Profit–Quantity Trade-Off Map and Counterfactual Simulation for SKU Portfolio Optimization in Community Pharmacies
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Abstract
This study examined whether data-driven SKU portfolio reallocation could improve profitability in a community pharmacy under a fixed-total-quantity condition. Using 24 months of transactional data from Thai Rungrueang Pharmacy Company Limited, the analysis covered 2,696 raw SKU records collected between September 2023 and August 2025. Products were classified into four clusters based on total profit and total quantity: High-Profit/High-Quantity, High-Profit/Low-Quantity, Low-Profit/High-Quantity, and Low-Profit/Low-Quantity. The results showed strong profit concentration. The High-Profit/High-Quantity cluster accounted for 94.84% of total sales and 96.11% of total profit. In comparison, the High-Profit/Low-Quantity cluster contributed an additional 1.92% of profit despite its small share of total quantity. At the branch-month level, gross margin ranged from 41.43% to 51.01%, with a standard deviation of 2.43 percentage points. Reconstructed counterfactual simulation showed that shifting 2.00% of total quantity from below-median-margin SKUs to the top 5.00% most profitable SKUs increased total profit by 2.33% and raised gross margin from 48.48% to 49.18%. A 5.00% shift increased total profit by 5.81% and raised gross margin to 50.20%. These findings indicate that modest portfolio reallocation can improve financial performance without increasing total quantity. The study suggests that community pharmacies may improve profitability by targeting data-driven assortment adjustments rather than relying solely on sales expansion.
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References
Athey, S., & Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3–32.
Blattberg, R. C., & Neslin, S. A. (1990). Sales promotion: Concepts, methods, and strategies. Prentice Hall.
Bolton, R. N., Shankar, V., & Montoya, R. (2007). Recent trends and emerging practices in retail pricing. M. Krafft & M. K. Mantrala (Eds.), Retailing in the 21st Century: Current and future trends (pp. 301–318). Springer Berlin Heidelberg.
Boonrit, N., Chaisawat, K., Phueakong, C., Nootong, N., & Ruanglertboon, W. (2024). Exploring community pharmacists’ attitudes in Thailand towards ChatGPT usage: A pilot qualitative investigation. Digital Health, 10, 20552076241283256. https://doi.org/10.1177/20552076241283256
Brynjolfsson, E., Hu, Y. J., & Smith, M. D. (2010). The longer tail: The changing shape of Amazon’s sales distribution curve. SSRN Electronic Journal, 2010, 1-13. https://doi.org/10.2139/ssrn.1679991
Dekimpe, M. G., & Hanssens, D. M. (1999). Sustained marketing and financial performance. Journal of Marketing Research, 36(4), 397–412.
Gaur, V., & Fisher, M. L. (2005). In-store experiments to determine the impact of assortment on sales. Production and Operations Management, 14(4), 377-387.
Gaur, V., Fisher, M. L., & Raman, A. (2005). An econometric analysis of inventory turnover performance in retail services. Management Science, 51(2), 181–194. https://doi.org/10.1287/mnsc.1040.0298
Heger, J., & Klein, R. (2024). Assortment optimization: A systematic literature review. OR Spectrum, 46(4), 1099–1161. https://doi.org/10.1007/s00291-024-00752-4
Kök, A. G., Fisher, M. L., & Vaidyanathan, R. (2009). Assortment planning: Review of literature and industry practice. In N. Agrawal & S. A. Smith (Eds.), Retail supply chain management (pp. 99–153). Springer. https://doi.org/10.1007/978-0-387-78902-6_6
Lal, R., & Rao, R. (1997). Supermarket competition: The case of everyday low pricing. Marketing Science, 16(1), 60–80. https://doi.org/10.1287/mksc.16.1.60
Mantrala, M. K., Levy, M., Kahn, B. E., Fox, E. J., Gaidarev, P., Dankworth, B., & Shah, D. (2009). Why is assortment planning so difficult for retailers? A framework and research agenda. Journal of Retailing, 85(1), 71–83. https://doi.org/10.1016/j.jretai.2008.11.006
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.
Rooderkerk, R. P., van Heerde, H. J., & Bijmolt, T. H. A. (2013). Optimizing retail assortments: A literature review and research agenda. Journal of Retailing, 89(4), 435–448. https://doi.org/10.1016/j.jretai.2013.09.002
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
van Ryzin, G., & Mahajan, S. (1999). On the relationship between inventory costs and variety benefits in retail assortments. Management Science, 45(11), 1496–1509. https://doi.org/10.1287/mnsc.45.11.1496
