Doctoral Student
Nathan Charles Craig
Nathan Craig is a doctoral student in Technology and Operations Management. He holds a BS in Industrial and Systems Engineering and an MS in Operations Research from the Ohio State University. His prior experience includes projects for Honda of America, Digital Automation Associates, American Electric Power, Intel, and Nationwide Children's Hospital.
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Working Paper
| HBS Working Paper Series
| 2013
The Impact of Supplier Reliability Tracking on Customer Demand - OK TO HARD DELETE
Nathan Craig, Nicole DeHoratius and Ananth Raman
To set inventory service levels, firms must understand how changes in service level affect customer demand. While the effects of service level changes have been studied empirically at the level of the end consumer, relatively little is known about the interaction between a retailer and a supplier. Using data from a manufacturer of branded apparel, we show increases in service level to be associated with statistically significant increases in retailer orders (i.e., demand, not just sales). Controlling for other factors that might affect demand, we find a 1 percent increase in historical service level to be associated with a 12 percent increase in demand from retailers, where historical service level is the type 1 service level performance of the apparel manufacturer over the prior year. Further, we find that retailers that order frequently exhibit a more substantial reaction to changes in service level, an outcome that is consistent with retailers learning about and reacting to changes in supplier service level. Our study not only provides the first empirical evidence of the impact of changes in service level on demand from retailers but also illustrates a method for estimating this relationship in practice.
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Working Paper
| 2012
Applying Management Science to Retail Store Liquidation
Nathan Craig and Ananth Raman
This paper introduces methods for increasing the efficiency of retail store liquidation, which we define as the time-constrained divestment of retail outlets through an in-store sale of inventory. The retail industry depends extensively on liquidation, not only as a means for investors to recover capital from failed ventures, but also to allow managers of going concerns to divest stores in efforts to enhance performance and to change strategy. The operations literature has examined product liquidation, but retail store liquidation differs significantly. This paper augments the literature by introducing techniques for improving operating decisions during retail store liquidations and by demonstrating the performance of these methods in the field.
Citation: Craig, Nathan, and Ananth Raman. "Applying Management Science to Retail Store Liquidation." Working Paper, November 2012.
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Working Paper
| HBS Working Paper Series
| 2013
The Impact of Supplier Reliability on Retailer Demand
Nathan Craig, Nicole DeHoratius and Ananth Raman
To set inventory service levels, firms must understand how changes in service level affect customer demand. While the effects of service level changes have been studied empirically at the level of the end consumer, relatively little is known about the interaction between a retailer and a supplier. Using data from a manufacturer of branded apparel, we show increases in service level to be associated with statistically significant increases in retailer orders (i.e., demand, not just sales). Controlling for other factors that might affect demand, we find a 1 percent increase in historical service level to be associated with a 12 percent increase in demand from retailers, where historical service level is the type 1 service level performance of the apparel manufacturer over the prior year. Further, we find that retailers that order frequently exhibit a more substantial reaction to changes in service level, an outcome that is consistent with retailers learning about and reacting to changes in supplier service level. Our study not only provides the first empirical evidence of the impact of changes in service level on demand from retailers but also illustrates a method for estimating this relationship in practice.
Keywords: Customer Satisfaction;
Forecasting and Prediction;
Learning;
Consumer Behavior;
Service Delivery;
Performance Expectations;
Apparel and Accessories Industry;
Service Industry;
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