In another blog we posed the question:  How can you be sure that you really have a policy for inventory planning and demand forecasting? We explained how an organization’s lack of understanding on the basics (how a forecast is created, how safety stock buffers are determined, and how/why these values are adjusted) contributes to poor forecast accuracy, misallocated inventory, and lack of trust in the whole process.

In this blog, we review 10 specific questions you can ask to uncover what’s really happening at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

Always start with a simple hypothetical example. Focusing on a specific problem you just experienced is bound to provoke defensive answers that hide the full story. The goal is to uncover the actual approach used to plan inventory and forecasts that has been baked into the mental math or spreadsheets.   Here is an example:

Suppose you have 100 units on hand, the lead time to replenish is 3 months, and the average monthly demand is 20 units?   When should you order more?  How much would you order? How will your answer change if expected receipts of 10 per month were scheduled to arrive?  How will your answer change if the item is the item is an A, B, or C item, the cost of the item is high or low, lead time of the item is long or short?  Simply put, when you schedule a production job or place a new order with a supplier, why did you do it? What triggered the decision to get more?  What planning inputs were considered?

When getting answers to the above question, focus on uncovering answers to the following questions:

1. What is the underlying replenishment approach? This will typically be one of Min/Max, forecast/safety stock, Reorder Point/Order Quantity, Periodic Review/Order Up To or even some odd combination

2. How are the planning parameters, such as demand forecasts, reorder points, or Min/Max, actually calculated? It’s not enough to know that you use Min/Max.  You have to know exactly how these values are calculated. Answers such as “We use history” or “We use an average” are not specific enough.   You’ll need answers that clearly outline how history is used.  For example, “We take an average of the last 6 months, divide that by 30 to get a daily average, and then multiply that by the lead time in days.  For ‘A’ items we then multiply the lead time average by 2 and for ‘B’ items we use a multiplier of 1.5.” (While that is not an especially good technical approach, at least it has a clear logic.)

Once you have a policy well-defined, you can identify its weaknesses in order to improve it.  But if the answer provided doesn’t get much further past “We use history”, then you don’t have a policy to start with.   Answers will often reveal that different planners use history in different ways.  Some may only consider the most recent demand, others might stock according to the average of the highest demand periods, etc.  In other words, you may find that you actually have multiple ill-conceived “policies”.

3. Are forecasts used to drive replenishment planning and if so, how? Many companies will say they forecast, but their forecasts are calculated and used differently. Is the forecast used to predict what on hand inventory will be in the future, resulting in an order being triggered?  Or is it used to derive a reorder point but not to predict when to order (i.e. I predict we’ll sell 10 a week so to help protect against stock out, I’ll order more when on hand gets to 15)? Is it used as a guide for the planner to help subjectively determine when they should order more?  Is it used to set up blanket orders with suppliers?  Some use it to drive MRP. You’ll need to know these specifics.  A thorough answer to this question might look like this: “My forecast is 10 per week and my lead time is 3 weeks so I make my reorder point a multiple of that forecast, typically 2 x lead time demand or 60 unit for important items and I use a smaller multiple for less important items.  (Again, not a great technical approach, but clear.)

4.  What technique is actually used to generate the forecast? Is it an average, a trending model such as double exponential smoothing, a seasonal model? Does the choice of technique change depend on the type of demand data or when new demand data is available? (Spare parts and high-volume items have very different demand patterns.) How do you go about selecting the forecast model? Is this process automated?  How often is the choice of model reconsidered?  How often are the model parameters recomputed? What is the process used to reconsider your approach?  The answer here documents how the baseline forecasts are produced.  Once determined, you can conduct an analysis to identify whether other forecasting methods would improve forecast accuracy.  If you aren’t documenting forecast accuracy and conducting “forecast value add” analysis then you aren’t in a position to properly assess whether the forecasts being produced are the best that they can be.  You’ll miss out on opportunities to improve the process, increase forecast accuracy, and educate the business on what type of forecast error is normal and should be expected.

5. How do you use safety stock? Notice the question was not “Do you use safety stock?” In this context, and to keep it simple, the term “safety stock” means stock used to buffer inventory against supply and demand variability.  All companies use buffering approaches in some way.  There are some exceptions though.  Maybe you are a job shop manufacturer that procures all parts to order and your customers are completely fine waiting weeks or months for you to source material, manufacture, QA, and ship.  Or maybe you are high-volume manufacturer with tons of buying power so your suppliers set up local warehouses that are stocked full and ready to provide inventory to you almost immediately.  If these descriptions don’t describe your company, you will definitely have some sort of buffer to protect against demand and supply variability.  You may not use the “safety stock” field in your ERP but you are definitely buffering.

