Configuring a Supply Chain to Reduce the Cost of Demand Uncertainty

J. H. Hammond; Marshall L. Fisher; Walter  Obermeyer; A.  Raman

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Chapter | Global Supply Chain and Technology Management | 1998

Configuring a Supply Chain to Reduce the Cost of Demand Uncertainty

by J. H. Hammond, Marshall L. Fisher, Walter Obermeyer and A. Raman

Keywords: Supply Chain Management; Cost Management; Demand and Consumers; Risk Management; Risk and Uncertainty;

Format: Print

Citation:

Hammond, J. H., Marshall L. Fisher, Walter Obermeyer, and A. Raman. "Configuring a Supply Chain to Reduce the Cost of Demand Uncertainty." In Global Supply Chain and Technology Management. Vol. 1, edited by Hau Lee and Shu Ming Ng, 76–90. POMS Series in Technology and Operations Management. Miami: Production and Operations Management Society (POMS), 1998.

About the Authors

Janice H. Hammond

Jesse Philips Professor of Manufacturing
Senior Associate Dean for Culture and Community

Technology and Operations Management

View Profile »
View Publications »

Ananth Raman

UPS Foundation Professor of Business Logistics
Chair, OPM

Technology and Operations Management

View Profile »
View Publications »

More from these Authors

Case | HBS Case Collection | November 2017 (Revised May 2019)

Wayfair: Fast Furniture?

Janice H. Hammond and Anna Shih

Keywords: Supply Chain; Logistics; Distribution; Web; Retail Industry; United States;

Citation:
Hammond, Janice H., and Anna Shih. "Wayfair: Fast Furniture?" Harvard Business School Case 618-036, November 2017. (Revised May 2019.) View Details

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Case | HBS Case Collection | November 2018

David Hysong and SHEPHERD Therapeutics

Ananth Raman, John Masko and Aldo Sesia

In 2016, David Hysong, at age 27, found out he had a rare, incurable cancer. Rather than wait around to die, Hysong, a recent graduate of Harvard Divinity School, decided to launch a biotechnology company called Shepherd Therapeutics to development treatments for his and other rare cancers. Two years later, the company had several drugs in promising stages of development but were at least 3 to 5 years away from commercialization, if they made it through clinical trials successfully. Only about 4% of drugs that enter clinical trials receive U.S. FDA approval. It is October 2018, and Shepherd is running out of money. Hysong needs to raise $5 million to fund operations for 2019 and at least $20 million for the following few years. But the company is too early to attract the type of venture capital he needs. Even if he got financing, Hysong worried that the company’s mission could be compromised by investors more interested in a return on their investment than saving lives, and he worried about the scaling challenges ahead.

Keywords: cancer; therapeutics; drugs; Health Care and Treatment; Business Startups; Product Development; Financing and Loans; Growth and Development Strategy; Problems and Challenges;

Citation:
Raman, Ananth, John Masko, and Aldo Sesia. "David Hysong and SHEPHERD Therapeutics." Harvard Business School Case 619-012, November 2018. View Details

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Case | HBS Case Collection | October 2014 (Revised August 2018)

Caesars Entertainment

Janice H. Hammond and Aldo Sesia

This case describes the introduction of a regression analysis model for forecasting guest arrivals to Caesars Palace hotel in Las Vegas, Nevada. The company will use the forecast to staff the front desk in the hotel. The staff is unionized and the company has little flexibility to change staffing levels on a short-term basis. The case is set in the context of industry overcapacity and lower customer demand.

The case describes several models that could be used to forecast guest arrivals, including a moving average technique and a multiple regression model. The multiple regression model includes over 40 independent variables, including dummy variables (e.g., to represent day of week, month, year, holidays, paydays) as well as continuous variables to represent customer segment and average daily room rate. The case contains tables showing the output of the regression model, and compares the fit of the moving average and regression models. The case allows students to understand how such a model is developed within an organization and to evaluate the models presented. Students may work with a data file with several years of historical data or they may work with the model description and output results in the case.

Keywords: Forecasting; staffing; gaming; gaming industry; hotel industry; Decision Making; Forecasting and Prediction; Human Resources; Selection and Staffing; Entertainment; Games, Gaming, and Gambling; Operations; Service Delivery; Service Operations; Accommodations Industry; Travel Industry; Tourism Industry; Food and Beverage Industry; Las Vegas;

Citation:
Hammond, Janice H., and Aldo Sesia. "Caesars Entertainment." Harvard Business School Case 615-031, October 2014. (Revised August 2018.) View Details

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Configuring a Supply Chain to Reduce the Cost of Demand Uncertainty

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