Demand-Driven Forecasting is an all-round book that introduces the concept of demand forecasting including all the forecasting methods and aspects that drives demand. It is a detailed read and goes down to various algorithms that determine and shape demand; more importantly how to put in effective strategies to maximise accuracy. Charles Chase is certainly a credible author in this area with his background/experience and delves into many case studies with both feedback, advice and lessons learned. One area that was missing in my opinion is the arena of machine learning which is something the organisation I work for has invested a lot of time and money into. Given the advent of big data and cloud (including elastic) computing machine learning will play a greater role in the world of demand forecasting.
Three key takeaways from the book:
1. Demand Management is done well when it is more than just forecasting. For example, when encompassing sensing, shaping and translating a demand response into a decision-making cycle that demand driven practitioners continuously fine-tuned (i.e. Shape) based on KPIs using the combination of data, analytics, technology and or domain knowledge.
2. A lot of companies think they are shaping future demand by meeting supply constraints however they are fitting demand to supply rather than supply to demand.
3. Combining of forecasts is a good rule of thumb in two situations. 1. When you are uncertain which forecasting method is most accurate and 2. When you are uncertain about the forecasting situation.