Worst Practices in Forecasting

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?

For ten years, the editorial team at Foresight: The International Journal of Applied Forecasting has been telling readers about the struggles and successes of forecasting professionals and doing all we can to educate them about methods and practices that really work. We do that with articles contributed by forecasting professionals as well as respected academics and authors of highly-regarded books.

As Founding Editor of Foresight, I’d like to invite you to join us for the upcoming Foresight Practitioner Conference entitled “Worst Practices in Forecasting: Today’s Mistakes to Tomorrow’s Breakthroughs.”

This 1.5-day event will take place in Raleigh, North Carolina, October 5-6, 2016. There we will take a hard look at common practices that may be inhibiting efforts to build better forecasts. Our invited speakers will share how they and others have uncovered and eliminated bad habits and worst practices in their organizations for dramatic improvements in forecasting performance.

Some of the topics to be addressed include:

• Use and Abuse of Judgmental Overrides

• Avoiding Dangers in Sales Force Input to Forecasts

• Improper Practices in Inventory Optimization

• Pitfalls in Forecast Accuracy Measurement

• Worst Practices in S&OP and Demand Planning

• Worst Practices in Forecasting Software Implementation

Foresight is published by the non-profit International Institute of Forecasters (IIF), an unbiased, non-commercial organization, dedicated to the generation, distribution and use of knowledge on forecasting in a wide range of fields. (Smart Software’s own Tom Willemain serves on Foresight’s Advisory Board.) Foresight is just one of the resources made available by the IIF. Additional publications, a host of online resources, an annual symposium and periodic workshops and conferences are available to all IIF members. The Smart Forecaster previously interviewed IIF past-president Dr. Mohsen Hamoudia. Visit the IIF site for information about joining.

(Len Tashman is the editor of Foresight: The International Journal of Applied Forecasting. The unusual practice-related conference he describes, upcoming in October 2016, will appeal to many of readers of The Smart Forecaster. For instance, those who have received Smart Software’s training have been alerted to the possibility that overriding statistical forecasts can backfire if done cavalierly. Two sessions at the conference focus on the use of judgement in the forecasting process. — Tom Willemain)

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The Right Forecast Accuracy Metric for Inventory Planning

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The Trouble With Turns

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

In our travels around the industrial scene, we notice that many companies pay more attention to inventory Turns than they should. We would like to deflect some of this attention to more consequential performance metrics.

Recall the definition: Turns = Annual dollar cost of goods sold / Average dollar value of inventory. If you sell $1 million of stuff in a year and have an average of $100,000 of stuff on the shelf each day, you are running at an impressive 10 Turns (Walmart runs at around 8). Supposedly, having high Turns signals efficient management, and keeping your Turns higher than competitors’ signals competitive advantage.

But as happens with most performance metrics, there is more to the story. Turns may be very salient to the CFO, but they can be a straightjacket to the COO. This is because Turns are not directly related to customer service; in fact, high Turns can be synonymous with low service levels and fill rates. S&OP consultant Darrin Oliver calls Turns his “pet peeve metric” because “the customer doesn’t care about Turns.”

Suppose you are unhappy with your current Turns value. What can you do to boost the number? Since Turns is a ratio, you can increase it by either increasing the numerator (goods sold) or decreasing the denominator (inventory). Increasing sales is more difficult because it requires the cooperation of the customer. Decreasing inventory is easier because it’s completely under your control: just make smaller replenishment orders, which also saves money in the short run. Indeed, you can get very enthusiastic and cut inventory to the bone. You end up with a better looking number for Turns—and a serious problem with stockouts, backorders, lost sales, lost customer good will and lost market share. Who’s sorry now?

