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The Applied Business Analytics Casebook
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The Applied Business Analytics Casebook
Applications in Supply Chain Management, Operations Management, and Operations Research
Matthew J. Drake, Ph.D., CFPIM
Pearson Education, Inc.
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Vice President, Publisher: Tim Moore
Associate Publisher and Director of Marketing: Amy Neidlinger
Executive Editor: Jeanne Glasser Levine
Operations Specialist: Jodi Kemper
Cover Designer: Chuti Prasertsith
Managing Editor: Kristy Hart
Project Editor: Katie Matejka
Copy Editor: Seth Kerney
Proofreader: Chuck Hutchinson
Indexer: Johnna VanHoose Dinse
Senior Compositor: Gloria Schurick
Manufacturing Buyer: Dan Uhrig
© 2014 by Matthew J. Drake
Publishing as Pearson
Upper Saddle River, New Jersey 07458
Pearson offers excellent discounts on this book when ordered in quantity for bulk purchases or special sales. For more information, please contact U.S. Corporate and Government Sales, 1-800-382-3419, corpsales@pearsontechgroup.com . For sales outside the U.S., please contact International Sales at international@pearsoned.com .
Company and product names mentioned herein are the trademarks or registered trademarks of their respective owners.
All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher.
Printed in the United States of America
First Printing November 2013
ISBN-10: 0-13-340736-5 ISBN-13: 978-0-13-340736-5
Pearson Education LTD.
Pearson Education Australia PTY, Limited.
Pearson Education Singapore, Pte. Ltd.
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Library of Congress Control Number: 2013946942
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For my wife, Nicole, and my daughter, Noelle. You are the inspiration for
everything that I accomplish.
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Table of Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
Part 1 Forecasting and Process Analysis . . . . . . . . . . . . . . . . . . . .1
Case 1 Forecasting Sales at Ska Brewing Company . . . . . . . . . . . . . . . 3
Eric Huggins, Fort Lewis College
Case 2 Maintaining Financial Success and Expanding into Other Markets at FeedMyPet.com . . . . . . . . . . . . . . . . . . . . . . 15
Charles A. Wood, Duquesne University
Case 3 Forecasting Offertory Revenue at St. Elizabeth Seton Catholic Church . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Matthew J. Drake, Duquesne University Ozgun Caliskan-Demirag, Pennsylvania State University—Erie, The Behrend College
Case 4 Pizza Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Kathryn Marley, Duquesne University Gopesh Anand, University of Illinois at Urbana–Champaign
Part 2 Optimization and Simulation . . . . . . . . . . . . . . . . . . . . . . .45
Case 5 Inventory Management at Squirrel Hill Cosmetics . . . . . . . . 47
Paul M. Griffin, Pennsylvania State University
Case 6 Safety Stock Planning for a Hong Kong Fashion Retailer . . . 65
Tsan-Ming (Jason) Choi, The Hong Kong Polytechnic University
Case 7 Network Design at Commonwealth Pipeline Company . . . . . 77
Matthew J. Drake, Duquesne University
Case 8 Publish or Perish: Scheduling Challenges in the Publishing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Beate Klingenberg and David Gavin, Marist College
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Part 3 Decision Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Case 9 Narragansett Brewing Company: Build a Brewery . . . . . . . . 101
John K. Visich, Christopher J. Roethlein, and Angela M. Wicks, Bryant University
Case 10 Aluminum Versus Plastic: A Life-Cycle Perspective on the Use of These Materials in Laptop Computers . . . . . . . . 107
Ryan Luchs, Drew Lessard, and Robert P. Sroufe, Duquesne University
Case 11 HealthCare’s Corporate Social Responsibility Program . . . . 131
Robert P. Sroufe and Marie Fechik-Kirk, Duquesne University
Case 12 PaperbackSwap.com: Got Books? . . . . . . . . . . . . . . . . . . . . . 143
Brandy S. Cannon and Louis A. Le Blanc, Berry College
Case 13 Stranded in the Nyiri Desert: A Group Case Study . . . . . . . 161
Aimée A. Kane and Mercy Shitemi, Duquesne University
Part 4 Advanced Business Analytics . . . . . . . . . . . . . . . . . . . . . .165
Case 14 Joe’s Coin Shop: Entry into Online Auctions . . . . . . . . . . . . 167
Charles A. Wood, Duquesne University
Case 15 Vehicle Routing at Otto’s Discount Brigade . . . . . . . . . . . . . 181
Matthew J. Drake, Duquesne University
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
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Acknowledgments
I am forever grateful to the efforts of all of the contributors to this book. Many of them have been friends and colleagues for a long time, but I met some others for the first time through working on this proj- ect. I look forward to many more years of collaboration with them. This book would not have become a reality without the contributors’ willingness to share their hard work with me. I am also indebted to Barry Render, Consulting Editor at FT Press, who invited me to work on this project, and to Jeanne Glasser Levine, Executive Editor at FT Press, whose guidance and advice was instrumental throughout the publication process.
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About the Author
Matthew J. Drake, Ph.D., CFPIM , is an Associate Professor of Supply Chain Management and the Director of International Business Programs in the Palumbo-Donahue School of Business at Duquesne University. Dr. Drake primarily teaches analytical courses in the Supply Chain Management program. He holds a B.S. in Business Administration from Duquesne University and an M.S. and Ph.D. in Industrial Engineering from the Georgia Institute of Technology. His first book, Global Supply Chain Management , was published by Busi- ness Expert Press in 2012. Dr. Drake’s research has been published in a number of leading journals including Naval Research Logistics , the European Journal of Operational Research , Omega , the International Journal of Production Economics , OR Spectrum , the Journal of Busi- ness Ethics , and Science and Engineering Ethics . Several of his previ- ous cases and teaching materials have been published in INFORMS Transactions on Education and Spreadsheets in Education .
Dr. Drake lives in suburban Pittsburgh, Pennsylvania, with his wife, Nicole; his daughter, Noelle; and his dog, Bismarck.
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Contributor List
Gopesh Anand is an Associate Professor of Process Management in the College of Business at the University of Illinois at Urbana– Champaign. His research is aimed at understanding continuous improvement of work processes and execution of operations strategy in organizations.
Ozgun Caliskan-Demirag is an Assistant Professor of Supply Chain Management in the Sam and Irene Black School of Business at Penn State Erie, The Behrend College. She holds a Ph.D. in Indus- trial and Systems Engineering from Georgia Tech, and her main research interests are in the areas of supply chain management, oper- ations/marketing interface, inventory management and decentralized resource allocation. Her work has appeared in journals such as Oper- ations Research , Production and Operations Management , Naval Research Logistics , and European Journal of Operational Research .
Brandy S. Cannon is employed as an accountant in the Business and Finance Office at Berry College, Mount Berry, Georgia, USA. She earned a B.S. in Accounting and the M.B.A. from the Campbell School of Business at Berry College.
Tsan-Ming (Jason) Choi is an Associate Professor in Fashion Business at The Hong Kong Polytechnic University. Over the past few years, he has actively participated in a variety of research proj- ects on supply chain management and systems engineering. He has authored/edited 10 research handbooks and published extensively in leading OR/OM journals such as Annals of Operations Research , Automatica , Decision Support Systems , European Journal of Opera- tional Research , IEEE Transactions on Automatic Control , Produc- tion and Operations Management , Service Science , Supply Chain Management , and various other IEEE Transactions . He is now an area editor/associate editor/guest editor of journals which include Annals of Operations Research ; Decision Sciences ; Decision Support
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Systems ; European Management Journal ; IEEE Transactions on Sys- tems, Man, and Cybernetics Part A: Systems; Information Sciences ; Journal of the Operational Research Society ; and Production and Operations Management .
Marie Fechik-Kirk , a Fulbright alumnus, earned an M.B.A. with a focus in sustainability at Duquesne University in 2009. Since then she has helped organizations from Bayer MaterialScience to The Hill School in reducing waste, increasing efficiency, and enhancing their reputation through sustainability initiatives.
David Gavin is an Associate Professor of Management at Marist College. He received his doctorate in Strategic Management from the University at Albany. His professional experience includes upper executive positions in the publishing, technology, food service, and retail industries. He has authored or co-authored articles appearing in the Journal of Business and Economics Studies , International Journal of Humanities and Social Science , and International Journal of Orga- nization Theory and Behavior .
Paul M. Griffin is a Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, where he serves as the Peter and Angela Dal Pezzo Department Head Chair. His research and teaching interests are in health and supply chain systems. Dr. Griffin earned a Ph.D. in Industrial Engineering from Texas A&M University.
Eric Huggins is an Associate Professor of Management at Fort Lewis College in Durango, Colorado. When he’s not busy teaching, working with student, or analyzing data from local companies, he enjoys spending time in the great outdoors of southwestern Colorado, and he can occasionally be found in the tasting room at Ska.
