An Approach of Decision-Making Support Based on Collaborative Agents for a Large Distribution Sector

International audienceThis paper applies the multi-agent systems paradigm to collaborative coordination and negotiation in a global distribution supply chain. Multi-agent computational environments are suitable for a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving contexts. An agent-based distributed architecture is proposed for better management of rush unexpected orders for which the quantity of product cannot be delivered partially or completely from the available inventory. This type of orders can be generated by unexpected swings in demand or unexpected exceptions (problem of production, problem of transportation, etc.). This paper proposes a first architecture and discusses an industrial case study

A collaborative decision-making approach for supply chain based on a multi-agent system

To improve the supply chain's performance under demand uncertainty and exceptions, various levels of collaboration techniques based on information sharing were set up in real supply chains (VMI, CPR, CPFR...). The main principle of these methods is that the retailers do not need to place orders because wholesalers use information centralization to decide when to replenish them. Although these techniques could be extended to a whole supply chain, current implementations only work between two business partners. With these techniques, companies electronically exchange a series of written comments and supporting data, which includes past sales trends, scheduled promotions, and forecasts. This allows participants to coordinate joint forecasting by focusing on differences in forecasts. But if the supply chain consists of autonomous enterprises, sharing information becomes a critical obstacle, since each independent actor is typically not willing to share with the other nodes its own strategic data (as inventory levels); That is why researchers proposed different methods and information systems to let the members of the supply chain collaborate without sharing all their confidential data and information. In this chapter we analyze some of the existing approaches and works and describe an agent-based distributed architecture for the decision-making process. The agents in this architecture use a set of negotiation protocols (such as Firm Heuristic, Recursive Heuristic, CPFR Negotiation Protocol) to collectively make decisions in a short time. The architecture has been validated on an industrial case study

Multi agent system for negotiation in supply chain management

Supply chain management (SCM) is an emerging field that has commanded attention and support from the industrial community. Supply chain (SC) is defined as the chain linking each entity of the manufacturing and supply process from raw materials through to the end user. In order to increase supply chain effectiveness, minimize total cost, and reduce the bullwhip effect, integration and coordination of different systems and processes in the supply chain are required using information technology and effective communication and negotiation mechanism. To solve this problem, Agent technology provides the distributed environment a great promise of effective communication. The agent technology facilitates the integration of the entire supply chain as a networked system of independent echelon. In this article, a multi agent system has been developed to simulate a multi echelon supply chain. Each entity is modeled as one agent and their coordination lead to control inventories and minimize the total cost of SC by sharing information and forecasting knowledge and using negotiation mechanism. The result showed a reasonable reduction in total cost and bullwhip effect

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

An agent-based disturbance handling architecture in manufacturing control

In industrial environments, disturbance handling is a major issue in reconfigurable manufacturing control systems, supporting the fast, effective and efficient response to the occurrence of unexpected disturbances. Those disturbances usually degrade the performance of the system, causing the loss of productivity and business opportunities, which are crucial roles to achieve competitiveness. This paper proposes an agent-based disturbance handling architecture that distributes the disturbance handling functions by several autonomous control units and considers the main types of shop floor disturbances that have impact at planning and scheduling level. The proposed architecture also integrates a prediction component, transforming the traditional “fail and recover” practices into “predict and prevent” practices

The process improvement dilemma in dynamic 3PL firms: A systems and agency lens

For the past several decades, firms have been shifting from contending as autonomous entities to working and competing as part of supply chains. In this context, warehousing, transportation, and distribution needs are being increasingly outsourced to third-party logistics (3PL) firms. 3PL providers operate in fast-moving, time-sensitive, and priority-changing supply chain environments, constantly demanding efficient, cost-effective, and routinized responses. To attain the ultimate end of maximizing efficiency, reducing costs, and improving customer satisfaction, scholars and supply chain industry opinion leaders alike talk about process improvement as part of a broader organizational learning strategy to be pursued in order to keep a competitive edge. This thesis explores the relationship between daily bottom-line pressures and prioritization and the design, implementation, and control of process improvement initiatives in complex and dynamic 3PL service providers. It uses a systems-agency lens to unveil intra- and inter-firm relations around process improvement activity and the links with organizational learning. The study utilized multi-case study-based qualitative-interpretive methods used in conjunction with system dynamics and agency tools. Data collection was carried out through in-depth interviews with 41 employees from two 3PL service providers and complemented by two collaborative enquiry exercises organized for each case study firm. Contrary to recommendations made by scholars and industry leaders, this thesis has found that day-to-day operational firefighting in 3PL scenarios revolving around managing multiple demands, conflicting priorities, and unexpected events often prevail over less tangible process improvement and broader organizational learning goals. This is aggravated by constant cost-reduction pressures centering on human resources headcount deemed critical for the development of learning and improvement practices. Consequently, there is little evidence that the case study firms demonstrate the necessary conditions for process improvement and organizational learning to actually take place. The study also revealed that when process improvement does happen, its focus mainly centers on customer satisfaction or cost-saving, rather than on the improvement of shop floor work routines aiming at operational effectiveness. It also shows process improvement to be more reactive and ad hoc as opposed to the continuous, widespread, and long-term-oriented practices associated with continuous improvement and organizational learning

Event Management for Sensing Enterprises with Decision Support Systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s40745-015-0034-z[EN] Sensing enterprises make use of new technologies to capture real-time information and fed constantly the decision making process. Decision support systems (DSS) are exposed to these real-time events and it is possible to start the decision process from scratch in case any unexpected internal and external events take place. Thus, an event monitoring and management system should interact with the DSS to manage events that might affect their decisions. It should act as a supra-system to identify when decisions made are still valid or need to be reanalysed. The traditional configuration of DSS (where they collect internal and external information of the organization and the decision-maker is involved in the decision-making process) should be extended to treat event management using a monitoring and management system, which monitors internal and external information and facilitate the introduction of no monitored events. This monitor and manager systems become more and more necessary due to the incessant incorporation of new technologies that enables the companies to be more context-sensitive. Furthermore, this new and/or more accurate information, which is obtained for the organization, requires a proper management.This research has been carried out in the framework of the project PAID-06-21Universitat Politècnica de València (Sistema de ayuda a la toma de decisiones ante decisiones no programadas en la planificación jerárquica de la producción) and GV/2014/010 Generalitat Valenciana (Identificación de la información proporcionada por los nuevos sistemas de detección accesibles mediante internet en el ámbito de las “sensing enterprises” para la mejora de la toma de decisiones en la planificación de la producción).Boza, A.; Alemany Díaz, MDM.; Cuenca, L.; Ortiz Bas, Á. (2015). Event Management for Sensing Enterprises with Decision Support Systems. Annals of Data Science. 2(1):103-109. https://doi.org/10.1007/s40745-015-0034-zS10310921Estupinyà P (2010) El ladrón de cerebros: Compartiendo el conocimiento científico de las mentes más brillantes. 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