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Analytics 2
The objective of Analytics 2 is to provide you with a strong background in predictive analytics. We will emphasize learning how to be an intelligent "consumer" of analytics. This, in turn, will help you make effective decisions as a manager using predictive analytics. We will use sophisticated statistical and predictive analytics methods to understand and anticipate the effects of our actions. These methods include confidence intervals, hypothesis testing, A/B testing, multiple regression, and supervised and unsupervised machine learning models such as neural networks and cluster analysis. We will apply these methods to problems from all organizational functions including management & strategy, operations, economics, marketing, finance & accounting, and to a variety of industries. In addition, you will become skilled in performing various analyses via hands-on experience using the R statistical programming language.
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Fundamentals of Web Programming
This hands-on minicourse will teach the basics of web programming (Javascript, HTML, and CSS). During the course, you will build a web-based e-commerce site from scratch. By the end of the course you will be able to reason effectively about designs for and potential capabilities of web applications. There are no prerequisites for the course.
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Tools for Improving Operations
This minicourse covers frameworks and tools designed to enhance operations performance. (The definition of operations used in this course is very broad: operations is fundamentally about execution in all types of contexts, from launching a new product to managing an evolving emergency situation.) The objectives are to equip future general managers, consultants, and operations managers with the perspectives and skills to effectively use operations as a competitive weapon and to develop facility with simple technical tools and frameworks which apply directly to operational decisions and can be useful in adding value to manufacturing and service organizations.
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Digital Operations
This course explores the transformative impact of digital technologies on operations management across various industries. Students will delve into the strategies, tools, and frameworks essential for implementing and managing digital operations. Key topics include digital operating model frameworks, digital platforms, automation and digital manufacturing, digital twin, cloud computing, and artificial intelligence and machine learning in operations. Through real-world case studies and practical applications, students will gain insights into how digital technologies and digital transformation can enhance operational efficiency, improve customer experience, and drive competitive advantage. The course will culminate in project presentations, allowing students to apply their knowledge to real-world scenarios and receive feedback from peers and the instructor. The course is roughly 70% qualitative and 30% quantitative. Simple linear regressions are needed for completing some assignments and can be implemented in Excel.
Note: This course was formerly titled “Data-Driven Analytics for Innovative Operating Models (DDA).” Students who have already completed that course should not enroll in Digital Operations, as there is considerable overlap in content.
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AI-Driven Analytics and Society
The past decade has seen extraordinary advances in AI and data-driven decision-making. These tools now shape, and often govern, nearly every dimension of how businesses and individuals operate. Yet alongside their promise, they have revealed themselves to be a mixed blessing, at best: algorithms interact with society in subtle ways that generate feedback loops, amplify bias, and create unintended consequences.
This seminar dives headfirst into these challenges. We’ll grapple with how firms can make sense of machine-learning predictions and act on them responsibly, what it really takes to keep data fair and unbiased, and how generative AI and large language models are transforming how organizations create value and compete. We’ll unpack why misinformation races ahead of truth on social media, driven by algorithms built to maximize engagement and amplify outrage. We’ll also examine the societal tradeoffs of automation, the human-technology interface, the emerging governance challenges around compute regulation and global AI supply chains, and the use of AI in the public sphere (policing, courts, and government services), where algorithms can shape civic life as powerfully as they shape markets.
The course is designed as an interactive forum: students will lead discussions, analyze case studies, and connect academic research to real-world practice. Like all Research-to-Practice seminars, the emphasis is on developing analytical rigor and critical perspective managers need to navigate the increasingly complex intersection of AI and society.
This course meets the Ethics and Social Responsibility (ESR) requirement.
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Management of Service Operations
In today's dynamic business landscape, the effective management of service operations is crucial for sustainable success. Building upon foundational knowledge from the first-year MBA operations management course, this elective explores some of the unique challenges inherent to service-based industries which connects operational performance to marketing and human resource frameworks. We will use an operational lens to understand how companies achieve and sustain service excellence discussing key topics such as customer behavior, change management, employee engagement, and cultivating a learning culture. We will use the case method to examine real-world challenges, discuss current events and directly observe local service businesses to enhance learning. Students will develop critical skills to improve delivery processes, enhance customer satisfaction, and drive operational excellence in service-driven environments.
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Optimization Modeling for Prescriptive Analytics
This mini course expands on the core Analytics sequence through a deeper dive into the art of modeling, centered around essential tools in optimization. The goal of course is to move beyond predictive analytics and build richer prescriptive models for optimizing business operations in a wide range of contexts. We will focus on modeling decision problems so they can be stated simply, solved efficiently, and, most importantly, translated into clear managerial insights.
