Demand management statistical forecast

In this section, it will be performed a literature review for each one of the four categories of the Demand management - Statistical Forecast, Sales and Operations Planning (S OP), Collaborative Plaiming and Forecasting Replenishment (CPFR) and Vendor managed Inventory (VMI). This review allowed identify the DDSC characteristics for each category which was used to develop the five level maturity model. 

For Demand Management, Statistical Forecast should be the basis due to the industry still applies the make to stock approach to optimize its asset base and reduce fixed cost. 

A good demand management process will enhance the S OP analysis by providing a consensus demand forecasting environment that incorporates statistical methodologies, dashboarding capabilities and... 

Implementing FVA into the demand management process requires that forecasts be recorded and saved before and after each cycle. Having the capabilities to store forecast history by a stream of activities (e.g., consensus forecast adjustments, managerial overrides, price lift calculations, etc.) is critical to measuring the value-added, or non-value-added, contribution to the overall process. Utilizing the statistical baseline forecast as the default is the key to establishing a benchmark to measure the effectiveness of all the touch points in the process. Unfortunately, few companies capture the appropriate data, or the level of detail on a historical basis, to conduct FVA. This is an opportunity. 

Performance of planners and forecast are tied to compensation and rewards. Senior management understands, support and value demand planning function. Forecast tools are in place for both short and long term forecast, and are used to automate statistical forecast process, increasing planners capability to simulate different models. 

For demand management, it is suggested to focus in Statistical Forecast and Vendor Managed Inventory as described below ... 

Demand can be estimated using forecasting. Forecasting is a necessary prerequisite for many of the methods and procedures used in operations management (Lewis, 1981, p. 241). Forecasting demand for goods and services requires the use of information, mathematical functions, and statistical analyses. 

Multiple forecasts for the same line of business within the organization are common (if any planning is even done). The gap between what is planned and what actually happens represents lost profits and lost opportunities. The new paradigm for retail supply chain management begins with an accurate view of customer demand. That demand drives planning for inventory, production, and distribution within some understood error parameters. Consumers will never be completely predictable. At the same time, prediction is bounded by limitations in our statistical and modeling sciences. We can... 

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