Aimée A. Kane holds a Ph.D. in organizational behavior and theory from the Tepper School of Business at Carnegie Mellon Uni- versity. She is an Assistant Professor of Management at the Palumbo- Donahue School of Business at Duquesne University. Her research,
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which focuses on how groups capitalize on the knowledge of their members, has appeared in several top publications, including the Academy of Management Annals and Organization Science .
Beate Klingenberg is an Associate Professor of Management at Marist College, with a focus on Operations Management and Decision Sciences. Her areas of research include sustainability and environmental management in operations, knowledge management in technology transfer settings, as well as operations management issues in real estate. Her publications appear in academic as well as practitioner publications. Her credentials include a master’s in Chemistry and Ph.D. in Physical Chemistry (both University of Erlangen-Nürnberg, Germany) as well as an M.B.A. from Marist College. Furthermore, she has extensive industry experience in technology transfer and project management.
Louis A. Le Blanc is Professor of Business Administration at the Campbell School of Business, Berry College, Mount Berry, Georgia, USA. He received a Ph.D. from Texas A&M University, followed by postdoctoral study at the University of Minnesota and Indiana Uni- versity. Dr. Le Blanc teaches courses in strategic use of information technology and operations management.
Drew Lessard is a strategy and analytics professional with expe- rience in Global Fortune 500 companies and has a current passion for startups. He holds an M.B.A. concentrating in Sustainability from Duquesne University and a Master of Arts in Economics from Boston University. He hails from Portland, Maine, and currently resides in Pittsburgh, Pennsylvania.
Ryan Luchs is an Assistant Professor of Marketing in the Palumbo-Donahue School of Business at Duquesne University. He teaches marketing and supply chain management courses to under- graduates and also teaches the Strategic Marketing course in the Sustainable M.B.A. curriculum. Dr. Luchs received a Ph.D. and an M.B.A. from the University of Pittsburgh and a B.S. in Chemical Engineering from Penn State University.
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Kathryn Marley is an Assistant Professor of Supply Chain Man- agement in the Palumbo-Donahue School of Business at Duquesne University. Her research interests include lean management and con- tinuous improvement programs, supply chain disruptions, and peda- gogical methods.
Christopher Roethlein is a Professor in the Management Department at Bryant University where he teaches courses in opera- tions management and supply chain management. He has a Ph.D. in Management Science and Information Systems from the University of Rhode Island; and his research interests include quality and com- munication within a supply chain, strategic initiatives through align- ment of supply chain goals, collaborative relationships, and leadership excellence. He has published in a numerous journals, and he was a co-winner of the 2011 Case Studies Award Competition presented by the Decision Sciences Institute.
Mercy Shitemi holds a B.S. in Informatics from Indiana Uni- versity and is currently completing a master’s degree in Information Systems Management at Duquesne University’s John F. Donahue Graduate School of Business. Mercy hails from Eldoret, Kenya.
Robert P. Sroufe is the Murrin Chair of Global Competitive- ness in the John F. Donahue Graduate School of Business and Direc- tor of Applied Sustainability within the Beard Institute at Duquesne University. Dr. Sroufe is an award-winning scholar and teacher. These awards include instructional innovation and best environmen- tal papers from the National Decision Sciences Institute. Within the M.B.A. Sustainability program, he develops and delivers courses on sustainable theories and models including life-cycle analysis, business applications of sustainability tools, and processes for new initiatives; and he oversees action-learning consulting projects every semester with corporate sponsors.
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John K. Visich is a Professor in the Management Department at Bryant University, where he teaches courses in operations manage- ment and supply chain management. He has a Ph.D. in Operations Management from the University of Houston, and his research inter- ests are in supply chain management, radio frequency identification, and corporate social responsibility. He has published in a numerous journals, and he was a co-winner of the 2011 Case Studies Award Competition presented by the Decision Sciences Institute.
Angela M. Wicks is an Associate Professor in the Manage- ment Department at Bryant University, where she teaches courses in operations management and project management. She has a Ph.D. in Operations Management from the University of Houston, and her research interests include hospital performance, patient satisfaction, and health care technology. She has published in numerous journals including the International Journal of Quality Assurance in Health- care , Hospital Topics , and the International Journal of Healthcare Technology and Management .
Charles A. Wood is an Assistant Professor in the Manage- ment Information Systems area at the Palumbo Donahue School of Business at Duquesne University in Pittsburgh, Pennsylvania. After spending over a decade in the “real world” as a systems analyst, team leader, manager, systems architect, and finally as the owner of a suc- cessful consulting company, Chuck returned to academia to complete an M.B.A. and a Ph.D. He has taught at several institutions, including Notre Dame and at the University of Minnesota.
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Preface
The field of business analytics has been thrust into the global spotlight in recent years. This surge in popularity is largely because of a barrage of books and periodical articles highlighting its potential to help firms create a competitive advantage. Although some techniques contained within the umbrella of business analytics, such as data min- ing, text mining, and neural networks, truly represent cutting-edge methodologies that mainly appear in advanced graduate courses, the building-block techniques of business analytics, such as statistical analysis, optimization, and decision trees, are mainstays in business- school curricula around the world.
Business analytics can be broadly defined as “the scientific pro- cess of transforming data into insight for better decision making.” 1 As a result of this focus on decision making, courses that cover material related to business analytics can benefit greatly from utilizing case studies as a supplement to the core analytical material. Case studies are an effective method for exposing students to the entire decision- making process because they put the student in a simulated active role as a decision maker who must perform the analysis and use the output to recommend a course of action.
Although cases are a mainstay of many graduate business courses, they are used somewhat less frequently in undergraduate courses. One reason for this lack of extensive case adoption in undergraduate courses is the preponderance of long cases published by the major case libraries. Cases appropriate for undergraduates need to be somewhat more focused because the students do not have as much experience as graduate students. Many textbooks include one- or two-page cases at the end of a chapter to illustrate the application of the techniques presented in the chapter. Because they are so short, these cases often amount to little more than a slightly expanded homework problem.
1 http://www.informs.org/About-INFORMS/What-is-Analytics
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This collection of cases is designed to supplement core material covering business analysis techniques in courses as varied as statistics, operations management, management science, supply chain mod- eling, and decision analysis. This book fills the gap in the library of business analytics case materials appropriate for undergraduate stu- dents with cases of moderate length. The cases are also appropriate for introductory-level graduate courses, as instructors can focus the analysis and discussion on more of the complex issues raised in the cases.
The cases in the collection are grouped by the primary analytical technique appropriate for each decision environment. Part 1 , “Fore- casting and Process Analysis,” includes three forecasting cases and one case that focuses on quality control and process improvement. Part 2 , “Optimization and Simulation,” contains cases that utilize the classic management science methods of optimization and simulation. The optimization cases address inventory control and logistics net- work design, and the simulation case addresses the management of process flows. Part 3 , “Decision Analysis,” includes cases that require the application of a variety of decision analysis tools from decision trees and factor rating to the Analytic Hierarchy Process (AHP), multi-criteria decision analysis, and group decision making. The deci- sion environments vary from facility location to sustainability manage- ment. Part 4 , “Advanced Business Analytics,” contains two advanced cases—one that is truly a “big data” case with a large data set and another centered on vehicle routing, a traditionally difficult problem in logistics.
It is my hope that the cases in this collection expose students to the power of business analytics and the utility of these techniques in the decision-making process. Students armed with an effective tool- box of analytical skills and techniques are well positioned to make thoughtful, reasoned decisions informed by data analysis for their
PREFACE xvii
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companies and organizations. These analytical skills are transferrable across companies and industries and can enhance students’ attractive- ness and value to employers throughout their careers.
Matthew J. Drake Pittsburgh, Pennsylvania, USA August 2013
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1 Forecasting and Process Analysis
1. Forecasting Sales at Ska Brewing Company 3
2. Maintaining Financial Success and Expanding into Other Markets at FeedMyPet.com 15
3. Forecasting Offertory Revenue at St. Elizabeth Seton Catholic Church 25
4. Pizza Station 33
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3
Case 1 Forecasting Sales at Ska Brewing
Company
Eric Huggins, Fort Lewis College
Background
Ska Brewing Company is a purveyor of fine craft beers located in Durango, Colorado. With its flagships Pinstripe Red Ale and True Blonde Ale, medal-winning Buster Nut Brown Ale and Steel Toe Stout, and seasonal Mexican Logger and Euphoria Pale Ale, Ska has enjoyed double-digit growth for more than a decade with no signs of slowing down. Learn more about Ska by visiting its tasting room at 225 Girard Street, Durango, Colorado, or online. 1
In the early ‘90s, founders/owners Dave and Bill were dissatisfied with watered-down corporate beer and decided to take matters into their own hands, literally. They began brewing their own beer in their basement, much to the delight of everyone who knew them. Eventu- ally, it became clear that they might be able to make a living doing what they loved to do, and they founded Ska Brewing Company in 1995 with third owner/founder Matt. Through hard work and a laser- like focus on brewing great beer, Ska continued to grow, and in 2008
1 http://www.skabrewing.com/
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the company moved into its $4.8 million, 24,000-square-foot world headquarters. In 2012, Ska brewed more than 25,000 barrels of beer (1 barrel = 2 standard kegs = 252 pints = 4,032 ounces), with sales exceeding $6.5 million.
