We often are asked, “Why is the software driving up the inventory?” The answer is that Smart isn’t driving it in either direction – the inputs are driving it, and those inputs are controlled by the users (or admins). Here are four things you can do to get the results you expect.

1. Confirm that your service level targets are commensurate with what you want for that item or group of items. Setting very high targets (95% or more) will likely drive inventory up if you have been coasting along at a lower level and are OK with being there. It’s possible you’ve never achieved the new higher service level but customers have not complained.  Figure out what service level has worked by evaluating historical reports on performance and set your targets accordingly. But keep in mind that competitors may beat you on item availability if you keep using your father’s service level targets.

2. Make sure your understanding of “service level” aligns with the software system’s definition. You may be measuring performance based on how often you ship within one week from receipt of the customer order, whereas the software is targeting reorder points based on your ability to ship right away, not within a week. Clearly the latter will require more inventory to hit the same “service level.” For instance, a 75% same-day service level may correspond to a 90% same-week service level. In this case, you are really comparing apples to oranges. If this is the reason for the excess stock, then determine what “same day” service level is needed to get you to your desired “same week” service level and enter that into the software. Using the less-stringent same-day target will drop the inventory, sometimes very significantly.

3. Evaluate the lead time inputs. We’ve seen instances in which lead times had been inflated to trick old software into producing desired results. Modern software tracks suppliers’ performance by recording their actual lead times over multiple orders, then it takes account of lead time variability in its simulations of daily operations. Watch out if your lead times are fixed at one value that was decided on in the distant past and isn’t current.

4. Check your demand signal. You have lots of historical transactions in your ERP system that can be used in many ways to determine the demand history. If you are using signals such as transfers, or you are not excluding returns, then you may be overstating demand. Spend a little time on defining “demand” in the way that makes most sense for your situation.

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.

The best inventory planning processes rely on statistical analysis to uncover relevant facts about the data. For instance:

1. The range of demand values and supplier lead times to expect.
2. The most likely values of item demand and supplier lead time.
3. The full probability distributions of item demand and supplier lead time.

If you reach the third level, you have the facts required to answer important operational questions, additional questions such as:

1. Exactly how much extra stock is needed to improve service levels by 5%?
2. What will happen to on-time-delivery if inventory is reduced by 5%?
3. Will either of the above changes generate a positive financial return?
4. More generally, what service level target and associated inventory level is most profitable?

When you have the facts and add your business knowledge, you can make more informed stocking decisions that will generate significant returns. You’ll also set proper expectations with internal and external stakeholders, ensuring there are fewer unwelcome surprises.

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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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.

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