrupted and misleading. We recently chanced upon documentation of a stark example of this problem at a nuclear power plant (see Scala, ­­­­­­­Needy and Rajgopal: Decision making and tradeoffs in the management of spare parts inventory at utilities. American Association of Engineering Management, 30th ASEM National Conference, Springfield, MO. October 2009). Scala et al. documented the usage history of a critical part whose absence would result in either a facility de-rate or a shutdown. The plant’s usage record for that part spanned more than eight years of data. During that time, the official usage history reported nine events in which positive demand occurred with sizes ranging from one to six units each. There were also five events marked by negative demands (i.e., returns to warehouse) ranging from one to three units each. Careful sleuthing discovered that the true usage occurred in just two events, both with demand of two units. Obviously, calculating the best Min/Max values for this item requires accurate demand data.

The Special Problem of Health and Safety

In the context of “regular” businesses, shortages of spare parts can damage both current revenue and future revenue (related to reputation as a reliable supplier). For an electric utility, however, Scala et al. noted a much greater level of consequence attached to stockouts of spare parts. These include not only a heightened financial and reputational risk but also risks to health and safety: “Ramifications of not having a part in stock include the possibility of having to reduce output or quite possibly, even a plant shut down. From a more long-term perspective, doing so might interrupt the critical service of power to residential, commercial, and/or industrial customers, while damaging the company’s reputation, reliability, and profitability. An electric utility makes and sells only one product: electricity. Losing the ability to sell electricity can be seriously damaging to the company’s bottom line as well its long-term viability.”

All the more reason for electric utilities to be leaders rather than laggards in the deployment of the most advanced probability models for demand forecasting and inventory optimization.

We often encounter Excel-based reorder point planning methods.  In this post, we’ve detailed an approach that a customer used prior to proceeding with Smart.  We describe how their spreadsheet worked, the statistical approaches it relied on, the steps planners went through each planning cycle, and their stated motivations for using (and really liking) this internally developed spreadsheet.

Their monthly process consisted of updating a new month of actuals into the “reorder point sheet.”  An embedded formula recomputed the Reorder Point (ROP) and order-up-to (Max) level.  It worked like this:

• ROP = LT Demand + Safety Stock
• LT Demand = average daily demand x lead time days (assumed constant to keep things simple)
• Safety Stock for long lead time parts = Standard deviation x 2.0
• Safety Stock for short lead time parts = Standard deviation x 1.2
• Max = ROP + supplier-dictated Minimum Order Quantity

Historical averages and standard deviations used 52-weeks of rolling history (i.e., the newest week replaced the oldest week each period).  The standard deviation of demand was computed using the “stdevp” function in Excel.

Every month, a new ROP was recomputed. Both the average demand and standard deviation were modified by the new week’s demand, which in turn updated the ROP.

The default ROP is always based on the above logic. However, planners would make changes under certain conditions:

1. Planners would increase the Min for inexpensive parts to reduce risk of taking an on-time delivery hit (OTD) on an inexpensive part.

2. The Excel sheet identified any part with a newly calculated ROP that was ± 20% different from the current ROP.

3. Planners reviewed parts that exceed the exception threshold, proposed changes, and got a manager to approve.

4. Planners reviewed items with OTD hits and increased the ROP based on their intuition. Planners continued to monitor those parts for several periods and lowered the ROP when they felt it is safe.

5. Once the ROP and Max quantity were determined, the file of revised results was sent to IT, who uploaded into their ERP.

6. The ERP system then managed daily replenishment and order management.

Objectively, this was perhaps an above-average approach to inventory management. For instance, some companies are unaware of the link between demand variability and safety stock requirements and rely on rule of methods or intuition exclusively.  However,  there are problems with their approach:

1. Manual data updates
The spreadsheets required manual updating. To recompute, multiple steps were required, each with their own dependency. First, a data dump needed to be run from the ERP system.  Second, a planner would need to open the spreadsheet and review it to make sure the data imported properly.  Third, they needed to review output to make sure it calculated as expected.  Fourth, manual steps were required to push the results back to the ERP system.