Answers might be provided such as “We don’t use safety stock because we forecast.”  Unfortunately, a good forecast will have a 50/50 chance of being over/under the actual demand.  This means you’ll incur a stock out 50% of the time without a safety stock buffer added to the forecast.  Forecasts are only perfect when there is no randomness. Since there is always randomness, you’ll need to buffer if you don’t want to have abysmal service levels.

If the answer isn’t revealed, you can probe a bit more into how the varying replenishment levers are used to add possible buffers which leads to questions 6 & 7.

6. Do you ever increase the lead time or order earlier than you truly need to?
In our hypothetical example, your supplier typically takes 4 weeks to deliver and is pretty consistent. But to protect against stockouts your buyer routinely orders 6 weeks out instead of 4 weeks.  The safety stock field in your ERP system might be set to zero because “we don’t use safety stock”, but in reality, the buyer’s ordering approach just added 2 weeks of buffer stock.

7. Do you pad the demand forecast?
In our example, the planner expects to consume 10 units per month but “just in case” enters a forecast of 20 per month.  The safety stock field in the MRP system is left blank but the now disguised buffer stock has been smuggled into the demand forecast.  This is a mistake that introduces “forecast bias.”  Not only will your forecasts be less accurate but if the bias isn’t accounted for and safety stock is added by other departments, you will overstock.

The ad-hoc nature of the above approaches compounds the problems by not considering the actual demand or supply variability of the item. For example, the planner might simply make a rule of thumb that doubles the lead time forecast for important items.  One-size doesn’t fit all when it comes to inventory management.  This approach will substantially overstock the predictable items while substantially understocking the intermittently demanded items. You can read “Beware of Simple Rules of Thumb for Managing Inventory” to learn more about why this type of approach is so costly.

The ad-hoc nature of the approaches also ignores what happens the company is faced with a huge overstock or stock out. When trying to understand what happened, the stated policies will be examined. In the case of an overstock, the system will show zero safety stock.  The business leaders will assume they aren’t carrying any safety stock, scratch their heads, and eventually just blame the forecast, declare “Our business can’t be forecasted” and stumble on. They may even blame the supplier for shipping too early and making them hold more than needed. In the case of a stock out, they will think they aren’t carrying enough and arbitrarily add more stock across many items not realizing there is in fact lots of extra safety stock baked into process.  This makes it more likely inventory will need to be written off in the future.

8. What is the exact inventory terminology used? Define what you mean by safety stock, Min, reorder point, EOQ, etc.  While there are standard technical definitions it’s possible that something differs, and miscommunication here will be problematic.  For example, some companies refer to Min as the amount of inventory needed to satisfy lead time demand while some may define Min as inclusive of both lead time demand and safety stock to buffer against demand variability. Others may mean the minimum order quantity.

9. Is on hand inventory consistent with the policy? When your detective work is done and everything is documented, open your spreadsheet or ERP system and look at the on-hand quantity. It should be more or less in line with your planning parameters (i.e. if Min/Max is 20/40 and typical lead time demand is 10, then you should have roughly 10 to 40 units on hand at any given point in time.  Surprisingly, for many companies there is often a huge inconsistency. We have observed situations where the Min/Max setting is 20/40 but the on-hand inventory is 300+.  This indicates that whatever policy has been prescribed just isn’t being followed.   That’s a bigger problem.

10. What are you going to do next?

Demand forecasting and inventory stocking policy need to be well-defined processes that are understood and accepted by everybody involved.  There should be zero mystery.

To do this right, the demand and supply variability must be analyzed and used to compute the proper levels of safety stock.   Adding buffers without an implicit understanding of what each additional unit of buffer stock is buying you in terms of service is like arbitrarily throwing a handful of ingredients into a cake recipe.  A small change in ingredients can have a huge impact on what comes out of the oven – one bite too sweet but the next too sour.  It is the same with inventory management.  A little extra here, a little less there, and pretty soon you find yourself with costly excess inventory in some areas, painful shortages in others, no idea how you got there, and with little guidance on how to make things better.

Modern inventory optimization and demand planning software with its advanced analytics and strong basis in forecast analysis can help a good deal with this problem. But even the best software won’t help if it is used inconsistently.

What We’re Up Against

As a third-generation Boston Red Sox fan, I’m disinclined to take advice from any New York Yankee ballplayer, even a great one but have to agree that sometimes, you just need to make a decision.   However, wouldn’t it be better if we knew the tradeoffs associated with each decision. Perhaps one road is more scenic but takes longer while the other is more direct but boring. Then you wouldn’t have to simply “take it” but could make an informed decision based on the advantages/disadvantages of each approach.

