Did you know that it was Benjamin Franklin who invented the lightning rod to protect buildings from lightning strikes? Now, it’s not every day that we must worry about lightning strikes, but in today’s unpredictable business climate, we do have to worry about supply chain disruptions, long lead times, rising interest rates, and volatile demand. With all these challenges, it’s never been more vital for organizations to accurately forecast parts usage, stocking levels, and to optimize replenishment policies such as reorder points, safety stocks, and order quantities.  In this blog, we’ll explore how companies can leverage innovative solutions like inventory optimization and parts forecasting software that utilize machine learning algorithms, probabilistic forecasting, and analytics to stay ahead of the curve and protect their supply chains from unexpected shocks.

Spare Parts Planning Solutions
Spare parts optimization is a key aspect of supply chain management for many industries. It involves managing the inventory of spare parts to ensure they are available when needed without having excess inventory that can tie up capital and space. Optimizing spare parts inventory is a complex process that requires a deep understanding of usage patterns, supplier lead times, and the criticality of each part for the business.

In this blog, our primary emphasis will be on the crucial aspect of inventory optimization and demand forecasting. However, other approaches highlighted below for spare parts optimization, such as predictive maintenance and 3D printing, Master Data Management, and collaborative planning should be investigated and deployed as appropriate.

1. Predictive Maintenance: Using predictive analytics to anticipate when a part is likely to fail and proactively replace it, rather than waiting for it to break down. This approach can help companies reduce downtime and maintenance costs, as well as improve overall equipment effectiveness.
2. 3D printing: Advancements in 3D printing technology are enabling companies to produce spare parts on demand, reducing the need for excess inventory. This not only saves space and reduces costs but also ensures that parts are available when needed.
3. Master Data Management: Data Management platforms ensure that part data is properly identified, cataloged, cleansed, and organized. All too often, MRO organizations hold the same part number under different SKUs. These duplicate parts serve the same purpose but require different SKU numbers to ensure regulatory compliance or security.  For example, a part used to support a government contract may be required be sourced from a US manufacturer to stay in compliance with “Buy America” regulations.  It’s critical that these part numbers be identified and consolidated into one SKU, when possible, to keep inventory investments in check.
4. Collaborative Planning: Collaborating with suppliers and customers to share data, forecasts, and plan demand can help companies reduce lead times, improve accuracy, and reduce inventory levels. Forecasting plays an essential role in collaboration as sharing insights on purchases, demand, and buying behavior ensures suppliers have the information they need to ensure stock availability for customers.

Inventory Optimization
Abraham Lincoln was once quoted as saying, “Give me six hours to chop down a tree, and I will spend the first four sharpening the axe”? Lincoln knew that preparation and optimization were key to success, just like organizations need to have the right tools, such as inventory optimization software, to optimize their supply chain and stay ahead in the market. With inventory optimization software, organizations can improve their forecasting accuracy, lower inventory costs, improve service levels, and reduce lead times. Lincoln knew that sharpening the axe was necessary to accomplish the job effectively without overexerting.  Inventory Optimization ensures that inventory dollars are allocated effectively across thousands of parts helping ensure service levels while minimizing excess stock.

Spare parts play a decisive role in maintaining operational efficiency, and the lack of critical parts can lead to downtime and reduced productivity. The sporadic nature of spare parts demand makes it difficult to predict when a specific part will be required, resulting in the risk of overstocking or understocking, both of which can incur costs for the organization.  Additionally, managing lead times for spare parts poses its own set of challenges. Some parts may have lengthy delivery times, necessitating the maintenance of adequate inventory levels to avoid shortages. However, carrying excess inventory can be costly, tying up capital and storage space.

Given the myriad of challenges facing materials management departments and spare parts planners, planning demand, stocking levels, and replenishment of spare parts without an effective inventory optimization solution is akin to attempting to chop down a tree with a very blunt axe! The sharper the axe, the better your organization will be able to contend with these challenges.

