Case Article—Mapping Business Problems to Analytics Solutions: Surrogate Experiential Learning in an MBA Introductory Data Science and Business Analytics Course

T his operations management case describes a waste-reduction project in a compounding pharmacy in a hospital. Every day, pharmacy technicians prepare a large number of patient-specific medication doses and then deliver these doses to various hospital units. With the rising cost of medications, pharmacy managers become concerned that a significant number of compounded medication doses are not administered to patients and are subsequently wasted. The students are asked to quantitatively evaluate proposed changes to the compounding and delivery process and to estimate savings from process reconfiguration. Two large data sets are provided with the case to facilitate hypothesis generation regarding probable causes of waste and to analyze proposed changes. The analysis will deepen students' spreadsheet skills as well as mathematical modeling of inventory problems. The case is presented in parts, and it is discussed over one-and-a half class meetings-simulating the steps of a field project: interviewing a client, framing the client's problem, formulating a data request, and then analyzing the data and delivering a recommendation. This case has been used in a core MBA operations management class; it could also be used in a health-care operations or in a business modeling course.

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confidence: 84%

Case Article—Medication Waste Reduction in an In-Hospital Pharmacy: A Case That Bridges Problem Solving Between a Traditional Case and an Industry Project

Dobson

Tilson

2016

“…Predictive and Prescriptive Analytics This case presents an opportunity for instructors to illustrate the life cycle of a data analytics project in a context that is different from a typical implementation in a company setting. The data analytics life cycle typically consists of six stages (see, e.g., EMC Educational Services (2015) and Pachamanova (2015)):…”

Section: Data Analytics Life Cycle and Interface Ofmentioning

confidence: 99%

Case Article—Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations

Pachamanova

Tilson

Dwyer-Matzky

2022

This case discusses a process improvement project aimed at maximizing the use of hospital capacity as the flu season looms. Dr. Erin Kelly heads the observation unit and turns to predictive models to improve the assignment of patients to her unit. The case covers three major themes: (1) data analytics life cycle and interface of predictive and prescriptive analytics in the context of process improvement, (2) design and ethical application of machine learning models, and (3) effecting organizational change to operationalize the findings of the analysis. Realistic data, R code, and Excel models are provided. The rich context of the case allows for discussing change management in a healthcare organization, analytics problem framing and model mapping, service process capacity analysis and Little’s law, data summaries and visualizations, interpretable machine learning algorithms, evaluations of predictive model performance, algorithmic bias, and dealing with dirty data. The case is appropriate for use in courses in machine learning, business analytics, operations management, and operations research, both at the advanced undergraduate level and at the master’s/MBA level.

“…Student Background Introduction to Data Science and Business Analytics is one of the elective core courses for the MBA business analytics concentration at our home institution. It focuses on the understanding of whether and how a business problem can be cast as an analytics problem as well as the steps for completing analytics projects (Pachamanova 2015). The course provides an overview of current analytical methods and software tools, and the main takeaway from the course is a process for recognizing patterns, necessary tools, and approaches for increasing the likelihood that analytics project outcomes are accepted and implemented in an organization.…”

Section: Analytics Course Summary Andmentioning

confidence: 99%

Writing to Learn: A Framework for Structuring Writing Assignments to Support Analytics Course Learning Goals

Getchell

Pachamanova

2022

Self Cite

Drawing on the scholarship of writing and learning, this article motivates the use of writing assignments in analytics courses and develops a framework for instructional design that advances both writing skills and discipline-specific learning. We translate a best practices set of foundational writing concepts into a matrix of design levers for analytics instructors and propose an instructional design process that balances discipline-specific learning goals with foundational writing concepts through specific writing activities. We summarize our experience applying the framework to a particular data science course and present some early evidence for favorable outcomes. The positive effect we observe extends beyond learning course concepts and includes increased student engagement and contributions to group work.