Demand Forecasting Best Practices by Nicolas Vandeput

Author:Nicolas Vandeput [Vandeput, Nicolas]
Language: eng
Format: epub, pdf
Publisher: Manning Publications Co.
Published: 2023-05-15T22:00:00+00:00

Hammer

100

150

20€

2,000€

3,000€

50

50

2,500

1,000€

1,000€

1,000,000€

Nail

1,500

1,000

0.1€

150€

100€

– 500

500

250,000

–50€

50€

2,500€

Total

1,612

1,160

2,750€

3,600€

– 452

552

290

850€

1,150€

581€

– 28%

34%

18%

31%

42%

21%

Note that you can weight products based on their costs, margins, or sale prices. Or even based on arbitrary weights to reflect their strategic importance.

As you can see in tables 11.3 and 11.4, RMSE scales poorly to product portfolios. And its value-weighted counterpart doesn’t scale well either. As RMSE is squaring errors, it will always put too much emphasis on high-volume items. RMSE might be a good KPI to assess the accuracy of a single product, but it shouldn’t be used over portfolios.

Pro Tip: Penalties for over- and under-forecasting?

There is a temptation to penalize (or weight) positive and negative forecast errors differently with a tool such as weighted errors. Obviously, in supply chains, the cost of having one product too many (extra holding costs or spoilage) or one product too few (unhappy clients, lost revenues) is not the same. Nevertheless, you will get biased forecasts by giving more importance to over or under forecasts. This will, in turn, gradually reduce the confidence in the overall forecasting process, until other teams and planners start creating their own projections because they do not trust the main demand forecast anymore. It is always better to balance company priorities regarding risks, costs, and service levels by setting proper service level targets and allocating the right amount of safety stock for each product. For more information, see my book Inventory Optimizations: Models and Simulations.

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