In the supply chain planning world, the most fundamental decision is how to balance item availability against the cost of maintaining that availability (service levels and fill rates). At one extreme, you can grossly overstock and never run out until you go broke and have to close up shop from sinking all your cash into inventory that doesn’t sell.  At the other extreme, you can grossly understock and save a bundle on inventory holding costs but go broke and have to close up shop because all your customers took their business elsewhere.

There is no escaping this fundamental tension. They way to survive and thrive is to find a productive and sustainable balance. To do that requires fact-based tradeoffs based on the numbers. To get the numbers requires software.

The general drift of things is obvious. If you decide to keep more inventory, you will have more Holding Costs, lower Shortage Costs, and possibly lower Ordering Costs. Whether this costs or saves money is impossible to know without some sophisticated analysis, but usually the result is that the Total Cost goes up. But if you do invest in more inventory, something will be gained, because you will offer your customers higher Service Levels and Fill Rates. How much higher requires, as you might guess, some sophisticated analysis.

Show Me the Numbers

This blog lays out what such an analysis looks like. There is no universal solution pointing you to the “right” decision. You might think that the right decision is the one that does best by your bottom line. But to get those numbers, you would need something rarely seen: an accurate model of customer behavior with regard to service level (check out our article “How to choose a target service level”) For example, at what point will a customer walk away and take their business elsewhere?  Will it be after you stock out 1% of the time, 5% of time, 10% of the time? Will you still keep their business as long as you fill back orders quickly?  Will it be after a back order of 1 day, 2 days? 3 weeks? Will it be after this happens one time on one an important part or many times across many parts?  While modeling the precise service level that will allow you to keep your customer while minimizing costs seems like an unapproachable ideal, another type of sophisticated analysis is more pragmatic. 

Inventory optimization and forecasting software can factor all associated costs such as the cost of stocking out, cost of holding inventory, and cost of ordering inventory in order to prescribe an optimal service level target that yields the lowest total cost. However, even that “optimal” service level is sensitive to changes in the costs making the results potentially questionable.  For example, if you don’t accurately estimate the precise costs (shortage costs are the most difficult) it will be tough to definitely state something like “If I increase my on-hand inventory by an average of one unit for all items in an important product family, my company will see a net gain of $170,500.  That gain increases until I get to 4 units.  At 4 units and higher, the return declines due to excessive holding costs. So, the best decision factoring projected holding, ordering, and stockout is to increase inventory by 3 units to see a net gain of over $500,000.  

Short of that ideal, you can do something that is simpler yet still extremely valuable: Quantify the tradeoff curve between inventory cost and item availability. While you won’t necessarily know the service level you should target, you will know the costs of varying service levels.  Then you can earn your big bucks by finding a good place to be on that tradeoff curve and communicating where you at risk, where you aren’t, and setting expectations with customers and internal stakeholders.  Without the tradeoff curve to guide you, you are flying blind with no way to rationally modify stocking policy.

A Scenario to Learn From

Let’s sketch out a realistic tradeoff curve. We start with a scenario requiring a management decision. The scenario we will use and associated assumptions about demand, lead times, and costs are detailed below:

Inventory Policy

• Periodic review – Reorder decisions made every 30 days
• Order-Up-To-Level (“S”) – Varied from 30 to 60 units
• Shortage Policy – Allow backorders, no lost orders

Demand

• Demand is intermittent
• Average = 0.8 units per day
• Standard deviation = 1.2 units per day
• Largest demand in a year ≈ 9
• % of days with no demand = 53%

Lead Time

• Random at either 7, 14 or 21 days with probabilities 70%, 20% and 10%, respectively

Cost Parameters

• Holding cost = $1 per day
• Ordering Cost = $10 per order without regard to size of order
• Shortage Cost = $100 per unit not immediately shipped from stock

We imagine an inventory control policy that is known in the trade as a “periodic review” or (T,S) policy. In this instance, the Review Period (“T”) is 30 days, meaning that every 30 days the inventory position is checked and an ordering decision is made. The order quantity is the difference between the observed number of units on hand and the Order-Up-To Quantity (“S”). So, if the end-of-month inventory is 12 units and S = 20, the order quantity would be S – 12 = 20 -1 2 = 8. The next month, the order quantity is likely to be different. If the inventory ever goes negative (backorders) during a review period, the next order tries to restore equilibrium by ordering more in order to fill those backorders. For example, if the inventory is -5 (meaning 5 units ordered by not available for shipping, the next order would be S – (-5) = S + 5. Details of the hypothetical demand stream, supplier lead times, and cost elements are shown in Figure 1 below. Figure 2 show a sample of daily demand and daily inventory over five review periods. Demand is intermittent, as is often true for spare parts, and therefore difficult to plan for.