Smart Software’s Axe is the Sharpest
Smart Inventory Optimization and Demand Planning Software uses a unique empirical probabilistic forecasting approach that results in accurate forecasts of inventory requirements, even where demand is intermittent. Since nearly 90% of spare and service parts are intermittent, an accurate solution to handle this type of demand is required.   Smart’s solution was patented in 2001 and additional innovations were recently patented in May of 2023 (announcements coming soon!).  The solution was awarded as a finalist in the APICS Technological Innovation Category for its role in helping transform the resource management industry.

The Role of Intermittent Demand
Intermittent demand does not conform to a simple normal or bell-shaped distribution that makes it impossible to forecast accurately with traditional, smoothing-based forecasting methods.  Parts and items with intermittent demand – also known as lumpy, volatile, variable or unpredictable demand – have many zero or low-volume values interspersed with random spikes of demand that are often many times larger than the average. This problem is especially prevalent in companies that manage large inventories of service and spare parts in industries such as aviation, aerospace, power and water supply and utilities, automotive, heavy asset management, high tech, as well as in MRO (Maintenance, Repair, and Overhaul).

Scenario Analysis
Smart’s patented and award-winning technology rapidly generates tens of thousands of possible scenarios of future demand sequences and cumulative demand values over an item’s lead time. These scenarios are statistically similar to the item’s observed data, and they capture the relevant details of intermittent demand without relying on the assumptions commonly made about the nature of demand distributions by traditional forecasting methods. The result is a highly accurate forecast of the entire distribution of cumulative demand over an item’s lead time. The bottom line is that with the information these demand distributions provide, companies can easily plan safety stock and service level inventory requirements for thousands of intermittently demanded items with nearly 100% accuracy.

Benefits
Implementing innovative solutions from Smart Software such as SmartForecasts for statistical forecasting, Demand Planner for consensus parts planning, and Inventory Optimization for developing accurate replenishment drivers such as min/max and safety stock levels will provide forward-thinking executives and planners with better control over their organization’s operations.  It will result in the following benefits:

1. Improved Forecasting Accuracy: Accurate demand forecasting is fundamental for any organization that deals with spare parts inventory management. Inventory optimization software uses sophisticated algorithms to analyze historical usage patterns, identify trends and forecast future demand with a high degree of accuracy. With this level of precision in forecasting, organizations can avoid the risk of overstocking or understocking their spare parts inventory.
2. Lower Inventory Costs: One major challenge that supply chain leaders face when dealing with spare parts inventory management is the cost associated with maintaining an optimal stock of spares at all times. By optimizing inventory levels using modern technology systems like artificial intelligence (AI), machine learning (ML), and predictive analytics, organizations can reduce carrying costs while ensuring they have adequate stocks available when needed.
3. Improved Service Levels: When it comes to repair and maintenance services, time is money! Downtime due to the unavailability of critical spare parts can result in lost productivity and revenue for businesses across industries such as manufacturing plants, power generation facilities, or data centers managing IT infrastructure equipment. Optimizing your spare parts inventory ensures that you always have the right amount on hand, reducing downtime caused by waiting for deliveries from suppliers.
4. Reduced Lead Times: Another benefit that accrues from accurate demand forecasting through modern warehouse technologies is reduced lead time in delivery which leads to better customer satisfaction since customers will receive their orders faster than before thus improving brand loyalty. Therefore, the adoption of new strategies driven by AI/ML tools creates value within supply chain operations leading to increased efficiency gains not only limited reductionism cost but also streamlining processes related to production scheduling, logistics transportation planning among others

Conclusion
Through the utilization of inventory optimization and demand planning software, organizations can overcome various challenges such as supply chain disruptions, rising interest rates, and volatile demand. This enables them to reduce costs associated with excess storage space and obsolete inventory items. By leveraging sophisticated algorithms, inventory optimization software enhances forecasting accuracy, ensuring organizations can avoid overstocking or under-stocking their spare parts inventory. Additionally, it helps lower inventory costs by optimizing levels and leveraging technologies like artificial intelligence (AI), machine learning (ML), and predictive analytics. Improved service levels are achieved as organizations have the right quantity of spare parts readily available, reducing downtime caused by waiting for deliveries. Furthermore, accurate demand forecasting leads to reduced lead times, enhancing customer satisfaction and fostering brand loyalty. Adopting such strategies driven by AI/ML tools not only reduces costs but also streamlines processes, including production scheduling and logistics transportation planning, ultimately increasing efficiency gains within the supply chain.

