Enhanced models for large scale supply chain tactical planning problems

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Supply chain is all about collaboration for managing the product demand smoothly. The supply chain planning becomes complex as the number of products (and thus partners) grows. Most of the difficulty in planning arises from the uncertainties in demand, supply, time, etc. The more the number of uncertain parameters, the more difficult the planning is. Natural resources based supply chains such as forest/agro product supply chain are one of such supply chains where uncertainties at all levels are high with huge number of products. To manage these uncertainties, either managers opt for high level of redundancy, that is, either buffer stocks at all levels across the supply chain or do multiple sourcing, etc. All the options come with advantages and disadvantages. Availability of multiple strategies makes decision making and supply chain planning more complex. To manage the supply chain smoothly, one of the options is to limit the number of products. However, businesses offer a portfolio of products to cater a large pool of customers. In this situation, to keep the supply chain lean, products are designed in such a way that most of the final products share lot of common components (raw material). In other words, a component can be used in many different products. There are two extremes: Designing the product specific component for each product; and designing a single universal component that can fit in every product. Both extremes have advantages and disadvantages and to balance the advantages and disadvantages associated with the both extremes, final products are segregated into different categories and each category shares the component. For instance, a roll of a specific width can be used to produce several final products. Determination of number of category and classification of products in categories is a very complex problem. In mathematical terms, the problem is known as set partition problem and in practice called the assortment problem. In supply chains we encounter numerous assortment type problems at various levels. For instance, selecting suppliers for multiple products with capacity restrictions, assigning retailers to warehouses, selecting contractors for multiple jobs with precedent constraints, resource assignments, etc. Resolving these problems are not easy due to non-linearity associated with capacity, cost and randomness. High waste and limited alternative supply (at the last moment) are very common in natural products based supply chains. Proper assortment planning is the only way to keep the supply chain smooth and operating cost minimal. In this research, our goal is to do systematic review of the relevant literature and enrich the existing knowledge by contributing novel tools to solve such complex problems. We will identify key tactical level planning problems and develop mathematical modeling based approaches. Based on the problem complexity, problem size and usage we will develop and test combination of approaches. Most effective and suitable approaches will be disseminated through publications and seminars. In particular, our main objectives are: 1) Develop and test strategies which are more suitable for supply chains under these circumstances. 2) Develop mathematical programming solution approaches which are effective and efficient in solving such complex models. 3) To develop /explore test cases for bench-marking the performance. The outcome of this research will be a set of planning tools that can help the industry in improved planning and thus more cost effective and competitive. Other outcomes of the research include training of several highly qualified personnel for research and further advancement of the field. The generated results will be shared through publications, conference presentations.