Here’s a lightly edited version of a story on this topic told by a very wise practitioner. “Back in my other life they were all about improving Turns. Why, I have no idea. So I pointed out the risks that you run. And they really weren’t interested. So we took our global inventories down to [a lower level], and then were breaking on stock left and right on a daily basis. Our turns were great, but we weren’t making any money, because we couldn’t get anything out the door, because we didn’t own it. The higher your turns, the lower your inventory’s going to have to be, or you’re just going to have really good flow. And in our industry that’s a very, very difficult thing to achieve. So if we can have reasonable Turns but still be in stock, I think that’s what we want to do. It can be very frustrating in an operations world to try to explain what we do every day and what the risks to the business are when the financial people are just looking at one or two metrics. They’re basically trying to plan the business in a vacuum, and it’s very difficult and very risky to do that.”

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselear Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

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Excess Inventory Hurts Customer Service!

Excess Inventory Hurts Customer Service!

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Managing the Inventory of Promoted Items

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

In a previous post, I discussed one of the thornier problems demand planners sometimes face: working with product demand data characterized by what statisticians call skewness — a situation that can necessitate costly inventory investments. This sort of problematic data is found in several different scenarios. In at least one, the combination of intermittent demand and very effective sales promotions, the problem lends itself to an effective solution.

Reviewing terms, recall that “service level” is the probability of not stocking out while waiting for a replenishment order to arrive, while “fill rate” is the percentage of demand that is satisfied immediately from stock. In my previous post, “The Scourge of Skewness”, I pointed out that a certain type of demand distribution, having a “long right tail”, will lead to fill rates that can be much lower than service levels. I also pointed out that sometimes the only way to improve the fill rate is to increase the target service level to an unusually high level, which can be expensive.

In this post, I’ll look at solving the problem in one special case: skewness resulting from effective sales promotions mixed with “intermittent demand”. Intermittent demand has a large proportion of zero values, with nonzero values mixed in at random. Successful sales promotions, obviously positive, have a downside: they can confuse the “demand signal” with spikes in your demand history, and can undermine forecasts and bias safety stock calculations. When intermittent demand and effective sales promotions are the source of your data’s skewness, methods exist to work around the problem to achieve both higher fill rates and more accurate demand forecasts.

How Promotions Increase Skewness

Successful promotions abruptly increase item demand. This creates anomalies, or “outliers”, which contribute to forming a skewed distribution. Knowing when promotions occurred in the past, we can adjust an item’s record of past demand. We produce an alternate demand history as if there had been no promotions, by replacing the outliers with values more representative of the “natural” level of demand. These adjustments reduce demand skewness. Reduced skewness can lead to significant reductions in both expected forecasts and safety stocks, which add together to form reorder points.

Successful promotions are likely to be repeated. When that happens, the promotion effects can be added in to demand forecasts to increase their accuracy. The effect of future promotions on inventory management will be to increase the risk of stockouts, so a sensible response is to work at the operational level to build up temporary supply, in a quantity keyed to the estimated impact of prior promotions on the effected items.

Using Event Modeling to Improve Demand Forecasting

It’s possible to model the impact of like events, and apply this to planned events in the future. Doing so can improve your forecast in two big ways: by projecting the demand jolt you expect from a planned event; and rationalizing the spikes in the past that were caused by events, making your baseline activity more visible and more accurately forecastable. We do a lot of this in SmartForecasts, so allow me to use our experience there to show you what I mean.

Event Modeling entails the following steps:
• Automatically estimating the impact of previous promotions (which is a useful result in itself).
• Adjusting historical demand to statistically remove the effect of promotions.
• Creating promotion-free forecasts.
• Revising the forecasts for any future time periods in which promotions are planned.

We call this this type of analysis “Promo forecasting”. We use the word “promotions” to describe what you do yourself to improve your results. We use “events” to describe what the world does to you, usually to your detriment; examples include strikes, power outages, warehouse fires and other unlucky happenings.

To understand how Event Modeling can help you cope with skewness when doing demand forecasting for high-volume items, consider Figures 1-3.

Figure 1 shows that this item’s demand pattern is clearly seasonal, and the forecast is both seasonal and “tight”, meaning that the forecast uncertainty interval (“margin of error”, shown in cyan lines) is very narrow.

Figure 2 shows an alternative history in which a promotion in June 2014 reversed the usual seasonal low associated with June sales. This demand pattern was forecasted using the Automatic forecasting tournament in SmartForecasts, as in Figure 1. This time, the promotion scrambled the seasonal pattern enough to create an inappropriate non-seasonal forecast, and one that has a much larger margin of error.

