Is there an equivalent to the Hippocratic Oath for forecasters? What harm(s) can a forecaster easily avoid? This blog explores some examples.

Having a good forecast is essential to demand planning. It ensures the right goods are produced at the right time and reach the right customers. What’s more is that demand planning is situated at the start of your supply chain, playing an integral part in a streamlined supply chain.

These “key tools” balance a need for simple with a need to handle the complexity of SCM – following the IBM adage – complexity exists whether you ignore it or not, best not to ignore it.

One of the many ways to improve the forecast is to forecast using a pyramid process, starting at the base, then forecasts the subsequent levels moving upwards. The objective of using the pyramid forecast process is to use the less detailed portions of the pyramid to improve the base level forecast of the pyramid.

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.

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.

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.

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.

Right-sizing inventory affects earnings before interest, tax, depreciation, and amortization (EBITDA) in several ways. Understand why it's both good and bad.