In Part 1 of this blog, we closed with the following question: “OK, intermittent demand creates a challenge, but I still need a demand estimate, what do I do!” Below we will provide an answer, but with a different orientation that begins with the question: “what is the purpose of the demand estimate?”
Historically, most of the key planning and computational activities (models, time series, machine learning, and other analytics) that support extended supply chain management (SCM) are “deterministic models”.
With each storm, there comes a bevy of forecasts put out by different computer models. These forecasts begin about 10 days out and change as the storm gets closer and closer. This blog tries to extract some learnings from this process of forecasting.
Scientific and system-driven Inventory Projection facilitates a quick decision-making process and enables a prompt analysis of alternative what-if scenarios. The following are the top 7 benefits of system driven inventory projection.
Should we combine the positive numbers and the negative numbers as we approach the essential business of forecasting future demand? Let us think this through.
Sometimes statistical prediction is confused with statistical forecasting. Forecasting can be considered a prediction model but not all prediction models can be considered forecast models.
Stop using traditional forecast accuracy metrics to measure forecast for sporadic demand patterns. Use this method instead.
Organizations need to regularly upgrade their products and launch new products to stay competitive and grow their businesses. However, a new product launch poses a totally new challenge – new product forecasting.
Selecting the right forecasting methods can be highly critical in how accurate your forecasts are. Unfortunately, there isn’t a golden ticket to forecasting which can essentially ensure accuracy. While the best-fit forecasting method is dependent on a business’ specific situation, understanding the types of forecasting methods can aid in your decision-making.
How to determine when to use a best-fit analysis and when to use prediction techniques for demand forecasting analysis.