Wahupa Blog

Our blog is a place where we place articles by ourselves and guest authors. These articles will be less formal and rigid than official documentation and whitepapers. The purpose is to provide thought-leadership as well as explain best practices here.

To Forecast, Or Not To Forecast?

November 20, 2017 - Stefan de Kok

Demand forecasting is under pressure. Supply chains are getting ever more fragmented, through globalization and portfolio expansion, causing the traditional forecasting methods that used to work somewhat adequately to deteriorate to levels that do more damage than good. Recognizing this, new schools of thought are appearing that aim to remove the dependency on forecasting. There is a lot to be said for this objective. Why indeed would you want to forecast something with uncertainty if you could just know it with certainty?

In this vein, great strides have been made with demand driven approaches such as demand sensing and DDMRP (Demand-Driven Material Requirements Planning). The former aims to remove latency and downstream bullwhip in the supply chain by capturing demand closer to the end consumer. In the extreme, Point-of-Sale (or POS) demand sensing, captures bar-code scans at the cash register. This means demand is exactly known in fine granularity days or weeks before it is received as a sales order. DDMRP drives improvement from that point back in a different way. It strategically places inventory buffers within the company to minimize effective lead times and streamline flow of product. It avoids forecasts, aiming to drive all replenishment from sales orders. In this way it removes uncertainty and reduces the bullwhip effect within the internal supply chain. In a perfect world, we can drive our entire supply chain using these approaches and never need a forecast.

But this is not a perfect world. Demand sensing only buys a limited extra amount of information lead time, and likewise DDMRP has a limit to how much it can reduce replenishment lead time. As a result, forecasts are still needed. But the same known weaknesses of traditional forecasts that spawned the innovation of these demand driven approaches also tend to blind-sight the minds behind the innovations to both the need and the means of how to create complete solutions that incorporate the forecasts they reject. This article aims to shed some light of where and how this can and should be done.


Probabilistic Forecasting vs Statistical Forecasting

October 30, 2017 - Stefan de Kok

Traditional forecasting approaches are becoming obsolete.

As supply chains are globalizing and product portfolios are growing, demand patterns are becoming lumpier and more intermittent. At the same time more companies are starting to accept that a monthly forecast by item is meaningless when purchases, production, and shipments need to be planned weekly by item, and by location. Even smooth, steady demand patterns by item/month become intermittent by item/week/location. Traditional demand forecast approaches, already failing to achieve adequate levels of accuracy, are deteriorating fast under these conditions. Even as forecasting systems are squeezing out extra slivers of accuracy, companies are witnessing consistent overall decline in achieved forecast accuracies. This trend is certain to continue.

But it gets worse.

Forecasts do not exist in isolation. They are created for a purpose, usually multiple purposes. For many companies, one of these purposes is to determine appropriate stocking levels as a safety to buffer against demand variability. The way the forecasts are determined however, does not allow a proper assessment of the shape and size of the uncertainty around the forecast. This uncertainty is the only accurate input to determine inventory buffers. Companies using traditional forecasting approaches need to resort to using demand variability or forecast error as input to safety stock formulas. The direct result of that is simultaneous out-of-stocks and excess inventory levels, excessive expediting and still not achieving service levels that those safety stocks promise to achieve. See for example in "Why You Keep Missing Your Service Level Targets" the gravity of the damage that occurs when you incorrectly assume a normal distribution of demand residuals, which is only one of the multiple problems of the above.

What is the alternative?

There are many alternatives. First and foremost, one should try to reduce any dependency on forecasts to the absolute minimum, because no matter what, forecasts will contain some amount of error. Wherever data and approaches are available that are more accurate, these should be preferred over a forecast for the same purpose. A number of so-called demand-driven approaches can be applied here, depending on data availability, budget and organizational readiness. In this sphere I consider DDMRP, demand sensing, and Point-of-Sale (POS) demand sensing.

However, one must realize that forecasts are a necessary evil. Demand-driven approaches can only take you so far without relying on a forecast. The reliance generally comes in two flavors:

these approaches still require some forecast to function efficiently, for example to determine buffer levels, and:
a hand-off needs to occur between time-horizons or functional areas where demand-driven can be applied and those where forecasts are the only option.
Traditional forecasting approaches are terribly equipped for either of these requirements. This is where probabilistic forecasting can save the day. Not only will it play nice with demand-driven approaches of any kind, it obliterates all the theoretical maximum levels of accuracy of the traditional forecasting approaches. This article explains the differences. READ MORE...