Believe it or not, I didn’t plan the alliteration. But that is my central point: So much of actual demand is unplanned. Which is fine as long as it is near to what was expected in terms of items purchased, period in which purchased, and the customer/region in which the purchase took place. But this does not appear to be the situation in many cases. So is forecasting fatally flawed? Lora Cecere has been writing about forecasting, principally within the CPG industry for many years. She has worked in industry, for a software vendor, and most recently as a highly respected analyst. In a recent blog Lora states that Mean Absolute Percentage Error (MAPE) for a one month lag was 31 percent + 12 percent. Data eight years ago for the same companies was an average of 36 percent + 10 percent MAPE. This made me sit up and listen. Especially when she went on to quote from her research while at AMR Research that Based on AMR Research correlations, a six percent forecast improvement could improve the perfect order by 10 percent and deliver a 10-15 percent reduction in inventory. In other words, there is a lot of benefit to getting the forecast right. But a range of highly respected CPG companies cannot do better than 31 percent MAPE, with a range of 19 percent to 43 percent? That caught my attention. Mostly because I am more familiar with the High-Tech/Electronics industry which has much shorter product life cycles than CPG and therefore more volatile or variable demand patterns. Of course it is difficult to be precise with industry classifications. Does Consumer Electronics fall into CPG, High-Tech/Electronics, or both? However we slice it, things like cell phones, tablets, cameras, etc have shorter product life cycles, greater seasonal variations in demand, and greater demand variability than do nearly all categories of CPG such as soap, washing powder, etc. In Consumer Electronics, and more generally High-Tech/Electronics I hear from companies that they seldom get their forecast accuracy, as measured by MAPE, above 50 percent, which is consistent with my observations about the characteristic differences with CPG. Higher demand variability/volatility would imply a lower forecast accuracy. Before anyone jumps down my throat, especially Lora, let my state unequivocally that everyone MUST forecast and that all companies should be demand driven. But … But where is the discussion about how best to satisfy the missing 31 percent demand in the case of CPG and 50 percent in the case of High-Tech/Electronics? Where is the discussion about the profitable response to the demand that is not anticipated? I feel as we are only having half the conversation. The half about forecasting. But if the best we can do is improve forecast accuracy from 64 percent to 69 percent over eight years in an industry segment with relative stable demand, I think we should be talking about supply chain agility and responsiveness. What amazes me is that since the early 1990’s we have been applying optimization engines, typically Linear Programming (LP), to the supply side. Ignoring for the moment the inherent issue of using linear models to represent highly non-linear systems, if you are basing your optimizations on inputs that are best 69 percent correct, are you not focusing on the wrong problem? Should you not be focusing on systems that enable you to detect true demand early and determine the best way to satisfy the unanticipated demand using the competing requirements of profitability and customer service? Of course you will need a supply chain that can execute in an agile and responsive manner consistent with your decision. Here is the rub: All our resources are limited. Time. Cash. People. So in this zero-sum game, where are you going to apply your energies? Spending eight years to improve the forecast by five percent, or working on the manner in which you satisfy the unanticipated demand in the most timely and profitable manner?