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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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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Utilities in the electrical, natural gas, urban water and wastewater, and telecommunications fields are all asset intensive. Generation, production, processing, transmission, and distribution of electricity, natural gas, oil, and water, are all reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management.

A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. Impeding these efforts are out-of-date IT systems, evolving security threats, frequent supply chain disruptions, and extreme demand variability.  However, the convergence of these challenges with mature cloud technology and recent advancements in data analytics, probabilistic forecasting, and technologies for data management, present utilities a generational opportunity to digitally transform their enterprise.

Here are seven digital transformations that require relatively small upfront investments but will generate seven-figure returns.

1. Inventory Management is the first step in MRO inventory optimization. It involves analyzing current inventory levels and usage patterns to identify opportunities for improvement. This should include looking for overstocked, understocked, or obsolete items.  New probabilistic forecasting technology will help by simulating future parts usage and predicting how current stocking policies will perform.  Pats planners can use the simulation results to proactively identify where policies should be modified.

2. Accurate forecasting and demand planning are very important in optimizing MRO service parts inventories. An accurate demand forecast is a critical supply chain driver. By understanding demand patterns that result from capital projects and planned and unplanned maintenance, parts planners can more accurately anticipate future inventory needs, budget properly, and better communicate anticipated demand to suppliers. Parts forecasting software can be used to automatically house an accurate set of historical usage that details planned vs. unplanned parts demand.

3. Managing suppliers and lead times are important components of MRO inventory optimization. It involves selecting the best vendors for the job, having backup suppliers that can deliver quickly if the preferred supplier fails, and negotiating favorable terms.  Identifying the right lead time to base stocking policies on is another important component. Probabilistic simulations available in parts planning software can be used to forecast the probability for each possible lead time that will be faced. This will result in a more accurate recommendation of what to stock compared to using a supplier quoted or average lead time.

4. SKU rationalization and master data management removes ineffective or out-of-date SKUs from the product catalog and ERP database. It also identifies different part numbers that have been used for the same SKU. The operating cost and profitability of each product are assessed during this procedure, resulting in a common list of active SKUs.  Master data management software can assess product catalogs and information stored in disparate data bases to identify SKU rationalizations ensuring that inventory policies are based on the common part number.

5.  Inventory control systems are key to synchronizing inventory optimization.    They provide a cost-efficient way for utilities to track, monitor, and manage their inventory. They helps ensure that the utility has the right supplies and materials when and where needed while minimizing inventory costs.

6. Continuous improvement is essential for optimizing MRO inventories. It involves regularly monitoring and adjusting inventory levels and stocking policies to ensure the most efficient use of resources. When operating conditions change, the utility must detect the change and adjust its operations accordingly. This means planning cycles must operate at a tempo high enough to stay up with changing conditions. Leveraging probabilistic forecasting to recalibrate service parts stocking policies each planning cycle ensures that stocking policies (such as min/max levels) are always up-to-date and reflect the latest parts usage and supplier lead times.

7. Planning for intermittent demand with modern Spare Parts Planning Software.  The result is a highly accurate estimate of safety stocks, reorder points, and order quantities, leading to higher service levels and lower inventory costs.   Smart Software’s patented probabilistic spare parts forecasting software simulates the probability for each possible demand, accurately determining how much to stock to achieve a utility’s targeted service levels.  Leveraging software to accurately simulate the inflow and outflow of repairable spare parts will better predict downtime, service levels, and inventory costs associated with any chosen pool size for repairable spares.

Managing spare parts inventories can feel impossible. You don’t know what will break and when. Feedback from mechanical departments and maintenance teams is often inaccurate. Planned maintenance schedules are often shifted around, making them anything but “planned.”   Usage (i.e., demand) patterns are most often extremely intermittent, i.e., demand jumps randomly between zero and something else, often a surprisingly big number. Intermittency, combined with the lack of significant trend or seasonal patterns, render traditional time-series forecasting methods inaccurate. The large number of part-by-locations combinations makes it impossible to manually create or even review forecasts for individual parts.   Given all these challenges, we thought it would be helpful to outline a number of do’s (and their associated don’ts).

1. Do use probabilistic methods to compute a reorder points and Min/Max levels
   Basing stocking decisions on average daily usage isn’t the right answer. Nor is reliance on traditional forecasting methods like exponential smoothing models. Neither approach works when demand is intermittent because they don’t take proper account of demand volatility. Probabilistic methods that simulate thousands of possible demand scenarios work best. They provide a realistic estimate of the demand distribution and can handle all the zeros and random non-zeros. This will ensure the inventory level is right-sized to hit whatever service level target you choose.
    
2. Do use service levels instead of rule-of-thumb methods to determine stocking levels
   Many parts planning organizations rely on multiples of daily demand and other rules of thumb to determine stocking policies. For example, reorder points are often based on doubling average demand over the lead time or applying some other multiple depending on the importance of the item. However, averages don’t account for how volatile (or noisy) a part is and will lead to overstocking less noisy parts and understocking more noisy parts.
    
3. Do frequently recompute stocking policies
   Just because demand is intermittent doesn’t mean nothing changes over time. Yet after interviewing hundreds of companies managing spare parts inventory, we find that fewer than 10% recompute stocking policies monthly. Many never recompute stocking policies until there is a “problem.” Across thousands of parts, usage is guaranteed to drift up or down on at least some of the parts. Supplier lead times can also change. Using an outdated reorder point will cause orders to trigger too soon or too late, creating lots of problems. Recomputing policies every planning cycle ensures inventory will be right-sized. Don’t be reactive and wait for a problem to occur before considering whether the Min or Max should be modified. By then it’s too late – it’s like waiting for your brakes to fail before making a repair. Don’t worry about the effort of recomputing Min/Max values for large numbers of SKU’s: modern software does it automatically. Remember: Recalibration of your stocking policies is preventive maintenance against stockout!
    
4. Do get buy-in on targeted service levels
   Inventory is expensive and should be right-sized based on striking a balance between the organization’s willingness to stock out and its willingness to budget for spares. Too often, planners make decisions in isolation based on pain avoidance or maintenance technicians’ requests without consideration of how spending on one part impacts the organization’s ability to spend on another part. Excess inventory on one part hurts service levels on other parts by disproportionally consuming the inventory budget. Make sure that service level goals and associated inventory costs of achieving the service levels are understood and agreed to.
    
5. Do run a separate planning process for repairable parts
   Some parts are very expensive to replace, so it is preferable to send them to repair facilities or back to the OEM for repair. Accounting for the supply side randomness of when repairable parts will be returned, and knowing whether to wait for a repair or to purchase an additional spare, are critical to ensuring item availability without inventory bloat. This requires specialized reporting and the use of probabilistic models.  Don’t treat repairable parts like consumable parts when planning.
    
6. Do count what is purchased against the budget – not just what is consumed
   Many organizations will allocate total part purchases to a separate corporate budget and ding the mechanical or maintenance team’s budget for parts that are used. In most MRO organizations, especially in public transit and utilities, the repair teams dictate what is purchased. If what is purchased doesn’t count against their budget, they will over-buy to ensure there is never any chance of stockout. They have literally zero incentive to get it right, so tens of millions in excess inventory will be purchased. If what is purchased is reflected in the budget, far more attention will be paid to purchasing only what is truly needed. Recognizing that excess inventory hurts service by robbing the organization of cash that could otherwise be used on understocked parts is an important step to ensuring responsible inventory purchasing.

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