Demand estimation and assortment planning in wireless communications

Xiaoyu Ma , Tianhu Deng , Boxiong Lan

Journal of Systems Science and Systems Engineering ›› 2016, Vol. 25 ›› Issue (4) : 398 -423.

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Journal of Systems Science and Systems Engineering ›› 2016, Vol. 25 ›› Issue (4) : 398 -423. DOI: 10.1007/s11518-015-5287-8
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Demand estimation and assortment planning in wireless communications

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Abstract

This paper provides an efficient and useful approach for demand estimation and assortment planning of cell phone cards in wireless communication industry. We use maximum likelihood estimation to estimate the primary demand and substitution probability of each cell phone card based on historical sales data. This estimation model is nonlinear, so we transform it to a mixed integer linear programming model by logarithmic transformations and piecewise linear approximation. On the basis of the estimation results, we can make assortment planning. Considering the resource of cell phone cards is limited, we jointly optimize the assortment and quantity planning of cell phone cards. In numerical study, we apply our approach to a large mobile service provider in China and find our approach can increase the revenue of this mobile service provider by 23.69%. Sensitivity analysis shows the mobile service provider should provide more assortments to increase revenue when the types of cell phone cards that can be assigned to each store are limited.

Keywords

Demand estimation / substitution behavior / assortment planning / optimization / wireless communications

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Xiaoyu Ma, Tianhu Deng, Boxiong Lan. Demand estimation and assortment planning in wireless communications. Journal of Systems Science and Systems Engineering, 2016, 25(4): 398-423 DOI:10.1007/s11518-015-5287-8

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References

[1]

Aydin G., Porteus E. L.. Joint inventory and pricing decisions for an assortment. Operations Research, 2008, 56(5): 1247-1255.

[2]

Aydin G., Ryan J. K.. Product line selection and pricing under the multinomial logit choice model. Working paper, Stanford University, 2000

[3]

Cachon G. P., Kok A. G.. Category management and coordination in retail assortment planning in the presence of basket shopping consumers. Management Science, 2007, 53(6): 934-951.

[4]

Cachon G. P., Terwiesch C., Xu Y.. Retail assortment planning in the presence of consumer search. Manufacturing & Service Operations Management, 2005, 7(4): 330-346.

[5]

Davis J. M., Gallego G., Topaloglu H.. Assortment optimization under variants of the nested logit model. Operations Research, 2014, 62(2): 250-273.

[6]

Fisher M., Vaidyanathan R.. A demand estimation procedure for retail assortment optimization with results from implementations. Management Science, 2014, 60(10): 2401-2415.

[7]

Gaur V., Honhon D.. Assortment planning and inventory decisions under a locational choice model. Management Science, 2006, 52(10): 1528-1543.

[8]

Guadagni P. M., Little J. D.. A logit model of brand choice calibrated on scanner data. Marketing Science, 1983, 2(3): 203-238.

[9]

Hubner A. H., Kuhn H.. Retail category management: state-of-the-art review of quantitative research and software applications in assortment and shelf space management. Omega, 2012, 40(2): 199-209.

[10]

Kok A. G., Fisher M. L.. Demand estimation and assortment optimization under substitution: methodology and application. Operations Research, 2007, 55(6): 1001-1021.

[11]

Kok A. G., Fisher M. L., Vaidyanathan R.. Assortment planning: review of literature and industry practice. Retail Supply Chain Management, 2009

[12]

Li Z.. A single-period assortment optimization model. Production and Operations Management, 2007, 16(3): 369-380.

[13]

Mantrala M. K., Levy M., Kahn B. E., Fox E. J., Gaidarev P., Dankworth B., Shah D.. Why is assortment planning so difficult for retailers. a framework and research agenda. Journal of Retailing, 2009

[14]

Rajaram K.. Assortment planning in fashion retailing: methodology, application and analysis. European Journal of Operational Research, 2001, 129(1): 186-208.

[15]

Rusmevichientong P., Shen Z.-J. M., Shmoys D. B.. Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Operations Research, 2010, 58(6): 1666-1680.

[16]

Rusmevichientong P., Shmoys D., Tong C., Topaloglu H.. Assortment optimization under the multinomial logit model with random choice parameters. Production and Operations Management, 2014, 23(11): 2023-2039.

[17]

Ryzin G. v., Mahajan S.. On the relationship between inventory costs and variety benefits in retail assortments. Management Science, 1999, 45(11): 1496-1509.

[18]

Smith S. A., Agrawal N.. Management of multi-item retail inventory systems with demand substitution. Operations Research, 2000, 48(1): 50-64.

[19]

Talluri K., Van Ryzin G.. Revenue management under a general discrete choice model of consumer behavior. Management Science, 2004, 50(1): 15-33.

[20]

Vulcano G. V., Ryzin G., Ratliff R.. Estimating primary demand for substitutable products from sales transaction data. Operations Research, 2012, 60(2): 313-334.

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