We reduce forecast error by up to 30%
Our fully automated forecasting model leverages data such as promotions, shortages, prices, holidays, and sell-outs to reduce forecast error by up to 30%. It even forecasts the sales of new products before they hit the market. Our model is used worldwide by manufacturers, distributors, retailers, and even software vendors.
Forecasting Accuracy Matters
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For every 1% of forecast improvement, a consumer goods company could achieve
- 2.7% reduction in finished goods inventory,
- 3.2% reduction in transportation costs, and
- 3.9% reduction in inventory obsolescence.
Gartner
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From our experience, 15% forecast accuracy improvement will deliver a 3% or higher pre-tax improvement.
The Institute of Business Forecasting
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Improving forecasting accuracy by 10 to 20% translates into a potential 5% reduction in inventory costs and revenue increases of 2 to 3%.
McKinsey Global Institute
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Simulations show that a 10% forecasting improvement would result in 6% shortage reduction or 4% inventory reduction.
But 50% of demand planners reviews worsen accuracy.
How can we do better?
Client Pains
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Low Accuracy & High Bias
Poor accuracy harms your supply chain: you’ll get too much inventory of the wrong products, and not enough of the right ones. You will face overstocks and shortages – often at the same time. These poor forecasts can be due to unreliable models or stakeholders inputting numbers to match their own agendas, rather than their actual predictions.
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Manual workload
Planners often spend their time reviewing historical demand, inputting promotions, accounting for shortages, and manually tweaking forecasting parameters. All these tasks should be automated through machine learning.
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No Added Value from Demand Planners
Once you automate low-value tasks, planners can focus on value-added tasks: ensuring clean input data for the automated model, and collecting insights beyond what the model knows. This way, planners can really start enriching the forecasts.
How is SupChains’ Inventory Model Different?
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Business Insights
Our model uses your business data to deliver accurate forecasts. It can be fed with promotional calendars (historical and future), prices, shortages, sell-out sales, client inventory levels, future orders, and the weather. Our model also captures product cannibalization, as well as overall trends and seasonality.
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Forecasting New Products
By design, our model can forecast brand new products even before they hit the market.
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Robust, Fast, Simple
We build our models to last. They process and clean your data, delivering clear inconsistency reports while running hundreds of checks before and after generating forecasts.
Test it yourself with our simulator
Impact 1
We increase service level while keeping same inventory
- – Extra revenue thanks to higher service level – computed based on lost sales
- – Extra profits thanks to higher service level – computed based on lost sales
- – Extra revenue thanks to higher service level – computed based on service level
- – Extra profits thanks to higher service level – computed based on service level
- in m$/year
Impact 2
We reduce inventory while keeping service level constant
- – Cash saved because of inventory reduction in m$
- – Savings thanks to inventory reduction in m$/year
- – Savings thanks to obsolete reduction in m$/year
- – Total savings in m$/year
- in m$/year
Typical Implementation Plan
Request your proof of concept
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Step 1
Scope definition
1 to 2 weeks
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Step 2
Data gathering and cleaning
A few months
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Step 3
Model creation
1 to 3 weeks
Why they trust us
Nicolas worked with our team at Marquis Data to introduce new and innovative approaches to reviewing our forecasting models and refining our overall demand forecasting strategy. He went well beyond his already popular text on the subject, enabling our leadership team to deepen their understanding of the current state and capabilities of machine-learning-powered demand forecasting. His coaching undoubtedly helped Marquis Data fine-tune our products and services, allowing us to deliver significantly more value and clarity to our clients. Working with an industry thought leader has had a measurable impact on the improvements of our Marquis IQ Inventory Intelligence solution.
Paul Ausserer – Founder & CEO, Marquis Data
I’ve been practicing forecast and supply data science with my teams for years. Yet, I still find a lot of value in Nicolas’ guidance. I know that in just a few hours of discussion, we’ll get straight to the point, avoid weeks of wandering, and cut through unnecessary complexities. Nicolas has a gift for convincing everyone to return to the essence of the problem and to imagine simple, elegant, and effective solutions.
Florent Cattaneo – Head of Data & AI, Foodles
We called on Nicolas to help us set up a forecasting tool for incoming flows. Nicolas quickly understood our needs, gathered, and centralized all the necessary data. He delivered a valuable tool that we now use regularly. Our forecasts are now reliable, easy to generate, and allow us to simulate different scenarios. His support was both effective and thoughtful. We benefited from his advice and experience.
Stéphanie Herrou – Demand Forecasting Manager, GENERATION
Demand forecast at zooplus is an important topic, with over 18k articles sold in almost 30 countries.
Nicolas showed us a structured way to evaluate forecast models. He coached us to improve our structure and the speed at which we could evaluate models. Thanks to this, we were able to evaluate dozens of models quickly, which significantly improved our way of working.
Michał Sierpień, Engineer at zooplus
Learn more about Forecasting
Read our book
Watch our video