Approaches for supply chain planning and scheduling

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Forming a supply chain is all about forming alliances with partners for managing the customer demand smoothly and competitively. As the supply chain grows (in terms of products, partners, etc.) the planning and coordination of various activities become more and more complex. In addition, most of the supply chains adding the online channels (stores) for selling the products. Such trend adding more pressure on the already complex supply chains - online customers prefer to order a small assortment of the products, expect an early delivery and are very price sensitive. In order to manage the core processes of the organization, various functions such as transportation, warehousing, packaging/logistics, etc. are outsourced. Managing the demand smoothly requires coordination between supply and demand and therefore coordination among all the partners. Sometimes such timely coordination or cooperation is not possible and that leads to product failure. One of our research goals is to analyze product recall risk, its impact on partnership and suitable strategies to manage such risks. Any small disruption in planning in any part of the supply chain has the potential to disrupt the whole supply chain. Planning at any part of the supply chain becomes difficult due to uncertainty (of demand, supply, capacity, etc.). Mathematical programming techniques are commonly used in such situations. These approaches are complex, based on aggregated data and thus most suitable for long term planning (in the shorter term, sometimes it is difficult to chase the adopted plan due to the spread of the randomness). As computing is more affordable and data gathering is easy, better decisions could be taken in a dynamic environment. In this proposal, our other major research goal to develop data-oriented solution approaches for short term operational planning and advanced optimization-based approaches for long term planning. In order to develop these approaches, we have identified a few common applied problems. Two MSc and two Ph.D. students will conduct a systematic review of the relevant literature and enrich the existing knowledge by contributing novel tools to solve such complex problems. Based on the problem complexity, problem size and usage we will develop and test a combination of approaches. Most effective and suitable approaches will be disseminated through publications and seminars. In particular, our main objectives are: 1) Develop data-driven optimization approaches for certain supply chain planning problems. 2) Develop mathematical programming approaches for stochastic planning problems. 3) Analyze supply chain partnerships for managing product recall risks. 4) Disseminate the work through research papers, conference presentations, and case studies.