Successful demand planning requires a stable and sustainable planning process that is continuously reviewed and improved.

If one is going to forecast demand into the future, it would make sense to get as true a picture as possible. For that, starting with historical demand would be the obvious choice. Choosing this data as the basis for forecasting would ensure the best possible projection out in the future.

These “key tools” balance a need for simple with a need to handle the complexity of SCM – following the IBM adage – complexity exists whether you ignore it or not, best not to ignore it.

In a previous blog post, we discussed how a high or low value of Coefficient of Variation (CV) impacts the first or second term of safety stock. Today we decided to put this to the test using real customer data - here we will discuss our findings.

In 1994, the IBM Micro-electronics Division, itself a fortune 100 size firm, put in place a major effort to create...

In one of my previous posts, I wrote about using coefficient of variation (CV) as a predictor of forecastability. In...

In our line of work at Arkieva, when we ask this question of business folks: What is your forecast accuracy?...

Sales is a fast-paced business; people who aren't focused on the here and now usually lose out on sales. As...

In our last post, Sujit discussed the importance of gathering input from participants as a way to mitigate biases. In...

When businesses try to predict their future demand, they often begin with a mathematics based statistical forecast. Once this is...