Ska was not alone in its success. Durango, a town with fewer than 20,000 people, has four long-term successful breweries/brewpubs, a brand new brewpub that opened in 2012, and another one in the works. Rather than considering these other breweries as competition, Ska has worked together with them (as well as others across the state of Colorado) to brew specialty beers for festivals and other occasions; Ska also contract brews beer for Steamworks Brewing Company (using its recipes) because Steamworks has exceeded its own brew- ing capacity. Owner Dave calls this unique relationship “coopitition.” Steamworks and Ska are just examples, however.
The craft brewing industry has seen phenomenal growth during the last three decades across the United States and in other coun- tries as well. According to the Brewers Association, 2 the craft brewing renaissance started in the late 1970s and saw periods of incredible growth during the 1990s. Historically, before Prohibition, small brew- eries were everywhere across the United States; the 18th Amendment caused most of the small breweries to go out of business, and only the larger breweries survived until the 21st Amendment repealed Prohi- bition 13 years later. It took several decades for smaller breweries to begin the resurgence that we see today.
But our concern is more specific: Will the growth and success at Ska continue? Can Ska anticipate how much beer it will produce, and what sales will be so that the company can plan wisely for the future? In fact, current plans are to increase brewing capacity yet again—a costly investment with potentially high returns. Is this a good decision or not? This is where you come in.
2 http://www.brewersassociation.org/pages/about-us/history-of-craft-brewing
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CASE 1 • FORECASTING SALES AT SKA BREWING COMPANY 5
Mission
Despite its success, Ska is still a relatively small operation. The company has one main numbers person, accountant Erik. In a nut- shell, Erik would like to predict Ska’s sales dollars and barrels sold for the current year, 2013. He has done some of this work on his own, but he would like you to confirm (or refute) his forecasts, and to do so in much greater detail, as Erik is too busy (presumably because he spends his days counting all of Ska’s money). To get you started, Exhibit 1.1 contains Ska’s total barrels (BBLS) sold and sales ($$$) over the previous 13 years. More precise monthly data is available in Exhibits 1.2 through 1.6 . Please note that this is actual (not phony textbook) data.
Even a cursory glance at the information in the table shows that both the number of barrels and sales are increasing annually at a pretty good rate. In fact, both values have shown tenfold growth between the years 2000 and 2012. What will these two numbers look like at the end of 2013? You might have studied forecasting techniques previ- ously, and ideally you learned that when forecasting real data, there is no “one-size-fits-all” approach; ahead you will try several approaches and then combine them to make a final prediction.
Your task is not only to forecast these two values for 2013, but to give Erik, Dave, Bill, and Matt a better picture of what is happening with their business overall. To do so, you will be asked to produce sev- eral graphs, both on annual and monthly bases, to consider growth as a percentage, and to consider the likely errors that go along with your forecasts. You will first be asked to learn a little more about the brew- ing industry in general, to give you a better idea of the current status of craft brewing. Your final report should be thorough, professional, and accurate. Good luck!
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Questions about Breweries
1. What is a craft brewery? How is it different from a brewpub? Go online and research these definitions. You should fairly eas- ily find a quantitative definition of the number of barrels pro- duced by a craft brewery (or microbrewery). For comparison, find out how many barrels are produced annually by a very large brewery such as Anheuser-Busch, MillerCoors, or Heineken. Write a paragraph or two with your findings and, as always, be sure to cite your sources.
2. Are there any local breweries in your area? If so, which catego- ries do they fall under? If not, why not? Discuss the feasibility and likely success or failure of a new brewery in your area. Of course, a cool name like Ska might be one of the keys to a new brewery’s success; what will you name your new brewery?
3. The claim was made earlier that the “craft brewing industry has seen phenomenal growth during the last three decades.” Go online and find evidence to support this claim. Specifically, how many craft breweries are there now compared to 30 years ago? How has the craft brewing market share grown (out of total beer sales)? How have the major breweries reacted to the growth of craft brewing? Write a paragraph or two with what you learn.
Questions about Ska’s Annual Data
4. Now onto Ska’s annual data: Use Microsoft Excel to draw scat- ter plots of both year versus barrels and year versus sales. (Hint: You might want to change the year range from 2000–2012 to 0–12 to simplify the equations of the curves that Excel will eventually fit to the data.) What kind of curve do both scatter plots look like? Consider the barrels data first; then repeat for the sales data:
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CASE 1 • FORECASTING SALES AT SKA BREWING COMPANY 7
a. Have Excel fit a linear trendline to the data and determine the equation of the line and the r 2 value. Interpret the slope of the line and the coefficient of determination. Is this a good fit?
b. The pattern on the graph should be clearly nonlinear. Now instead, have Excel fit an exponential curve to the data and again determine the equation of the curve and the r 2 value. Is this a better fit?
c. Using the equation for the curve from 4b, plug in 13 (or 2013) to get your first forecast. Does it seem reasonable, or does it seem too low or too high? (Note: To see where the forecast falls, Excel will let you extend the curve by one period when you draw the trendline. When you format the trendline, forecast forward one period.)
Be sure to do 4a–4c for both barrels and sales.
5. Now draw a scatter diagram of barrels versus sales. This pattern should appear quite linear. Fit a line to the data and interpret both the slope of the line (Hint: 1 barrel = 2 kegs) and the coef- ficient of determination. Can you reasonably conclude that the more beer Ska produces, the more money it makes?
6. Reconsider the graphs from question 4. Although the growth does appear to be exponential, your predictions in 4c shouldn’t quite look right. Let’s try it another way: Consider the last four points on each graph, from 2009 to 2012. Ignoring the rest of the data, do those four points appear to have an (obvious) pattern?
a. Using only the last four years’ data, fit a line for both barrels and for sales. Interpret both the slope and r 2 value for each line.
b. Plug a 13 into each line to get your second forecast for bar- rels and sales in 2013. How confident do you feel with these predictions?
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7. Your predictions in question 6 might seem pretty good, but take it one step further:
a. For both barrels and sales, determine the MAD for each of your predictions. If you are not familiar with the concept of MAD, go online and search for “mean absolute deviation.” You should quickly find a website that explains the concept and shows you how to calculate it. What are your forecasts for 2013 including the MAD? What information does the MAD tell you?
b. Repeat 7a but now for the MAPE, or mean absolute per- centage error. Interpret the MAPE.
c. As one final check, repeat what you did in question 6 but this time use the data from 2008 to 2011 to predict 2012 and compare your prediction for 2012 to the actual value. Do this for both barrels and sales. Does this forecasting method appear to be promising?
8. In both 4c and 6b, you forecasted barrels and sales for 2013. Consider one more way to do this before you make your final decision. Determine the percentage growth for both bar- rels and sales for each year. For example, from 2000 to 2001, barrels increased from 2,595 to 3,025, or a growth rate of (3025 – 2595)/2595 = 17%. Calculate these rates for years 1 to 12 for both columns of data.
a. Determine the average and median growth rates for both barrels and sales.
b. Considering only sales, draw a scatter plot of year versus sales growth. Do any of the growth rates look like outliers? (Hint: Recall that Ska moved into its new world headquar- ters in 2008, increasing its brewing capacity tremendously.)
c. The outliers in 8b might be obvious, but they aren’t always so easy to identify. So, use a box plot (Tukey’s Method) to find the outliers. For each column of percentage data,
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CASE 1 • FORECASTING SALES AT SKA BREWING COMPANY 9
determine the first and third quartiles; these are the points where 25% of the data are below and 25% of the data are above, respectively. (Hint: Use Excel’s =quartile() function to find both Q 1 and Q 3 .) Calculate the IQR = Q 3 – Q 1 and the range of “typical” values (Q 1 – 1.5*IQR, Q 3 + 1.5*IQR). Any data point within the range is typical, whereas any point outside the range is atypical, or an outlier. What are the two outliers for each column in this case?
d. Eliminate the outliers and recalculate the average and median growth rates for both barrels and sales. Multiply these growth rates by the 2012 actual values for barrels and sales and make your third (and final ) set of forecasts for 2013. How do you feel about these predictions?
e. As a side note, Erik, the accountant, asked the owners to do a quick, back-of-the-beer-coaster estimate of what growth would be for 2013. Their immediate response was “20%.” Would you say that Dave, Bill, and Matt are guessing, or do they know their business very well?
