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Abstract. This article suggests ways to frame classroom discussion around the business value of models in data science, predictive analytics, and management science classes. We consider an example in which predictive analytics is used to determine the inputs to prescriptive models for customer service, and illustrate how calculations of business value enter the process of creating recommendations for business stakeholders. A review of predictive and prescriptive techniques and how they map to business problems is provided to explain the context for the exercise, and the level of analytics maturity of organizations is discussed in connection with the use of predictive and prescriptive analytics. This example presents a unified view of concepts from traditionally disparate areas of analytics, making it suitable as a capstone or an ongoing project in a data science or business analytics course.

T he traditional approach to teaching hypothesis testing, based on test statistics, is often perceived as lengthy and convoluted. This perception is of particular concern in business schools where the main focus of statistics education should be on providing practical decision-making tools to future managers. This paper discusses the results of a two-year experiment incorporating a more intuitive graph-based introduction to hypothesis testing that places the concept of p-value in a central role. Using this innovative approach at our institution we decreased class coverage time and improved students' understanding and retention with excellent results.

EXECUTIVE SUMMARYThis paper explores the effect of learning curve cost behavior, as opposed to linear, on lot sizing. The first portion of the paper develops optimizing models for the independent demand situation. The second portion examines lot sizing for dependent demand, developing a lot sizing rule similar to Part Period Balancing.After examining the shortcomings of previous attempts at the independent demand lot sizing problem, two models are derived. Excluding material costs (for an assembly operation, the cost of all components), the optimal lot size is Seen to vary linearly with demand and inversely with the carrying cost rate.

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