Several authors have also proposed disruption management strategies for SCRM. Zsidisin and Ritchie (2008) and Juttner (2005) provide comprehensive reviews of the literature and models used for an effective SCRM. The SCM literature is rife of studies that investigate supply chain risk phenomena and provide models for the analysis and mitigation of several types of supply chain risks (financial, operational and strategic) that occur both in the supply and demand side of supply chains. The paper concludes with a discussion of the limitations and managerial implications of the framework and potential extension of the research. 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 second section presents the analytical approach that has been utilized, the process for the development of the framework and its features in detail.
Supply chain risk manager software#
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 remainder of the paper is organised in four sections. 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. 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. 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). 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. 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). The use of multi-agent modelling (a sub category of artificial intelligence) can be an alternative decision making tool for collaboration within supply chains. It is also characterized by inflexibility for the reconfiguration of supply chains processes and high development and maintenance costs (Botta–Genoulaz et al., 2005). 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). The conventional IT, however, (which is based on legacy systems) has not provided sustainable solutions for collaborative Supply Chain Management (SCM). 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). Many successful modern organizations have shifted from an opportunistic dogma of cooperation to a synergetic ethos of collaboration and aligned their supply chain processes. 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.Ĭonstructive 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). The wide range of risks along the supply chain (both from supply and demand side) may impose negative implications upon supply chain performance.
This increased complexity raises the level of uncertainty and risks that companies are faced with Manuj and Mentzer (2008). 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.
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