Questions about Ska’s Monthly Data
Another concern at Ska is seasonal variation. The brewery is much busier during the summer months than during the winter months. Two possible explanations for this phenomenon are that 1) people simply buy more beer during the summer, and 2) Ska releases two very popular seasonal beers, Mexican Logger and Euphoria Pale Ale, at the beginning and end of the summer season. To get a better han- dle on the seasonal variations at Ska, your task is to draw some clear pictures of what’s happening (sometimes called data visualization ).
To achieve this goal, consider Exhibits 1.2–1.6 with the complete monthly data for all 13 years. You will see the barrels information in
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white and the sales information in gray. Use this data to display the seasonal patterns at Ska:
9. Thirteen years provides 156 months’ worth of data. In Excel, develop one column from 1 to 156. In the next column, list the barrels sold for each year in chronological order (so the first 12 data points will be the 196.5–238.1 from year 2000, the next 12 will be the 243.2–258.9 from year 2001, and so on.) (Hint: You can build this using simple cut/copy and paste, or there’s likely a better way.) In the third column, list all the monthly sales data.
a. Graph a scatter plot of both month versus barrels and month versus sales.
b. Fit exponential curves to both graphs.
c. Look carefully at the last four years of each graph. When does Ska tend to get busier during these four years? Does each graph indicate that summertime is crunch time? Which months in particular appear to be the busiest?
10. For the final forecasts for 2013, predict each month of 2013 and add them to the scatter plot from question 9. As you did in question 4, use only the last four years from 2009 to 2012 to forecast 2013.
a. For each month, make a linear forecast using the monthly data from 2009 to 2012. So, for example, to predict barrels for January 2013, use the data points 706.6, 1017.3, 1272.4, and 1484.9, and make a straightforward linear prediction. Do this for both barrels and sales for each month.
b. Now, add these forecasted values onto the scatter plots from question 9. Make the forecasted values a different color from the actual data to make them stand out and label the final graphs accordingly. These two graphs should give the stakeholders at Ska a clear picture of what 2013 might look like, depending on how accurate the forecasts end up being.
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(Note: Adding these extra points to a pre-existing scatter plot in Excel is a little tricky. To do so, right-click on the scatter plot itself and choose Select Data. Click the Add but- ton and add your forecasted values as a new series of data.) According to the two graphs (including actual monthly data and forecasted values), when will Ska be busiest in 2013?
Conclusion
Congratulations, you have just completed a very thorough analy- sis of Ska Brewing Company’s production in barrels and sales figures. At this point, it might be worth reconsidering how accurate forecasts will help Ska. According to Erik, “An accurate sales budget is the root of the entire budgeting process.” In addition, Dave says that accu- rate forecasts would help “tremendously,” allowing Ska to “increase efficiencies from a production standpoint,” and help “make decisions about whether or not Ska could enter any new markets.”
Now it’s time to tie everything together and make your best fore- cast for 2013 for both barrels and sales, including some kind of esti- mate of the error term. Carefully combine your forecasts from 4c, 6b, 7a, 7b, and 8d. Be bold and use a large font— you are an expert now!
Year Forecasted Barrels Forecasted Sales
2013
(Note to students: The actual values for 2013 have not yet been realized as I (the author) prepare this case study. When they become available in early 2014, I will get them from Ska and record them. If you are curious about how good your final forecasts actually were, send them to Dr. Eric Huggins, 3 and I’ll reply with the actual values when they become available.)
3 huggins_e@fortlewis.edu
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Exhibits
Exhibit 1.1 Barrels Sold and Sales Volume at Ska Brewing Company Year BBLS $$$
2000 2,595 $521,050
2001 3,025 $629,866
2002 3,465 $739,153
2003 4,031 $883,378
2004 4,525 $1,011,409
2005 5,273 $1,234,628
2006 6,268 $1,481,759
2007 7,289 $1,754,272
2008 7,943 $2,080,795
2009 11,681 $3,179,390
2010 16,026 $4,376,982
2011 21,258 $5,317,535
2012 25,771 $6,553,145
Exhibit 1.2 Monthly Data for Barrels and Sales (2000–2002) 2000 2001 2002
BBLS $$$ BBLS $$$ BBLS $$$
Jan 196.5 $40,458 243.2 $53,093 290.80 $62,989
Feb 193.2 $35,615 239.9 $48,819 254.80 $53,912
Mar 229.7 $43,306 241.3 $49,782 267.50 $56,477
Apr 190.2 $34,885 214.2 $44,515 252.30 $54,720
May 195.1 $40,879 227.6 $50,671 306.90 $67,387
Jun 261.9 $53,378 309.3 $64,764 323.80 $68,196
Jul 230.2 $46,850 292.5 $59,947 336.80 $71,179
Aug 247.7 $50,118 327.9 $67,821 326.00 $69,643
Sep 210.6 $43,872 226.2 $46,102 272.70 $57,426
Oct 203.3 $41,805 242.9 $50,403 262.90 $55,944
Nov 198.2 $39,317 201.3 $40,892 248.70 $55,639
Dec 238.1 $50,569 258.9 $53,058 321.70 $65,643
Total 2.594.7 $521,050 3.025.2 $629,866 3.464.9 $739,153
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Exhibit 1.3 Monthly Data for Barrels and Sales (2003–2005) 2003 2004 2005
BBLS $$$ BBLS $$$ BBLS $$$
Jan 336.3 $74,218 336.6 $74,072 411.1 $93,769
Feb 269.4 $57,567 317.6 $69,847 374.7 $83,907
Mar 284.8 $58,822 405.6 $86,334 396.2 $95,687
Apr 273.5 $59,089 344 $76,337 388.4 $91,712
May 367.0 $81,849 391.5 $94,853 435.9 $101,683
Jun 383.0 $85,636 492.1 $110,477 526.3 $122,572
Jul 388.3 $87,031 410.9 $90,578 492.1 $114,097
Aug 416.1 $91,560 418.4 $90,768 492.7 $114,925
Sep 292.5 $65,976 411.3 $90,308 449.6 $103,899
Oct 386.0 $84,438 309.6 $69,961 434.3 $104,706
Nov 266.0 $58,368 308.7 $69,073 433.4 $102,354
Dec 368.1 $78,824 378.9 $88,800 437.9 $105,317
Total 4031 $883,378 4525.2 $1,011,409 5272.6 $1,234,628
Exhibit 1.4 Monthly Data for Barrels and Sales (2006–2008) 2006 2007 2008
BBLS $$$ BBLS $$$ BBLS $$$
Jan 455.9 $107,422 598.7 $141,177 581.5 $153,098
Feb 437.2 $101,485 512.4 $124,511 628.7 $163,893
Mar 619.7 $140,082 560.3 $133,152 658.3 $164,180
Apr 368.6 $88,973 628.5 $142,942 628.5 $176,973
May 635.2 $149,576 621.9 $151,621 685.7 $177,043
Jun 587.5 $139,916 780.1 $182,735 661.8 $168,823
Jul 597.8 $141,982 641.9 $152,912 780.8 $201,482
Aug 557.1 $133,007 728.6 $176,702 725.5 $190,317
Sep 567.6 $132,330 571.3 $136,517 626.8 $156,337
Oct 478.3 $115,470 641.2 $159,959 676 $186,388
Nov 424.1 $100,695 418.8 $107,104 518.5 $138,374
Dec 538.8 $130,823 585 $144,939 770.9 $203,889
Total 6267.8 $1,481,759 7288.7 $1,754,272 7943 $2,080,795
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Exhibit 1.5 Monthly Data for Barrels and Sales (2009–2011) 2009 2010 2011
BBLS $$$ BBLS $$$ BBLS $$$
Jan 706.6 $193,481 1,017.3 $267,782 1,272.4 $319,313
Feb 641.3 $170,674 853.3 $225,592 1,275.9 $323,726
Mar 884.8 $228,095 1,124.2 $356,604 1,333.2 $342,353
Apr 862.4 $232,372 999.1 $274,723 1,356 $361,315
May 1,061.3 $288,188 1,434.1 $377,369 2,471.1 $612,500
Jun 1,110 $304,763 1,673.3 $439,907 2,276.3 $564,599
Jul 1,269.5 $337,825 1,626.7 $430,999 2,102.3 $518,422
Aug 1,269.7 $342,121 1,871.7 $485,822 2,556.2 $623,860
Sep 1,147.5 $320,011 1,398 $407,577 1,631.4 $412,091
Oct 1,107.4 $304,756 1,649.4 $450,234 2,140.4 $530,636
Nov 766.1 $221,514 1,111.2 $315,238 1,258.1 $313,034
Dec 854.8 $235,591 1,267.5 $345,135 1,584.3 $395,686
Total 11,681.4 $3,179,390 16,025.8 $4,376,982 21,257.6 $5,317,535
Exhibit 1.6 Monthly Data for Barrels and Sales (2012) 2012
BBLS $$$
Jan 1,484.9 $375,117
Feb 1,520.9 $391,677
Mar 1,624.2 $426,746
Apr 2,136.1 $535,876
May 2,622.2 $659,204
Jun 2,349.6 $582,670
Jul 2,635 $663,534
Aug 2,292.9 $564,901
Sep 2,495.2 $636,399
Oct 2,856.7 $727,822
Nov 2,088.3 $539,011
Dec 1,664.7 $450,188
Total 25,770.7 $6,553,145
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Case 2 Maintaining Financial Success and
Expanding into Other Markets at FeedMyPet.com
Charles A. Wood, Duquesne University
Introduction
The first-ever 10-Q quarterly financial reports have just been filed with the SEC (U.S. Securities and Exchange Commission), and John McCloud is very happy with his company’s performance so far. Feed- MyPet.com just conducted its first IPO last month, and has raised an amazing $89 million after only one year in business (see Exhibit 2.1 ). After starting FeedMyPet.com last February, just a little over a year ago, Cindy Jones, FeedMyPet.com’s COO (Chief Operating Operator), joined John McCloud, founder and CEO (Chief Executive Officer) of FeedMyPet.com, to review the year’s company activities.