Every class will be entirely “hands on,” with an emphasis on active problem solving and case-based learning. Each of the first three weeks will center on a core optimization technique, with the first session devoted to modeling skills and the second applying those skills to a data-driven case. We begin with linear programming, expanding from the types of problems introduced in core Analytics to richer models of strategic planning and control. From there, we turn to optimization with logical and binary constraints, emphasizing applications like investment planning, ride-hailing services, and compute scheduling. We then conclude with non-linear models and heuristic solution methods, which allow us to analyze problems in revenue management, election analytics, and system design. Throughout, we will balance classical management settings with contemporary challenges. Cases and examples will draw from diverse areas such as cloud resource allocation, prescriptive policing, data mining, personalized advertising, and product design in consumer markets.
The final three sessions shift from theory to practice, bringing optimization to life through real-world cases led by industry experts and the instructor. Each class will feature a guest speaker who shares their perspective on designing and implementing optimization in high-stakes settings, highlighting both the opportunities and pitfalls of operationalizing these methods. Students will be expected to actively engage, applying the modeling tools and analytical skills developed in the first three weeks to interpret, challenge, and extend the insights raised in these practitioner-led discussions. By the end of the course, students will not only have deepened their command of optimization modeling, but will also have built the skill to communicate the insights of their models in ways that influence managerial action.
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Data, Models, and Decisions
DMD builds on the core Analytics courses and explores a mix of advanced analytics topics. Each class is centered around a practical data-driven case. In addition to discussing a new application, each case introduces one or two new analytics tools. The overall course philosophy is ``data, models, and decisions,'' where models cover both predictive and prescriptive aspects of analytics. The primary software is Excel, and our focus is to build data-driven models for the purposes of (1) extracting business insights and (2) developing some fundamental understanding behind such models. Analytics tools used in the course include logistic regression, optimization, Monte Carlo simulation, and data visualization. Applications include revenue management of an auto lender's portfolio, managing readmissions to a hospital, workforce scheduling for a ride-hailing service, insurance management in aviation, and portfolio optimization for fantasy sports. The intended audience focuses on MBA students who wish to deepen their knowledge of analytics tools and their applications in business contexts, with a particular emphasis on data-driven decision-making to address complex business challenges.
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Human Behavior in Operations Management
Operations management is an academic discipline that studies the design, management and improvement of the business processes governing both the transformation of raw materials into finished goods and services - and the delivery of those goods and services to customers. In OM, we aim for both efficiency (minimal waste of resources) and effectiveness (meeting customer requirements). Although people have always been a critical component of any operating system, historically, the field has focused on manufacturing environments. Like other traditionally quantitative, model-driven fields that have come to adopt behavioral perspectives, such as economics and finance, the study of behavioral operations has developed in the last 20 years.
Today, with the service sector accounting for 76% of GDP and 85% of employment, understanding the myriad ways that human psychology and behavior affects the efficiency and effectiveness of operating systems is more important than ever before. In this course, we will review papers that use experimental methods to investigate a topic in operations management. We will discuss the operational challenge, why understanding human behaviors are important to the efficiency or effectiveness goals, how the researchers approached the study and how business leaders might incorporate the research findings into managerial practice. In addition, students will have the opportunity to consider ways that they might design and test their own theories through experimentation in their future managerial practice.
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VBA Programming
This minicourse teaches the fundamentals of computer programming using MS Excel's macro language, Visual Basic for Applications (VBA), as the language of instruction. The course starts by teaching students to simplify and extend code generated by Excel's macro recorder, and then builds on that base toward developing applications that analyze information and enhance decision making. Special attention will be given to mastering good programming style and building a solid base to continue learning.
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Supply Chain Management
A supply chain is comprised of all parties involved, directly or indirectly, in fulfilling a customer demand. The integrated management of this network is a critical determinant of success in today’s competitive environment. Companies like Amazon, Inditex, Intel, Johnson & Johnson, Lenovo, and Walmart are proof that excellence in supply chain management is a must for financial strength and industry leadership.
With increasing competition around the globe, supply chain management is both a challenge and an opportunity for companies. Hence a strong understanding of supply chain management concepts and the ability to recommend improvements should be in the toolbox of all managers. The objective of this course is to introduce you to the key concepts and techniques that will allow you to analyze, manage and improve supply chain processes for different industries and markets. At completion of this course, you will have the skills to assess supply chain performance and make recommendations to increase supply chain competitiveness.
The course covers a wide range of supply chain topics including supply chain network design, inventory management, strategic sourcing, supply chain contracting, and supply chain disruptions. Each topic will be discussed using a combination of models, case discussions, and readings. We will use a data-driven approach where tools and analysis start with realistic data.