There’s a lot of really good information out there about S&OP, supply chain management, demand forecasting, business intelligence… but you’re busy. You’re probably too busy to keep up with everything. Seriously – do you have time to read the scores of books and journals and blogs that promise to fill you in on everything? Somehow, you need to find a way to filter out what you need to know, which is the best advice, and what really isn’t worth the free download.  There are some terrific resources out there, if you know where to look. Sometimes, however, the most obvious filter is to go straight to the top of the field. A few words from the acknowledged experts addressing the most important questions of the day can save you a lot of time and money.

Along these lines, some of our folks have posted thought-provoking discussions with Lora Cecere, a partner with Altimeter Group, and Blake Johnson, a Stanford University professor, on our website. These are short videos in a series – you only have to listen to what you need to know right now. You can always come back for other topics. Yes, we’re going to ask you to register once, but after that it’s a free ride.  In the series’ first installment, Lora Cecere discusses the business challenges that are driving S&OP adoption. You’ll learn why Lora has concluded that companies with strong S&OP processes and tools are flourishing in today’s economy, and in fact, are able to sense changes in demand five times faster than their competitors. Lora is a partner with Altimeter Group and is known as a supply chain visionary who understands technology. She brings seven years of industry analyst expertise coupled with two decades of manufacturing, marketing and software expertise.…

What’s your forecast accuracy telling you? Stop and ask a few questions
Forecast accuracy is an important performance metric in any effective S&OP process, but it can be measured in various ways. Comparing your company’s accuracy to an industry standard will be difficult to impossible if you don’t know the details behind the measurement. More importantly, the metric needs to resonate within your organization as a meaningful indicator of forecast relevance. So then…
What details should one consider for forecast accuracy measurement?

Here’s the Steelwedge Top Six:
1. Aggregation level: Are you measuring accuracy at a product SKU or family level? What about other hierarchy levels? Odds are your accuracy will appear to be better at an aggregated level such as family. This happens because variability of forecasts and actuals tend to cancel out one another as data is combined. The result is a smoothing of results and lowering of error calculations. Recommendation: Measure accuracy at the same level as the majority of forecasts are captured.
2. Error Calculation: In its most basic form, accuracy is a measure of the difference between a prediction and what actually happened. How far off were we? Error is equal to the difference between forecast and actual. Often, this is captured as a percentage value called percent error. Mean absolute percent error (MAPE) calculates the average of errors. Since we don’t want positives and negatives to cancel out each other, we use the absolute values of each error. There are other methods, but MAPE is fairly common. Weighted MAPE is a method used to give greater importance (weight) to items with greater activity. Amount of activity may be defined as the proportion a particular item is of the total. Recommendation: Keep it simple. Make sure people understand the measurement and how…