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. 

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.

Whether you are using ‘Min/Max’ or ‘reorder point’ and ‘order quantity’ to determine when and how much to restock, your approach might deliver or deny huge efficiencies. Key mistakes to avoid:

1. Not recalibrating regularly
2. Only reviewing Min/Max when there is a problem
3. Using Forecasting methods not up to the task
4. Assuming data is too slow moving or unpredictable for it to matter

“We have over 150,000 SKU x Location combinations. Our demand is intermittent. Since it’s slow moving, we don’t need to recalculate our reorder points often. We do so maybe once annually but review the reorder points whenever there is a problem.” – Materials Manager.

This reactive approach will lead to millions in excess stock, stock outs, and lots of wasted time reviewing data when “something goes wrong.” Yet, I’ve heard this same refrain from so many inventory professionals over the years. Clearly, we need to do more to share why this thinking is so problematic.

It is true that for many parts, a recalculation of the reorder points with up-to-date historical data and lead times might not change much, especially if patterns such as trend or seasonality aren’t present. However, many parts will benefit from a recalculation, especially if lead times or recent demand has changed. Plus, the likelihood of significant change that necessitates a recalculation increases the longer you wait. Finally, those months with zero demands also influence the probabilities and shouldn’t be ignored outright. The key point though is that it is impossible to know what will change or won’t change in your forecast, so it’s better to recalibrate regularly.

This standout case from real world data illustrates a scenario where regular and automated recalibration shines—the benefits from quick responses to changing demand patterns like these add up quickly. In the above example, the X axis represents days, and the Y axis represents demand. If you were to wait several months between recalibrating your reorder points, you’d undoubtedly order far too soon. By recalibrating your reorder point far more often, you’ll catch the change in demand enabling much more accurate orders.

Rather than wait until you have a problem, recalibrate all parts every planning cycle at least once monthly. Doing so takes advantage of the latest data and proactively adjusts the stocking policy, thus avoiding problems that would cause manual reviews and inventory shortages or excess.

The nature of your (potentially varied) data also needs to be matched with the right forecasting tools. If records for some parts show trend or seasonal patterns, using targeting forecasting methods to accommodate these patterns can make a big difference. Similarly, if the data show frequent zero values (intermittent demand), forecasting methods not built around this special case can easily deliver unreliable results.

Automate, recalibrate and review exceptions. Purpose built software will do this automatically. Think of it another way: is it better to dump a bunch of money into your 401K once per year or “dollar cost average” by depositing smaller, equally sized amounts throughout the year. Recalibrating policies regularly will yield maximized returns over time, just as dollar cost averaging will do for your investment portfolio.

How often do you recalibrate your stocking policies? Why?

The Covid19 pandemic has placed unusual stress on public transit agencies. This stress forces agencies to look again at their spare parts planning processes, which is a key driver up ensuring uptime and balancing service parts inventory costs.

This blog focuses on bus systems and their practices for spare parts management and planning. However, there are lessons here for other types of public transit, including rail and light rail.

Back in 1995, the Transportation Research Board (TRB) of the National Research Council published a report that still has relevance. System-Specific Spare Bus Ratios: A Synthesis of Transit Practice stated

The purpose of this study was to document and examine the critical site-specific variables that affect the number of spare vehicles that bus systems need to maintain maximum service requirements. … Although transit managers generally acknowledged that right-sizing the fleet actually improves operations and lowers cost, many reported difficulties in achieving and consistently maintaining a 20 percent spare ratio as recommended by FTA… The respondents to the survey advocated that more emphasis be placed on developing improved and innovative bus maintenance techniques, which would assist them in minimizing downtime and improving vehicle availability, ultimately leading to reduced spare vehicles and labor and material costs.

Grossly simplified guidelines like “keep 20% spare buses” are easy to understand and measure but grossly mask more detailed tactics that can provide more tailored policies that better steward taxpayer dollars spent on spare parts while ensuring the highest levels of availability. If operational reliability can be improved for each bus, then fewer spares are needed.

One way to keep each bus up and running more often is to improve the management of inventories of spare parts – specifically by forecasting service parts usage and the required replenishment policies more accurately. Here is where modern supply chain management can make a significant contribution. The TRB noted this in their report:

Many agencies have been successful in limiting reliance on excess spare vehicles. Those transit officials agree that several factors and initiatives have led to their success and are critical to the success of any program [including] … Effective use of advanced technology to manage critical maintenance functions, including the orderly and timely replacement of parts… Failure to have available service parts and other components when they are needed will adversely affect any maintenance program.

As long as managers are cognizant of the issues and vigilant about what tools are available to them, the probability of buses [being] ‘out for no stock’ will greatly diminish.”

Effective spare parts inventory management requires a balance between “having enough” and “having too much.” What modern service parts planning software can do is make visible the tradeoff between these two goals so that transit managers can make fact-based decisions about spare parts inventories.

There are enough complications in finding the right balance to require moving beyond simple rules of thumb such as “keep ten days’ worth of demand on hand” or “reorder when you are down to five units in stock.” Factors that drive these decisions include both the average demand for a part, the volatility of that demand, the average replenishment lead time (which can be a problem when the part arrives by slow boat from Germany), the variability in lead time, and several cost factors: holding costs, ordering costs, and shortage costs (e.g., lost fares, loss of public goodwill).

Innovative supply chain analytics and spare parts planning software uses advanced probabilistic forecasting and stochastic optimization methods to manage these complexities and provide greater parts availability at lower cost. For instance, Minnesota’s Metro Transit documented a 4x increase in return on investment in the first six months of implementing a new system. To read more about how public transit agencies are exploiting innovative supply chain analytics, see:

• Optimizing Parts Management at BC Transit
• Smart Software and Metro Transit
• Smart Software Helps Metro-North Railroad Keep the Trains Running On-Time