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14814REVI SED: SEPT EMBER 30 , 2020T EA CH ING NOT EData Analytics Simulation: StrategicDecision MakingThis guide was prepared by Babson College Professor Tom Davenport for the sole purpose of aiding classroom instructors in the use of the Data Analytics Simulation: Strategic Decision Making (HBP No...

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4814 REVI SED: SEPT E MBE R 30 , 2020

T EA CH ING NOT E

Data Analytics Simulation: Strategic Decision Making

This guide was prepared by Babson College Professor Tom Davenport for the sole purpose of aiding classroom instructors in the use of the Data Analytics Simulation: Strategic Decision Making (HBP No. 7050). It provides analysis and questions that are intended to present alternative approaches to deepening students’ comprehension of business issues and energizing classroom discussions. The guide and the simulation are developed solely as the basis for class discussion and are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. Copyright © 2016 Harvard Business School Publishing. To order copies or request permission to reproduce materials, call 1–800–545–7685, write Harvard Business Publishing, Boston, MA 02163, or go to http://www.hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying,

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recording, or otherwise—without the permission of Harvard Business Publishing.

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Table of Contents Quick Facts............................................................................................................................................. 5 Key Information ........................................................................................................................................ 5 Running the Simulation............................................................................................................................. 5 Learn More................................................................................................................................................ 5 Getting Started............................................................................................................................................. 6 Teaching Note .............................................................................................................................................. 6 Overview ................................................................................................................................................... 6 Introduction .............................................................................................................................................. 6 Explanation of the Model ...................................................................................................................... 7 Teaching Objectives .................................................................................................................................. 8 The Student Experience ............................................................................................................................ 9 Overview ................................................................................................................................................ 9 Successful strategies............................................................................................................................ 10 The Forecast Demand Tool and the Production Decision.................................................................... 11 Suggested Uses ....................................................................................................................................... 12 Assigning the Simulation...................................................................................................................... 12 Debriefing the Simulation.................................................................................................................... 13 Critical Takeaways ............................................................................................................................... 14 Teaching Data Analytics in an Online Setting ......................................................................................... 14 Before Simulation Play......................................................................................................................... 14 During Simulation Play......................................................................................................................... 15 Debriefing the Simulation.................................................................................................................... 15 Technical Guide .................................................................................................................................. 17 Adopting the Simulation and Enabling Student Access ..........................................................................17 Simulation Overview .................................................................................................................................. 19 Student User Screens .............................................................................................................................. 19 Simulation Architecture ....................................................................................................................... 19

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Prepare ................................................................................................................................................19 Dashboard............................................................................................................................................ 20 Reports................................................................................................................................................. 20 Reports à Income Statement ............................................................................................................. 20 Reports à Production v. Demand ....................................................................................................... 21 Reports à Pricing ................................................................................................................................ 21 Reports à Social Media ......................................................................................................................22 Data Explorer ....................................................................................................................................... 22 Geographic Demand ............................................................................................................................ 23 Make Decisions .................................................................................................................................... 24 Make Decisions à Units to Produce ................................................................................................... 24 Make Decisions à Units to Produce à Forecast Demand.................................................................. 25 Make Decisions à Channel Price ........................................................................................................25 Make Decisions à Formulation........................................................................................................... 26 Make Decisions à Product Features and Positioning ......................................................................... 26 Make Decisions à Trade Channel Spend ............................................................................................ 26 Make Decisions à Media Spend ......................................................................................................... 27 Make Decisions à Target Market Segment for Decisions................................................................... 27 Decision History ................................................................................................................................... 28 Faculty Administration Screens .............................................................................................................. 29 Overview .............................................................................................................................................. 29 Simulation Status ................................................................................................................................. 30 Best Scores........................................................................................................................................... 30 View Users ........................................................................................................................................... 31 Facilitator Materials............................................................................................................................. 31 Settings ................................................................................................................................................ 32 Run Simulation..................................................................................................................................... 32 Debrief Slides ....................................................................................................................................... 32 Appendices ................................................................................................................................................. 33 Appendix A: Simulation Case .................................................................................................................. 33

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Data-Driven Management of Blue Detergent ...................................................................................... 33 Exhibit 1 ............................................................................................................................................... 35 Exhibit 2 ............................................................................................................................................... 35 Exhibit 3 ............................................................................................................................................... 36 Appendix B: Three Types of Analytics ..................................................................................................... 37 Descriptive Analytics............................................................................................................................ 37 Predictive Analytics.............................................................................................................................. 37 Prescriptive Analytics ........................................................................................................................... 37 Appendix C: Author Videos ..................................................................................................................... 38 Author Introduction Videos with Tom Davenport ............................................................................... 38 Author Debriefing Session ................................................................................................................... 38 Appendix D: Forecast Demand Detailed Discussion ............................................................................... 39 Forecast Demand Tool and the Production Decision........................................................................... 39 Forecasted Demand............................................................................................................................. 39 Making the Production Decision.......................................................................................................... 41 How to Set Production to Maximize Profitability Based on Forecasted Demand ................................ 42 The Newsvendor Problem ................................................................................................................... 44 Laundry Detergent Inventory and Variable Costs................................................................................ 44 Appendix E: Supplementary Materials ................................................................................................... 46 Appendix F: Release Notes ..................................................................................................................... 47

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QUICK FACTS

TEACHING NOTE

TECHNICAL GUIDE

APPENDICES

Quick Facts Key Information Author

Tom Davenport

Players/Scenarios/Roles

Single player

Asynchronous Play?