Finally, Figure 3 shows how Promo forecasting handles the same promoted scenario, retaining a seasonal forecast and building into the forecast an estimate of the effect of a planned repeat promotion in 2015.

The Case of Intermittent Demand

In Figure 1, the item was a high-volume finished good and the task was demand forecasting. Promo modeling is also useful when dealing with the task of setting safety stocks and reorder points for items with intermittent demand, whether the items are finished goods, components or spare parts. Intermittent demand very often has a skewed distribution that makes it difficult to achieve high item availability with a small investment in inventory.

Figure 4 illustrates the problem that a successful promotion can accidentally create for inventory management. If such a spike arises from the natural, un-promoted demand, then the only way to maintain high fill rates is to provide safety stocks large enough to cope with these random surges. In this case, the big spike in demand of 500 units in February 2013 was the result of a one-time promotion.

Taking Account of Promotions to Improve Inventory Management

Unwittingly treating the spike in the example above as part of the natural demand variability results in a poor fill rate. To achieve a target service level of, say, 95% with a lead time of one month would require a reorder point of 38 units, computed as the sum of an expected forecast over the one month replenishment lead time of 21 units supplemented by a safety stock of 17 units. This investment would result in a disappointing fill rate of only 36%.

However, recognizing that the spike is a one-time promotion and replacing the 500 units with 0 obviously would make a big difference. The reorder point would drop from 38 units to 31 (the sum of an expected demand of 7 units and a safety stock of 24 units) and the fill rate would increase to 94%.

Of course, it is not ok to just throw out inconvenient demand spikes whenever they make life uncomfortable; there has to be a valid “business story” behind the adjustment of historical demand. If the spike is the result of a data processing error, then by all means, fix it. If the spike coincides with a promotion, then replacing the spike with, say, the median demand (often zero, as in this example) will result in a much more sustainable inventory investment that still meets aggressive performance targets. Future promotions of the same type on the same item will require some extra effort to prepare for the temporary surge in demand, but the recommended reorder point will be correct in the long run.

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselear Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

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The Scourge of Skewness

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Demand planners have to cope with multiple problems to get their job done. One is the Irritation of Intermittency. The “now you see it, now you don’t” character of intermittent demand, with its heavy mix of zero values, forces the use of advanced statistical methods, such as Smart Software’s patented Markov Bootstrap algorithm. But even within the dark realm of intermittent demand, there are degrees of difficulty: planners must further cope with the potentially costly Scourge of Skewness.

Skewness is a statistical term describing the degree to which a demand distribution is not symmetrical. The classic (and largely mythic) “bell-shaped” curve is symmetric, with equal chances of demand in any time period falling below or above the average. In contrast, a skewed distribution is lopsided, with most values falling either above or below the average. In most cases, demand data are positively skewed, with a long tail of values extending toward the higher end of the demand scale.

Bar graphs of two time series
Figure 1: Two intermittent demand series with different levels of skewness
Figure 1 shows two time series of 60 months of intermittent demand. Both are positively skewed, but the data in the bottom panel are more skewed. Both series have nearly the same average demand, but the one on top is a mix of 0’s, 1’s and 2’s, while the one on the bottom is a mix of 0’s, 1’s and 4’s.

What makes positive skewness a problem is that it reduces an item’s fill rate. Fill rate is an important inventory management performance metric. It measures the percentage of demand that is satisfied immediately from on-hand inventory. Any backorders or lost sales reduce the fill rate (besides squandering customer good will).

Fill rate is a companion to the other key performance metric: Service level. Service level measures the chance that an item will stock out during the replenishment lead time. Lead time is measured from the moment when inventory drops to or below an item’s reorder point, triggering a replenishment order, until the arrival of the replacement inventory.

