Designing forecasting and service-level-based inventory strategies through product segmentation
Submitted: 2026-03-31
|Accepted: 2026-05-12
|Published: 2026-07-16
Copyright (c) 2026 Claudemir Tramarico, Nubia Velasco

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
ABC/XYZ, demand pattern, forecasting strategies, inventory control, service level
Supporting agencies:
Abstract:
In the context of supply chain improvement, segmenting products aligns with demand patterns and contributes to strategic decisions to support better resource allocation, allowing businesses to achieve desirable service levels. This paper provides feasible strategies that strengthen sales forecasting and service-level-based inventory management, emphasizing the ABC/XYZ segmentation to improve the flexibility and responsiveness of the supply chain process in dynamic market environments. This work applies the ABC/XYZ classification to 540 finished goods, revealing that 59% fall into category C, with cluster CZ2 (27%) and CZ1 (25%) predominating. Category B accounts for 23% of items, while category A represents 18%. Based on this segmentation, service-level targets were defined at 95%, 90%, 80%, and 60%, depending on product criticality and demand variability. The results highlight that sporadic demand clusters (CZ1 and CZ2) require demand-driven replenishment, while high-value stable-demand items (AX) benefit from high service levels and statistical forecasting. These findings demonstrate potential improvements in forecast accuracy, inventory responsiveness, and cost-service trade-offs, offering a structured pathway for managers to balance service levels with inventory efficiency. Overall, the segmentation results provide actionable insights for reducing inefficiencies and improving cost-to-serve in dynamic supply chain environments.
References:
Arnold, J. R. T., Chapman, S. N., & Clive, L. M. (2008). Introduction to materials management (6th ed.). Pearson Prentice Hall.
Babu Kri shnaraj, R., & Meenakshi, P. K. (2016). A study on ABC-XYZ analysis in a pharmacy store. International Journal of Mathematics and Statistics Invention, 4(9), 24–26.
Badr, H. , & Ahmed, W. (2023). A comprehensive analysis of demand prediction models in supply chain management. American Journal of Industrial and Business Management, 13(12), 1353–1376. https://doi.org/10.4236/ajibm.2023.1312075
Barbosa, L. H., Marins, F. A. S., & Tramarico, C. L. (2025). Assessment of sales and operations planning maturity in an automotive manufacturer. Revista de Gestão e Secretariado, 16(8), e5164. https://doi.org/10.7769/gesec.v16i8.5164
Boylan, J. E., & Syntetos, A. A. (2021). Intermittent demand forecasting: Context, methods and applications. Wiley.https://doi.org/10.1002/9781119135289
Castro, J. I., Muniz, J., Jr., Bernardes, E., & Tramarico, C. L. (2021). Logistics projects based on radio frequency identification: Multi-criteria assessment of Brazilian aircraft industry. Pesquisa Operacional, 41, e244928. https://doi.org/10.1590/0101-7438.2021.041.00244928
Chase, C . W. (2016). Next generation demand management: People, process, analytics, and technology. Wiley. https://doi.org/10.1002/9781119449591
Choi, J. H., Choi, S., & Suresh, N. C. (2025). Inventory and financial performance in the pharmaceutical industry: What transpired over the last two decades? International Journal of Operations & Production Management, 45(1), 301–328.https://doi.org/10.1108/IJOPM-07-2022-0461
Driessen , M., & van den Bremer, W. (2024). How to do ABC/XYZ properly for better inventory optimization. The Journal of Business Forecasting, 43(2), 22–27. https://eyeonplanning.com/wp-content/uploads/2024/09/ihVNZcDD.pdf
Durach, C. F., Peers, Y., Wang, Y., & Repasky, T. (2024). Mitigating upstream disruptions: Effects of extended inventories in first- and second-tier suppliers. Journal of Operations Management, 70(8), 1261–1285. https://doi.org/10.1002/joom.1332
Fogarty, D. W., Blackstone, J. H., & Hoffmann, T. R. (1991). Production and inventory management (2nd ed.). SouthWestern Publishing.
Gatewood , A. K., & Drake, M. J. (Eds.). (2025). ASCM supply chain dictionary (19th ed.). ASCM.
Gillilan d, M. (2010). The business forecasting deal: Exposing myths, eliminating bad practices, providing practical solutions. Wiley.
