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Having worked in demand planning, S&OP and supply planning for several years, I have found that organizations often try to improve forecasts as a means to improve the overall supply chain. Indeed—looking at the pure math—improved forecast accuracy enables us to reduce end-to-end variability and optimize inventory and production efficiency. But the consistent focus on optimizing item accuracy takes our attention away from the real issues, i.e., our ability to manage variations and uncertainty to add value to our supply chain.

Regardless of our ability to improve accuracy by a certain number of points, we will still be short of 100%. The fact is we pay far less attention to driving business value than we do hunting for the next few points of improvement. In all the companies I have worked for, and the many companies I worked with during my years in consulting, I am yet to come across business processes that manage uncertainty in the forecast in an efficient way.

Using forecast accuracy as an input to the size of buffers needed and to assess the uncertainty in the plans is well established in many companies. However, in the operational execution, the acknowledgement of variability is absent. We still discuss the largest deviations between the forecast and actuals as opposed to deviations that are out of range. With that, our planning systems take us back to the target inventory level even though the consumption of our inventory may be perfectly in line with the expected variability, which may be different from forecasts. This kind of strategy causes a long sequence of changes all the way through the value chain, resulting in the well-documented bullwhip effect.

From a strictly forecasting perspective, measurement of forecast accuracy provides very little insight to improve it. Often, the way to improve accuracy is out of the hands of those working with the forecast. Accuracy is often affected by the way we incentivize our customers with different payment terms and shipping charges. I see three important steps that are needed to improve the way we manage uncertainty of the forecast.

Focus On Bias Rather Than On Accuracy

If we want to improve our forecasts, we should focus on forecast errors that are systemic such as forecast bias. Bias in a forecast is very harmful to the value chain. Measuring bias will help the business call out incorrect use of statistical models, where the sales history needs cleansing, and where qualitative “intelligence” that is manually added to the forecast is not intelligent enough. Bias is a main source of forecast error, which can be taken care of.

Using Segmentation For Allocating Forecasting Resources

Another way of dealing with accuracy is to assess the error against the natural variability in sales. A portfolio of items can be classified into segments based on their historical variability. Based on that, we can determine an expected level of error. The best way, therefore, is to chase errors that exceed our expected levels, and not ones that have large deviations. Say we have two items that are selling on average at a rate of 100 units per week. Item A has a historic variation of 30% and item B has a historic variation of 75%. Let’s say we sold 140 units of item A and 150 units of item B last week. Traditionally, we would investigate item B because the deviation of 50 units is larger than that of item A. Knowing that historically item A has a low variation and item B has a high variation, we should spend more time on understanding what happened to item A and investigate whether it needs re-forecasting, and not on item B which performed well within expectation.

Further, we should pay more attention to high value items, and less to others. As shown in Figure 1, we can expect higher deviations in products in segments 1 and 4, and lower deviations in segments 2 and 3. By looking at the volume (or revenue) as opposed to only deviations, we can identify the most important items to concentrate on. We don’t need to spend time on items in segment 4 (“exception only”) because they are of low importance to the business and have high natural deviations. Assessing deviations out of range provides an opportunity to refine our statistical models (as in the case of segment 2), as well as suggest how we can further improve the forecast of products in segment 1 by collaborating more with our stakeholders such as sales representatives.

Don’t Change Operational Planning If Deviations Are Within The Expected Range

Most planning systems work by trying to meet the target inventory. In that case, the plan is changed every time there is a change in actual demand compared to expected demand, no matter how small it is. In the network of inventory points and production sites, these little changes add up to much bigger changes. The cost associated with these changes throughout the value chain is in most cases not measured and accounted for.

Therefore, the best strategy is to have a system that stops re-planning whenever inventory is within acceptable range. In supply chain planning, it is often seen as an issue when actual inventory on hand is below target safety stock. Safety stock is meant to take care of uncertainty. If it does not, we are not using safety stock properly. Instead we are biasing our planning system, and unnecessarily putting pressure upstream. Every time inventory goes below a threshold, the system tries to replenish it. To avoid this, we should have a planning system like a forecast range. It should use inventory target ranges instead of fixed inventory targets. In other words, when the projected inventory is within certain limits, no change to the plan is necessary. When it exceeds the expected limit, as shown in Figure 2, the plan may have to be changed.

By using these three steps we can not only manage uncertainty more efficiently but also reduce cost. Some planning systems such as Kanban (reorder point planning) and pull-based decoupling points are good ways of reducing over reaction to variability in our operational planning. However, we do need a way to manage planning within ranges of forecast as well as inventory. In order to start reaping the rewards of managing uncertainty in this way, we must change how we think about variability and stop pointlessly chasing forecast accuracy.

 

This article was originally published in the Winter 2018/2019 issue of the Journal of Business Forecasting. Subscribe to get it delivered to your door quarterly, or become a member and get subscription to the journal plus discounted events, members only tutorials, access to the entire IBF knowledge library, and more.