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.

What do you do when you are forecasting an intermittently demanded item, such as a spare part, with average demand of less than one unit per month?  Most of the time the demand is zero, but the part is significant in a business sense; it can’t be ignored and must be forecasted to be sure you have adequate stock.

Your choices tend to center around a few options:

Option 1:  Round up to 1 each month, so your annual forecast is 12.

Option 2:  Round down to 0 each month, so your annual forecast is 0.

Option 3:  Forecast “same as same month last year” method so the forecast matches last year’s actual.

There are obvious disadvantages to each option and not much advantage to any of them.  Option 1 often results in a significant over forecast.  Option 2 often results in a significant under-forecast.  Option 3 results in a forecast that is almost guaranteed to miss the actual significantly since the demand isn’t likely to spike in the exact same period. If you MUST forecast the item, then we would normally recommend option 3 since it is the most likely answer that the rest of the business would understand. 

But a better way is to not forecast it at all in the usual sense and instead use a “predictive reorder point“ keyed to your desired service level. To calculate a predictive reorder point, you can use Smart Software’s patented Markov bootstrap algorithm to simulate all possible demands that could occur over the lead time, then identify the reorder point that will yield your target service level.

You can then configure your ERP system to order more when on-hand inventory breaches the reorder point rather than when you are forecasted to hit zero (or whatever safety stock buffer is entered). 

This makes for more common-sense ordering without the unneeded assumptions that are required to forecast an intermittently demanded, low-volume part.

What’s the difference, and why it matters for inventory planning.

Those new to the parts planning game are often confused by the many variations in the names of parts. This blog points out distinctions that do or do not have operational significance for someone managing a fleet of spare parts and how those differences impact inventory planning.

For instance, what is the difference between “spare” parts and “replacement” parts? In this case, the difference is their source. A spare part would be purchased from the equipment’s manufacturer, whereas a replacement part would be purchased from a different company. For someone managing a fleet of spares, the difference would be two different entries in their parts database: the source would be different, and the unit price would probably be different. It is possible that there would also be a difference in the useful life of the parts from the two sources. The “OEM” parts might be more durable than the cheaper “aftermarket” parts. (Now we have four different terms describing these parts.) These distinctions would be salient for optimizing an inventory of spares. Software that computes optimal reorder points and order quantities would arrive at different answers for parts with different unit costs and different rates of replacement.

Perhaps the largest distinction is between “consumable” and “repairable” or “rotable” parts. The key distinction between them is their cost. It is foolish to try to repair a stripped screw; just throw it out and use another one. But it is also foolish to throw out a $50,000 component if it can be repaired for $5,000. Optimizing the management of inventory for fleets of each type of part requires very different math. With consumables, the parts can be regarded as anonymous and interchangeable. With “rotatables”, each part must essentially be modeled individually. We treat each as cycling through states of “operational,” “under repair,” and “standby/spare.” Decisions about repairable parts are often handled by a capital budgeting process, and the salient analytical question is, “what should be the size of our spares pool?”

There are other distinctions that can be drawn among parts. Criticality is an important attribute. The consequences of part failure can range from “we can take our time to get a replacement” to “this is an emergency; get those machines back in action pronto”. When working out how to manage parts, we must always strike a balance between the benefits of having a larger stock of parts and the dollar costs. Criticality shifts the balance toward playing it safe with larger inventories. In turn, this dictates higher planning targets for part availability metrics such as service levels and fill rates, which will lead to larger reorder points and/or order quantities.

If you Google “types of spare parts”, you will discover other classifications and distinctions. From our perspective at Smart Software, the words matter less than the numbers associated with parts: unit costs, mean time before failure, mean time to repair and other technical inputs to our products that work out how to manage the parts for maximum benefit.

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.