Balancing Act

Managing inventory, like managing anything, involves balancing competing priorities. Do you want a lean inventory? Yes! Do you want to be able to say “It’s in stock” when a customer wants to buy something? Yes!

But can you have it both ways? Only to a degree. If you lean into leaning your inventory too aggressively, you risk stockouts. If you stamp out stockouts, you create inventory bloat. You are forced to find a satisfactory balance between the two competing goals of lean inventory and high item availability.

Striking a Balance

How do you strike that balance? Too many inventory planners “guestimate” their way to some kind of answer. Or they work out a smart answer once and hope that it has a distant sell-by date and keep using it while they focus on other problems. Unfortunately, shifts in demand and/or changes in supplier performance and/or shifts in your own company’s priorities will obsolete old inventory plans and put you right back where you started.

It is inevitable that every plan has a shelf life and has to be updated. However, it is definitely not best practice to replace one guess with another. Instead, each planning cycle should exploit modern supply chain software to replace guesswork with fact-based analysis using probability math.

Know Thyself

The one thing that software cannot do is compute a best answer without knowing your priorities. How much do you prioritize lean inventory over item availability? Software will predict the levels of inventory and availability caused by any decisions you make about how to manage each item in your inventory, but only you can decide whether any given set of key performance indicators is consistent with what you want.

Knowing what you want in a general sense is easy: you want it all. But knowing what you prefer when comparing specific scenarios is more difficult. It helps to be able to see a range of realizable possibilities and mull over which seems best when they are laid out side by side.

See What’s Next

Supply chain software can give you a view of the tradeoff curve. You know in general that lean inventory and high item availability trade off against each other, but seeing item-specific tradeoff curves sharpens your focus.

Why is there a curve? Because you have choices about how to manage each item. For instance, if you check inventory status continuously, what values will you assign to the Min and Max values that govern when to order replenishments and how much to order. The tradeoff curve arises because choosing different Min and Max values leads to different levels of on hand inventory and different levels of item availability, e.g., as measured by fill rate.

A Scenario for Analysis

To illustrate these ideas, I used a digital twin  to estimate how various values of Min and Max would perform in a particular scenario. The scenario focused on a notional spare part with purely random demand having a moderately high level of intermittency (37% of days having zero demand). Replenishment lead times were a coin flip between 7 and 14 days. The Min and Max values were systematically varied: Min from 20 to 40 units, Max from Min+1 units to 2xMin units. Each (Min,Max) pair was simulated for 365 days of operation a total of 1,000 times, then the results averaged to estimate both the average number of on hand units and the fill rate, i.e., percentage of daily demands that were satisfied immediately from stock. If stock was not available, it was backordered.

Results

The experiment produced two types of results:

• Plots showing the relationship between Min and Max values and two key performance indicators: Fill rate and average units on hand.
• A tradeoff curve showing how the fill rate and units on hand trade off against each other.

Figure 1 plots on hand inventory as a function of the values of Min and Max. The experiment yielded on hand levels ranging from near 0 to about 40 units.  In general, keeping Min constant and increasing Max results in more units on hand. The relationship with Min is more complex: keeping Max constant,  increasing Min first adds to inventory but at some point reduces it.

Figure 2 plots fill rate as a function of the values of Min and Max.  The experiment yielded fill rate levels ranging from near 0% to 100%.  In general, the functional relationships between the fill rate and the values of Min and Max mirrored those in Figure1.

Figure 3 makes the key point, showing how varying Min and Max produces a perverse pairing of the key performance indicators. Generally speaking, the values of Min and Max that maximize item availability (fill rate)  are the same values that maximize inventory cost (average units on hand). This general pattern is represented by the blue curve. The experiments also produced some offshoots from the blue curve that are associated with poor choices of Min and Max, in the sense that other choices dominate them by producing the same fill rate with lower inventory.

Conclusions

Figure 3 makes clear that your choice of how to manage an inventory item forces you to trade off inventory cost and item availability. You can avoid some inefficient combinations of Min and Max values, but you cannot escape the tradeoff.

The good side of this reality is that you do not have to guess what will happen if you change your current values of Min and Max to something else. The software will tell you what that move will buy you and what it will cost you. You can take off your Guestimator hat and do your thing with confidence.

Figure 1 On Hand Inventory as a function of Min and Max values

Figure 2 Fill Rate as a function of Min and Max values

Figure 3 Tradeoff curve between Fill Rate and On Hand Inventory

Forecasting inventory requirements is a specialized variant of forecasting that focuses on the high end of the range of possible future demand.

For simplicity, consider the problem of forecasting inventory requirements for just one period ahead, say one day ahead. Usually, the forecasting job is to estimate the most likely or average level of product demand. However, if available inventory equals the average demand, there is about a 50% chance that demand will exceed inventory and result in lost sales and/or lost good will. Setting the inventory level at, say, ten times the average demand will probably eliminate the problem of stockouts, but will just as surely result in bloated inventory costs.

The trick of inventory optimization is to find a satisfactory balance between having enough inventory to meet most demand without tying up too many resources in the process. Usually, the solution is a blend of business judgment and statistics. The judgmental part is to define an acceptable inventory service level, such as meeting 95% of demand immediately from stock. The statistical part is to estimate the 95th percentile of demand.