Inventory management software, such as Smart Software’s SmartForecasts, can analyze demand patterns to calculate the reorder point required to achieve a specified service level target. To hit a 95% service level for the item in the top panel of Figure 1, assuming a lead time of 1 month, the required reorder point is 3; for the bottom item, the reorder point is 1. (The first reorder point is 3 to allow for the distinct possibility that future demand values will exceed the largest values, 2, observed so far. In fact, values as large as 8 are possible.) See Figure 2.

Histograms of two time series
Figure 2: Distributions of total demand during a replenishment lead time of 1 month
(Figure 2 plots the predicted distribution of demand over the lead time. The green bars represent the probability that any particular level of demand will materialize.)

Using the required reorder point of 3 units, the fill rate for the less skewed item is a healthy 93%. However, the fill rate for the more skewed item is a troubling 44%, even though this item too achieves a service level of 95%. This is the scourge of skewness.

The explanation for the difference in fill rates is the degree of skewness. The reorder point for the more skewed item is 1 unit. Having 1 unit on hand at the start of the lead time will be sufficient to handle 95% of the demands arriving during a 1 month lead time. However, the monthly demand could reach above 15 units, so when the more skewed unit stocks out, it will “stock out big time”, losing a much larger number of units.

Most demand planners would be proud to achieve a 95% service level and a 93% fill rate. Most would be troubled, and puzzled, by achieving the 95% service level but only a 44% fill rate. This partial failure would not be their fault: it can be traced directly to the nasty skewness in the distribution of monthly demand values.

There is no painless fix to this problem. The only way to boost the fill rate in this situation is to raise the service level target, which will in turn boost the reorder point, which finally will reduce both the frequency of stockouts and their size whenever they occur. In this example, raising the reorder point from 1 unit to 3 units will achieve a 99% service level and boost fill rate to a respectable, but not outstanding, 84%. This improvement would come at the cost of essentially tripling the dollars tied up in managing this more skewed item.

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselear Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

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A Check on Forecast Automation with the Attention Index

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

A new metric we call the “Attention Index” will help forecasters identify situations where “data behaving badly” can distort automatic statistical forecasts (see adjacent poem). It quickly identifies those items most likely to require forecast overrides—providing a more efficient way to put business experience and other human intelligence to work maximizing the accuracy of forecasts. How does it work?

Classical forecasting methods, such as the various flavors of exponential smoothing and moving averages, insist on a leap of faith. They require that we trust present conditions to persist into the future. If present conditions do persist, then it is sensible to use these extrapolative methods—methods which quantify the current level, trend, seasonality and “noise” of a time series and project them into the future.

But if they do not persist, extrapolative methods can get us into trouble. What had been going up might suddenly be going down. What used to be centered around one level might suddenly jump to another. Or something really odd might happen that is entirely out of pattern. In these surprising circumstances, forecast accuracy deteriorates, inventory calculations go wrong and general unhappiness ensues.

One way to cope with this problem is to rely on more complex forecasting models that account for external factors that drive the variable being forecasted. For instance, sales promotions attempt to disrupt buying patterns and move them in a positive direction, so including promotion activity in the forecasting process can improve sales forecasting. Sometimes macroeconomic indicators, such as housing starts or inflation rates, can be used to improve forecast accuracy. But more complex models require more data and more expertise, and they may not be useful for some problems—such as managing parts or subsystems, rather than finished goods.

If one is stuck using simple extrapolative methods, it is useful to have a way to flag items that will be difficult to forecast. This is the Attention Index. As the name suggests, items to be forecast with a high Attention Index require special handling—at least a review, and usually some sort of forecast adjustment.

The Attention Index detects three types of problems:

An outlier in the demand history of an item.
An abrupt change in the level of an item.
An abrupt change in the trend of an item.
Using software like SmartForecasts™, the forecaster can deal with an outlier by replacing it with a more typical value.

An abrupt change in level or trend can be dealt with by omitting, from the forecasting calculations, all data from before the “rupture” in the demand pattern—assuming that the item has switched into a new regime that renders the older data irrelevant.

