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 Impeding these efforts are out-of-date IT systems, evolving security threats, frequent supply chain disruptions, and extreme demand variability.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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Why Days of Supply Targets Don’t Work when Computing Safety Stocks

CFOs tell us they need to spend less on inventory without impacting sales.  One way to do that is to move away from using targeted day of supply to determine reorder points and safety stock buffers.   Here is how a days of supply model works:

1. Compute average demand per day and multiply the demand per day by supplier lead time in days to get lead time demand
2. Pick a days of supply buffer (i.e., 15, 30, 45 days, etc.). Use larger buffers being used for more important items and smaller buffers for less important items.
3. Add the desired days of supply buffer to demand over the lead time to get the reorder point. Order more when on hand inventory falls below the reorder point

Here is what is wrong with this approach:

1. The average doesn’t account for seasonality and trend – you’ll miss obvious patterns unless you spend lots of time manually adjusting for it.
2. The average doesn’t consider how predictable an item is – you’ll overstock predictable items and understock less predictable ones. This is because the same days of supply for different items yields a very different stock out risk.
3. The average doesn’t tell a planner how stock out risk is impacted by the level of inventory – you’ll have no idea whether you are understocked, overstocked, or have just enough. You are essentially planning with blinders on.

There are many other “rule of thumb” approaches that are equally problematic.  You can learn more about them in this post. 

A better way to plan the right amount of safety stock is to leverage probability models that identify exactly how much stock is needed given the risk of stock-out you are willing to accept.   Below is a screenshot of Smart Inventory Optimization that does exactly that.  First, it details the predicted service levels (probability of not stocking out) associated with the current days of supply logic.  The planner can now see the parts where predicted service level is too low or too costly.  They can then make immediate corrections by targeting the desired service levels and level of inventory investment. Without this information, a planner isn’t going to know whether the targeted days of safety stock is too much, too little, or just right resulting in overstocks and shortages that cost market share and revenue. 

Whether you understand it in detail yourself or rely on trustworthy software, math is a fact of life for anyone in inventory management and demand forecasting who is hoping to remain competitive in the modern world.

At a conference recently, the lead presenter in an inventory management workshop proudly proclaimed that he had no need for “high-fallutin’ math”, which was explained to mean anything beyond sixth-grade math.

Math is not everyone’s first love. But if you really care about doing your job well, you can’t approach the work with a grade school mentality. Supply chain tasks like demand forecasting and inventory management are inherently mathematical. The blog associated with edX, a premier site for online college course material, has a great post on this topic, at https://www.mooc.org/blog/how-is-math-used-in-supply-chain. Let me quote the first bit:

Math and the supply chain go hand and hand. As supply chains grow, increasing complexity will drive companies to look for ways to manage large-scale decision-making. They can’t go back to how supply chains were 100 years ago—or even two years ago before the pandemic. Instead, new technologies will help streamline and manage the many moving parts. The logistics skills, optimization technologies, and organizational skills used in supply chain all require mathematics.

Our customers don’t need to be experts in supply chain math, they just need to be able to wield the software that contains the math. Software combines users’ experience and subject matter expertise to produce results that make the difference between success and failure. To do its job, the software can’t stop at sixth-grade math; it needs probability, statistics, and optimization theory.

It’s up to us software vendors to package the math in such a way that what goes into the calculations is all that is relevant, even if complicated; and that what comes out is clear, decision-relevant, and defensible when you must justify your recommendations to higher management.

Sixth-grade math can’t warn you when the way you propose to manage a critical spare part will mean a 70% chance of falling short of your item availability target. It can’t tell you how best to adjust your reorder points when a supplier calls and says, “We have a delivery problem.” It can’t save your skin when there is a surprisingly large order and you have to quickly figure out the best way to set up some expedited special orders without busting the operating budget.

So, respect the folk wisdom and don’t bring a knife to a gunfight.

When deciding on the right stocking parameters for spare parts and service parts, it is important to distinguish between consumable and repairable service parts.  These differences are often overlooked by service parts planning software and can result in incorrect estimates of what to stock.  Different approaches are required when planning for consumables vs. repairable spare parts.

First, let’s define these two types of spare parts.

• Consumable parts are spares contained within the equipment which are replaced rather than repaired when they fail. Examples of consumable parts include batteries, oil filters, screws, and brake pads.  Consumable spare parts tend to be lower-cost parts for which replacement is cheaper than repair or repair may not be possible.
• Repairable parts are parts that are capable of being repaired and returned to service after failing due to causes like wear and tear, damage, or corrosion. Repairable service parts tend to be more expensive than consumable parts, so repair is usually preferable to replacement. Examples of repairable parts include traction motors in rail cars, jet engines, and copy machines.

Traditional spare parts planning software fail to do the job

Traditional parts planning software is not well-adapted to deal with the randomness in both the demand side and the supply side of MRO operations.

Demand-Side Randomness
Planning for consumable spare parts requires calculation of inventory control parameters (such as reorder points and order quantities, min and max levels, and safety stocks). Planning to manage repairable service parts requires calculation of the right number of spares. In both cases, the analysis must be based on probability models of the random usage of consumables or the random breakdown of repairable parts.  For over 90% of these parts, this random demand is “intermittent” (sometimes called “lumpy” or “anything but normally distributed”). Traditional spare parts forecasting methods were not developed to deal with intermittent demand. Relying on traditional methods leads to costly planning mistakes. For consumables, this means avoidable stockouts, excess carrying costs, and increased inventory obsolescence. For repairable parts, this means excessive equipment downtime and the attendant costs from unreliable performance and disruption of operations.

Supply-Side Randomness
Planning for consumable spare parts must take account of randomness in replenishment lead times from suppliers. Planning for repairable parts must account for randomness in repair and return processes, whether provided internally or contracted out. Planners managing these items often ignore exploitable company data. Instead, they may cross their fingers and hope everything works out, or they may call on gut instinct to “call audibles” and then hope everything works out.  Hoping and guessing cannot beat proper probability modeling. It wastes millions annually in unneeded capital investments and avoidable equipment downtime.