| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | March 24, 2012 |
| Latest Amendment Date: | March 24, 2012 |
| Award Number: | 1162034 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Georgia-Ann Klutke
gaklutke@nsf.gov (703)292-2443 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | July 1, 2012 |
| End Date: | June 30, 2017 (Estimated) |
| Total Intended Award Amount: | $234,894.00 |
| Total Awarded Amount to Date: | $234,894.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
77 MASSACHUSETTS AVE CAMBRIDGE MA US 02139-4301 (617)253-1000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
77 Massachusetts Avenue Cambridge MA US 02139-4301 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SERVICE ENTERPRISE SYSTEMS |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
The research goal of this award is to design operational models to provide decision support for markdown pricing by e-tailers. The models will include strategic customer behavior in the presence of business rules, and will have the potential to be applied operationally. Existing models for MDO with strategic customers make strong assumptions about customer behavior that are difficult to estimate or validate with the data that can practically be collected. Our research focuses on pricing models that can be estimated with customer visit data. Such data is already being collected by e-tailers through user logins and cookies. Such information would not be practical to collect for brick-and-mortar stores, the traditional context for the MDO problem. The research plan is to first try to understand the impact of limited strategic (i.e. returning but myopic) customers on the optimal prices for an e-tailer in a single-item setting. Next, we will build upon the foundation such MDO models by additionally considering business rules - these are practically important hard constraints that retailers impose on the sequence of prices. Finally, we will generalize our models to the case of multiple items. We will analyze these models first from a theoretical standpoint but also will exploit the relationships between them and test them in practice using real data. This research will employ methodologies from a variety of fields with the long term goal to deepen our understanding on issues in dynamic pricing as they relate to the retail industry and beyond. We will develop an integrated framework, models and methods for the application of stochastic and robust optimization to key pricing problems.
If successful this research will fill a gap between theory and practice in the existing research that will transform the pricing processes of e-tailers. It will empower them to benefit from a better understanding of strategic customer behavior. This is vital because e-commerce an increasing segment of retail business. Further, we believe that the applications of this research go beyond the field of pricing. We will share our integrated framework, models and methods, to help both academics and practitioners. From an educational perspective, the results of this project will serve as components in teaching modules at MIT. These include modules in core courses for which the PI already has shared responsibility. This project lends itself ideally to mentoring undergraduate and graduate students in research on tractable practice-based optimization.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This research project has focused on tractable dynamic pricing with business rules. We have been investigating two important problems within this area; markdown pricing and promotion pricing. Incorporating business rules is an important aspect of the problem that nevertheless, makes the problem hard to investigate. In addition another goal of this reseach has been to incorporate in our models important consumer behavior; such as returning customer behavior, promotion/markdown fatigue and stockpilling effects. Nevertheless, although these effects are realistic and important to consider, they make the underlying models become intractable. In the course of this research we have been able to introduce and analyze models for the problems discussed above, that incorporate these important effects. In particular, we first introduce predictive models for demand and then subsequently, we introduce optimization models that use the demand models we came up with, in order to propose pricing strategies. As these underlying models are hard to solve, we devisegoodd approximation methods and provide guarantees on the quality of these approximations. We have investigated these approximations both theoretically and computationally starting with synthetic data but then also testing these methods with with real data from companies. Finally, we were fortunate to conduct a pilot with a retailer on our models and methods.
Overall, we are able to show that we can predict demand well, with MAPEs of the order of 9-30%. In addition our pricing models are improving profits at the order of 3-9% over current practice. As in this industry margins are thin, these improvements are significant.
The outcomes of this research include
1.Several papers. Some of these papers have gotten accepted or are at advanced publication stages and finally, other papers that are still under review.
2. Several presentations. These presentations have been given either at conferences (MSOM, INFORMS) or at various universities.
3. This project has also allowed the PI to train students in this area with the goal top bridge theory and practice. These include students at all levels: PhDs, Masters and Undergraduates.
4. Furthermore, the PI has written a case on the topic based on her experience on this research in order to dissiminate the results and ideas of this research to a broader audience of PhD, Masters and undergraduate students. This case has been successfully used by the PI at several courses at MIT and also at NYU Stern.
Last Modified: 10/04/2017
Modified by: Georgia Perakis
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