Coursera Operations Research

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Operationsresearch is the use of statistical analysis and mathematical optimization techniques to help organizations solve problems and improve decision-making. The ability to harness vast amounts of data on day-to-day operations has created opportunities to rigorously optimize processes for cost, quality control, inventory management, and other goals, making operations research an important part of many businesses.

The tools of operations research are similar to those of other fields relying heavily on quantitative analysis and statistics. Operational data is input into programs such as Microsoft Excel and Solver, R, and Python, where mathematical optimization techniques such as linear programming (LP) are applied to find the best solution for business problems. Monte Carlo simulations and other probabilistic analyses may also be used to discover areas of sensitivity and risk.

Operations research is a core competency of careers in operations management, supply chain management, and logistics. This skill is particularly highly valued by businesses in manufacturing, transportation, healthcare, and other industries where companies must compete to deliver goods and services with complex supply chains in cost-sensitive markets.

These data-driven insights are typically applied according to process management and process improvement frameworks such as Six Sigma and Lean, which strive to reduce variation and eliminate waste in operations.

The skills and experience that you might need to already have before starting to learn operations research may include knowledge of mathematical and engineering methods, understanding of the fundamentals of business, and some background in linear programming, a math technique to solve systems of linear constraints. All of these can help you fully grasp the basics of operations research. Furthermore, having a background that includes computer algorithms and optimization techniques can help you to better analyze optimization problems in computer science, civil engineering, economics, and management. This multifaceted field is also linked with machine learning, as some operations researchers are sampling machine learning methods to test various metrics in a particular optimization process.

The kind of people who are best suited for operations research work are often quantitative thinkers who are detailed and analytical. These people use mathematics formulas to help companies study labor needs, cost scenarios, product distribution, and other factors. In doing this work, they need to be curious, methodical, rational, and logical. Because operations researchers understand how algorithms, math, and statistics may impact workflows, budgets, employee staffing, and profitability, they also tend to be the kind of people who are investigative in nature, using their intellect and curiosity in the work.

When looking to enhance your workforce's skills in Operations Research, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.

This course provides a solid foundation in mathematical programming and its applications in business analytics, covering everything from linear programming to integer programming and portfolio optimization.

Towards the end, the course introduces you to machine learning concepts like simple linear regression and support vector machines (SVM), bridging the gap between operations research and data analysis.

Professor Terwiesch is the co-author of Matching Supply with Demand, a widely used text-book in Operations Management that is now in its third edition. Based on this book, Professor Terwiesch has launched the first Massive Open Online Course (MOOC) in business on Coursera. By now, well over half a million students enrolled in the course.

Professor Terwiesch has researched with and consulted for various organizations. From small start-ups to Fortune 500 companies, he has helped companies become more innovative, often by implementing innovation tournament events and by helping to restructure their innovation portfolio. He holds a doctoral degree from INSEAD and a Diploma from the University of Mannheim.

Christian Terwiesch, Bradley R. Staats, Marcelo Olivares, Vishal Gaur (2019), A Review of Empirical Operations Management over the Last Two Decades, Manufacturing and Service Operations Management.

Tan (Suparerk) Lekwijit, Christian Terwiesch, David Asch, Kevin Volpp (Forthcoming), Evaluating the Efficacy of Connected Healthcare: An Empirical Examination of Patient Engagement Approaches and Their Impact on Readmission.

Abstract: Connected healthcare is a form of health delivery that connects patients and providers through connected health devices, allowing providers to monitor patient behavior and proactively intervene before an adverse event occurs. Unlike the costs, the benefits of connected healthcare in improving patient behavior and health outcomes are usually difficult to determine. In this study, we examine the efficacy of a connected health system that aimed to reduce readmissions through improved medication adherence. Specifically, we study 1,000 patients with heart disease who received electronic pill bottles that tracked medication adherence. Patients who were non-adherent received active social support that involved different types of feedback such as text messages and calls. By integrating data on adherence, intervention, and readmission, we aim to (1) investigate the efficacy of connected healthcare in promoting medication adherence, (2) examine the relationship between medication adherence and readmission, and (3) develop a dynamic readmission risk-scoring model that considers medication adherence and use the model to better target non-adherent patients. Our findings suggest that patients are more likely to become adherent when they or their partners receive high levels of intervention that involve personalized feedback and when the intervention is escalated quickly and consistently. We also find that long-term adherence to two crucial heart medications, statins and beta-blockers, is strongly associated with reduced readmission risk. Lastly, using counterfactual simulation, we apply the dynamic readmission risk-scoring model to our setting and find that, when using an intervention strategy that prioritizes high-risk patients, we obtain 10% fewer readmissions than we would obtain without considering readmission risk while using the same effort level from the patient support team.

The word "operations" derives from the Latin "opus," and opus means work. So by definition, operations is about work. This course offers an introduction to operations management. After completing the course, you will be able to use a systematic approach to analyze and improve your work in health care settings. The course includes an examination of inefficiencies resulting from the three system inhibitors: waste, variability, and inflexibility. And it provides strategies for engaging in the ongoing process of reducing these negative impacts without sacrificing quality of care. Major units also cover health care delivery processes, lean ops, agility, and managing the service organization. You will practice identifying key performance indicators in health care systems, forecasting demand, predicting utilization and variability, determining staffing levels, and recommending process improvements and innovations to improve client satisfaction.

Matching supply with demand is an enormous challenge for firms: excess supply is too costly, inadequate supply irritates customers. In the course, we will explore how firms can better organize their operations so that they more effectively align their supply with the demand for their products and services. Throughout the course, we illustrate mathematical analysis applied to real operational challenges--we seek rigor and relevance. Our aim is to provide both tactical knowledge and high-level insights needed by general managers and management consultants. We will demonstrate that companies can use (and have used) the principles from this course to significantly enhance their competitiveness.

Seminar on distribution systems models and theory. Reviews current research in the development and solution of models of distribution systems. Emphasizes multi-echelon inventory control, logistics management, network design, and competitive models.

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