Abstract
Customers prefer the availability of a range of products when they shop online. This enables them to identify their needs and select products that best match their desires. This is addressed through assortment planning. Some customers have strong awareness of what they want to purchase and from which provider. When considering customer taste as an abstract concept, such customers’ decisions may be influenced by the existence of the variety of products and the current variant market may affect their initial desire. Previous studies dealing with assortment planning have commonly addressed it from the retailer’s point of view. This paper will provide customers with a ranking method to find what they want. We propose that this provision benefits both the retailer and the customer. This study provides a customer-oriented assortment ranking approach. The ranking model facilitates browsing and exploring the current big market in order to help customers find their desired item considering their own taste. In this study, a scalable and customised multi-criteria decision making (MCDM) method is structured and utilised to help customers in the process of finding their most suitable assortment while shopping online. The proposed MCDM method is tailored to fit the big data environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Saberi, Z., et al.: Stackelberg game-theoretic approach in joint pricing and assortment optimizing for small-scale online retailers: seller-buyer supply chain case. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA). IEEE (2018)
Saberi, Z., et al.: Online retailer assortment planning and managing under customer and supplier uncertainty effects using internal and external data. In: 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE). IEEE (2017)
Saberi, Z., et al.: Stackelberg model based game theory approach for assortment and selling price planning for small scale online retailers. Future Gener. Comput. Syst. 100, 1088–1102 (2019)
Hart, C., Rafiq, M.: The dimensions of assortment: a proposed hierarchy of assortment decision making. Int. Rev. Retail Distrib. Consum. Res. 16(3), 333–351 (2006)
Flamand, T., et al.: Integrated assortment planning and store-wide shelf space allocation: an optimization-based approach. Omega 81, 134–149 (2018)
Kök, A.G., Fisher, M.L., Vaidyanathan, R.: Assortment planning: review of literature and industry practice. In: Retail Supply Chain Management, pp. 175–236. Springer (2015)
Kunnumkal, S., Martínez-de-Albéniz, V.: Tractable approximations for assortment planning with product costs. Oper. Res. 67, 436–452 (2019)
Flores, A., Berbeglia, G., Van Hentenryck, P.: Assortment optimization under the sequential multinomial logit model. Eur. J. Oper. Res. 273(3), 1052–1064 (2019)
Kautish, P., Sharma, R.: Managing online product assortment and order fulfillment for superior e-tailing service experience: an empirical investigation. Asia Pac. J. Market. Logistics 4, 1161–1192 (2019)
Melacini, M., et al.: E-fulfilment and distribution in omni-channel retailing: a systematic literature review. Int. J. Phys. Distrib. Logistics Manag. 48(4), 391–414 (2018)
Argouslidis, P., et al.: Consumers’ reactions to variety reduction in grocery stores: a freedom of choice perspective. Eur. J. Mark. 52(9/10), 1931–1955 (2018)
Li, Z., Lu, Q., Talebian, M.: Online versus bricks-and-mortar retailing: a comparison of price, assortment and delivery time. Int. J. Prod. Res. 53(13), 3823–3835 (2015)
Wang, R., Sahin, O.: The impact of consumer search cost on assortment planning and pricing. Manag. Sci. 64(8), 3649–3666 (2017)
Sauré, D., Zeevi, A.: Optimal dynamic assortment planning with demand learning. Manuf. Serv. Oper. Manag. 15(3), 387–404 (2013)
Goyal, V., Levi, R., Segev, D.: Near-optimal algorithms for the assortment planning problem under dynamic substitution and stochastic demand. Oper. Res. 64(1), 219–235 (2016)
Pan, X.A., Honhon, D.: Assortment planning for vertically differentiated products. Prod. Oper. Manag. 21(2), 253–275 (2012)
Azadeh, A., et al.: Z-AHP: A Z-number extension of fuzzy analytical hierarchy process. In: 2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST). IEEE (2013)
Shokri, H., et al.: An integrated AHP-VIKOR methodology for Facility Layout design. Ind. Eng. Manag. Syst. 12(4), 389–405 (2013)
Aboutorab, H., et al.: ZBWM: the Z-number extension of Best Worst Method and its application for supplier development. Expert Syst. Appl. 107, 115–125 (2018)
Nawaz, F., et al.: An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowl.-Based Syst. 159, 120–131 (2018)
Zhang, Y., et al.: Ranking scientific articles based on bibliometric networks with a weighting scheme. J. Informetr. 13(2), 616–634 (2019)
Asadabadi, M.R., Chang, E., Saberi, M.: Are MCDM methods useful? A critical review of analytic hierarchy process (AHP) and analytic network process (ANP). Cogent Eng. 6, 1623153 (2019). (just-accepted)
Rezaei, J.: Piecewise linear value functions for multi-criteria decision-making. Expert Syst. Appl. 98, 43–56 (2018)
Mitchell, R.: Web Scraping with Python: Collecting More Data from the Modern Web. O’Reilly Media Inc, Sebastopol (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Saberi, M., Saberi, Z., Aasadabadi, M.R., Hussain, O.K., Chang, E. (2020). A Customer-Oriented Assortment Selection in the Big Data Environment. In: Chao, KM., Jiang, L., Hussain, O., Ma, SP., Fei, X. (eds) Advances in E-Business Engineering for Ubiquitous Computing. ICEBE 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-34986-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-34986-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34985-1
Online ISBN: 978-3-030-34986-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)