Figure 1: Different choices of inventory policy (order up to), associated costs, and service levels

Figure 2: Details of five months of system operation given one of the polices

Inventory Planning Software Is Our Friend

Software encodes the logic of the operation of the (T,S) system, generates many hypothetical but realistic demand scenarios, calculates how each of those scenarios plays out, then looks back on the simulated operation (here, 10 years or 3,650 consecutive days) to calculate cost and performance metrics.

To reveal the tradeoff curve, we ran several computational experiments in which we varied the Order-Up-To Level, S. The plots Figure 2 show the behavior of the on-hand inventory in “richest” alternative with S = 60. In the snippet shown in Figure 2, the on-hand inventory never comes close to stocking out. You can read that too ways. One, a bit naïve, is to say “Good, we’re well protected.” The other, more aggressive, is to say, “Oh no, we’re bloated. I wonder what would happen if we reduced S.”

The Tradeoff Curve Revealed

Figure 3 shows the results of reducing S from 60 down to 30 in steps of 5 units. The table shows that Total Cost is the sum of Holding Cost, Ordering Cost, and Shortage Cost. For the (T,S) policy, the ordering cost is always the same, since an order is placed like clockwork every 30 days. But the other components of cost respond to the changes in S.

Figure 3: The experimental results and corresponding tradeoff curve showing how changing the Order-Up-To Level (“S”) impacts both Service Level and Total Annual Cost

Note that the Service Level is always lower than the Fill Rate in these scenarios. As a professor, I always think of this difference in terms of exam grading. Each replenishment cycle is like a test. Service Level is about the probability of a stockout, so it’s a like the grade on pass/fail exam with one question that must be answered perfectly. If there is no stockout in a cycle, that’s an A. If there is a stockout, that’s an F. It doesn’t matter if it’s one unit that’s not supplied or 50 – it’s still an F. But Fill Rate is like a question that is graded with partial credit. So being short one of ten units gets you 90% Fill Rate for that cycle, not 0%. It’s important to understand the difference between these two important metrics for inventory planning – check out this vlog describing service level vs. fill rate via an interactive exercise in Excel.

The plot in Figure 3 is the real news. It pairs Total Cost and Service Level for various levels of S. If you read the graph right to left, it tells us that there are dramatic cost savings to be had by reducing S with very little penalty in terms of reduced item availability. For instance, reducing S from 60 to 55 saves close to $800 per year on this one item while reducing service level just a bit from (essentially) 100% to a still-impressive 99%. Cutting S some more does the same, though not as dramatically. If you read the graph left to right, you see that moving up from S = 30 to S = 35 costs about $1,000 per year but improves Service Level from an F grade (45%) to at least a C grade (71%). After that, pushing S higher costs progressively more while gaining progressive less.

The tradeoff curve doesn’t give you an answer to how to set the Order-Up-To Level, but it does let you evaluate the costs and benefits of each possible answer. Take a minute and pretend that this is your problem: Where would you want to be along the tradeoff curve?

You may object and say you hate your choices and want to change the game. Is there escape from the curve? Not from the general curve, but you might be able to shape a less painful curve. How?

You may have other cards to play. One avenue is to try to “shape” the demand so that it is less variable. The demand plot in Figure 2 shows a lot of variability. If you could smooth out the demand, the whole tradeoff curve would shift down, making every choice less expensive. A second avenue is to try to reduce the mean and variability of supplier lead times. Achieving either would also shift the curve down to make the choice less painful. Check out our article on how suppliers influence your inventory costs

Summary

The tradeoff curve is always with us. Sometimes we may be able to make it more friendly, but we always to pick our spot along it. It is better to know what you’re getting for any choice of inventory policy than to try to guess, and the curve gives you that.  When you have an accurate estimate of that curve, you are no longer flying blind when it comes to inventory planning. 

An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items.

In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock.

In the meantime, however, your company’s accumulated inventory control policies keep on doing what they do, even if they are leaking money. A good manager will make time to listen to the “background noise” even when he or she hears loud crashing in the warehouse.

Consider the current settings for your inventory control parameters (e.g., reorder points and order quantities). It’s easy to think of these as “fire and forget” decisions. But these settings usually accumulate over time and end up comprising a mish-mash of forgotten judgement calls that may be misaligned with your current operating environment. Many factors can drift away from their previous levels, such as supplier lead times, ordering costs, or average item demand. These changes can force invisible tradeoffs that are not to your best advantage.

It’s wise to revisit these control settings now and then to see if it’s possible to align your day-to-day operations with current realities. Of course, it would be infeasible for a busy manager to manually calculate the effects of changing the control settings on, say, 10,000 items. But that’s what modern inventory optimization and demand planning software is for: making large scale analytical tasks feasible. Such software will allow you to automatically process new information and compute adjustments at scale. The result will be easy wins – many of which would otherwise go unrealized.  And continuously saving a little here and there adds up to significant dollars when you are managing thousands of items.

