Our focus in this blog series has been to establish forecast accuracy targets. In very general terms, the goal should be to add value to the business through the forecasting process. We have however focused on the forecast value add and using that to create a minimum acceptable forecast accuracy target in the previous blog. Now we will take that a step further and talk of ways of improving it.
We have all seen it in the news. Covid-19 outbreaks, labor shortages at the port as well as in trucking, and port delays coupled with high demand from consumers are causing major supply chain issues. Shipping containers are in short supply, or perhaps a better way to say it is that they are waiting to be unloaded or loaded resulting in a shortage. In this blog, let us try to enlist some possible future impacts of this situation. Let us look at it from the perspectives of the different players in the supply chain.
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 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.
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.
Arm your supply chain planners with the right software tools so they can turn your data into better business decisions.
Learn how to combine very different metrics/KPIs towards a composite score, this is a question that comes up quite often in my conversations with customers.
Recently, we have heard about several product shortages and other issues in the supply chain. Let us try and make sense of some of these in this blog. As I live in the US, my experience is somewhat limited to what happened here.