How does currency demonetization affect statistical forecasting? The Government of India recently enacted the policy to demonetize Rupees 500 and 1,000 banknotes. ( ₹500 and ₹1,000). All bank notes of these denominations ceased to be legal tender on November 9, 2016. A Google search on this will reap rich returns; here is an article on this topic on
This Halloween, while kids dress up in cute costumes to go trick-or-treating and homes gear up with their most ghoulish décor ever, there might be a real life “bogeyman” lurking in the shadows. A serious Excel error could cost a business thousands or millions of dollars. And while this kind of monster may not be
Is it possible to use a company's public financial data to strategically benchmark inventory? Bram answers this in the two part conclusion to his 'Inventory Benchmarking’ blog series.
The day is nearing where the next CEO will be a former VP of Supply Chain. Continue to develop your skills and you will become a great fit for that position in the future.
Sometimes companies implement a forecasting system but do not realize the anticipated gains in the forecast accuracy. Very often, it is not the actual software but the setup that is to be blamed. And I do not mean the setup at the technical (parameter level) but more the process level. Read this blog post on how to get the setup right.
Supply Chain Planning deals with the future and therefore uncertainty with economy. As a result, the planners sometimes have to question other people’s assumptions. Whether for this or some other reason, they do not get credit that is due to them for the great job they do. Eric Wilson from Tempur Sealy gives them the due credit and then some and calls them the super heroes of business. We agree!
Are the researchers in the academic world aligned with the expectations and needs of the business world when it comes to the world of forecasting? Or are the two very different? What can the academe do that will be of value to the business folks? Read on to see some of my thoughts on this topic.
Have you ever wondered whether you should forecast: Bottom-up? Top-down? Middle-Out? It turns many in the demand planning profession do. Read along to see why you can do all of that and more with attribute based demand planning.
Should one forecast at the SKU-Customer-Location level and aggregate the results up? Or should one forecast at the product-family-region level and dis-aggregate the results down? Or should one do both and try to triangulate the results? Which method gives the best results to the business? Let us look at this together.
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 (σ) / Mean (µ)