Collaborative forecasting

World Wide Web, supported by, 246, 256 Collaborative customer/demand planning, 968 Collaborative forecasting and replenishment, 779-780, 785... 

Under Collaborative Forecasting, Planning, and Replenishment (CPFR), the retailer shares with the manufacturer the logjc associated with the order, so as to permit store-level monitoring of stock avadabihty and improved performance. Under CPFR, along with a retailer order, retailers submit both retail orders as well as logic for each order. This logic will... 

If it were based on effort, the relationships should be stronger. There is no supply chain that has sponsored more industry programs to build value chain collaboration than the consumer-driven value chain. They were many. Over the last 10 years, there have been a myriad of programs to improve collaboration BCR, collaborative forecasting and replenishment, the Voluntary Inter-industry Commerce Solutions Association, and the Foundation for Strategic Sourcing Association. However, the industry has made little progress. There are four fundamental issues ... 

For the APS to be effective for collaborative forecasting and planning throughout the supply chain, it is necessary that a reliable method for passing information between the different APS systems be in use. When a supplier has only one or two major customers, it is possible for them to have the same type of software as their customer. When a supplier has many customers, they cannot have software that matches each of their customer s needs. 

Helms, M.M., Ettkin, L.P., Chapman., S. (2000). Supply chain forecasting—collaborative forecasting supports supply chain management. Business Process Management Journal, 6(5), 392-407. 

The research community has responded to the E-Business challenge. Despite the infamous dot-com bust in the early 2000 s, scores of research initiatives, workshops, technical papers, and special journal issues have been devoted to the subject. E-Business remains a critical subject not only in the research community, but also in corporate boardrooms. Instead of the revolution that would replace every facet of business, the rise of E-Business might be viewed as the emergence of new economic intermediaries that offer opportunities for innovation. These new intermediaries offer different means to respond to market demands (e.g., Internet vs. traditional channels), to facilitate sourcing, procurement, and price discovery (e.g., electronic auctions), and to develop new mechanisms for coordination and execution (e.g., dynamic pricing, revenue management, and collaborative forecasting). 

Models for collaborative forecasting need to consider all of the following components simultaneously (i) The demand process (ii) The forecasting pro-... 

The information structure In the basic form of the supply chain, day-to-day exchange of information is not possible, except for order placement. On the other hand, in a collaborative forecasting setting, sharing of forecast data and inventory status may be possible. Some of the models we shall present later are quite flexible in terms of their capability of handling a variety of information structures. We shall generally assume that a fair amount of static information is commonly known to the members of the supply chain. This includes the cost structure of the supply chain, the characteristics of the demand process, the types of forecasting processes used at each member s location. 

In this section we briefly describe a linear state space model that serves as a building block for the main models of collaborative forecasting processes we present in this chapter. We then present a well-known forecasting technique associated with this model namely, the Kalman filter. Let Xt be a finite, n-dimensional vector process called the state of the system. In the context of inventory management, this vector may consist of early indicators of future demand in the channel, actual demand realizations at various points of the channel, and so forth. Suppose that the state vector evolves according to ... 

The Markovian model. We find the Markovian framework appealing due to its potential applications in the analysis of collaborative forecasting settings. While we refer the reader to Song and Zipkin (1993,1996)... 

In our integrated model for collaborative forecasting ( 4 and thereafter) we shall assume that (10.11) always holds, or alternatively, we shall permit negative values of zt. This assumption is essential when we expand our discussion to more complex information-rich settings. 

The purpose of this section is to demonstrate that in addition to the development of forecasting and control policies for supply chain inventory systems, the framework above provides the means for assessing the benefits of information usage and information sharing practices, one of which is collaborative forecasting. For example, in the model of Aviv (2002a) that will be described in 5 below, the difference between various supply chain settings is determined in terms of the information available to the retailer and the supplier at the times they need to forecast future demand. 

In this section, we summarize an inventory model for collaborative forecasting studied in Aviv (2002a). In Aviv s model, the demand follows an autoregressive statistical time-series of order 1, as follows ... 

Collaborative Forecasting and its Impact on Supply Chain space follows the evolution Xt = FXt-i 4- Vt where... 

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