Yes

In-Class or Out-of-Class Play

Either

Teaching Points

Analytics, Decision analysis, Decision making, Improving Performance, Product Management, Strategy formulation

Target Audience

Undergraduate, graduate, or executive students in any course where the above subjects are taught

Accompanying/ Supplemental Material

Teaching Note; Debrief Slides

Approx. Time Required

Student Preparation: 10-15+ minutes, Simulation Play: 45-75 minutes, Debrief: 60-120 minutes

Running the Simulation Technical specifications for this simulation can be found here: (A) Technical Specifications, (B) System Check. The typical steps for setting up and running this simulation are as follows: 1. Ensure all students are populated into the simulation. 2. On the Simulation Setup screen, open simulation for play. 3. Monitor student progress if running simulation in class (there may be questions about technical functionality, etc. [p. 14]) 4. End simulation and begin debrief (p. 9) See also: Technical Guide, Adopting the Simulation and Enabling Student Access

Learn More To learn more about this simulation, carefully review the Teaching Note and take advantage of any or all of the following resources: • Free trial • Overview video

• Simulation Preview • Webinar recording

For these and other resources visit: https://hbsp.harvard.edu/product/7050-HTM-ENG

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QUICK FACTS

TEACHING NOTE

TECHNICAL GUIDE

APPENDICES

Getting Started This teaching note is designed for administrators of the Data Analytics Simulation: Strategic Decision- Making (HBP Product No. 7050). •

The Teaching Note section gives specific information related to the learning objectives and teaching opportunities inherent in the simulation.

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The Technical Guide section reviews the process by which a faculty member adopts the simulation through the Harvard Business for Educators website and extends that access to students.

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The Simulation Overview section provides screenshots and description of user and administrative screens.

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Several Appendices provide information about the background case for the simulation, the three types of analytics, and links to the author videos included in the simulation.

Teaching Note Overview After giving an overview of the simulation and outlining some technical requirements, we review in detail the various tasks and challenges posed to the students. In doing so we provide details on the pedagogical concepts that each task allows covering and on the types of analyses and thought processes that students are expected to perform (using the tools and information available to them as part of the simulation) while attempting to respond to each task they are confronted with. We also discuss how to customize the tool, prepare for class, and teach using the set of exercises that are part of the simulation. This Facilitator’s Guide was last updated on September 30, 2020. For detailed release notes, see Release Notes.

Introduction Data and analytics are widely considered to be one of the most effective ways to improve decision-making within a company today.a Many organizations, both large and small, are using data from operations and from customer and supplier interactions to improve and optimize their strategic and tactical decisions. Analytics can be used directly with customers in offers and promotions, or they can inform internal decisions within a company. This latter purpose is the focus of the simulation.

a

Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of W inning, Harvard Business Publishing, 2007.

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QUICK FACTS

TEACHING NOTE

TECHNICAL GUIDE

APPENDICES

The student in this simulation is expected to play the role of a product or marketing manager for a single brand at a consumer products firm, Kelsey-White. The company has a new data-driven CEO, and she has instigated the development of K-W Vision, a tool for manipulating and analyzing market data and performance measurement outcomes. The type of analysis done in K-W Vision is similar to that done by Procter & Gamble, which provided insights for the development of the simulation. The data, however, are not from any specific company, and Procter & Gamble has several successful brands in the real detergent market. The goal of this simulation is to turn around the lagging performance of one brand of detergent, Blue, which has several competitors in the U.S. marketplace. The underlying assumption of the simulation is that students who make extensive use of the data and analytics to make their decisions will be more successful in sales, profit, and market share growth than those who guess or use their intuition. There are several underlying trends and patterns in the data that, if noticed and acted upon by the student, will maximize the positive outcomes of the simulation. The majority of the analyses to be done in the simulation involve descriptive analytics. In these there are only visual analytical outcomes such as line graphs or bar charts. However, there is also a forecasting model that exposes students to some level of predictive analytics.b

Explanation of the Model Data Analytics Model Description The model for the Data Analytics simulation is a highly arrayed model written in Python, a technical computing language frequently used for data science problems. Because the model uses arrays extensively, we use NumPy, a Python package that helps with using multidimensional arrays. The model simulates market dynamics for a fictitious laundry detergent company and its competitors. Students who run the data analytics simulation represent Blue. There are four competitors in the market. Blue's competitors include Turbo, Fresh, and store brands. The dominant competitor in this market is Turbo with close to 50% market share. In addition to calculating market share, the model calculates operating profit, revenue, prices, sales, US households, media consumed by US households over time, and several intermediate variables. Sales are divided into multiple categories, including sales by price, sales by brand, sales by formulation, sales by trade channel, and sales by state. Students can filter all of the above variables, including all sales types, US households, media, and profits, and revenue by five filters – regions, incomes, races, household sizes, and household ages. Each of these five filters have preset categories as listed below: •

Regions: Northeast, Southwest, Central, and West

b A description of descriptive, predictive, and prescriptive analytics is provided in a short video:

https://hbr.org/vi deo/23868161...