Like many new startups, FeedMyPet.com had a rough time get- ting started. Expenditures were high, especially in the area of adver- tising, which was necessary to increase name recognition. There were also some problems with an inadequate business plan formulation and a lack of initial market research that caused some industry analysts (“negativos” as McCloud calls them) to be unenthusiastic about the company, but the market has spoken, and McCloud couldn’t help but smile as he thought of the investing community—the true visionaries
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in the business world—who flocked to the recently held IPO. The additional capital will allow the firm to accomplish amazing things.
FeedMyPet.com is an online business that sells pet food, pet accessories, and pet supplies direct to consumers over the Internet. The public offering raised so much money that FeedMyPet.com was able to purchase its main competitor, KennelTime.com, leaving it to rule the entire online pet marketplace. McCloud attributes his vast success to several factors:
• FeedMyPet.com has a dedicated support staff of 280 people (whereas most online pet shops have around 30 employees). This staff is much more responsive to customer needs than the skeleton staff at other online pet product companies.
• FeedMyPet.com offers free shipping. Dog food bags and cans are heavy, and the cost of shipping can approach the cost of the dog food. Some competitors have tried to pass these costs on to the consumer, thus alienating the client base; but FeedMyPet. com did not make that mistake and has been rewarded with the largest market share of any online pet product retailer.
• FeedMyPet.com has an advertising campaign that spans across a variety of media, including TV, print, radio, web-based ads, and even its own FeedMyPet.com magazine. This marketing was important, especially right before the IPO, when advertis- ing drove up the price of the initial stock!
The advertising campaign started last year with a 5-city advertis- ing campaign rollout and has now expanded to 10 cities, and has finally gone nationwide with a $1.4 million Super Bowl ad that introduced the country to its answer as to why customers should shop at FeedMy- Pet.com: “Because Pets are People Too!” The cute ad featured a large man in a dog costume, won several awards, and had the highest recall of any ad that ran during the Super Bowl. Name recognition was at an all-time high. After the ad, FeedMyPet.com went public with an IPO that raised millions. McCloud is sure that extensive advertising has
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played a large part in FeedMyPet.com’s success and that the advertis- ing budget will continue to grow to ensure that the company remains successful.
To deliver pet supplies, FeedMyPet.com made significant invest- ments in infrastructure such as computer networking and data ware- housing. FeedMyPet.com’s management maintained that the company needed to realize a revenue run rate that supported this infrastructure build-out. McCloud’s fellow executives believe that revenue needs to approach $400 million to hit the break-even point, and that it will take a minimum of four to five years to hit that run rate. But investors are still on board; with the stock price on the way up, McCloud perceives no problem with cash flow at FeedMyPet.com.
As McCloud and Jones contemplate the future, they discuss two important topics that require Jones to conduct some additional investigation:
• Maintaining financial success— Clearly, FeedMyPet.com has become the de facto leader in the online pet products industry in a fairly short time. Investment and revenues have followed this market leadership. How might FeedMyPet.com’s leadership leverage into other ventures? And how does FeedMyPet.com not only dissuade future online competitors, but also coerce current offline “bricks and mortar” shoppers to move their shopping for pet products online?
• New marketing plan— Marketing has clearly been the key to FeedMyPet.com’s success. The Super Bowl ad drove up invest- ment, and all the previous marketing campaigns have increased market share. Now that FeedMyPet.com is flush with capital, additional marketing plans should be considered.
With so much available cash, McCloud feels comfortable in attack- ing these issues. McCloud and Jones have discussed various responses to these topics, and the $89 million that they have raised in the recent IPO will fund strategies that heretofore have not been possible.
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Maintaining Financial Success
Jones’s first task is to ensure FeedMyPet.com maintains its strong financial position. Both McCloud and Jones agree that FeedMyPet. com is in an excellent financial position, especially after the last infu- sion of $89 million in capital from the recent IPO. McCloud knows that there is a reason for the investor exuberance. Exhibit 2.5 (taken from data found in Exhibit 2.3 ) graphically shows the explosive growth in sales since the company’s inception in the second quarter of last year.
The growth in revenue has been a boon to the company. Total assets have increased 25% from the second quarter to the third quar- ter. Exhibit 2.6 (taken from data found in Exhibit 2.2 ) shows the increase in asset value of the company during the same period.
McCloud and Jones discuss how the explosive growth in revenue has sent a strong message to the investor community that FeedMyPet. com is not just a niche small company, but a major player in online retail, and a safe yet profitable investment.
In addition, there are some challenges when operating in the pet supplies industry. Dog food is heavy and costly to ship. Also, to gain market share and name recognition before competitors could swoop into this lucrative market, FeedMyPet.com was very aggressive in the initial pricing of its products, paying $16 million for goods sold to cus- tomers for $7 million. This means that in that initial period, for every dollar that FeedMyPet.com paid employees, pet food manufacturers such as Purina, and delivery services such as UPS, it charged the cus- tomer about 44 cents. BusinessWeek 1 notes that operating margins for pet products retailers are typically much higher; for example, offline “bricks and mortar” pet supplies companies such as Petco typically post a profit margin of up to 4.5%. Jones is confident that after the convenience of online pet delivery catches on, FeedMyPet.com will
1 Arlene Weintraub and Robert D. Hof, “For Online Pet Stores, It’s Dog-Eat- Dog,” BusinessWeek , March 6, 2000.
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19
be able to command an even higher 20%–30% operating margin. Customers are very loyal, and appreciate having pet products, like dog toys and bird food, shipped directly to them rather than forcing the customers to visit a store.
The question remains as to how to best ensure that the success of FeedMyPet.com continues into the future. McCloud and Jones both understand that weak financial positions can drive down stock price and can be exploited by new entrants into the lucrative online pet products market space. Jones’s first task is to examine the financial statements (found in the exhibits) to examine how to continue this strong performance into the foreseeable future.
New Marketing Plan
Jones’s second task is to formulate a new marketing plan that picks up from the highly successful previous marketing plan. McCloud believes that marketing was the key to FeedMyPet.com’s initial suc- cess, and Jones is expected to devise a plan to continue that market- ing success...no small feat, to be sure! There are several advertising mediums that are available to FeedMyPet.com:
• TV advertising— This is the most expensive type of advertis- ing, but the amazing results with the Super Bowl ad show its effectiveness.
• Magazine/newspaper— Magazines ads are one of the most targeted advertising mediums. For example, an advertiser can select a pet magazine in which to advertise. Newspapers facili- tate geographic targeting.
• Direct mail— Direct mail allows a company to target an indi- vidual, regardless of online access. It is often inexpensive, and a company can purchase a potential customer list based on demographic information.
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• Telemarketing— Telemarketing requires a temporary service or employees, but allows a script to be delivered to the target over the phone lines. However, some states enforce a do-not- call list that might interfere with the marketing effort.
• Search engine optimization (SEO)— This is probably the most cost-effective method of online advertising. With SEO, you design your web pages and links to optimize their appear- ance within a web page within the “free” listings, which are then often clicked by the end user.
• Pay per click (PPC)— PPC advertising is when search engines charge companies for each click. The bright side of this adver- tising is that you only pay for the clicks you receive. The down- side is that many end users avoid clicking on advertisements.