While no index is perfect, the Attention Index does a good job of focusing attention on the most problematic demand histories. This is demonstrated in the two figures below, which were produced with data from the M3 Competition, well known in the forecasting world. Figure 1 shows the 20 items (out of the contest’s 3,003) with the highest Attention Index scores; all of these have grotesque outliers and ruptures. Figure 2 shows the 20 items with the lowest Attention Index scores; most (but not all) of the items with low scores have relatively benign patterns.

If you have thousands of items to forecast, the new Attention Index will be very useful for focusing your attention on those items most likely to be problematic.

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

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The Right Forecast Accuracy Metric for Inventory Planning

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Traditional forecasting accuracy metrics aren’t applicable when the goal is to optimize inventory. It’s “service level accuracy” that matters because just setting a service target doesn’t mean you’ll actually achieve it. Poor accuracy here has extremely costly implications. The right way to measure accuracy for inventory planning is to focus on the accuracy of the service level projection. This blog explains why and details how to calculate the metric.

Infrequent Updates to Inventory Planning Parameters Costs Time, Money, and Hurts Service

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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. Ensuring that these inputs are optimized regularly will dramatically improve customer service levels and will reduce the amount of unnecessary inventory spending.

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5 Demand Planning Tips for Calculating Forecast Uncertainty

Those who produce forecasts owe it to those who consume forecasts, and to themselves, to be aware of the uncertainty in their forecasts. This note is about how to estimate forecast uncertainty and use the estimates in your demand planning process. We focus on forecasts made in support of demand planning as well as forecasts inherent in optimizing inventory policies involving reorder points, safety stocks, and min/max levels.

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Too Much or Too Little Inventory?

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Do you know which items have too much or too little inventory? What if you knew? How would you go about cutting overstocks while still ensuring a competitive service level? Would you be able to reduce stockouts without incurring a prohibitively expensive inventory increase? How would these changes impact service levels, costs and turns—for individual items, groups of items and overall?

Most companies know they have too much or too little inventory but lack a key ingredient for optimizing inventory: Service Level-Driven Demand Planning. To take action, you must know how much inventory is needed to satisfy the service level you require. More fundamentally, you need to know the specific service level that will result from your current inventory policies, the gap to be addressed and its financial implications.

Many organizations, especially those with intermittent demand, find this to be an exceptionally challenging trial and error process.

Moving to a service level-driven approach will overcome this challenge and ensure that rebalancing inventory improves service level performance at a lower cost. Start with the most accurate demand forecast possible, calibrate for forecast risk and then determine your optimal inventory position. In a recent webinar, I demonstrated Service Level-Driven Demand Planning and how SmartForecasts can be used to drive this process:

1. Measure the service levels that will be achieved at current inventory levels and with your current inventory policy.
2. Identify items that will achieve high service levels (98%+) but at prohibitively high cost.
3. Identify items that are at high risk of stockout (service levels < 75%).
4. Run multiple what-if scenarios based on a different prioritization of service levels by item or item groups. Choose the scenario that optimizes financial constraints with service objectives.
5. Quantify cash savings from reducing overstocks and the costs to increase inventory when service levels are unacceptably low.
6. Take action to establish new service level-driven reorder points, order quantities and inventory levels to meet your service targets and budget.

To view the webinar replay, please click here and complete the registration request.

Gregory Hartunian serves as President of Smart Software and as a member of the Board of Directors. A graduate of The F.W. Olin School for Business at Babson College, he formerly served as Vice President, Sales and Operations.

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5 Demand Planning Tips for Calculating Forecast Uncertainty

Those who produce forecasts owe it to those who consume forecasts, and to themselves, to be aware of the uncertainty in their forecasts. This note is about how to estimate forecast uncertainty and use the estimates in your demand planning process. We focus on forecasts made in support of demand planning as well as forecasts inherent in optimizing inventory policies involving reorder points, safety stocks, and min/max levels.

Excess Inventory Hurts Customer Service!

Excess Inventory Hurts Customer Service!

Many companies adopt a “customer first, better to have the inventory and not need it” approach to inventory planning. While well intentioned, this approach often ignores the role that diminishing returns and opportunity costs play in inventory management impacting the organizations ability to quickly respond to demand.

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