How to Set Up a Forecasting 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 temporal). In this article, I propose an original 4-dimensions forecasting framework that will enable you to set up a tailor-made forecasting process for your supply chain. I like to use this framework to kick off any forecasting project.

The 4-Dimensions Forecasting Framework

Demand Forecasting to Support Decision-Making

Supply chains are living organisms making hundreds — if not thousands — of…


As explained in my book Inventory Optimization: Models & Simulation, for better or for worse, inventories lie everywhere in supply chains. The central question of how much inventory is needed and where it is needed is often an endless debate among colleagues, especially when the game of politics (rather than quantitative/qualitative analysis) drives decisions.

Inventory Done Right

Stocking products helps companies around the globe to supply their clients on time and provide a buffer against any unforeseen event. Moreover, since holding inventory disconnects the production process from the sales process, it allows planners to produce longer production batches, decreasing production costs.

In other…


The article below is a summary of one of my LinkedIn posts. If you are interested in such debates, let’s connect! I would like to thanks the following people for their insightful remarks in the original discussion: Tatiana Usuga, Bruno Vinicius Gonçalves, Chris Mousley, Zachary MacLean, and Levent Ozsahin.

I recently received the following question on LinkedIn.

“We sell certain products once a year within a short period (like 20–40 days). The following year, the same upgraded product or a completely new one will be sold. Do you have any advice to forecast those short life-cycle products?”

Forecasting short life-cycle…


The article below is a summary of one of my LinkedIn posts. If you are interested in such debates, let’s connect! I would like to thanks the following people for their insightful remarks in the original discussion: James Cowan, Ruchin Gaur, Danny Bloem, Roel van Gemert, Jeff Baker, and Walter Reynders.

Capturing real demand is difficult for supply chains. If not impossible.

The real demand is your clients’ initially requested product, quantity, and delivery date. Demand is not your actual sales. Sales are constrained by the inventory at hand or, more generally, by supply availability. In other words, demand is…


Are you using (S)ARIMA(X) to forecast supply chain demand?
I see five reasons why you should not.

  1. 💾 ARIMA requires a long historical horizon, especially for seasonal products. Using three years of historical demand is likely not to be enough. Short Life-Cycle Products. Products with a short life-cycle won’t benefit from this much data. Forecasting demand at a higher hierarchical level might help. But it will come with other challenges (reconciliation, loss of accuracy).
  2. 💻 Running ARIMA on a wide dataset is (extremely) time-consuming as each SKU needs to be optimized separately. If it takes 1 second to optimize one…


The article below is a summary of one of my LinkedIn posts. If you are interested in such debates, let’s connect! I would like to thanks the following people for their insightful remarks in the original discussion: Timothy Brennan, Chris Davies, Valery Manokhin, Leonardo Cabrera, Charlie Kantz, Karl-Eric Devaux, Spyros Makridakis, and Dyci Manns Sfregola.

Supply chain demand planners often ask themselves: What is the best model to forecast my demand? What are the best practices I should follow to improve (or set up) my forecast (and make it more useful for my supply chain)?

It is impossible to give…


The article below is a summary of one of my LinkedIn posts. If you are interested in such debates, let’s connect! I would like to thanks the following people for their insightful remarks in the original discussion: Timothy Brennan, Chris Davies, Valery Manokhin, Leonardo Cabrera, Charlie Kantz, Karl-Eric Devaux, Spyros Makridakis, and Dyci Manns Sfregola.

Supply chain demand planners often ask themselves: What is the best model to forecast my demand? What are the best practices I should follow to improve my forecast?

It is impossible to give a definitive, absolute answer to those questions. There no silver bullet model…


The article below is a summary of one of my LinkedIn posts. If you are interested in such debates, let’s connect!

I would like to thanks the following people for their insightful remarks in the original discussion: Valery Manokhin, Nick Cronshaw, Robert van Dijk, Thomas Meersseman, Wassim Tabbara, Archit Patel, Chris Davies, Joris De Smet, Aleksandra Barteczek, Paul Balcaen, Karl-Eric Devaux, and Rohit Anand.

❓ COVID shook supply chains in 2020. How should you forecast future demand when everything is changing and you lack relevant data?

🥉Flag Outliers (Simple Solution)

The most straightforward response to an unusual demand-period is to flag it as an…


The article below is a summary of one of my LinkedIn posts. If you are interested in such debates, let’s connect!

I would like to thanks the following people for their insightful remarks in the original discussion: Adolph Vogel, John Skelton, Thamin Rashid, Paul Tolsma, Karl-Eric Devaux, Andy Robson, Leonardo Cabrera, Leen Klijn, Chris Davies, and Navdeep Agarwal.

📊 Should You Optimize Your Forecast?

Forecasting low-volume products have always been a challenge. Historically, Croston models have been used to forecast intermittent demand (see my article Forecasting Intermittent Demand with the Croston Model). More recently, Nikolaos Kourentzes proposed a temporal aggregation method (see it here). …


The article below is an extract from my book Data Science for Supply Chain Forecast, available here. You can find my other publications here. I am also active on LinkedIn.

This article is following another article on the theoretical introduction to simple exponential smoothing. You can find it here. You can find a similar article on how to make the same model with Python here.

Do It Yourself — Simple Exponential Smoothing with Python

Simple smoothing function

We will define a function simple_exp_smooth that takes a time series d as input and returns a pandas DataFrame df with the historical demand, the forecast, and the error. The function also takes extra_periods as…

Nicolas Vandeput

Consultant ✒️Book Author 📚1️⃣ “Inventory Optimization: Models and Simulations” 2️⃣ “Data Science for Supply Chain Forecast”

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