• Email marketing— Email marketing, often called spamming, is reviled by many recipients. However, the positive side is that you can easily send out millions of emails for free after you have the email addresses, and a very small percentage of conversions can result in a large increase in sales.
Jones is aware of the various costs of each advertising medium (see Exhibit 2.4 ). FeedMyPet.com has proven itself willing to spend quite a lot for advertising. Exhibit 2.7 shows how much FeedMyPet. com has spent on advertising since its inception; although, a leveling off has been observed in the last two quarters, perhaps indicating an optimal advertising level for the company.
Final Thoughts on Analysis
Jones has to hit the ground running on these two goals. First, she needs to isolate any potential problem areas in the financial state- ments. With $89 million, the company is sure to be set for years to come, and it will benefit the company to leverage its new revenue to
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21
make the company even more profitable and competitive. Second, she has to develop a marketing plan that allows the company to con- tinue its upward path. Perhaps viewing the numbers as a percentage of sales or developing a nonlinear trend line in Excel to project future financial statement values is in order.
Exhibits
Exhibit 2.1 FeedMyPet.com IPO Capital Raised Statement Price Per Share Shares Total Revenue
Public offering price $12.00 8,000,000 $96,000,000
Underwriting discount $0.88 8,000,000 $7,040,000
Proceeds, before expenses, to FeedMyPet.com
$11.12 8,000,000 $88,960,000
Exhibit 2.2 Quarterly FeedMyPet.com Balance Sheet Data Last Year This Year
Third Quarter Fourth Quarter First Quarter
Cash and cash equivalents
$52,172 $43,482 $84,137
Current assets $54,275 $51,967 $110,096
Total assets $69,695 $86,846 $137,750
Current liabilities $8,934 $10,903 $16,402
Total liabilities $10,231 $11,028 $17,376
Total stockholders’ equity, including convertible preferred stock
$59,464 $75,818 $120,374
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Exhibit 2.3 FeedMyPet.com Statement of Operations (in Thousands) Last Year This Year
Second Quarter
Third Quarter
Fourth Quarter First Quarter
Net sales $47 $682 $6,202 $9,181
Cost of goods sold ($91) ($2,119) ($13,884) ($15,018)
Gross profit ($44) ($1,438) ($7,682) ($5,837)
Operating expenses:
Marketing and sales 1,346 12,832 36,811 34,626
Product development 1,949 2,633 3,175 3,223
General and administrative
1006 1,446 2,653 2,797
Amortization of stock- based compensation
-- 1,367 1174.8 1,290
Total operating expenses
($4,301) ($18,277) ($43,814) ($41,936)
Operating profit (EBIT)
($4,345) ($19,715) ($51,497) ($47,773)
Interest income $148 $692 $589 $868
Net income ($4,198) ($19,022) ($50,908) ($46,906)
Outstanding shares 1,744 1,744 1,760 9,760
Earnings per share (EPS) (in dollars)
($2.41) ($10.91) ($28.92) ($4.81)
Exhibit 2.4 Advertising Medium Costs 2 Setup Process Setup Cost Cost of Media
National TV ad spot
Design + production
$50,000–$750,000 $35,000 to $2 million per 30-second spot
National magazine
Design $1,500–$20,000 $3,000–$25,000 per full-page ad per issue
National newspaper ad
Design $1,500–$20,000 ~$28,000 per half- page ad per day
Direct mail Design $1,500–$15,000 ~$2.20 per addressee
2 From WebPageFX, 2009, last accessed Feb. 26, 2013, available at http://www. webpagefx.com/blog/business-advice/the-cost-of-advertising-nationally-broken- down-by-medium/ .
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http://www.webpagefx.com/blog/business-advice/the-cost-of-advertising-nationally-brokendown-by-medium/
http://www.webpagefx.com/blog/business-advice/the-cost-of-advertising-nationally-brokendown-by-medium/
http://www.webpagefx.com/blog/business-advice/the-cost-of-advertising-nationally-brokendown-by-medium/
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Setup Process Setup Cost Cost of Media
Telemarketing Script writing $1,000–$4,000 $20–$60 per hour per outbound caller
National SEO Website configuration
$4,000–$10,000 ~$500/month to Internet marketer
National PPC Campaign configuration
$4,000–$10,000 5¢–$3 per qualified visitor
National email marketing
Email template design
$4,000–$10,000 ~$500/month to Internet marketer
Exhibit 2.5 Net Sales Quarter by Quarter
$10,000
Second Quarter Third Quarter Fourth Quarter First Quarter
$9,000
$8,000
$7,000
$6,000
$5,000
$4,000
$3,000
$2,000
$1,000
$0
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Exhibit 2.6 Asset Growth in the Previous Three Quarters
$160,000
$140,000
$120,000
$100,000
$80,000
$60,000
$40,000
$20,000
$0
Third Quarter Fourth Quarter First Quarter
Exhibit 2.7 FeedMyPet.com Quarterly Expenditures on Advertising
$40,000
Second Quarter Third Quarter Fourth Quarter First Quarter
$35,000
$30,000
$25,000
$20,000
$15,000
$10,000
$5,000
$0
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Case 3 Forecasting Offertory Revenue at St.
Elizabeth Seton Catholic Church *
Matthew J. Drake, Duquesne University
Ozgun Caliskan-Demirag, Pennsylvania State University—Erie, The Behrend College
Introduction
Fr. Clyde Jarreau could not sleep early in the morning of Octo- ber 10, 2005. The evening before, he had presided over his parish’s monthly finance committee meeting, where concerned parishioners examined the church’s monthly financial statements and provided recommendations to keep the organization on track financially. At the previous night’s meeting, a few of the committee members continued to voice their concern that spending was out of control. The church’s bank account balances had fallen sharply for the sixth month in a row, and the committee members were worried that the church would run out of funds sometime early in 2006.
Fr. Jarreau appreciated their commitment to the parish, but he did not need them to remind him of the church’s financial struggles. As the pastor of the church, he was greeted by the stack of unpaid bills dominating his desk every time he entered his office. He also saw the
* Finalist in the 2011 INFORMS Case Competition
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stagnant, if not dwindling, weekly offertory collection figures that he had to publish in the weekly bulletin. With expenses increasing with- out the additional revenue from collections to cover them, Fr. Jarreau knew that he would have many more sleepless nights if he could not find a way for the church to live within its financial means.
After contemplating the problem over a cup of coffee in the rec- tory’s kitchen, Fr. Jarreau knew that he could not construct a solution to such a big problem by himself. He decided to place a call in the morning to a few of his most trusted advisors on the finance commit- tee. These people had been parishioners at the church for more than 15 years, predating himself by a half dozen or so years. They knew the history of the parish over this time and had seen the financial position deteriorate over the past few years, as well. Fr. Jarreau knew that they would do anything they could to help the parish; he only hoped that he was not reaching out to them too late.
St. Elizabeth Seton Catholic Church
Fr. Jarreau’s parish, St. Elizabeth Seton, was founded in 1976 in Daphne, Alabama, a small Gulf town just outside of Mobile. The par- ish grew rapidly throughout the 1980s and 1990s as a large number of workers from the northern United States moved into the south, chas- ing both displaced jobs and better weather. Although the church itself is the same size as the original building built in 1976, the parish con- ducted two successful capital campaigns in the subsequent decades after the parish was founded. The first campaign, kicked off in 1985, raised funds to build an educational building for religious education classes for children and adults. The second campaign, begun in 1996, enabled the church to build new offices for its staff and parishioner organizations.
By the year 2000, membership in the church was strong, and the cash reserves were rising each month as parishioners gave generously
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each week. With the crash of the dot-com bubble in late 2001, how- ever, offertory revenue slid in 2002, and a few years passed before it showed signs of any significant recovery. In an effort to revitalize the church and in keeping with the historical 10-year cycle, Fr. Jarreau spearheaded a new capital campaign toward the end of 2004 with the goal of raising money to build a new recreational hall for the church. This would enable the parish to hold more fellowship activities, as well as generate additional sources of revenue by hosting wedding receptions and other banquets. Unfortunately for the pastor, these new revenue streams would only begin after the building was com- pleted in early 2007.
When Fr. Jarreau initially discussed the new capital campaign in the summer of 2004, several members of the finance committee were worried that many parishioners would simply direct a large portion of their weekly offertory contribution to the new capital campaign. This would severely hinder the church’s ability to meet its normal operat- ing expenses. Luckily, however, Fr. Jarreau’s explanation of the capi- tal campaign had largely convinced the parishioners to support it in addition to maintaining their normal weekly offertory contributions. The offertory figures thus far in 2005 appeared to be unaffected by the capital campaign.
