Development of an Inventory Management Strategy Model (Two-Products) Based on Demand Predicting in Digital Supply Chain Networks by Combining Data Analysis Methods

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Industrial Engineering, Semnan University, Semnan, Iran.

2 M.Sc., Department of Industrial Engineering, Semnan University, Semnan, Iran.

Abstract

Recording and classifying commerce data electronically within the new age of information and development has many benefits for sellers and consumers in the online supply chain. We can predict customer purchase behavior patterns by analyzing this classified data. In recent years, digital stores have received additional attention due to their advantages. On the other hand, the performance of these stores is directly associated with the performance of their suppliers. Hence, supply chain management is essential during this sales system. In this investigation, the classification methods (WFRM), demand prediction analysis (Binary Logistic Regression), data classification (Discriminant Analysis), time series analysis (Trend Analysis), and mathematical modeling have been used to select suppliers to order led to the development of a management strategy to prevent shortages and reduce the average inventory and costs of sending and producing for suppliers. It ultimately covers a 29% error in sales data for assigning suppliers to orders.

Keywords

Main Subjects

References

Abbaspour Ghadim Bonab, A., & yousefi nejad attari, m. (2022). A predictive data-driven state-dependent decision approach to determine inventory system states for critical spare parts. Journal of Quality Engineering and Production Optimization, 7(2), 205-231. https://doi.org/10.22070/jqepo.2022.16129.1233
Akhtar, P., Frynas, J. G., Mellahi, K., & Ullah, S. (2019). Big data‐savvy teams’ skills, big data‐driven actions and business performance. British Journal of Management, 30(2), 252-271.
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34(5), 1785-1792. https://doi.org/https://doi.org/10.1016/j.jksuci.2019.12.011
Beheshtinia, M. A., Falsafi, P., Qorbani, A., & Jalinouszade, H. (2022). Evaluating and ranking digital stores’ suppliers using TOPKOR method. International Journal of Engineering, 35(11), 2155-2163.
Brito, P. Q., Soares, C., Almeida, S., Monte, A., & Byvoet, M. (2015). Customer segmentation in a large database of an online customized fashion business. Robotics and Computer-Integrated Manufacturing, 36, 93-100.
Chaffey, D., Edmundson-Bird, D., & Hemphill, T. (2019). Digital business and e-commerce management. Pearson UK.
Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management, 19(3), 197-208.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
Chen, X., Fang, Y., Yang, M., Nie, F., Zhao, Z., & Huang, J. Z. (2017). Purtreeclust: A clustering algorithm for customer segmentation from massive customer transaction data. IEEE Transactions on Knowledge and Data Engineering, 30(3), 559-572.
Chen, Y., Liu, L., Zheng, D., & Li, B. (2023). Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model. Journal of Retailing and Consumer Services, 74, 103433. https://doi.org/https://doi.org/10.1016/j.jretconser.2023.103433
Cheng, C.-H., & Chen, Y.-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36(3), 4176-4184.
Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration, 9, 1-13.
Constantinides, E., & Fountain, S. J. (2008). Web 2.0: Conceptual foundations and marketing issues. Journal of direct, data and digital marketing practice, 9(3), 231-244.
Côrte-Real, N., Ruivo, P., Oliveira, T., & Popovič, A. (2019). Unlocking the drivers of big data analytics value in firms. Journal of business research, 97, 160-173.
Dibb, S., & Wensley, R. (2002). Segmentation analysis for industrial markets: Problems of integrating customer requirements into operations strategy. European journal of marketing.
Dutta, S., Bhattacharya, S., & Guin, K. K. (2015). Data mining in market segmentation: a literature review and suggestions. Proceedings of Fourth International Conference on Soft Computing for Problem Solving,
Farajian, M. A., & Mohammadi, S. (2010). Mining the banking customer behavior using clustering and association rules methods. International Journal of Industrial Engineering & Production Research, 21(4), 239-245.
Frost, R. D., & Strauss, J. (2016). E-marketing. Routledge.
Gallego-García, S., & García-García, M. (2020). Predictive sales and operations planning based on a statistical treatment of demand to increase efficiency: A supply chain simulation case study. Applied Sciences, 11(1), 233.
Güçdemir, H., & Selim, H. (2015). Integrating multi-criteria decision making and clustering for business customer segmentation. Industrial Management & Data Systems.
Heldt, R., Silveira, C. S., & Luce, F. B. (2021). Predicting customer value per product: From RFM to RFM/P. Journal of Business Research, 127, 444-453. https://doi.org/https://doi.org/10.1016/j.jbusres.2019.05.001
Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.
Kim, K.-j., & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert systems with applications, 34(2), 1200-1209.
Kumar, A., Shankar, R., & Aljohani, N. R. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial marketing management, 90, 493-507.
Liu, Y., Li, H., Peng, G., Lv, B., & Zhang, C. (2015). Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market. Annals of Operations Research, 233(1), 263-279.
McCarty, J. A., & Hastak, M. (2007). Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. Journal of business research, 60(6), 656-662.
Ming-Chih, T., Yi-Ting, T., & Ching-Wei, L. (2011). Generalized linear interactive model for market segmentation: The air freight market. Industrial marketing management, 40(3), 439-446.
Mitchell, V.-W., & Wilson, D. F. (1998). Balancing theory and practice: A reappraisal of business-to-business segmentation. Industrial marketing management, 27(5), 429-445.
Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications, 36(2), 2592-2602.
Pawar, K. S., & Driva, H. (2000). Electronic trading in the supply chain: a holistic implementation framework. Logistics Information Management.
Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: a case study. Marketing Intelligence & Planning.
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.
Sadeghi, H. (2019). A forecasting system by considering product reliability, POQ policy, and periodic demand. Journal of Quality Engineering and Production Optimization, 4(2), 133-148. https://doi.org/10.22070/jqepo.2020.5087.1123
Tabianan, K., Velu, S., & Ravi, V. (2022). K-means clustering approach for intelligent customer segmentation using customer purchase behavior data. Sustainability, 14(12), 7243.
Tavakoli, M., Molavi, M., Masoumi, V., Mobini, M., Etemad, S., & Rahmani, R. (2018). Customer segmentation and strategy development based on user behavior analysis, RFM model and data mining techniques: a case study. 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE),
Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553.
Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmentation. John Wiley & Sons.
Wang, S.-C., Tsai, Y.-T., & Ciou, Y.-S. (2020). A hybrid big data analytical approach for analyzing customer patterns through an integrated supply chain network. Journal of Industrial Information Integration, 20, 100177.
Weinstein, A. (2006). A strategic framework for defining and segmenting markets. Journal of Strategic Marketing, 14(2), 115-127.
Wixom, B. H., Yen, B., & Relich, M. (2013). Maximizing value from business analytics. MIS Quarterly Executive, 12(2).
Wong, E., & Wei, Y. (2018). Customer online shopping experience data analytics: Integrated customer segmentation and customised services prediction model. International Journal of Retail & Distribution Management.
Wu, J., Shi, L., Yang, L., XiaxiaNiu, Li, Y., XiaodongCui, Tsai, S.-B., & Zhang, Y. (2021). User value identification based on improved RFM model and k-means++ algorithm for complex data analysis. Wireless Communications and Mobile Computing, 2021, 1-8.
Wu, R.-S., & Chou, P.-H. (2011). Customer segmentation of multiple category data in e-commerce using a soft-clustering approach. Electronic Commerce Research and Applications, 10(3), 331-341.
Advances in Industrial Engineering
Volume 60, Issue 1
June 2026
Pages 165-184

Files

Share

How to cite

Statistics