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Most organizations today are proud to proclaim their "customer oriented" approach, and it comes as no surprise to learn that "customer centric strategies" are often the preferred topic of discussion in the board-room. But fashionable as customer relationship management might be, it is inescapably dependent on its less glamorous cousin-supply chain management (SCM)-for sustainable competitive advantage. Indeed, organizations that give SCM the shortcut, sooner or later find themselves plummeting into a chasm of inefficiency that often ends in bankruptcy and ruin.

The ultimate goal of any SCM would be completely seamless collaboration between all entities in the supply chain, with comprehensive information sharing for predictive and proactive control to bring in efficiencies that would be impossible in reactive or retrospective modes. It's no secret that such perfection is still a distant dream. Yet, new strategies and models are helping progressive organizations inch closer to some semblance of SCM nirvana.

Challenges
In essence, the supply chain of an organization includes the network of all suppliers, factories, warehouses, distributors and retailers involved in the process of transforming the requisite raw materials or components into finished goods or services and delivering them to customers. Simply stated, the objective of any SCM is the integration and optimization of all the components, links and processes involved.

SCM can be looked at from different angles, but in general every SCM system essentially comprises five key steps-planning, sourcing, manufacturing, delivering and handling returns. SCM involves strategic, tactical and operational decision-making. At the strategic level, decisions need to be taken regarding selection of suppliers, factory and warehouse locations, transportation routes, etc. Planning for the optimal match of supply with demand happens through tactical-level decisions and finally, the operational level ensures that these plans are executed effectively.

But, because of the large number of variables and the general uncertainty surrounding the supply chain, developing an effective SCM for sustainable competitive advantage is complex and challenging. When problems develop within a single supply chain entity and can be locally addressed, the implications are limited; on the other hand, problems of a more global nature require dynamic adjustments across supply-chain functions, and this could prove to be rather tricky. In fact, the efficient and coordinated achievement of organisation-level goals is highly dependent on how well the tactical and operational levels of the supply chain are managed.

Intelligent Software Agents
In the past, object-oriented (OO) system design was the method of choice for SCM systems. This is gradually being replaced by agent-oriented (AO) system design, which could be considered as an extension of OO, to enable a higher level of integration and better effectiveness of the SCM system.

The AO software architecture, based on neural network technology, helps manage the supply chain better at the tactical and operational levels. It views the supply chain as a set of intelligent software agents. Each agent is responsible for one or more activities in the supply chain, while at the same time interacting with other agents in the planning and execution of their responsibilities. (An agent is nothing but an autonomous, goal-oriented software process that operates asynchronously).

One approach that is gaining popularity relies on the use of a multi-agent system, providing generic, reusable, and guaranteed components and services for communication, conversational coordination and role-based organization modelling. Using these components, it is possible to develop a supply chain architecture that can support complex cooperative work and manage exceptional situations arising out of unforeseen events or uncertainty in the supply chain. This approach was first postulated by Mark Fox et al, based at the Enterprise Integration Laboratory of the University of Toronto in Canada.

Perfect integration of components in the supply chain is essential for optimisation of performance. But, as discussed earlier, uncertainties in the dynamics of the enterprise and the market render this difficult. Thus, dynamic revisions to plans are a given, and the SCM must be able to coordinate these revisions across the supply chain effectively and in real time.

Intelligent Supply Chain Management (ISCM) addresses these coordination problems at the tactical and operational levels. It is composed of a set of cooperating, intelligent agents, each performing one or more supply-chain functions, and coordinating their decisions with other agents. The underlying philosophy of the design provides for:

• Coordination that allows software agents to cooperatively manage change.

• An agent problem-solving approach that enables software agents to cooperate with one another and reason together in their exploration of alternative solutions to problems.

• Agency and support tools that enable users to build multi-agent systems with minimal programming effort, based on trusted, reusable components.