Cash Flow Analysis
Fr. Jarreau was able to arrange a meeting with his trusted finance committee members, John Gust and Charlie Stewart, a few nights later. When they arrived in his office around 7 p.m., Fr. Jarreau wasted no time summarizing the problem facing the church. “Our bank balances have consistently fallen throughout this year. At this rate, it looks like the parish is going to be out of money by this time next year. What do you guys think we should do?” Charlie and John had thought that they were going to have to open Fr. Jarreau’s eyes to
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the church’s financial problems in this meeting, but now it was obvi- ous that the pastor understood them all too well. After a brief sigh of relief, Charlie started, “John and I have been members of the finance committee for years, and we’ve got a lot of ourselves invested in this parish. We’ve been trying to suggest subtly that the church’s spend- ing was getting out of control, but now it appears as if the time for subtlety has passed. We need to drastically rein in expenses.”
Fr. Jarreau recalled their previous concerns but could not recon- cile one aspect of the church’s financial operations. “But the Archdio- cese requires that we have a balanced budget each year. They won’t accept a budget from us that isn’t balanced. How could we be in this situation with a balanced budget?”
John chimed in, “Well, Father, the problem seems to be with the budget process itself. In my opinion, we’ve been doing the whole thing backwards. We have been asking each department head to sub- mit his or her expected expenses for the upcoming year, and we have most of our discussions as a committee about these expenses.
“I’m not saying that expenses aren’t important, but we haven’t spent nearly enough time trying to estimate the revenue from our weekly offertory collections. In the past, we’ve basically just estimated whatever revenue we needed to cover the estimated expenses and plugged that number into the budget without any real thought as to whether we could actually expect to collect those amounts. We need to start the budget process with the revenue piece this year and make sure that we have a realistic estimate of our collections. Then we can try to estimate expenses that coincide with these revenue projections.”
Fr. Jarreau liked what he had just heard from John and Charlie. It was obvious to him that the old budgeting process had some fatal flaws which could not be allowed to continue. The advisors’ recom- mendations made a lot of sense to him. He knew that the department heads would complain about the significant spending cuts that would likely be required with a more realistic revenue estimate, but the financial viability of the entire parish was at stake. The department
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heads would simply have to prioritize between expenditures that they absolutely had to make and those that they could live without. It seemed like a much better idea to allow increased spending later on if collections turned out to be higher than expected, rather than have to cut expenditures that the department heads had planned to make as of the beginning of the year.
Budgeting for 2006
Because the budgeting process was scheduled to begin at the November meeting of the finance committee, Fr. Jarreau decided to call an additional meeting in the meantime to inform the committee about the new focus on revenue projection during the budgeting pro- cess. At this meeting, he asked the group for suggestions about how the offertory revenue could be predicted.
Frank Lawson, a vocal member of the committee but one who usually spent more time looking at his watch at meetings than actually contemplating the issues at hand before he spoke, characteristically blurted, “Why don’t we just use the current year’s actual offertory and be done with it? Whatever we collected this past January can be the forecast for this coming January. We should be spending more time thinking up additional ways to raise money beyond the collection basket. We need to be increasing the revenue to enable us to meet the expenses that we have now.”
Trying in vain to conceal her exasperation, Megan Fisher, demand planner at a local consumer packaged goods company, responded quickly,
“You’ve been on this committee long enough, Frank, to know that the offertory collection is overwhelmingly the largest component of the church’s total revenue. Any additional fundraising that we do is fine, but it’s not going to totally make up for offertory projections that are way off.
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“One of the most important parts of my job is to produce weekly forecasts of demand for our various product lines to make sure that we plan to have enough units to satisfy our customers. Why don’t I take some time over the next few weeks and use some of the models that I use at work to forecast the offertory collections for each month of next year? That can be a starting point for our budget meeting in November.”
Megan turned to Ernie Jackson, the church’s bookkeeper. “Ernie, how much past data about the offertory collections can you get me? The more the better!”
“I think I can get you the last four years’ worth of data. That shouldn’t be a problem. Oh, and I’ll also get you a list of the dates of the Holy Days for each year. That should have some kind of effect on the collections in those months because parishioners are obligated to attend Mass those days.”
“Sounds good, Ernie. That’s a great point about the Holy Days. I wasn’t thinking about those. I wonder if the offertory revenue is related to any other factors. I’m going to have to think about those when I run the models. I’ll let you know if I need any more data from you.”
As the meeting wound down, Fr. Jarreau started to feel a little better about the church’s future. Certainly some difficult financial decisions were on the horizon, but at least the committee had a plan that they were committed to and should help to stabilize the net cash outflows. He prayed that Megan would get the whole budgeting pro- cess off to a good start by producing a good forecast.
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Exhibits
Exhibit 3.1 Monthly Offertory Revenue from July 2001 to September 2005 Month 2001 2002 2003 2004 2005
January $110,492.56 $92,298.44 $98,005.33 $131,627.02
February $90,979.03 $78.930.37 $114.943.12 $90,711.98
March $128,952.91 $111,539.47 $88,289.13 $108,976.43
April $79,301.47 $102,117.76 $100,502.85 $123,005.88
May $76,936.52 $79.484.64 $111,646.53 $93,311.73
June $94,806.21 $101,758.24 $83,580.73 $82,907.85
July $99,061.10 $77,038.89 $85,851.77 $81,039.41 $97970.72
August $89,066.57 $82,764.19 $98.602.05 $107,677.54 $78,723.84
September $115,003.28 $104,756.91 $79,139.66 $85,619.97 $83,625.49
October $86,224.72 $79,724.52 $79,178.51 $111,837.81
November $92,264.05 $96,470.47 $115,691.27 $82,599.90
December $181,938.85 $160,005.98 $155,950.77 $158,685.01
60000
80000
100000
120000
140000
160000
180000
200000
20 01
M 07
20 01
M 09
20 01
M 11
20 02
M 01
20 02
M 03
20 02
M 05
20 02
M 07
20 02
M 09
20 02
M 11
20 03
M 01
20 03
M 03
20 03
M 05
20 03
M 07
20 03
M 09
20 03
M 11
20 04
M 01
20 04
M 03
20 04
M 05
20 04
M 07
20 04
M 09
20 04
M 11
20 05
M 01
20 05
M 03
20 05
M 05
20 05
M 07
20 05
M 09
Exhibit 3.2 Graph of Monthly Offertory Revenue from July 2001 to September 2005
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Exhibit 3.3 List of Catholic Holy Days of Obligation or Major Feast Days from 2001–2006 Holy Day/Feast 2001 2002 2003 2004 2005 2006
Solemnity of Mary
Jan 1 Jan 1 Jan 1 Jan 1 Jan 1 Jan 1
Ash Wednesday Feb 28 Feb 13 Mar 5 Feb 25 Feb 9 Mar 1
Easter Sunday Apr 15 Mar 31 Apr 20 Apr 11 Mar 27 Apr 16
Ascension May 24 May 9 May 29 May 20 May 5 May 25
Assumption of Mary
Aug 15 Aug 15 Aug 15 Aug 15 Aug 15 Aug 15
All Saints’ Day Nov 1 Nov 1 Nov 1 Nov 1 Nov 1 Nov 1
Immaculate Conception
Dec 8 Dec 8 Dec 8 Dec 8 Dec 8 Dec 8
Christmas Day Dec 25 Dec 25 Dec 25 Dec 25 Dec 25 Dec 25
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Case 4 Pizza Station
Kathryn Marley, Duquesne University
Gopesh Anand, University of Illinois at Urbana–Champaign
Background
Established in 1980, Pizza Station is located in the trendy down- town area of Salina, Pennsylvania. Situated within walking distance of Salina State College, the restaurant initially offered in-house dining and a variety of food items on its menu. However, as competition among local restaurants grew, Pizza Station’s staff decided to limit their offerings to delivery of pizzas in early 2001. Since then, they have developed a loyal following among customers who have come to expect quick and reliable delivery of good-quality pizza from the res- taurant. Nevertheless, in the past two years, manager Tom Smith has noticed that customer complaints have increased significantly. With new pizza outlets and other restaurants opening up in the area every year, Tom is concerned that unless changes can be made quickly, Pizza Station will lose market share, and might eventually have to close its doors permanently.
Pizza Station operates seven days a week. On Sunday through Thursday, the hours are noon through 1 a.m. On Fridays and Sat- urdays, the hours are noon through 3 a.m. The busiest hours are on Fridays and Saturdays between 9 p.m. and 2 a.m. Demand for pizza
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varies throughout the week and times of day. From Sunday through Thursday, the average daily demand is 300 pizzas. Fridays and Sat- urdays are busier, with average demand increasing to 650 pizzas per day. On these two days, the demand during each busy 9 p.m.–2 a.m. period averages 400 pizzas. Currently, Pizza Station is promising a delivery time of 45 minutes to customers.
