Using Coefficient of Variation as a Guide for Safety Stocks

In one of my previous posts, I wrote about using coefficient of variation (CV) as a predictor of forecastability. In this post, I will talk about how it can be used to indicate a sensitivity of lead time towards the safety stock calculations. To quickly remind the reader first: The formula for CV = StdDev

Do You Use Coefficient Of Variation To Determine Forecastability?

Key Point: Coefficient of Variation is not a perfect measure of forecastability. However, if used properly, it can add value to a business’s forecasting process. In the world of forecasting, one of the key questions to consider is the forecastability of a particular set of data. For example, a salesman might consistently be better at

Reporting Forecast Accuracy At Sales and Operations Planning Meetings

You have a favorite forecast accuracy metric(s) you’ve been practicing within the organization for a while, and now you think you are ready to bring it to the Sales and Operations Planning (S&OP) meeting as a Key Performance Indicator (KPI) of your demand planning process. But you are not sure exactly how to go about

By |2019-04-13T23:09:59-04:00August 4th, 2015|Demand Planning, Forecasting, S&OP, Supply Chain|2 Comments

The Family Tree of MAPE

I saw this news article on CNN (here) about our planet’s earth bigger, older cousin. Quite an interesting discovery if you ask me. However, it got me to thinking about the family tree of Mean Absolute Forecast Error (MAPE), a subject that I am a little bit familiar with. A few weeks ago, I wrote about

By |2019-04-13T23:09:59-04:00July 28th, 2015|Forecasting|5 Comments

How To Measure BIAS In Forecast

I spent some time discussing MAPE and WMAPE in prior posts. In this post, I will discuss Forecast BIAS. Forecast BIAS can be loosely described as a tendency to either Forecast BIAS is described as a tendency to either over-forecast (meaning, more often than not, the forecast is more than the actual), or under-forecast (meaning, more often

By |2019-04-13T23:10:00-04:00July 21st, 2015|Demand Planning, Forecasting|8 Comments

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