2. One Size Fits All Safety Stock
Or in this case, “one of two sizes fit all”. The choice of using 2x and 1.2x standard deviation for long and short lead time items respectively equates to service levels of 97.7% and 88.4%.    This is a big problem since it stands to reason that not every part in each group requires the same service level.  Some parts will have higher stock out pain than others and vice versa. Service levels should therefore be specified accordingly and be commensurate with the importance of the item.  We discovered that they were experiencing OTD hits on roughly 20% of their critical spare parts which necessitated manual overrides of the ROP.  The root cause was that on all short lead time items they they were planning for an 88.4% service level target. So, the best they could have gotten was to stock out 12% of the time even if “on plan.”   It would have been better to plan service level targets according to the importance of the part.

3. Safety stock is inaccurate.  The items being planned for this company are spare parts to support diagnostic equipment.  The demand on most of these parts is very intermittent and sporadic.  So, the choice of using an average to compute lead time demand wasn’t unreasonable if you accept the need for ignoring variability in lead times.  However, the reliance on a Normal distribution to determine the safety stock was a big mistake that resulted in inaccurate safety stocks.  The company stated that its service levels for long lead time items ran in the 90% range compared to their target of 97.7%, and that they made up the difference with expedites.  Achieved service levels for shorter lead time items were about 80%, despite being targeted for 88.4%.    They computed safety stock incorrectly because their demand isn’t “bell shaped” yet they picked safety stocks assuming they were.  This simplification results in missing service level targets, forcing the manual review of many items that then need to be manually “monitored for several periods” by a planner.  Wouldn’t it be better to make sure the reorder point met the exact service level you wanted from the start?  This would ensure you hit your service levels while minimizing unneeded manual intervention.

There is a fourth issue that didn’t make the list but is worth mentioning.  The spreadsheet was unable to track trend or seasonal patterns.  Historical averages ignore trend and seasonality, so the cumulative demand over lead time used in the ROP will be substantially less accurate for trending or seasonal parts. The planning team acknowledged this but didn’t feel it was a legitimate issue, reasoning that most of the demand was intermittent and didn’t have seasonality.  It is important for the model to pick up on trend and seasonality on intermittent data if it exists, but we didn’t find their data exhibited these patterns.  So, we agreed that this wasn’t an issue for them.  But as planning tempo increases to the point that demand is bucketed daily, even intermittent demand very often turns out to have day-of-week and sometimes week-of-month seasonality. If you don’t run at a higher frequency now, be aware that you may be forced to do so soon to keep up with more agile competition. At that point, spreadsheet-based processing will just not be able to keep up.

In conclusion, don’t use spreadsheets. They are not conducive to meaningful what-if analyses, they are too labor-intensive, and the underlying logic must be dumbed down to process quickly enough to be useful.  In short, go with purpose-built solutions. And make sure they run in the cloud.

When managing service parts, you don’t know what will break and when because part failures are random and sudden. As a result, demand patterns are most often extremely intermittent and lack significant trend or seasonal structure. The number of part-by-location combinations is often in the hundreds of thousands, so it’s not feasible to manually review demand for individual parts. Nevertheless, it is much more straightforward to implement a planning and forecasting system to support spare parts planning than you might think.

This conclusion is informed by hundreds of software implementations we’ve directed over the years. Customers managing spare parts and service parts (the latter for internal consumption/MRO), and to a lesser degree aftermarket parts (for resale to installed bases), have consistently implemented our parts planning software faster than their peers in manufacturing and distribution.

The primary reason is the role in manufacturing and distribution of business knowledge about what might happen in the future. In a traditional B2B manufacturing and distribution environment, there are customers and sales and marketing teams selling to those customers. There are sales goals, revenue expectations, and budgets. This means there is a lot of business knowledge about what will be purchased, what will be promoted, whose opinions need to be accounted for. A complex planning loop is required. In contrast, when managing spare parts, you have a maintenance team that fixes equipment when it breaks. Though there are often maintenance schedules for guidance, what is needed beyond a standard list of consumable parts is often unknown until a maintenance person is on-site. In other words, there just isn’t the same sort of business knowledge available to parts planners when making stocking decisions.