Tom recently hired Kate Fox, a business major from Salina State College, to manage the weekend shift. He asked her for assistance in identifying the necessary changes that would enable Pizza Station to decrease complaints, increase customer satisfaction, and win back lost customers. Kate recently completed a Lean Six Sigma training course as one of her business school classes; and, eager to apply some of the things she learned, she sat down with Tom to discuss the situation.
“I don’t know where we went wrong and, frankly, I don’t know where to begin!” exclaimed Tom. “All I know is that our troubles seem to have suddenly multiplied since last January when the students came back to campus. Things started off normally, but over the next three months I noticed a steadily increasing number of complaints.” Tom pulled out a file folder from the bottom of a stack on his desk. Inside were papers of varying sizes with notes scribbled on them. He squinted as he tried to read them. “This customer said the crust was too thin, while this one said the delivery time was too long.” As he read from the stack of mismatched notes, Kate realized that they were going to have to implement a better system for capturing customer feedback—and fast, if they were going to turn this place around.
“Okay, Tom, I get the idea,” said Kate. “Let’s start at the begin- ning. We need to approach this problem from a systematic process improvement perspective—which first involves figuring out what is the voice of the customer.” Tom looked confused. Kate continued, “The voice of the customer (or VOC) consists of customer require- ments, which is what the customer is expecting Pizza Station to deliver. There is no chance that customers are going to keep ordering pizza from Pizza Station if these expectations are not being met. So
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you need to capture this information to know where to begin to make changes.”
“Sounds great, Kate,” said Tom. “Let’s get started!”
A customer satisfaction study was commissioned to figure out the voice of the customer (VOC). It pointed to delivery time and crust thickness as being critical to quality (CTQ) characteristics. An anal- ysis of recent sales data revealed that the most commonly ordered crust from Pizza Station was the unique medium crust. In addition, three focus groups with eight customers each revealed that the ideal medium pizza crust was found to be between 4.25 mm and 5.75 mm. To measure what the process was actually producing (voice of the process, or VOP), Kate took a sample of five medium pizza crusts every day over a period of 30 days, and measured their thickness. The data that she collected is provided in Exhibit 4.1 .
Kate’s training in Lean Principles also prompted her to talk with the employees who actually work on the pizza-making line. As she told Tom, people working on the frontlines of any process know the most about how the work is done. From the spirited discussion that Kate had with the staff, it soon became apparent that they believed the task of order-taking had problems. So, she asked them to collect data on this task. For 30 days, they took samples of 50 orders every day, inspected them, and recorded all the errors involved. This data is provided in Exhibit 4.2 .
Next, choosing one of the busiest times at Pizza Station, Kate walked the process to map the value stream for pizza-making and delivery. She explained to Tom that this exercise was aimed at 1) getting some measurements of different tasks in the process, and 2) gaining additional insights into the current length of delivery time. Because the peak demand period for Pizza Station is Friday nights between 9 p.m. and 2 a.m., Kate and Tom walked the process at that time and observed the following steps involved in making pizzas. Their observations are described in the following sections.
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There are a total of five employees in the store on Friday nights, along with nine delivery drivers on staff. The pizza-making process begins with orders received by phone. Next, pizzas are assembled and baked. Finally, the pizzas are cut, boxed, labeled, and delivered to the customers.
Ordering
There are no designated operators who answer the phones at Pizza Station. The phones are answered by whoever is “nearby” at the time. That can be any of the four employees who are working the pizza line during the shift, with the exception of the employee who is dedicated to the baking process. It is estimated that each of the four employees spends 20% of her time answering phones. Kate watched the order-taking process for 15 minutes. During that time, orders for 20 pizzas were received. After the customer places the order, the employee who took the order informs the customer about the price and the estimated delivery time. The order is written on a note pad and hung on a board for the assembly station workers to retrieve as they become available. Kate observed during those 15 minutes that there were orders for 5 pizzas waiting to be assembled; an order waits on average 225 seconds before moving to assembly.
Pizza Assembly
Each of the four employees dedicates 60% of her time to assem- bling pizza. After an order is received, an assembly worker retrieves a ball of pizza dough from the refrigerator at the back of the store. Employees only retrieve one ball of pizza dough at a time. It takes, on average, three minutes for one employee to walk to the refrigera- tor and back every time an order is received. The worker begins by
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flattening the dough to the desired thickness and forming the crust. Next, the worker drizzles oil on the crust and assembles the pizza, which includes adding sauce, cheese, toppings, and seasonings. Kate noticed that this process took an average of 90 seconds per pizza. After the pizza is completed, it is placed on a tray until there is an available rack in the oven. Kate observed six pizzas waiting to be baked. A pizza waits 270 seconds before baking on average.
Baking
The oven used for baking the pizzas is set at 500 degrees to ensure crisp and efficient baking. The baking process takes nine minutes and the oven can hold six pizzas at a time. There is one worker at the oven station, who dedicates 100% of her time to putting the pizzas in the oven and removing them, as well as monitoring the baking time of each pizza. After the pizza is baked, it is removed and placed on a large wooden tray. Kate observed 15 pizzas waiting to be cut. A pizza waits an average of 675 seconds before moving to the next station.
Cutting/Boxing/Labeling
The four employees dedicate 20% of their time to cutting and boxing the pizzas. When the pizza is removed from the oven, a worker uses a metal cutter to cut the pizza into the appropriate number of slices. Then he assembles the box and places the cut pizza in the box. The employee then goes to the order station, retrieves the address information from the order slip, and writes this information on the pizza box. The boxed pizza is placed on the delivery counter and waits for delivery. Cutting the pizza takes an average of 5 seconds. Making the box takes 20 seconds. Placing the pizza in the box and closing the lid takes 5 seconds on average. Retrieving the order information and
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writing the information on the box takes 90 seconds on average. Kate observed 10 pizzas waiting to be delivered, and that, on average, the pizzas wait 450 seconds before being taken out for delivery.
Delivery
There are nine dedicated delivery drivers. When a driver returns from a delivery, she checks to see whether there is another pizza wait- ing to be delivered. If there is, she proceeds with delivering that pizza. If there is not, she is free to wait in the break room until there is a pizza waiting to be delivered. Each delivery driver delivers one order at a time. Because the majority of Pizza Station customers live within a five-mile radius of the restaurant, a worker is usually able to deliver a pizza and be back in the restaurant within 18 minutes; therefore, the delivery time to the customer is approximately 9 minutes.
Suppliers
Tom Smith is in charge of ordering all the supplies and ingredi- ents for the restaurant. The pizza dough is made by a local bakery and delivered once a week by truck to Pizza Station. Each delivery consists of 2,500 pizza crusts. After the crusts are received, they are stored in a large refrigerator in the back of the storeroom. Because the delivery day varies, on average there are 1,250 balls of dough on hand.
Analysis
After walking the process with Tom, Kate started scrutinizing the current state of operations as depicted in the value stream map to con- sider potential areas of improvement. Tom was skeptical but excited.
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“Let’s get going, Kate; I don’t want to lose one more customer if I can help it. Let’s get Pizza Station back on top where we belong!”
Assume you are a new addition to Kate’s Lean Six Sigma team. Answer the following questions:
1. Based on the sample data on pizza thickness collected for the 30-day period and presented in Exhibit 4.1 , construct an X-bar–R chart. Include the information on customer require- ments obtained through the focus groups, conduct a process capability analysis, and interpret its result.
2. Based on the data on errors in order-taking provided in Exhibit 4.2 , construct a Pareto chart to identify the areas that should be top priorities for Pizza Station.
3. Compute the DPMO and sigma level of the order-taking task using the data in Exhibit 4.2 .
4. Using the data on total daily errors provided in Exhibit 4.2 , conduct an analysis of variance (ANOVA) test to determine whether there is a significant difference in errors on different days of the week.
5. Construct the appropriate control chart (based on the nature of the data collected) for the total number of defects or errors per 50 orders shown in Exhibit 4.2 . Is this process in statistical control?
6. What is the Takt time for this process (in seconds)? (Note: Because the information was gathered during the Friday eve- ning shift, use that time period for this analysis.)
7. Draw a Current State Map of this pizza-making process. Pizza Station is quoting a delivery lead time of 45 minutes to its cus- tomers. What is the total lead time between ordering and deliv- ery? Is Pizza Station capable of meeting this promise based on your Current State Map calculations?
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8. Develop a list of the symptoms that indicate problem areas in the pizza-making and delivery value stream. Provide sugges- tions for how the problems underlying the symptoms might be reduced or eliminated.
9. Draw a Future State Map of the pizza-making process, incor- porating the changes you suggested in #8.
10. Develop an implementation plan for your suggested improve- ments at Pizza Station.
Exhibits
Exhibit 4.1 Pizza Crust Thickness (in Millimeters) Sample Number Observations
1 2 3 4 5
1 5.33 5.44 5.21 5.3 5.2
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