Coordination and Negotiation
In ISCM, the resolution of problems in the supply chain is viewed as a process of constraint satisfaction or optimisation. Here, the software agents communicate individual constraints to each other, thus influencing the problem-solving behaviour. Coordination occurs when agents develop plans that satisfy not only their own internal constraints but also the constraints of other agents. Negotiation occurs when constraints that cannot be satisfied are modified by the subset of agents directly concerned. Such distributed multi-agent technology needs to be Web-enabled, in light of the fact that global integration and management of the supply chain often uses the Internet for cost-effective connectivity and interactivity.

For ISCM, the most common and typical agents that need to be developed are as follows:

Order acquisition agent: This agent is responsible for acquiring orders from customers. In the process, it is capable of carrying out price negotiations, setting due dates, and so on. It also manages the modification or cancellation of orders. Any modifications in orders are communicated to the logistics agent. When plans violate constraints imposed by the customer (such as due date violation), the order acquisition agent negotiates with the customer and the logistics agent for a feasible alternative plan.

Logistics agent: This agent is responsible for coordinating the suppliers, factories and distribution centres of the organisation with a view to achieving the goals of the supply chain, including on-time delivery and cost minimisation. It manages the movement of products or materials across the supply chain from the supplier to the final consumer.

Transportation agent: This agent is responsible for the assignment and scheduling of transportation resources to satisfy requests for movement of goods and materials as specified by the logistics agent. It can consider a variety of transportation assets and transportation routes in the construction of its schedules.

Scheduling agent: This agent is responsible for scheduling of all activities in the factories; schedules would automatically change depending on constraints or potential for optimisation. This intelligent agent is also capable of doing "what-if" analysis, to explore possibilities for new orders and such like. It generates schedules that are sent to the dispatching agent for execution. It assigns resources and start times to activities that are feasible, while at the same time optimizing certain criteria such as minimising work-in-progress or slack. It can generate a schedule from scratch or repair an existing schedule that has violated some constraints.

Resource agent: The resource agent merges the functions of inventory management and purchasing. It dynamically manages the availability of resources so that the schedule can be executed. It estimates resource demand and determines resource order quantities. It is responsible for selecting suppliers that minimise costs and maximise delivery. This agent generates purchase orders and monitors the delivery of resources. When resources do not arrive as expected, it assists the scheduler in exploring alternatives to the schedule by generating new plans.

Dispatching agent: This agent performs the order release and real-time floor control functions as directed by the scheduling agent. It operates autonomously as long as the factory performs within the constraints specified by the scheduling agent. When deviations from schedule occur, the dispatching agent communicates them to the scheduling agent for repair. Given degrees of freedom in the schedule, the dispatcher makes decisions on what to do next. Here, the dispatcher must balance the cost of performing the activities, the amount of time in performing the activities, and uncertainties on the factory floor.

Feedback Agent: One of the unique features of Intelligent Supply Chain Management is the incorporation of a feedback agent. Using mathematical models such as regression analysis, the task of this agent is to continuously monitor deviation between predicted outcomes and actuals. As the feedback re-enters the system and provides input to the other neural network based agents, they are able to modify their own behaviour and dynamically bring in enhanced efficiencies into the supply chain.

Conclusion
Traditional Supply Chain Management systems have been of limited utility, because of organizational constraints, the existence of legacy systems, or limitations in the algorithms. In recent times, the evolution of agent oriented SCM has brought in sophisticated levels of intelligence into the system, enabling significant improvements in efficiency at the tactical and operational levels of the supply chain. Intelligent Supply Chain Management with agent-based technology provides for near-optimal adaptive business and knowledge management strategies, despite the uncertainties present in the real world. Best of all, ISCM provides for an in-built feedback mechanism that dynamically modifies the supply chain for sustained optimal performance, thus helping the organization achieve its strategic and business goals.

Dr Kaustubh Chokshi
The author is CEO of Intelligent Business Systems (IBS), a UK-based AI Enterprise Solutions company, which has recently expanded into India.
Dr Chokshi has a PhD in Artificial Intelligence from the University of Sunderland, UK.
He can be contacted at kaustubh.chokshi@intelligentsystems.biz

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