Supply Chain Management

We consider a strategic supply chain planning problem formulated as a two-stage stochastic integer programming (SIP) model. The strategic decisions include site locations, choices of production, packing and distribution lines, and the capacity increment or decrement policies. The SIP model provides a practical representation of real-world discrete resource allocation problems in the presence of future uncertainties which arise due to changes in the business and economic environment. Such models that consider the future scenarios (along with their respective probabilities) not only identify optimal plans for each scenario, but also determine a hedged strategy for all the scenarios. We (1) exploit the natural decomposable structure of the SIP problem through Benders decomposition, (2) approximate the probability distribution of the random variables using the generalized lambda distribution, and (3) through simulations, calculate the performance statistics and the risk measures for the two models, namely the expected-value and the here-and-now.

Today’s marketplace is becoming increasingly dynamic and volatile. As consumers become more sophisticated, they demand the right product at the right time, at the right price, and at the right place. Whereas quality was the competitive weapon of the 80s, customer responsiveness, or time-to-market is the differentiator today. In many industries, hyper-competition is forcing many enterprises to fundamentally change the way business is conducted in order to service. Given these challenges, traditional paradigms for business management are ineffective. At the same time, businesses face tremendous pressure from their stakeholders to increase ROA, profit contribution and customer responsiveness. Given the complexity of a typical supply chain, supply chain planning systems (also known Given the complexity of a typical supply chain, supply chain planning systems (also known as Advanced Planning Systems) enable companies to intelligently manage the activities of the supply chain. Every company must perform five basic activities or processes within a supply chain: buy, make, move, store and sell. Within each of these processes, there are short-term decisions (which product should be put on the truck?) and long-term decisions (do we need a new factory to meet demand?). Through the intelligent application of constraint-based principles, we can reduce system inertia by reducing capacity on non-constrained resources without a corresponding increase in system nervousness or instability. Multi-enterprise planning capabilities of an intelligent system should include support for the various command and control structures as well as organizational aspects of the supply chain. The ability to model multiple authority domains and support autonomy with interdependence among the various business functions within a supply chain can provide a great deal of flexibility in managing system inertia. Ideally, the distributed architecture of a decision support system should provide global visibility to the various business units or functions to make decisions that meet both local business objectives as well as the global objective of the entire supply chain. In this white paper we discuss the optimal allocation and utilisation of resources using mathematical optimization techniques. We explain in non-technical terms the success and growing importance of optimization techniques in efficiently processing complex supply chain problems in a cross-section of industry. We highlight the differences between strategic and tactical supply chain models. We set out the sequence of actions, the necessary system issues, the decision making issues and managerial aspects in Supply chain. Through this paper the reader will: 1. appreciate the significance of using optimization techniques in making decisions on industrial problems, 2. be aware of real-life case-studies highlighting the benefits of supply-chain optimization, 3. have access to the relevant and the most recent articles and books on supply chain.