Consider this example. Company A uses a periodic review inventory system. Every 30 days, they check on-hand inventory for all their items and decide how much replenishment stock to order. Each of their 10,000 items has a specified Order-Up-To Level that determines the size of their replenishment orders.

For instance, suppose Item 1234 has an Order-Up-To Level of 74, determined by factoring in the average item demand of 1.0 units per day, an average replenishment lead time of 8 days, and a target fill rate of 90% for this item. The choice of 74 as the Order-Up-To Level lets Company A meet its 90% fill rate target for Item 1234, but it also results in an average on hand inventory level of 40 units. At $1,500 per unit, this item alone represents $45,000 of inventory investment.

Now supposed that average item demand were to drift up from 1.0 to 1.2 units/day. Without anyone noticing, the fill rate for Item 1234 would drop to 82%!

Now suppose demand were to shift in the other direction and drift down to 0.8 units/day. As with the increase in average demand from 1.0 to 1.2 units/day, this kind of change is difficult to see when looking at a plot (see Figure 1) but can have a significant operational impact. In this case, the fill rate would zoom to a generous 96% but on hand inventory would also zoom: from 40 units to 46. Those six extra units would represent $9,000 in excess inventory.

Figure 1: Samples of daily demand with two different average values.  The difference in demand is unnoticeable to the naked eye but if not accounted for will have a large operational impact on inventory spend and service levels

Now imagine similar small shifts happening unnoticed across a full fleet of 10,000 inventory items. The total financial impact of all such shifts would be sufficient to get onto the radar of any CFO.  Trying to keep on top of this turbulence would be impossible if done manually but modern inventory optimization software could calculate the proper adjustments automatically as frequently as your company can handle, even daily helping you realize substantial improvements in service levels, inventory efficiency, while lowering stockout and holding costs!

To test software solutions via a series of empirical competition can be a considerable option. For forecasting / demand planning, a traditional “hold out” test in which 2014-2018 data are provided to software vendors and 2019 is held out for later comparison against forecasts provided by competing vendors. The company then measures forecast error and bias. This approach is advocated nearly universally for assessing forecast accuracy. It’s a good way to assess monthly or weekly forecast accuracy, but it is minimally useful if you have a different objective: Optimizing inventory.

In our last blog, we discussed how to pick a targeted service level. We indicated that just because you set a target (or a system recommends a target) doesn’t mean you’ll actually achieve the target. The right way to measure accuracy if you are interested in optimizing stock levels is to focus on the accuracy of the service level projection. This will account for both lead time demand and safety stock.

Setting a target service level is a strategic decision about inventory risk management. Inventory software does the tactical work by computing reorder points (a.k.a. mins) meant to achieve a user-defined target or that will achieve a system-calculated optimal target. But if the software uses the wrong demand model, the achieved service level will miss the target, sometimes significantly. The result of this error will be either shortages or inventory bloat, depending on the direction of the miss.

Forecasting is a means to an end. The end is to optimize inventory levels. Because demand is uncertain, companies that need to provide even moderate service levels must stock more than the forecast, often much more. But doesn’t low forecast error mean lower safety stock? The better my forecasts, the lower my inventory? Yes, true. But what matters when determining the required inventory are both accurate forecasts of the most likely demand and accurate estimates of the variability around the most likely demand.

Especially with long tailed, intermittent demand, traditional forecast accuracy assessments over a conventional 12 month forecast horizon miss the point three ways.

– First, the relevant time scale for inventory optimization is the replenishment lead time, which is usually much shorter than 12 months. Demand during lead times measured in days or weeks has volatility that gets averaged out over long forecast horizons. This is bad because factoring in the effect of volatility is essential to calculation of optimal reorder points.

– Second, forecast accuracy assessed over a multi-month forecast horizon focuses on the typical error in a typical month within the horizon. In contrast, inventory optimization requires a focus on cumulative demand, not period-by-period demand.

– Third, and most important is that forecast error metrics are focused on the middle of the demand distribution, aiming to estimate the most likely demand. But setting reorder points involves estimating high percentiles of the cumulative demand distribution over a lead time. Estimating the middle a bit better but having no clue about, say, the 95th percentile, is not helpful.

