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Aligning Supply Chain Metrics to Improve Value

Supply Chain Shaman

In follow-up qualitative interviews, one of the largest issues with organizational alignment was metric definition and a clear definition of supply chain excellence. In my post Mea Culpa, I reference my work with the Gartner Supply Chain Hierarchy of Metrics. Error is error, but is it the most important metric? My answer is no.

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Can Improving Forecast Accuracy Address Our Demand Planning Woes?

Logistics Viewpoints

If “the forecast is always wrong,” is improving forecast accuracy even the solution to our demand planning woes? Artificial intelligence and machine learning ( AI/ML ) can improve forecast accuracy, but a bigger problem is the failure to set accurate expectations around forecasting models, not the accuracy of the models themselves.

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The Forecasting Accuracy Bugaboo

Logistics Viewpoints

When it comes to running a company, when things break down executives have traditionally said “we need to improve our forecasting!” Would better forecasting accuracy be a good thing? Unfortunately, most companies cannot, and will never be able to, consistently rely on highly accurate forecasts. Absolutely!

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The Top Five Benefits of Using Machine Learning for Demand Forecasting

Logility

Machine Learning for demand forecasting has matured to a level of accuracy, transparency and replicability that translates into transformative results, including in these five areas: Accuracy, transparency, thoroughness of analytical options and results. Let’s take a closer look at each one. Accuracy and transparency.

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What’s Your Forecast Accuracy Target for 2019?

ToolsGroup

Editor’s Note: Two years ago we posted a blog about how to set an annual forecast accuracy target and it was one of our most popular topics. It seems as if everyone is looking to improve their forecasting performance. How much can you realistically expect to improve your forecast accuracy each year?

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A New Decade: Give Science A Chance

Supply Chain Shaman

The SAS forecasting system implemented in 2019 was not tested for model accuracy. An example for this client would be to use 2017 and 2018 history to forecast 2019. So, I asked the questions, “Is your data forecastable? Data at this level of variability is complicated to forecast.) The reason? The answer?

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Don’t Wait to Innovate: 3 Key Benefits of Causal Forecasting

Logility

That’s precisely what demand forecasting feels like for many businesses today. Enter causal forecasting. Unfortunately, many companies hesitate to use causal forecasting, thinking it’s too complicated or resource-hungry. What is Causal Forecasting? That’s where causal forecasting comes into play.