A Manufacturer-Retailer Supply Chain with Fuzzy Customer Demand: A Vertical Cooperative Advertising and Pricing Model

Supply chain transformation is necessary for the survival and growth of an organization; the more effective the transformation, the more likely the company is to thrive. In response to the dramatic changes in the business landscape over the last few years, many companies are launching business transformation programs to drive sweeping changes in their supply chain processes. These transformations are required to match the ever-growing customer demand and drive competition in the market. The supply chain transformation can be evident in exploring new sourcing networks, establishing collaborative forecasting processes, optimizing networks and inventories, reducing supply chain complexities, improving margins, etc. In today's world, it is no longer an option whether or not to opt for supply chain business transformations. Instead, it is a strategic mandate in order to stay relevant in the industry. This chapter provides the analysis of the most common transformation failures and suggests a practical framework leveraging some of the best practices in change management.

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A Computational Intelligence Approach to Supply Chain Demand Forecasting

Machine Learning ◽

10.4018/978-1-60960-818-7.ch603 ◽

2012 ◽

pp. 1551-1565 ◽

Cited By ~ 1

Author(s):

Nicholas Ampazis

Keyword(s):

Supply Chain ◽

Computational Intelligence ◽

Demand Forecasting ◽

Business Environment ◽

Chain Structure ◽

Support Vector ◽

Customer Demand ◽

Public Data ◽

Vector Machines ◽

Supply Chain Structure

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

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Data Warehousing and Mining in Supply Chains

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch092 ◽

2011 ◽

pp. 586-591 ◽

Cited By ~ 1

Author(s):

Richard Mathieu

Keyword(s):

Supply Chain ◽

Manufacturing Process ◽

Data Warehousing ◽

Raw Materials ◽

Distribution Channels ◽

Transformation Process ◽

Customer Demand ◽

Product Availability ◽

Product Manufacturing ◽

Flow Of Information

Every finished product has gone through a series of transformations. The process begins when manufacturers purchase the raw materials that will be transformed into the components of the product. The parts are then supplied to a manufacturer, who assembles them into the finished product and ships the completed item to the consumer. The transformation process includes numerous activities (Levary, 2000). Among them are • Designing the product • Designing the manufacturing process • Determining which component parts should be produced in house and which should be purchased from suppliers • Forecasting customer demand • Contracting with external suppliers for raw materials or component parts • Purchasing raw materials or component parts from suppliers • Establishing distribution channels for raw materials and component parts from suppliers to manufacturer • Establishing of distribution channels to the suppliers of raw materials and component parts • Establishing distribution channels from the manufacturer to the wholesalers and from wholesalers to the final customers • Manufacturing the component parts • Transporting the component parts to the manufacturer of the final product • Manufacturing and assembling the final product • Transporting the final product to the wholesalers, retailers, and final customer Each individual activity generates various data items that must be stored, analyzed, protected, and transmitted to various units along a supply chain. A supply chain can be defined as a series of activities that are involved in the transformation of raw materials into a final product, which a customer then purchases (Levary, 2000). The flow of materials, component parts, and products is moving downstream (i.e., from the initial supply sources to the end customers). The flow of information regarding the demand for the product and orders to suppliers is moving upstream, while the flow of information regarding product availability, shipment schedules, and invoices is moving downstream. For each organization in the supply chain, its customer is the subsequent organization in the supply chain, and its subcontractor is the prior organization in the chain.

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