Consider this hypothetical example. If Vendor A forecasts 20 units with 110% error and Vendor B forecasts 22 units with 105% error, then Vendor B has an advantage in the forecasting game. But if you want a high service level and the demand is intermittent, you’ll have to stock a lot more than 20 or 22 units. Let’s assume you select Vendor B’s technology to plan stocking levels. You then notice that when planning reorder points to achieve a 95% service level, you often fall short – way more often that the expected 5% of the time. You come to realize that Vendor B’s approach completely underestimates the safety stock required to achieve the target service target. Focusing on vendors’ forecast error isn’t going to help. You will come to wish that you had verified Vendor A and B’s service level accuracy. Now you are stuck arbitrarily adjusting Vendor B’s service level targets to compensate for the shortfall.

So what’s needed in vendor competitions is assessment of their systems’ abilities to accurately forecast the inventory required to meet a given service level over an item’s replenishment lead time. Narrowly focusing on measuring forecast error is not appropriate if the mission is managing inventory. This is especially true for long tail items with intermittent demand or items that have medium to high volume but don’t have a demand distribution that looks like the classic “bell shaped curve” (Normal distribution).

The remainder of this blog explains how to test the accuracy of software’s service level calculations, so you can monitor the risk of missing your service level targets. We recommend this accuracy test over traditional “forecast versus actuals” tests because it provides much more insight into how reorder point recommendations will influence inventory levels and customer service.

Office staff are analyzing The Right Forecast Accuracy Metric for Inventory Planning

Service Level Defined

Consider a single inventory item. When inventory drops to or below the reorder point, a replenishment order is generated. This starts a period of risk that lasts as long as the replenishment lead time. During the period of risk, there might be enough incoming demands to create backorders or lost sales. The service level is the probability that there are no backorders or stockouts during the replenishment lead time. Critical items might be given very high target service levels, say 99%, whereas other items might have more relaxed targets, such as 75%. Whatever the target service level, it is best to hit that target.

Calculating Service Level

The service level for an individual item can only be estimated by repeated comparison of observed lead time demand against the calculated reorder point. These estimates take a lot of time: at least dozens of lead times. But fleet-wise service level can be estimated using data compiled over a single lead time.

Let’s do an example. Suppose you have demand histories for 1,000 items over 365 days and that (for simplicity) all items have 45-day lead times. For each item, follow these steps to estimate the fleet-wise achieved service level:

Step 1: Step aside (“hold out”) the most recent 45 days of demand (or however many days is closest to your typical lead times). Compute their sum, which is the most recent value of the actual lead time demand. This is the ground truth to be used to estimate the achieved service level.

Step 2: Use the prior 320 days of demand history to forecast the required inventory to hit a range of service level targets, say 90%, 95%, 97%, and 99%.

Step 3: Check whether the observed lead time demand is less than or equal to the reorder point. If it is, count this as a win; otherwise, count it as a loss. For instance, if the reorder point is 15 units but the most recent lead time demand is 10 units, then this is a win, since the reorder point is high enough to cover a lead time demand of 10 without any shortage. However, if the most recent lead time demand is 18 units, there would be a stockout, and 3 units would either be backordered or counted as lost sales.

Step 4: Working across all items, and all service level targets, tally the percentage of tests for each service level target that resulted in a win. This is the achieved service level. If the target was 90% and 853 of the 1,000 units record a win, then the achieved service level is 85.3%.

Example

Consider a real-world example. The data are daily demand histories of 590 medical supply items used in an internationally famous clinic. For simplicity, we assume each item has a lead time of 45 days. We evaluate target service levels of 70%, 90%, 95% and 99%.
We compare two demand models. The “Normal” model assumes that daily demand has a Normal (“bell-shaped”) distribution. This is the classic assumption used in most introductory textbooks on inventory control and in many software products. Classic though it may be, it is often an inappropriate model of demand for spare parts or supplies. The “Probability Forecast” model takes explicit account of the intermittent nature of demand.

Exhibit 1 shows the results. Column J shows the actual demand over the final 45 observations. The computed reorder points for the Advanced Model are shown in columns L-O.  The computed reorder points for the Normal model are not displayed.  Columns Q-T and V-Y hold the results of the tests for whether the reorder points were high enough to handle the lead time demands in column J.

The final results (yellow cells) show a clear difference between the Normal and Probability (Advanced) demand models. Both did a good job of hitting the 70% service level target, but estimating higher service levels is a more delicate calculation, and the Probability model does a much better job. For instance, the Normal model’s supposed 99% service level turned out to be only 94.4%, while the Probability model hit the target with a 98.5% achieved service level.

Implications

Utilizing the more accurate method achieved the targeted service level, while the less accurate method did not. If the less accurate method is used then real and costly business decisions will be made on the false assumption that a higher service level will be achieved. For example, if a Service Level Agreement (SLA) is based on these results and a 99% service level is committed to, the supplier would actually be five times more likely to stock out than planned (service level promised = 99% or 1% stockout risk vs. service level achieved = 94.5% or 5.5% stock out risk)! This means financial penalties will be incurred five times more often than expected.

