A multi-agent approach for modeling and analysis of supply chain networks

Abstract

Supply Chain Management (SCM) involves the integration, planning, sourcing, production and delivery of materials, energy, information and money across an entire company or organization. Particularly in the chemical process industry (CPI), the main aim is to expand the traditional boundaries of the system studied-the manufacturing plant-to include the whole network of suppliers, customers and facilities that are associated with the company through exchange relationships. This concept emerged as a subject in business management in the early 1980s, and in the 1990s the CPI recognized the need for an integrated view of the entire supply chain (SC). Since then, an increasing number of studies and proposals from both academia and industry have been presented in journals, congresses, workshops and other media. It is widely thought that the SCM problem of the CPI sector involves horizontal integration across departmental divisions and vertical integration or coordination of the strategic planning, scheduling and operational execution layers. This whole setting is usually expressed through a massive amount of operational data and decision-making processes that are related to each cycle of SCM: stock replenishment, manufacturing, customer orders and procurement of raw materials and services. Due to this complexity, an efficient solution for the SCM problem that is widely accepted in the industry has not been found. SCM issues are often approached in a disconnected manner and even using manual procedures, which leads to operations with high inventories, inefficient capital utilization and increased economic, social and environmental costs. Complaints have also been reported as to the lack of powerful, easy-to-use, PC-based tools that are able to find solutions to SCM problems at different levels.