Anybody doing the job knows that managing inventory can be stressful. Common stressors include: Customers with “special” requests, IT departments with other priorities, balky ERP systems running on inaccurate data, raw material shortages, suppliers with long lead times in far-away countries where production often stops for various reasons and more. This note will address one particular and ever-present source of stress: demand variability.

Everybody Has a Forecasting Problem

Suppose you manage a large fleet of spare parts. These might be surgical equipment for your hospital, or repair parts for your power station. Your mission is to maximize up time. Your enemy is down time. But because breakdowns hit at random, you are constantly in reactive mode. You might hope for rescue from forecasting technologies. But forecasts are inevitably imperfect to some degree: the element of surprise is always present.  You might wait for Internet of Things (IOT) tech to be deployed on your equipment to monitor and detect impending failures, helping you schedule repairs well in advance. But you know you can’t meter up the thousands of small things that can fail and disable a big thing.

So, you decide to combine forecasting with inventory management and build buffers or safety stock to protect against surprise spikes in demand. Now you have to work out how much safety stock to maintain, knowing that too little means vulnerability and too much means bloat.

Suppose you handle finished goods inventories for a make-to-stock company. Your problem is essentially the same as in managing service parts: You have external customers and uncertain demand. But you may also have additional problems in terms of synchronizing multiple suppliers of components that you assemble into finished goods. The suppliers want you to tell them how much of their stuff to make so you can make your stuff, but you don’t know how much of your own stuff you’ll need to make.

Finally, suppose you handle finished goods in a build-to-order company. You might think that you no longer have a forecasting problem, since you don’t build until you are paid to build. But you do have a forecasting problem. Since your finished goods might be assembled from a mixture of components and sub-assemblies, you have to translate some forecast of finished goods demand to work out a forecast of those components. Otherwise, you will go to make your finished goods and discover that you don’t have a required component and have to wait until you can re-actively assemble everything you need. And your customers might not be willing to wait.

So, everybody has a forecasting problem.

What Makes Forecasting Difficult

Forecasting can be quick, easy and dead accurate – as long as the world is simple. If demand for your product is 10 units every week, month after month, you can make very accurate forecasts. But life is not quite like that. If you’re lucky and life is almost like that – maybe weekly demand is more like {10, 9, 10, 8, 12, 10, 10…} — you can still make very accurate forecast and just make minor adjustments around the edges. But if life is as it more often is – maybe weekly demand looks like {0, 0, 7, 0, 0, 0, 23, 0 …} – demand forecasting is difficult indeed. The key distinction is demand variability: it’s the zigging and zagging that creates the pain.

Safety Stock Takes Over Where Forecasting Leaves Off

Statistical forecasting methods are an important part of the solution. They let you squeeze as much advantage as possible from the historical patterns of demand your company has recorded for each item. The job of forecasts is to describe what is typical, which provides the base on which to cope with randomness in demand. Statistical forecasting techniques work by finding “big picture” features in demand records, such as trend and seasonality, then projecting those into the future. They all implicitly assume that whatever patterns exist now will persist, so 5% growth will continue, and July demand will always be 20% higher than February demand. To get to that point, statistical forecasting methods use some form of averaging to smother the “noise” in the demand history.

But then the rest of the job falls on inventory management, because the atypical, random component of future demand will still be a hassle in the future. This inevitable level of uncertainty has to be handled by the “shock-absorber” called safety stock.

The same methods that produce forecasts of trend and/or seasonality can be used to estimate the amount of forecast error. This has to be done carefully using a method called “holdout analysis”.  It works like this. Suppose you have 365 observations of daily demand for Item X, which has a replenishment lead time of 10 days. You want to know how many units will be demanded over some future 10-day period. You might input the first 305 days of demand history into the forecasting technique and get forecasts for the next 10 days, days 306-315.

The answer gives you one estimate of the 10-day total demand. Importantly, it also gives you one estimate of the variability around that forecast, i.e., the forecast error, the difference between what actually happened in days 306-315 and what was forecasted. Now you can repeat the process, this time using the first 306 days to forecast the next 10, the first 307 days to forecast the next 10, etc. You end up with 52 honest estimates of the variability of total demand over a 10-day lead time. Suppose 95% of those estimates are less than 28 units. Then 28 units would be a pretty safe safety stock to add to the forecast, since you will run into shortages only 5% of the time.

Modern statistical software does these calculations automatically. It can ease at least one of the chronic headaches of inventory management by helping you cope with demand variability.

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.