Suppose that planners knew the target service level would not be met but were stuck using an inaccurate model. They would still need a way to increase inventory and achieve the desired level of service. What might they choose to do? We have observed situations where the planner enters a higher service level target than needed in order to “trick” the system into delivering the required service level. In the above example, the Normal model needed to have a 99.99% service level entered before it could achieve a target service level of 99%. This change resulted in achieving a 99% service but more than doubled the inventory investment when compared to the Advanced model.

Implementing a Service Level Accuracy Test

At Smart Software, we’ve encouraged many of our customers to conduct the test of service level accuracy as a way for them to assess our and other vendors’ claims during the software selection process. Missing the service level target has extremely costly implications resulting in substantial over stocks or under stocks.  So, test service level accuracy before deploying a solution to identify situations when the modeling fails. Don’t assume that you will achieve the service level you decide to target (or that the system recommends). To request an Excel spreadsheet that serves as a template for a service level accuracy test, email your contact information to info@smartcorp.com and enter “Accuracy Template” in the subject line.

You can’t properly manage your inventory levels, let alone optimize them, if you don’t have a handle on exactly how demand forecasts and stocking parameters (such as Min/Max, safety stocks, and reorder points, and order quantities) are determined.

Many organizations cannot specify how policy inputs are calculated or identify situations calling for management overrides to the policy.   For example, many people can say they rely on a particular planning method such as Min/Max, reorder point, or forecast with safety stock, but they can’t say exactly how these planning inputs are calculated.  More fundamentally, they may not understand what would happen to their KPI’s if they were to change Min,Max, or Safety Stock. They may know that the forecast relies on “averages” or “history” or “sales input”, but specific details about how the final forecast is arrived at are unclear.

Often enough, a company’s inventory planning and forecasting logic was developed by a former employee or vanished consultant and entombed in a spreadsheet.  It otherwise may rely on outdated ERP functionality or ERP customization by an IT organization that incorrectly assumed that ERP software can and should do everything. (Read this great and, as they say, “funny because it’s true,” blog by Shaun Snapp about ERP Centric Strategies.)  The policy may not have been properly documented, and no one currently on the job can improve it or use it to best advantage.

This unhappy situation leads to another, in which buyers and inventory planners flat out ignore the output from the ERP system, forcing reliance on Microsoft Excel to determine order schedules.  Ad hoc methods are developed that impede cohesive responses to operational issues and aren’t visible to the rest of the organization (unless you want your CFO to learn the complex and finicky spreadsheet).  These methods often rely on rules of thumb, averaging techniques, or textbook statistics without a full understanding of their shortcomings or applicability.  And even when documented, most companies often discover that actual ordering strays from the documented policy.  One company we consulted for had on hand inventory levels that were routinely 2 x’s the Max quantity!  In other words, there isn’t really a policy at all.

In summary, many current inventory and demand forecast “systems” were developed out of distrust for the previous system’s suggestions but don’t actually improve KPI’s.  They also force the organization to rely on a few employees to manage demand forecasting, daily ordering, and inventory replenishment.

And when there is a problem, it is impossible for the executive team to unwind how you got there, because there are too many moving parts.  For example, was the excess stock the fault of an inaccurate demand forecast that relied on an averaging method that didn’t account for a declining demand?  Or was it due to an outdated lead time setting that was higher than it should’ve been?  Or was it due to a forecast override a planner made to account for an order that just never happened?  And who gave the feedback to make that override?  A customer? Salesperson?

Do you have any of these problems?  If so, you are wasting hundreds of thousands to millions of dollars each year in unnecessary shortage costs, holding costs, and ordering costs.  What would you be able to do with that extra cash?  Imagine the impact that this would have on your business.

This blog details the top 10 questions that you can ask in order to uncover what’s really happening at your company.  We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

Inventory planning parameters, such as safety stock levels, reorder points, Min/Max settings, lead times, order quantities, and DDMRP buffers directly impact inventory spending and ability to meet customer demand. Based on these parameter settings, your ERP system makes daily purchase order suggestions.

Ensuring that these inputs are understood and optimized regularly will substantially reduce wasteful inventory spending and dramatically improve customer service levels.

Given the importance of getting these planning parameters right, we spend a lot of time during our consultations asking (1) how these parameter values are calculated and (2) how often they are updated. Most often the methods for calculating the parameter values are rule of thumb. You can read about why using rule of thumb approaches is so problematic here  – Beware of Simple Rules of Thumb for Managing Inventory.