Yes, that is a disadvantage, but it also has an upside: there is no need to produce a period-by-period consensus demand forecast with all the work that requires. When planning spare parts, you can usually skip many steps required for a typical manufacturer, distributor, or retailer. These skippable steps include:  

1. Building forecasts at different levels of the business, such as product family or region.
2. Sharing the demand forecast with sales, marketing, and customers.
3. Reviewing forecast overrides from sales, marketing, and customers.
4. Agreeing on a consensus forecast that combines statistics and business knowledge.
5. Measuring “forecast value add” to determine if overrides make the forecast more accurate.
6. Adjusting the demand forecast for known future promotions.
7. Accounting for cannibalization (i.e., if I sell more of product A, I’ll sell less of product B).

Freed from a consensus-building process, spare parts planners and inventory managers can rely directly on their software to predict usage and the required stocking policies. If they have access to a field-proven solution that addresses intermittent demand, they can quickly “go live” with more accurate demand forecasts and estimates of reorder points, safety stocks, and order suggestions.  Their attention can be focused on getting accurate usage and supplier lead time data. The “political” part of the job can be limited to obtaining organization consensus on service level targets and inventory budgets.

​

Service-Level-Driven Service Parts Planning is a four-step process that extends beyond simplified forecasting and rule-of-thumb safety stocks. It provides service parts planners with data-driven, risk-adjusted decision support.

Step 1. Ensure that all stakeholders agree on the metrics that matter. All participants in the service parts inventory planning process must agree on the definitions and what metrics matter most to the organization. Service Levels detail the percentage of time you can completely satisfy required usage without stocking out. Fill Rates detail the percentage of the requested usage that is immediately filled from stock. (To learn more about the differences between service levels and fill rate, watch this 4-minute lesson here.) Availability details the percentage of active spare parts that have an on-hand inventory of at least one unit. Holding costs are the annualized costs of holding stock accounting for obsolescence, taxes, interest, warehousing, and other expenses. Shortage costs are the cost of running out of stock including vehicle/equipment down time, expedites, lost sales, and more. Ordering costs are the costs associated with placing and receiving replenishment orders.

Step 2. Benchmark historical and predicted current service level performance. All participants in the service parts inventory planning process must hold a common understanding of predicted future service levels, fill rates and costs and their implications for your service parts operations. It is critical to measure both historical Key Performance Indicators (KPIs) and their predictive equivalents, Key Performance Predictions (KPPs). Leveraging modern software, you can benchmark past performance and leverage probabilistic forecasting methods to simulate future performance. By stress testing your current inventory stocking policies against all plausible scenarios of future demand, you will know ahead of time how current and proposed stocking policies are likely to perform.

Step 3. Agree on targeted service levels for each spare part and take proactive corrective action when targets are predicted to miss. Parts planners, supply chain leadership, and the mechanical/maintenance teams should agree on the desired service level targets with a full understanding of the tradeoffs between stockout risk and inventory cost. By leveraging what-if scenarios in modern parts planning software, it is possible to compare alternative stocking policies and identify those that best meets business objectives. Agree on what degree of stockout risk is acceptable for each part or class of parts. Likewise, determine inventory budgets and other cost constraints. Once these limits are agreed, take immediate action to avoid stockouts and excess inventory before they occur. Use your software to automatically upload modified reorder points, safety stock levels, and/or Min/Max parameters to your Enterprise Resource Planning (ERP) or Enterprise Asset Management (EAM) system to adjust daily parts purchasing.

Step 4. Make it so and keep it so. Empower the planning team with the knowledge and tools it needs to ensure that you strike agreed-upon balance between service levels and costs by driving your ordering process using optimized inputs (forecasts, reorder points, order quantities, safety stocks). Track your KPI’s and use your software to identify and address exceptions. Don’t let reorder points grow stale and outdated.  Recalibrate the stocking policies each planning cycle (at least once monthly) using up-to-date usage history, supplier lead times, and costs. Remember: Recalibration of your service parts inventory policy is preventive maintenance against both stockouts and excess stock.

​