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 best in class supply chain planning process and software including demand planning(DM), central planning, available to promise, et al. I was fortunate to be an original member and had the opportunity to work extensively on
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
In our line of work at Arkieva, when we ask this question of business folks: What is your forecast accuracy? Depending on who we ask in the same business, we can get a full range of answers from 50% (or lower) to 95% (or higher). How is this possible? Imagine a management team being given
Sales is a fast-paced business; people who aren’t focused on the here and now usually lose out on sales. As mentioned in Jelle’s blog, sales persons would rather be out in the field selling, which is the reason why they feel it would be a waste of their resources sitting in their office forecasting. Yet,
In our last post, Sujit discussed the importance of gathering input from participants as a way to mitigate biases. In this post, I will explain why the sales team should be required participants and what their influence is on the forecasting process.
When businesses try to predict their future demand, they often begin with a mathematics based statistical forecast. Once this is established, some users are asked to provide overrides that can make this statistical forecast better. Typically, this is asked of users who have a client facing role.