This blog will focus on the frequency of updates. When we interview companies and ask them how often they update planning parameters, the answer we nearly always hear is “every day!” A follow up question or two most often reveals that this just isn’t true. What “every day” actually means in practice is this: Every day, the ERP system suggests dozens to hundreds of purchase orders and/or production jobs. The planner, let’s call him Peter, reviews these orders daily and decides whether to release, modify, or cancel them. If the order suggestion doesn’t “feel right”, Peter reviews the planning inputs and modifies the order if necessary. For example, Peter may feel there is already enough inventory on hand. To “fix” the issue, he will reduce the reorder point and cancel the order. Or if he feels that the order should have been placed weeks ago, Peter may expedite the order and increase the reorder point and order quantity to ensure there will be plenty of stock the next time.

The principal flaws with this approach are that it is reactive and incomplete. Here is why:

Reactive

It only assesses the handful of items marked for replenishment on any given day but not others. The trigger for reviewing an item is when the ERP suggests an order, and that will only happen when the reorder point or Min is breached. If the Min is too high and breaches earlier than it should have, an unneeded order will be placed unless caught by the planner. If the Min is too low, well, it is too late to fix the error. No matter how large the order suggestion is, you still have to wait to be resupplied and since the order was suggested late, a stockout during the replenishment period is highly probable. Where is the “planning” in such a process? As one customer put it, “Our former process was, in hindsight, spent managing the outputs and not the inputs.”

Incomplete

What about the thousands of other items that have a Min/Max, safety Stock, Reorder Point, or other parameters that isn’t being reassessed given the updated demand and supply data. The planner isn’t reviewing any of these items which means problems aren’t being identified in advance. Compounding the problem is that when Peter does make a change he doesn’t have any tools to assess the quality of his changes. If he modifies the min/max settings he doesn’t know the specific impact this will have on inventory value, ordering costs, holding costs, stock outs, and service levels. He only knows that an increase in inventory will likely improve service and increase costs. He doesn’t know for example whether his inventory has reached a point of diminishing returns. When inventory decisions are made with only a very rough understanding of the trade offs it creates more problems downstream. You wouldn’t want your carpenter making rough estimates of their measurements yet it’s commonplace for inventory planning professionals to do so with millions of dollars in inventory spend at stake.

How Often Do Most Companies Update Parameters?

So how often do most companies make system-wide updates to their planning parameters such as reorder points, safety stocks, Min/Max settings, lead times, and order quantities? Typically, mass updates occur quarterly, annually, and in some cases never – the only times changes are made are when an order is triggered by ERP. Not exactly agile.

The biggest reason given for not intervening more often is that it takes too much time. Most companies set these key parameters using very unwieldy Excel programs or ERP applications that simply aren’t designed to conduct systemic inventory planning. This is where inventory optimization software can help.

Using inventory optimization software and probability forecasting to update key planning parameters frequently, say every week or month instead of quarterly or annually, enables you quickly respond to changes in your business. You can seize on cost saving opportunities, as when demand turns down and you can reduce reorder points and/or order quantities and possibly cancel outstanding orders. Or you can respond to problems, as when demand increases threaten your service level commitments to customers, or supplier lead times increase and require re-computation of reorder points.

How to do it Right

The key is establishing an agreed upon set of performance and inventory value metrics and letting the software monitor the state of play in the background and alert you to exceptional situations. This is simply one more way of saying that, once systems have been established, you want to go forward using management by exception. You can set ranges within which things can bubble along as they normally do, but once a critical parameter like “stock out risk exceeds a pre-defined level” or “inventory value or costs exceeds a pre-defined level,” the software can provide a daily alert and can also recommend a response, such as raising a reorder point. With this level of automated assistance, it becomes possible to keep your finger on the pulse of the inventory without being overwhelmed by the sheer volume of data.

For example, you may choose an initial set of inventory parameters as the policy because you could see from the software that it meets your service level goals within your inventory budget. You may let the system prescribe service level targets for you and be comfortable with the settings because inventory value is within the budget. However, if demand gets less predictable than historically, you won’t be able to achieve the same level of service without an increase in inventory. An exception report will identify this and enable you to make an informed decision on what to do. You can decide to modify the policy or keep it the same. If you keep it the same, you now know the additional risks and change in inventory costs. This can be communicated to all stake holders so that there aren’t any surprises.

Plan Don’t React

Rather than being constantly in reactive mode, you can handle what really needs to be handled and still have some time to do forward thinking. For instance, you can do “what if” analyses on such issues as which supplier lead times would yield the biggest payoff if reduced, or whether service level targets should be adjusted to account for shifts in customer criticality, or similar policy issues. After all, it’s not as if you are not going to end up with a full daily agenda, it’s just a question of whether you can elevate that agenda to a more strategic level. So if you are spending all of your “planning” time managing the outputs of your ERP instead of constructively reviewing and optimizing the inputs, it is time to reassess your inventory planning process.