To set up a perfect demand forecasting process, you need to get four things right: granularity, temporality, metrics, and process. — When it comes to demand forecasting, most supply chains rely on populating 18-month forecasts with monthly buckets. Should this be considered a best practice, or is it merely a by-default, overlooked choice? I have seen countless supply chains forecasting demand at an irrelevant aggregation level — whether material, geographical or…

Forecasting future demand despite historical shortages is a massive challenge for demand planners. This article presents three techniques to estimate unconstrained demand during shortages: fill in missing values based on historical figures, use your forecasting tool, or use historical demand drivers. Finally, we will see how forecasting demand using more granular time buckets might help with data quality.

This is the first part of a supply chain optimization project focusing on demand planning. In this two-part proof-of-concept, SupChains and DragonRitter (a forecasting-as-a-service platform) helped a Pharma Distributor to reduce its inventory while optimizing its service level. The POC showed a forecast error reduction of 25% and an expected…

Using cutting-edge machine learning models, SupChains delivered a forecasting model for an international FMCG reducing its forecast error by 30% compared to the benchmark and cutting its bias to virtually zero. The client used the forecast to plan its 2023 purchasing budget. Business Situation The client, an international FMCG retailer operating more…

Planners usually set targets as dynamic period coverages or fixed quantities. As described in this article, both techniques have their pro and cons. In both cases, you will have to rely on a solid automated quantitative model to refresh your targets regularly and focus on exception and alert management. — Before discussing how to express safety stocks, let’s recap some critical points. Many planners and practitioners confuse safety stocks, reorder points, and order up-to level. These three concepts are intertwined but are different. Let’s review them one by one:

In this article, I outline six drivers that influence how much service level you need to provide to your clients to maximize your profits (while minimizing your costs and risks). I conclude it by introducing a service level targets classification matrix. — Inventory is the connection between your supply chain and your clients. Stocking products allows you to decouple demand from supply and grants your clients instant access to your goods. Higher service levels will only bring more satisfaction to your clients — but it will come at a financial cost and…

Using cutting-edge machine learning models, SupChains delivered a forecasting model that helped an international manufacturer to reduce their forecast error by 20% compared to the benchmark. The client is now using this model as a baseline for its monthly demand planning process. On top of the extra forecasting accuracy resulting…

Service level metrics often confuse planners. This is dangerous — especially if you deal with inventory targets — as using different definitions will result in very different numbers and KPIs. This article highlights the main types of service levels, illustrate them with examples, and discuss their pro and cons — no more confusion. — This article is an extract from my book Inventory Optimization: Models and Simulations.