It is important to measure and improve the forecast accuracy at the right level of aggregation. If you measure at too high a level, your accuracy picture will look better than what it needs to be as the data at high (aggregated) levels is more forecastable. By contrast, at too low a level...
A successful demand planning process accurately forecasts demand and revenue streams, and subsequently drives the next steps in the S&OP process which are Inventory, Supply Planning, and Optimization. Therefore, it is a crucial step in an organization's S&OP process.
A company’s total inventory consists of many types of stock such as strategic, anticipation, safety, cycle, and unplanned. Cycle stock is most connected to the demand forecast; it is expected to be sold as the forecast becomes real demand. Safety stock on the other hand is extra stock to deal with the variability of the demand or supply. As such, it is not always linked to forecasting accuracy.
Learn the best approach to setting forecast accuracy targets and how to set expectations for your management team.
Having access to an accurate forecast is very beneficial for businesses. If used correctly, it can provide better margins, increase market shares, and many other positive results. At a more tactical level, it can help reduce the costs associated with meeting the customer demand and make the supply chain more efficient.
Learn how a simple binomial model can help anticipate the future including COVID-19 breakthrough cases just as models help a firm estimate it's future.
COVID-19 direct and ancillary events have made clear that uncertainty is an inherent part of the demand-supply network structure. Every firm, on a regular basis, faces “risk situations" such as manufacturing excursion, unexpected new demand or loss of demand, component supplier interruption, etc. This has placed risk management and rapid intelligent response (RIR) front and center in SCM discussions.
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 this blog we briefly cover some key insights for successful time series forecasting: (a) Profiling the Shape of the Curve is the first stage, and the first step is assessing if the time series is stationary. (b) The forecast method identified must capture the shape and be able to project the shape across time. (c) There are limits in historical and no amount of “fancy math” can overcome them.
Some time ago, I had been trying to help a business improve its statistical forecasting. We tried different parameters and different forecasting algorithms but the statistical forecast for about half of the products could not be improved no matter what we tried. We decided to do a deep dive to understand the reason.