Outside of work, you may have heard the famous dictum “Correlation is not causation.” It may sound like a piece of theoretical fluff that, though involved in a recent Noble Prize in economics, isn’t relevant to your work as a demand planner. Is so, you may be only partially correct.

Extrapolative vs Causal Models

Most demand forecasting uses extrapolative models. Also called time-series models, these forecast demand using only the past values of an item’s demand. Plots of past values reveal trend and seasonality and volatility, so there is a lot they are good for. But there is another type of model – causal models —that can potentially improve forecast accuracy beyond what you can get from extrapolative models.

Causal models bring more input data to the forecasting task: information on presumed forecast “drivers” external to the demand history of an item. Examples of potentially useful causal factors include macroeconomic variables like the inflation rate, the rate of GDP growth, and raw material prices. Examples not tied to the national economy include industry-specific growth rates and your own and competitors’ ad spending. These variables are usually used as inputs to regression models, which are equations with demand as an output and causal variables as inputs.

Forecasting using Causal Models

Many firms have an S&OP process that involves a monthly review of statistical (extrapolative) forecasts in which management adjusts forecasts based on their judgement. Often this is an indirect and subjective way to work causal models into the process without doing the regression modeling.

To actually make a causal regression model, first you have to nominate a list of potentially-useful causal predictor variables. These may come from your subject matter expertise. For example, suppose you manufacture window glass. Much of your glass may end up in new homes and new office buildings. So, the number of new homes and offices being built are plausible predictor variables in a regression equation.

There is a complication here: if you are using the equation to predict something, you must first predict the predictors. For example, sales of glass next quarter may be strongly related to numbers of new homes and new office buildings next quarter. But how many new homes will there be next quarter? That’s its own forecasting problem. So, you have a potentially powerful forecasting model, but you have extra work to do to make it usable.

There is one way to simplify things: if the predictor variables are “lagged” versions of themselves. For example, the number of new building permits issued six months ago may be a good predictor of glass sales next month. You don’t have to predict the building permit data – you just have to look it up.

Is it a causal relationship or just a spurious correlation?

Causal models are the real deal: there is an actual mechanism that relates the predictor variable to the predicted variable. The example of predicting glass sales from building permits is an example.

A correlation relationship is more iffy. There is a statistical association that may or may not provide a solid basis for forecasting. For example, suppose you sell a product that happens to appeal most strongly to Dutch people but you don’t realize this. The Dutch are, on average, the tallest people in Europe. If your sales are increasing and the average height of Europeans is increasing, you might use that relationship to good effect. However, if the proportion of Dutch in the Euro zone is decreasing while the average height is increasing because the mix of men versus women is shifting toward men, what can go wrong? You will expect sales to increase because average height is increasing. But your sales are really mostly to the Dutch, and their relative share of the population is shrinking, so your sales are really going to decrease instead. In this case the association between sales and customer height is a spurious correlation.

How can you tell the difference between true and spurious relationships? The gold standard is to do a rigorous scientific experiment. But you are not likely to be in position to do that. Instead, you have to rely on your personal “mental model” of how your market works. If your hunches are right, then your potential causal models will correlate with demand and causal modeling will pay off for you, either to supplement extrapolative models or to replace them.

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.

Looking for Trouble in Your Inventory Data

In this video blog, the spotlight is on a critical aspect of inventory management: the analysis and interpretation of inventory data. The focus is specifically on a dataset from a public transit agency detailing spare parts for buses.

Can Randomness be an Ally in the Forecasting Battle?

When we try to understand the complex world of logistics, randomness plays a pivotal role. This introduces an interesting paradox: In a reality where precision and certainty are prized, could the unpredictable nature of supply and demand actually serve as a strategic ally?
The quest for accurate forecasts is not just an academic exercise; it’s a critical component of operational success across numerous industries. For demand planners who must anticipate product demand, the ramifications of getting it right—or wrong—are critical. Hence, recognizing and harnessing the power of randomness isn’t merely a theoretical exercise; it’s a necessity for resilience and adaptability in an ever-changing environment.

The Objectives in Forecasting

A forecast is a prediction about the value of a time series variable at some time in the future. For instance, one might want to estimate next month’s sales or demand for a product item. A time series is a sequence of numbers recorded at equally spaced time intervals; for example, unit sales recorded every month. The objectives you pursue when you forecast depend on the nature of your job and your business. Every forecast is uncertain; in fact, there is a range of possible values for any variable you forecast. Values near the middle of this range have a higher likelihood of actually occurring, while values at the extremes of the range are less likely to occur.

Inventory Optimization for Manufacturers, Distributors, and MRO

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