When not dealing with intermittent demand, you can often estimate the required inventory level by assuming a bell-shaped (Normal) curve of demand, estimating both the middle and the width of the bell curve, then using a standard statistical formula to estimate the desired percentile. The difference between the desired inventory level and the average level of demand is called the “safety stock” because it protects against the possibility of stockouts.

When dealing with intermittent demand, the bell-shaped curve is a very poor approximation to the statistical distribution of demand. In this special case, Smart leverages patented technology for intermittent demand that is designed to accurately forecast the ranges and produce a better estimate of the safety stock needed to achieve the required inventory service level.

Reveal how forecasts are used with these 4 questions.

Often companies will insist that they “don’t use forecasts” to plan inventory.  They often use reorder point methods and are struggling to improve on-time delivery, inventory turns, and other KPIs. While they don’t think of what they are doing as explicitly forecasting, they certainly use estimates of future demand to develop reorder points such as min/max.

Regardless of what it is called, everyone tries to estimate future demand in some way and uses this estimate to set stocking policies and drive orders. To improve inventory planning and make sure you aren’t over/under ordering and creating large stockouts and inventory bloat, it is important to understand exactly how your organization uses forecasts. Once this is understood, you can assess whether the quality of the forecasts can be improved.

Try getting answers to the following questions. It will reveal how forecasts are being used in your business – even if you don’t think you use forecasts.

1.  Is your forecast a period-by-period estimate over time that is used to predict what on-hand inventory will be in the future and triggers order suggestions in your ERP system?

2. Or is your forecast used to derive a reorder point but not explicitly used as a per-period driver to trigger orders? Here, I may predict we’ll sell 10 per week based on the history, but we are not loading 10, 10, 10, 10, etc., into the ERP. Instead, I derive a reorder point or Min that covers the two-period lead time + some amount of buffer to help protect against stock out. In this case, I’ll order more when on hand gets to 25.

3. Is your forecast used as a guide for the planner to help subjectively determine when they should order more?  Here, I predict 10 per week, and I assess the on-hand inventory periodically, review the expected lead time, and I decide, given the 40 units I have on hand today, that I have “enough.” So, I do nothing now but will check back again in a week.

4. Is it used to set up blanket orders with suppliers? Here, I predict 10 per week and agree to a blanket purchase order with the supplier of 520 per year. The orders are then placed in advance to arrive in quantities of 10 once per week until the blanket order is consumed.

Once you get the answers, you can then ask how the estimates of demand are created.  Is it an average? Is it deriving demand over lead time from a sales forecast?  Is there a statistical forecast generated somewhere?  What methods are considered? It will also be important to assess how safety stocks are used to protect against demand and supply variability.  More on all of this in a future article.

​If you had your head up lately, you may have noticed some additional madness off the basketball court: The failure of Silicon Valley Bank. Those of us in the supply chain world may have dismissed the bank failure as somebody else’s problem, but that sorry episode holds a big lesson for us, too: The importance of stress testing done right.

The Washington Post recently carried an opinion piece by Natasha Sarin called “Regulators missed Silicon Valley Bank’s problems for months. Here’s why.” Sarin outlined the flaws in the stress testing regime imposed on the bank by the Federal Reserve. One problem is that the stress tests are too static. The Fed’s stress factor for nominal GDP growth was a single scenario listing presumed values over the next 13 quarters (see Figure 1). Those 13 quarterly projections might be somebody’s consensus view of what a bad hair day would look like, but that’s not the only way things could play out.  As a society, we are being taught to appreciate a better way to display contingencies every time the National Weather Service shows us projected hurricane tracks (see Figure 2). Each scenario represented by a different colored line shows a possible storm path, with the concentrated lines representing the most likely.  By exposing the lower probability paths, risk planning is improved.

When stress testing the supply chain, we need realistic scenarios of possible future demands that might occur, even extreme demands.   Smart provides this in our software (with considerable improvements in our Gen2 methods).  The software generates a huge number of credible demand scenarios, enough to expose the full scope of risks (see Figure 3). Stress testing is all about generating massive numbers of planning scenarios, and Smart’s probabilistic methods are a radical departure from previous deterministic S&OP applications, being entirely scenario based.

The other flaw in the Fed’s stress tests was that they were designed months in advance but never updated for changing conditions.  Demand planners and inventory managers intuitively appreciate that key variables like item demand and supplier lead time are not only highly random even when things are stable but also subject to abrupt shifts that should require rapid rewriting of planning scenarios (see Figure 4, where the average demand jumps up dramatically between observations 19 and 20). Smart’s Gen2 products include new tech for detecting such “regime changes”  and automatically changing scenarios accordingly.

Banks are forced to undergo stress tests, however flawed they may be, to protect their depositors. Supply chain professionals now have a way to protect their supply chains by using modern software to stress test their demand plans and inventory management decisions.

Figure 1: Scenarios used the Fed to stress test banks.

Figure 2: Scenarios used by the National Weather Service to predict hurricane tracks

Figure 3: Demand scenarios of the type generated by Smart Demand Planner

Figure 4: Example of regime change in product demand after observation #19