How Modified BCG Matrix-Based Product Portfolio Optimization Changes Consumer Purchase Behavior: Evidence from Vending Machine Transactions
DOI:
https://doi.org/10.35870/ijmsit.v6i2.8278Keywords:
Modified BCG Matrix, Product Portfolio Optimization, Consumer Purchase Behavior, Interrupted Time Series, Smart Vending Machine, Retail AnalyticsAbstract
Product portfolio optimization is essential in automated retail because vending machines operate under severe storage constraints, requiring every Stock Keeping Unit (SKU) to contribute effectively to overall sales performance. However, conventional portfolio management approaches, including the Boston Consulting Group (BCG) Matrix, rely on market-level indicators that are difficult to apply in vending machine environments where external market share data are unavailable. This study proposes a Modified BCG Matrix by replacing Relative Market Share with Relative Sales Performance (RSP) and Market Growth Rate with Sales Growth Trend (SGT) to enable SKU-level portfolio optimization using internal transaction data. A quantitative longitudinal design with an Interrupted Time Series (ITS) approach was employed using 167,018 valid transactions collected from 11 smart vending machines operating in Cikarang, Indonesia, between September 2025 and June 2026. The Modified BCG Matrix guided operational decisions, including facing allocation, replenishment priority, product rotation, replacement, and product delisting. The implementation reduced the active product portfolio from 253 to 146 SKUs (42.3%), increased average daily transaction volume by 28.53%, increased average daily sales revenue by 22.19%, and produced statistically significant immediate intervention effects based on segmented regression analysis. These findings demonstrate that data-driven product portfolio optimization not only improves operational efficiency and sales performance but also reshapes consumer purchase behavior. This study extends the application of the traditional BCG Matrix from the Strategic Business Unit (SBU) level to the SKU level and provides an evidence-based decision-support framework for automated retail portfolio management.
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Awamleh, F. T., Bustami, A., Alarabiat, Y., & Sultan, A. (2024). Data-driven decision-making under uncertainty: Investigating OLAP's mediating role to leverage business intelligence analytics for entrepreneurship. Journal of Systems and Management Sciences, 14(2), 523–541. https://doi.org/10.33168/jsms.2024.0822
Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348–355. https://doi.org/10.1093/ije/dyw098
Czerniachowska, K., Lutosławski, K., & Fojcik, M. (2022). Heuristics for shelf space allocation problem with vertical and horizontal product categorization. Procedia Computer Science, 207, 195–204. https://doi.org/10.1016/j.procs.2022.09.076
Davis, G. B., & Olson, M. H. (1984). Management information systems: Conceptual foundations, structure, and development. McGraw-Hill.
Ding, X., Chen, C., Li, C., & Lim, A. (2021). Product demand estimation for vending machines using video surveillance data: A group-lasso method. Transportation Research Part E: Logistics and Transportation Review, 150, 102335. https://doi.org/10.1016/j.tre.2021.102335
García-Vidal, G., Sánchez-Rodríguez, A., Pérez-Campdesuñer, R., & Martínez-Vivar, R. (2023). Contribution margin and quantity matrix to analyze the product portfolio in the context of SMEs: Criticism of the BCG matrix and its alternatives. Cogent Business & Management, 10(3), 2233272. https://doi.org/10.1080/23311975.2023.2233272
Kumoro, C. J., & Rachmat, B. (2022). Faktor-Faktor Penentu Adopsi E-Wallet Ovo Di Provinsi Jawa Timur. Jurnal Manajerial, 9(01), 52–72. https://doi.org/10.30587/jurnalmanajerial.v9i01.2816
Kumoro, C. J., Ryandini, E. Y., & Samin, N. (2024). Faktor-faktor yang mempengaruhi adopsi pembayaran QR Code Indonesian Standard (QRIS) di toko fisik. Journal of Innovation in Management, Accounting and Business, 3(2), 97–112. https://doi.org/10.56916/jimab.v3i2.865
Laudon, K. C., & Laudon, J. P. (2004). Management information systems: Managing the digital firm (8th ed.). Pearson Education.
Li, X., Shen, Q., & Yang, T. (2024). Design and optimization of multidimensional data models for enhanced OLAP query performance and data analysis. Applied and Computational Engineering, 69, 161–166. https://doi.org/10.54254/2755-2721/69/20241365
Marinelli, L., Fiano, F., Gregori, G. L., & Daniele, L. M. (2021). Food purchasing behaviour at automatic vending machines: The role of planograms and shopping time. British Food Journal, 123(5), 1821–1836. https://doi.org/10.1108/BFJ-05-2020-0418
Nemoto, G., & Hiraishi, K. (2024). A POMDP-based approach to assortment optimization problem for vending machine. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E107-A(6), 909–918. https://doi.org/10.1587/transfun.2023EAP1036
Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. https://doi.org/10.2307/1913610
Perfetti, A., Pietrini, R., Scarpi, D., & Gistri, G. (2025). Snack dilemma: How vending machines influence choice of virtue and vice foods. Journal of Retailing and Consumer Services, 87, 104369. https://doi.org/10.1016/j.jretconser.2025.104369
Power, D. J., & Heavin, C. (2017). Decision support, analytics, and business intelligence (2nd ed.). Business Expert Press.
Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299–309. https://doi.org/10.1046/j.1365-2710.2002.00430.x
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Copyright (c) 2026 Chowal Jundy Kumoro, Lambok Rommy Sulaeman, Gredinov Sumanta Malsad, Mochamad Arya Maulana
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