Just as the renowned astronomer Copernicus transformed our understanding of astronomy by placing the sun at the center of our universe, today, we invite you to re-center your approach to inventory management. And while not quite as enlightening, this advice will help your company avoid being caught in the gravitational pull of inventory woes—constantly orbiting between stockouts, surplus gravity, and the unexpected cosmic expenses of expediting?

In this article, we’ll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We’ll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we’ll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we’ll explore ways to enhance your service-level-driven inventory plan consistently.

In service-oriented businesses, the consequences of stockouts are often very significant.  Achieving high service levels depends on having the right parts at the right time. However, having the right parts isn’t the only factor. Your Supply Chain Team must develop a consensus inventory plan for every part, then continuously update it to reflect real-time changes in demand, supply, and financial priorities.

Managing inventory with Service-level-driven planning combines the ability to plan thousands of items with high-level strategic modeling. This requires addressing core issues facing inventory executives:

• Lack of control over supply and associated lead times.
• Unpredictable intermittent demand.
• Conflicting priorities between maintenance/mechanical teams and Materials Management.
• Reactive “wait and see” approach to planning.
• Misallocated inventory, causing stockouts and excess.
• Lack of trust in systems and processes.

The key to optimal service parts management is to grasp the balance between providing excellent service and controlling costs. To do this, we must compare the costs of stockout with the cost of carrying additional spare parts inventory. The costs of a stockout will be higher for critical or emergency spares, when there is a service level agreement with external customers, for parts used in multiple assets, for parts with longer supplier lead times, and for parts with a single supplier. The cost of inventory may be assessed by considering the unit costs, interest rates, warehouse space that will be consumed, and potential for obsolescence (parts used on a soon-to-be-retired fleet have a higher obsolescence risk, for example).

To arbitrate how much stock should be put on the shelf for each part, it is critical to establish consensus on the desired key metrics that expose the tradeoffs the business must make to achieve the desired KPIs. These KPIs will include Service Levels that tell you how often you meet usage needs without falling short on stock, Fill Rates that tell you what percentage of demand is filled, and Ordering costs detail the expenses incurred when you place and receive replenishment orders. You also have Holding costs, which encompass expenses like obsolescence, taxes, and warehousing, and Shortage costs that pertain to expenses incurred when stockouts happen.

An MRO business or Aftermarket Parts Planning team might desire a 99% service level across all parts – i.e., the minimum stockout risk that they are willing to accept is 1%. But what if the amount of inventory needed to support that service level is too expensive? To make an informed decision on whether there is going to be a return on that additional inventory investment, you’ll need to know the stockout costs and compare that to the inventory costs. To get stockout costs, multiply two key elements: the cost per stockout and the projected number of stockouts. To get inventory value, multiply the units required by the unit cost of each part. Then determine the annual holding costs (typically 25-35% of the unit cost). Choose the option that yields a total lower cost. In other words, if the benefit associated with adding more stock (reduced shortage costs) outweighs the cost (higher inventory holding costs), then go for it. A thorough understanding of these metrics and the associated tradeoffs serves as the compass for decision-making.

Modern software aids in this process by allowing you to simulate a multitude of future scenarios. By doing so, you can assess how well your current inventory stocking strategies are likely to perform in the face of different demand and supply patterns. If anything falls short or goes awry, it’s time to recalibrate your approach, factoring in current data on usage history, supplier lead times, and costs to prevent both stockouts and overstock situations.

Enhance your service-level-driven inventory plan consistently.

In conclusion, it’s crucial to assess your service-level-driven plan continuously. By systematically constructing and refining performance scenarios, you can define key metrics and goals, benchmark expected performance, and automate the calculation of stocking policies for all items. This iterative process involves monitoring, revising, and repeating each planning cycle.

The depth of your analysis within these stocking policies relies on the data at your disposal and the configuration capabilities of your planning system. To achieve optimal outcomes, it’s imperative to maintain ongoing data analysis. This implies that a manual approach to data examination is typically insufficient for the needs of most organizations.

For information on how Smart Software can help you meet your service supply chain goals with service-driven planning and more, visit the following blogs.

–   “Explaining What  Service-Level Means in Your Inventory Optimization Software”  Stocking recommendations can be puzzling, especially when they clash with real-world needs.  In this post, we’ll break down what that 99% service level means and why it’s crucial for managing inventory effectively and keeping customers satisfied in today’s competitive landscape.

–  “Service-Level-Driven Planning for Service Parts Businesses” 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.

–   “How to Choose a Target Service Level.” This is a strategic decision about inventory risk management, considering current service levels and fill rates, replenishment lead times, and trade-offs between capital, stocking and opportunity costs.  Learn approaches that can help.

–   “The Right Forecast Accuracy Metric for Inventory Planning.”  Just because you set a service level target doesn’t mean you’ll actually achieve it. If you are interested in optimizing stock levels, focus on the accuracy of the service level projection. Learn how.

