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Data-Driven Supply Chain Optimization
Q What is data-driven supply chain optimization, and why is it important? A Data-driven supply chain optimization applies prescriptive analytics in mathematical programming and operations research (OR) to provide decision support for supply chain decision-making at the strategic, tactical, and operational levels. By properly capturing the decisions to make (decision variables), the performance met- ric to optimize (objective function), and the requirements and/or limitations to satisfy (con- straints), an optimization model can prescribe the best (or better) solutions for supply chain network design (strategic), supply chain con- figuration and production planning (tactical), and resource allocation, routing, and schedul- ing (operational). Its data-driven feature makes it possible to separate a model from its input data, and to prescribe optimal solutions and recommen- dations that are adaptive to today’s changing and volatile business environment. Custom designed data-driven supply chain optimi- zation tools, tailored to the unique business setting and decision needs of a company, are key to the company’s competitive advantage and success. Q What are the new trends and opportunities for data-driven supply chain optimization? A First, fast advancement of information technology (IT) and vast availability of big-data, in volume, velocity, and variety, make it possible to address and solve innovative supply chain optimization problems that were not solvable before. Examples include advanced manufacturing with Internet- of-Things (IoT), climate-smart food and agriculture supply chain, and efficient and
resilient supply chains in healthcare, energy, and telecommunication. Second, three dimensions of complexity for supply chain optimization are emerg- ing: dynamics, uncertainty, and multiple decision-makers (game-theoretic setting). These call for the integration of multiple tech- niques in analytics and artificial intelligence (AI): descriptive, predictive, and prescriptive. Moreover, modern supply chain optimiza- tion applications must address multiple (often conflicting) performance metrics, e.g., effi- ciency, cost, profitability, equity, resilience, and sustainability. Q What is the best practice for developing industrial-strength supply chain optimization applications? A First, successful development and deployment of supply chain optimization applications require collaboration and concerted work of a team of subject matter experts, optimization modelers, and software developers, supported by stakeholders and leadership. Second, incremental modeling is recom- mended for model building. That is, start with modeling the core decision needs to get a prototype for proof-of-concept; then progres- sively add new features and components with expanded complexity, e.g., those addressing dynamics, uncertainty, or the game-theoretic setting. Last but not least, engage users and stake- holders from the beginning to the end. Seek their inputs and feedback for modeling build- ing, use case, scenario analysis, graphical user interface (GUI) design, and most importantly, evaluation and assessment of the decision- support and managerial insights provided by the optimization application.
Haitao Li, Ph.D. Professor and Chair Supply Chain & Analytics Department Founding Director Laboratory of Advanced Supply Chain Analytics College of Business Administration University of Missouri – St. Louis lihait@umsl.edu umsl.edu 314-516-5890
20 Inbound Logistics • June 2022
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