Optimization plan for excess warehouse storage in e-commerce–based plant shops: a case study for Chinese plant industrial
Abstract
The rapid development of e-commerce in China has played a critical role in the development of the national economy and ongoing modernization. The plant industry is unique among industries that employ e-commerce sales models because its products exhibit special characteristics such as high death and damage rates. Therefore, its e-commerce and logistical requirements are stricter than in other industries and, as a result, excess warehouse storage can be extremely difficult for e-commerce–based plant shops to manage. Numerous studies have indicated the need to identify a product’s most up-to-date market conditions, as well as the type, function, and size of warehouses. Therefore, based on a case study, this study proposes an optimization plan for solving excess warehouse storage in e-commerce–based plant shops. First, sales volume data of the case company, Enterprise A, were analyzed to predict future sales. Then, entropy and the technique for order preference by similarity to an ideal solution were used to construct the decision-making model. Finally, a cloud warehouse–based optimization plan was proposed to solve excess warehouse storage in e-commerce–based plant shops. This plan can serve as a reference for decision-makers or executives in e-commerce–based plant shops when handling excess warehouse storage.
Keyword : plant industry, excess warehouse storage, cloud warehouse, exponential smoothing, multiple criteria decision making (MCDM)
How to Cite
Lin, H.-L., Cho, C.-C., Ma, Y.-Y., Hu, Y.-Q., & Yang, Z.-H. (2019). Optimization plan for excess warehouse storage in e-commerce–based plant shops: a case study for Chinese plant industrial. Journal of Business Economics and Management, 20(5), 897-919. https://doi.org/10.3846/jbem.2019.10188
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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China’s e-Commerce Research Center. (2017). China’s e-Commerce of plant market statistic report.Dev, S., Aherwar, A., & Patnaik, A. (2019). Material selection for automotive piston component using entropy-VIKOR method. Silicon. https://doi.org/10.1007/s12633-019-00110-y
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Gavurova, B., Belas, J., Kocisova, K., & Kliestik, T. (2017). Comparison of selected methods for performance evaluation of Czech and Slovak commercial banks. Journal of Business Economics and Management, 18(5), 852-876. https://doi.org/10.3846/16111699.2017.1371637
Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Hooshmand, R., & Antucheviciene, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1-19. https://doi.org/10.3846/16111699.2016.1278559
Govindan, K., Agarwal, V., Darbari, J. D., & Jha, P. C. (2019). An integrated decision making model for the selection of sustainable forward and reverse logistic providers. Annals of Operations Research, 273(1-2), 607-650. https://doi.org/10.1007/s10479-017-2654-5
Hoseinpour, M., Sadrnia, H., Ghobadian, B., & Tabasizadeh, M. (2019). Exhaust emission characteristics of a diesel engine on gasoline fumigation: an experimental investigation and evaluation using the MCDM method. International Journal of Environmental Science and Technology, 16(2), 995-1004. https://doi.org/10.1007/s13762-018-1667-1
Hu, J. (2018). E-commerce big data computing platform system based on distributed computing logistics information. Cluster Computing. https://doi.org/10.1007/s10586-018-2074-6
Hu, K. Y., & Chang, T. S. (2010). An innovative automated storage and retrieval system for B2C e-commerce logistics. International Journal of Advanced Manufacturing Technology, 48(1-4), 297-305. https://doi.org/10.1007/s00170-009-2292-4
Hwang, C., & Yoon, K. (1981). Multiple attribute decision making: Methods and application. New York: Springer Publications. https://doi.org/10.1007/978-3-642-48318-9
Hyndman, R. J., Akram, M., & Archibald, B. C. (2008). The admissible parameter space for exponential smoothing models. Annals of the Institute of Statistical Mathematics, 60(2), 407-426. https://doi.org/10.1007/s10463-006-0109-x
Jiao, Z. (2016). Service mode and development trend of the “last-mile delivery” of e-commerce logistics. Contemporary Logistics in China, 239-261. https://doi.org/10.1007/978-981-10-1052-1_11
Koster, R. B. M. D., Johnson, A. L., & Roy, D. (2017). Warehouse design and management. International Journal of Production Research, 55(21), 6327-6330. https://doi.org/10.1080/00207543.2017.1371856
Lewis, C. D. (1982). Industrial and business forecasting model. London: Butterworths.
Li, A. H. F. (2017). E-commerce and taobao villages: A promise for China’s rural development? China Perspectives, 3, 57-62.
Lin, H. L., & Cho, C. C. (2018). An ideal model for a merger and acquisition strategy in the information technology industry a case study for investment in the Taiwanese industrial personal computer sector. Journal of Testing and Evaluation. https://doi.org/10.1520/JTE20170106
Liu, S. (2018). E-shop transshipment selection evaluation based on cloud model. Journal of Shanghai Jiaotong University (Science), 23(5), 643-649. https://doi.org/10.1007/s12204-018-1978-x
Liu, J., Liu, C., Zhang, L., & Xu, Y. (2019). Research on sales information prediction system of e-commerce enterprises based on time series model. Information Systems and e-Business Management. https://doi.org/10.1007/s10257-019-00399-7
Mohammed, A. (2019). Towards a sustainable assessment of suppliers: an integrated fuzzy TOPSIS-possibilistic multi-objective approach. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03167-5
Ramanathan, R. (2010). E-commerce success criteria: determining which criteria count most. Electronic Commerce Research, 10(2), 191-208. https://doi.org/10.1007/s10660-010-9051-3
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Singh, J., Sharma, S. K., & Srivastava, R. (2019). AHP-Entropy based priority assessment of factors to reduce aviation fuel consumption. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-019-00758-0
Stankevičienė, J., Kraujalienė, L., & Vaiciukevičiūtė, A. (2017). Assessment of technology transfer office performance for value creation in higher education institutions. Journal of Business Economics and Management, 18(6), 1063-1081. https://doi.org/10.3846/16111699.2017.1405841
Stanujkic, D., Zavadskas, E. K., Karabasevic, D., Turskis, Z., & Kersuliene, V. (2017). New group decision-making ARCAS approach based on the integration of the SWARA and the ARAS methods adapted for negotiations. Journal of Business Economics and Management, 18(4), 599-618. https://doi.org/10.3846/16111699.2017.1327455
Vercher, E., Corberan-Vallet, A., Segura, J. V., & Bermudez, J. D. (2012). Initial conditions estimation for improving forecast accuracy in exponential smoothing. TOP, 20(2), 517-533. https://doi.org/10.1007/s11750-011-0221-9
Wakabayashi, K., Suzuki, K., Watanabe, A., & Karasawa, Y. (2014). Analysis and suggestion of an e-commerce logistics solution: effects of introduction of cloud computing based warehouse management System in Japan. In P. Golinska (Ed.), Logistics operations, supply chain management and sustainability (pp. 567-573). Springer. https://doi.org/10.1007/978-3-319-07287-6_40
Xu, H. S., Ma, C., Lian, J. J., Xu, K., & Chaima, E. (2018). Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou. China, Journal of Hydrology, 563, 975-986. https://doi.org/10.1016/j.jhydrol.2018.06.060
Yang, C., Lan, S., & Huang, G. Q. (2019). Revenue sharing model in New Hong Kong’s warehousing business paradigm. Journal of Ambient Intelligence and Humanized Computing, 10(3), 883-892. https://doi.org/10.1007/s12652-018-0822-3
Zhang, M., Huang, G. Q., Xu, S. X., & Zhao, Z. (2016). Optimization based transportation service trading in B2B e-commerce logistics. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1287-x