چارچوب مبتنی بر چند عامل برای مدیریت ریسک زنجیره تامین
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|744||2011||9 صفحه PDF||سفارش دهید||1 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Purchasing and Supply Management, Volume 17, Issue 1, March 2011, Pages 23–31
The high level of complexity of supply chains and the inherent risks that exist in both the demand and supply of resources – especially in economic downturns – are recognized as major limiting factors in achieving high levels of supply chain performance. The use of modern information technology (IT) decision support systems is fast becoming an indispensable tool for designing and managing complex supply chain systems today. This paper develops a framework for the design of a multi-agent based decision support system for the management disruptions and mitigation of risks in manufacturing supply chains.
The increasing call for mass customization in many industries has made today’s global supply chains very complex, requiring a multitude of parallel information and physical flows to be controlled to ensure high customer service levels. This increased complexity raises the level of uncertainty and risks that companies are faced with Manuj and Mentzer (2008). The wide range of risks along the supply chain (both from supply and demand side) may impose negative implications upon supply chain performance. There is an eminent need for organizations to have necessary strategies to manage these risks and disruptions, so that they can achieve the necessary level of agility for effective mass customization. Constructive collaboration among business partners in supply chains is vital in any attempt to mitigate risks and ameliorate disruptions, to achieve responsiveness and to offer a high customer service level (Hallikas et al., 2004). Many successful modern organizations have shifted from an opportunistic dogma of cooperation to a synergetic ethos of collaboration and aligned their supply chain processes. The use of Information and Communication Technology (ICT) tools is perceived as a paramount facilitator for the realisation of this collaborative perception, offering the capabilities of information sharing, customer sensitivity and process integration (Wu and Angelis, 2007). The conventional IT, however, (which is based on legacy systems) has not provided sustainable solutions for collaborative Supply Chain Management (SCM). It lacks real-time adaptability in supply chains and focuses on dyadic contexts of collaboration rather than collaboration amongst a plethora of partners (Akkermans et al., 2003). It is also characterized by inflexibility for the reconfiguration of supply chains processes and high development and maintenance costs (Botta–Genoulaz et al., 2005). The use of multi-agent modelling (a sub category of artificial intelligence) can be an alternative decision making tool for collaboration within supply chains. In computer science, an agent can be defined as a software entity, which is autonomous to accomplish its design objectives, considered as a part of an overall objective, through the axiom of communication and coordination with other agents (Gilbert, 2007). Through this paradigm of software architecture, the management of supply chain processes can be perceived as facilitated by several autonomous decision making entities (software agents), each responsible for specific activities and performing different roles. These agents interact and cooperate with other agents, within and across organizations, in order to solve problems beyond their individual knowledge or expertise, and to promote a higher performance for the entire system (Stone and Veloso, 2000). In this paper, a multi-agent based framework is proposed as the conceptual basis for the design of a DSS that facilitates collaborative disruption risk management in manufacturing supply chains. The framework supports the fulfilment of production, event and disruption risk management constituted by coordination, communication and task agents and draws on principles and theories of SCM, agent based simulation and computer science. The remainder of the paper is organised in four sections. In the first section, the usefulness of a multi-agent system (MAS) framework for supply chain risk management (SCRM) is discussed through a brief review of an expansive SCRM literature, a comparison between conventional IT solutions and MAS and a discussion of the application of software agents to different supply chain problems. The second section presents the analytical approach that has been utilized, the process for the development of the framework and its features in detail. With the use of a hypothesized scenario, the third section presents the processes for supply chain disruption management that an MAS designed with the logical structure of the proposed framework will follow. The paper concludes with a discussion of the limitations and managerial implications of the framework and potential extension of the research.
نتیجه گیری انگلیسی
In recent years, disruptions have become more common in supply chains, due to the increasing supply chain complexity and demands for more agility that increase supply chain risks. There are certainly many benefits from the utilization of IT systems in SCRM initiatives. Inter-Organisational ICT tools offer opportunities to effectively support the management of supply chain activities, to communicate and share information in a speedy and reliable way, to reduce information asymmetries across the supply chain and to lead to the identification of events that have the potential to create disruptions in supply chain processes. The advantages of a multi-agent SCRM model over conventional ICT tools for risk management are multitude. The real-time adaptability and the learning capability through algorithms that is embedded in the model can lead to a more efficient response to information asymmetries amongst supply chain partners. This in turn can lead to more effective SCRM and synchronization of supply chain processes, which in turn can ameliorate the impacts of the bullwhip effect. Due to its simple and straight forward programming effort (with the use of freely available developing tools for an MAS) (Stone and Veloso, 2000), the model has the potential to incorporate conventional supply continuity planning systems as well with the use of “wrappers”, and/or be part of existing risk assessment tools like critical path analysis tools or geographic information systems, as it can be built in mobile software environments. The proposed agent based model builds the foundations for effective disruptions management under a collaborative basis, through the facilitation of software agents and through the utilization of previous successful corrective actions as cases for future decisions. Due to its generalised process for risk management, the framework has the potential to proactively mitigate a series of risks at the operational and tactical levels of SCM, without perceiving risk management as an individual process from supply chain actors. It can also propose and execute rectification strategies for disruptive situations, offering an integrated decision making framework for SCM. At the operational level, it can deal with risks associated with an order fulfilment that arise on each node of a supply chain (e.g. manufacturer, suppliers) concerning the sourcing of subcomponents, manufacturing and delivery of products. At the tactical level, it can significantly facilitate planning of orders, providing substantial information for planning orders. Fuzzy logic can be incorporated to simulate human action in decision making (Bodendorf and Zimmermann, 2005). The framework focuses on demand driven supply chains rather than supply chains driven by forecasts, since for the latter risk can be mitigated through stock piling of inventories, in order to achieve high throughput but low volume. The adaptation of the proposed framework to the single character of a supply chain (e.g. lean, agile) is therefore of paramount importance for its success. Future work is currently directed on the performance of the proposed framework using an agent based simulation.