The course aims to introduce students to the main quantitative decision-making problems in production, inventory, and supply chain management. Its primary objective is to present the analytical foundations of operations management through the use of mathematical models and optimization techniques. The course focuses on classical inventory theory under both deterministic and stochastic demand, covering single-period and multi-period settings. It also aims to familiarize students with heuristic and exact solution approaches for dynamic inventory problems. Another goal is to provide a basic understanding of uncertainty and probability as they apply to operational decisions. The course further aims to illustrate how inventory decisions interact across different stages of a supply chain, with particular attention to wholesaler–retailer coordination. Finally, the course seeks to bridge theory and practice by introducing spreadsheet-based tools for solving operational decision problems.

At the end of the course, students will be able to formulate production and inventory management problems using appropriate quantitative models. They will be able to analyze and solve deterministic inventory problems, including EOQ-based models, multi-product systems, and dynamic lot sizing problems, using both heuristic methods and exact algorithms. Students will be able to model and solve inventory problems under demand uncertainty, including the newsvendor problem, applying basic probabilistic reasoning. They will be able to evaluate coordination mechanisms in simple two-stage supply chains and assess the impact of contractual arrangements between a wholesaler and a retailer. From an operational standpoint, students will be able to implement inventory and optimization models using Excel and standard solver tools. They will be able to interpret numerical results, compare alternative solutions, and justify managerial decisions based on quantitative evidence.

The course is structured around four main thematic blocks, each addressing a key set of decision-making problems in supply chain and operations management. In the introductory part of the course, students will be introduced to fundamental production planning and transportation problems. This part will also provide the foundations of mathematical programming, with a focus on formulating optimization models and solving them using standard tools, in particular the Excel Solver. This initial block aims to equip students with the basic modeling skills that will be used throughout the course. The second part of the course focuses on inventory optimization under known demand in a single-period setting. Students will study classic inventory control models, starting from the Economic Order Quantity (EOQ) framework and its main extensions. These include EOQ models with quantity discounts, resource-constrained systems, and multi-product inventory problems. Alongside the theoretical analysis, this part of the course includes an applied component, in which students will work on practical exercises and implement inventory models using Excel. The third part of the course addresses inventory management under known demand in a multi-period context, with particular attention to the Dynamic Lot Sizing Problem (DLSP). Students will first examine commonly used heuristic policies, such as constant order quantity, constant order period, part-period balancing, and the Silver–Meal method. They will then study the optimal solution approach through the Wagner–Whitin algorithm. As in the previous block, this section also includes an applied component, where students will solve multi-period inventory problems using Excel-based implementations. The final part of the course is devoted to inventory problems under uncertain demand. The Newsvendor model will be introduced as the basic framework for decision-making under demand uncertainty. Building on this model, the course will analyze inventory systems involving a wholesaler and a retailer, and will study supply chain coordination through contracts. In particular, students will examine wholesale price, buy-back, and revenue-sharing contracts, focusing on how these mechanisms can be used to improve coordination and performance in a two-agent supply chain setting.

1) Nahmias, S. Lennon Olsen T. (2015). Production and Operations Analysis. Waveland, Long Grove, IL, ISBN 1-4786-2306-3. 2) Cachon, G. (2003). Part II: Supply Chain Coordination, Handbooks in Operations Research and Management Science.

The course is delivered through a combination of frontal lectures and applied activities. Frontal lectures are used to introduce the theoretical concepts and analytical frameworks required to address decision-making problems in production, inventory, and supply chain management. During these lectures, the instructor presents the material using structured PowerPoint presentations, with the aim of providing students with clear and systematic explanations of models, assumptions, and solution approaches. Each theoretical topic is followed by a dedicated problem-solving session designed to foster an experiential understanding of the concepts discussed in class. These sessions include both traditional exercises, to be developed using paper-and-pencil calculations, and practical applications in which students formulate and solve decision problems using spreadsheet models. In particular, students are guided in the use of Excel and standard solver tools to implement optimization and inventory models introduced during the lectures. Throughout the course, three major applied assignments based on Excel are planned. These practical activities are carried out in small groups and focus respectively on the solution of production and transportation planning problems, single-period multi-product inventory problems under known demand, and dynamic lot sizing problems in a multi-period setting. These group-based exercises are intended to strengthen students’ ability to translate theoretical models into operational decision-support tools and to interpret quantitative results in a managerial context.

For attending students, the final grade is based on a combination of practical assignments and individual assessments, designed to evaluate both problem-solving skills and theoretical understanding. Students’ performance in applied decision-making is assessed through three in-class practical assignments focused on the solution of problems using Excel. These assignments are carried out in small groups and concern production and transportation planning problems, single-period multi-product inventory problems under known demand, and dynamic lot sizing problems in a multi-period setting. The maximum score for these practical Excel-based assignments is 12 points overall, distributed across the three activities. Additional bonus points may be awarded for the practical assignments according to criteria that will be specified during the course. In addition, attending students are required to take two individual written tests, both held in class during regular lecture hours. The first written test takes place around the sixth week of the course and covers theoretical topics and exercises related to the first part of the program. The second written test is held at the end of the course and focuses on the theoretical topics and exercises related to the second part of the program. Each written test contributes a maximum of 7 points to the final grade. Clear indications regarding the content and structure of the two written tests will be provided by the instructor during the course. Finally, attending students are required to take an individual oral exam, which takes place after the end of the course. The oral exam focuses on the theoretical topics not already covered in the two written tests and contributes a maximum of 6 points to the final grade. The overall maximum score for attending students is 32 points (plus any additional bonus points obtained in the practical Excel-based assignments). A grade of 30.5 or higher is awarded with honors (cum laude). Students who choose to attend the course as non-attending students are assessed through a single individual oral exam at the end of the course. This exam covers the entire program and evaluates students’ knowledge of theoretical concepts, their ability to solve exercises, and their understanding of the solution methods introduced during the course.

A minimum grade of 27/30 and participation during the course.