Managing spare parts presents numerous challenges, such as unexpected breakdowns, changing schedules, and inconsistent demand patterns. Traditional forecasting methods and manual approaches are ineffective in dealing with these complexities. To overcome these challenges, this blog outlines key strategies that prioritize service levels, utilize probabilistic methods to calculate reorder points, regularly adjust stocking policies, and implement a dedicated planning process to avoid excessive inventory. Explore these strategies to optimize spare parts inventory and improve operational efficiency.

Bottom Line Upfront

​1.Inventory Management is Risk Management.

2.Can’t manage risk well or at scale with subjective planning – Need to know service vs. cost.

3.It’s not supply & demand variability that are the problem – it’s how you handle it.

4.Spare parts have intermittent demand so traditional methods don’t work.

5.Rule of thumb approaches don’t account for demand variability and misallocate stock.

6.Use Service Level Driven Planning  (service vs. cost tradeoffs) to drive stock decisions.

7.Probabilistic approaches such as bootstrapping yield accurate estimates of reorder points.

8.Classify parts and assign service level targets by class.

9.Recalibrate often – thousands of parts have old, stale reorder points.

10.Repairable parts require special treatment.

Do Focus on the Real Root Causes

Intermittent Demand

• Slow moving, irregular or sporadic with a large percentage of zero values.
• Non-zero values are mixed in randomly – spikes are large and varied.
• Isn’t bell shaped (demand is not Normally distributed around the average.)
• At least 70% of a typical Utility’s parts are intermittently demanded.

Normal Demand

• Very few periods of zero demand (exception is seasonal parts.)
• Often exhibits trend, seasonal, or cyclical patterns.
• Lower levels of demand variability.
• Is bell-shaped (demand is Normally distributed around the average.)

Don’t rely on averages

• OK for determining typical usage over longer periods of time.
• Often forecasts more “accurately” than some advanced methods.
• But…insufficient for determining what to stock.

Don’t Buffer with Multiples of Averages

Example:  Two equally important parts so let’s treat them the same.
We’ll order more  when On Hand Inventory ≤ 2 x Avg Lead Time Demand.

Do use Service Level tradeoff curves to compute safety stock

Standard Normal Probabilities

OK for normal demand. Doesn’t work with intermittent demand!

Don’t use Normal (Bell Shaped) Distributions

• You’ll get the tradeoff curve wrong:

– e.g., You’ll target 95% but achieve 85%.

– e.g., You’ll target 99% but achieve 91%.

• This is a huge miss with costly implications:

– You’ll stock out more often than expected.

– You’ll start to add subjective buffers to compensate and then overstock.

– Lack of trust/second-guessing of outputs paralyzes planning.

Why Traditional Methods Fail on Intermittent Demand: 

Traditional Methods are not designed to address core issues in spare parts management.

Need: Probability distribution (not bell-shaped) of demand over variable lead time.

• Get: Prediction of average demand in each month, not a total over lead time.
• Get: Bolted-on model of variability, usually the Normal model, usually wrong.

Need: Exposure of tradeoffs between item availability and cost of inventory.

• Get: None of this; instead, get a lot of inconsistent, ad-hoc decisions.

Do use Statistical Bootstrapping to Predict the Distribution:

Then exploit the distribution to optimize stocking policies.

How does Bootstrapping Work?

24 Months of Historical Demand Data.

Bootstrap Scenarios for a 3-month Lead Time.

Bootstrapping Hits the Service Level Target with nearly 100% Accuracy!

• National Warehousing Operation.

Task: Forecast inventory stocking levels for 12,000 intermittently demanded SKUs at 95% & 99% service levels

Results:

At 95% service level, 95.23% did not stock out.

At 99% service level, 98.66% did not stock out.

This means you can rely on output to set expectations and confidently make targeted stock adjustments that lower inventory and increase service.

Set Target Service Levels According to Order Frequency & Size

Recalibrate Reorder Points Frequently

• Static ROPs cause excess and shortages.
• As lead time increases, so should the ROP and vice versa.
• As usage decreases, so should the ROP and vice versa.
• Longer you wait to recalibrate, the greater the imbalance.
• Mountains of parts ordered too soon or too late.
• Wastes buyers’ time placing the wrong orders.
• Breeds distrust in systems and forces data silos.

Do Plan Rotables (Repair Parts) Differently

Summary

1.Inventory Management is Risk Management.

2.Can’t manage risk well or at scale with subjective planning – Need to know service vs. cost.

3.It’s not supply & demand variability that are the problem – it’s how you handle it.

4.Spare parts have intermittent demand so traditional methods don’t work.

5.Rule of thumb approaches don’t account demand variability and misallocate stock.

6.Use Service Level Driven Planning  (service vs. cost tradeoffs) to drive stock decisions.

7.Probabilistic approaches such as bootstrapping yield accurate estimates of reorder points.

8.Classify parts and assign service level targets by class.

9.Recalibrate often – thousands of parts have old, stale reorder points.

10.Repairable parts require special treatment.

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%!

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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.

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.

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