The forecasting project begins with a project definition phase. It gives a well-defined framework for approving the forecasting effort based on well-described business needs, allocated resources, and approved funding. As most practitioners already know, the next block—data preparation—often takes most of the time and the lion's share of the cost. It usually requires data extraction from internal and external sources and a lot of tricks to transfer the initial disarray in the data into a time series database acceptable for modeling and forecasting. The appropriate tricks are discussed in detail in Chapters 5 and 6.

      The block for variable reduction and selection captures the corresponding activities, such as various data mining and modeling methods, that are used to take the initial broad range of potential inputs (Xs) that drive the targeted forecasting variables (outputs, Ys) to a short list of the most statistically significant factors. The next block includes the various forecasting techniques that generate the models for use. Usually, it takes several iterations along these blocks until the appropriate forecasting models are selected, reliably validated, and presented to the final user. The last step requires an effective consensus building process with all stakeholders. This loop is called the model development cycle.

      The last three blocks in the generic flowchart in Figure 2.1 represent the key activities when the selected forecasting models are transferred from a development environment to a production mode. This requires automating some steps in the model development sequence, including the monitoring of data quality and forecasting performance. Of critical importance is tracking the business performance metric as defined by its key performance indicators (KPIs), and tracking the model performance metric as defined by forecasting accuracy criteria. This loop is called the model deployment cycle in which the fate of the model depends on the rate of model performance degradation. In the worst-case scenario of consistent performance degradation, the whole model development sequence, including project definition, might be revised and executed again.

2.2.2 Key Steps

      Each block of the work process is described by defining the related activities and detailed substeps. In addition, the expected deliverables are discussed and illustrated with examples when appropriate.

      Project definition steps

      The first key step in the work process—project definition—builds the basis for forecasting applications. It is the least formalized step in the sequence and requires proactive communication skills, effective teamwork, and accurate documentation. The key objectives are to define the business motivation for starting the forecasting project and to set up as much structure as possible in the problem by effective knowledge acquisition. This is to be done well before beginning the technical work. The corresponding substeps to accomplish this goal as well as the expected deliverables from project the definition phase are described below.

      Project objectives definition

      This is one of the most important and most often mishandled substeps in the work process. A key challenge is defining the economic impact from the improved forecasts through KPIs such as reduced cost, increased productivity, increased market share, and so on. In the case of demand-driven forecasting, it is all about getting the right product to the right customer at the right time for the right price. Thus, the value benefits can be defined as any of the following (Chase 2009):

      It is strongly recommended to quantify each of these benefits (for example, “a 15% reduction in customer back orders on an annual basis relative to the accepted benchmark”).

      An example of an appropriate business objective for a forecasting project follows:

      More accurate forecasts will lead to proactive business decisions that will consistently increase