“It is said that the present is pregnant with the future” – Voltaire Forecasting, therefore, is an attempt to deduce the future from the present. It is both, art and science. We will explore the practice of forecasting demand in the short to medium term. Within the constraints of economic and technology trends, demand forecasting drives the planning process in most businesses.
In this week’s What-if Wednesday post, Arkieva Supply Chain Optimization Consultant – Abhishek Shah – shares the results of a what-if demand forecast simulation using the Winters multiplicative beta parameter.
In this week's What-if Wednesday post, Arkieva Supply Chain Optimization Consultant - Abhishek Shah - shares the results of a what-if demand forecast simulation using the Winters multiplicative alpha parameter.
In the previous What-if Wednesday previous posts, we experimented with the three parameters of Holt-Winters method, namely; alpha, beta, and gamma. This week we are going to run some tests with the damping factor.
Seasonality forecast simulation: what is the effect of changing the gamma parameter in Holt-Winters forecasting method?
In our previous blog, we experimented with Alpha - the intercept parameter of the Holt-Winters method - to see how the forecast gets affected as the weight changes. This week we are going to run similar tests with Beta, the trend parameter. Learn how you can use your recent sales trends to improve future forecasts.
The forecasting process in demand planning sets the stage for all other planning activities including bill of material (BOM) and finished goods inventory optimization. Demand forecasts can also affect perfect order fulfillment rates, customer satisfaction and ultimately bottom line results.
In webinar on B2B Demand Sensing, we discuss practical examples of how upstream manufacturers are using B2B Demand sensing to reduce inventory costs and identify additional sales opportunities.
What-if Wednesday: Forecast Simulation – What Happens if You Change The Alpha Parameter in a Forecasting Method?
From time to time, clients ask me about the effect of changing a parameter of a forecasting method. Recently, I got the idea of creating a series of blog posts where I would simulate the results of tweaking these parameters. Join me as we unpack a couple simple ways to improve your forecasts.
Last week I wrote about the potential benefits in forecasting results based on removing outliers from one-time events. A key question that came up because of that post was this: How long does a change in demand as a result of an event (whether up or down) impact forecasts in the future? Rather than theorize