Guru, R. R., Mitra, S., Jadhav, S. J., Maikano, A. K., & Kumar, R. (2024). Buffer stock inventory control mechanism: An approach of minimizing the buffer stock level through segmentation at a tertiary care rural hospital. Cureus, 16(8), e67423. https://doi.org/10.7759/cureus.67423
Hammond, J. S., Keeney, R. L., & Raiffa, H. (1999). Smart choices: A practical guide to making better decisions. Harvard Business School Press.
Jacobs, F. R., Berry, W. L., Whybark, D. C., & Vollmann, T. E. (2011). Manufacturing planning and control for supply chain management: APICS/CPIM certification edition (6th ed.). McGraw-Hill.
Jonsson, P., Öhlin, J., Shurrab, H., Bystedt, J., Sheikh Muhammad, A., & Verendel, V. (2024). What are the root causes of material delivery schedule inaccuracy in supply chains? International Journal of Operations & Production Management, 44(13), 34–68. https://doi.org/10.1108/IJOPM-12-2022-0806
Kırmızı, S. D., Ceylan, Z., & Bulkan, S. (2024). Enhancing inventory management through safety-stock strategies—A case study. Systems, 12(7), 260. https://doi.org/10.3390/systems12070260
Kubasako va, I., & Kubanova, J. (2024). Utilization of the intersection of ABC and XYZ analysis in stock planning in the warehouse by COVID period. Acta Logistica, 11(3), 461–472. https://doi.org/10.22306/al.v11i3.532
March, J . G. (1994). A primer on decision making: How decisions happen. Free Press.
Martin, S., & Rasch, A. (2024). Demand forecasting, signal precision, and collusion with hidden actions. International Journal of Industrial Organization, 92, 103036. https://doi.org/10.1016/j.ijindorg.2023.103036
Moharram ian, A., & Pasandideh, S. H. R. (2025). The optimal dedication of ordering policy to group B items within ABC analysis: Three tunefully leading metaheuristics. Journal of Industrial and Management Optimization, 21(2), 853–877. https://doi.org/10.3934/jimo.2024108
Munitić, N. F., Baržić, M., Jelić, L., & Lešić, V. (2023). Utilization of ABC/XYZ analysis in retail sales forecasting using multivariate LSTM method. In 2023 IEEE International Conference on Industrial Technology (ICIT) (pp. 1–6). IEEE. https://doi.org/10.1109/ICIT58465.2023.10143038
Murray, P. W., Agard, B., & Barajas, M. A. (2015). Forecasting supply chain demand by clustering customers. IFACPapersOnLine, 48(3), 1834–1839. https://doi.org/10.1016/j.ifacol.2015.06.353
Murray, P. W., Agard, B., & Barajas, M. A. (2018). Forecast of individual customer’s demand from a large and noisy dataset. Computers & Industrial Engineering, 118, 33–43. https://doi.org/10.1016/j.cie.2018.02.007
Narayana n, P., Somasundaram, J., & Seifert, M. (2025). Risk-averse algorithmic support and inventory management. European Journal of Operational Research, 322(3), 993–1004. https://doi.org/10.1016/j.ejor.2024.11.013
Niekurza k, M., & Mikulik, J. (2026). Inventory segmentation and demand forecasting as tools supporting sustainable resource management in a manufacturing company. Sustainability, 18(8), 4047. https://doi.org/10.3390/su18084047
Pandey, R., Chatterjee, D., & Rungtusanatham, M. (2023). The effects of tie strength and data integration with supply base on supply disruption ambiguity and its impact on inventory turnover. International Journal of Operations & Production Management, 43(3), 428–465. https://doi.org/10.1108/IJOPM-03-2022-0199
Perera, H. N., Fahimnia, B., & Tokar, T. (2020). Inventory and ordering decisions: A systematic review on research driven through behavioral experiments. International Journal of Operations & Production Management, 40(7/8), 997–1039.https://doi.org/10.1108/IJOPM-05-2019-0339
Sargent, R. G. (2009). Verification and validation of simulation models. In Proceedings of the 2009 Winter Simulation Conference (WSC) (pp. 162–176). IEEE. https://doi.org/10.1109/WSC.2009.5429327
Scholz-R eiter, B., Heger, J., Meinecke, C., & Bergmann, J. (2012). Integration of demand forecasts in ABC-XYZ analysis: Practical investigation at an industrial company. International Journal of Productivity and Performance Management,61(4), 445–451. https://doi.org/10.1108/17410401211212689
Silva, A . M., & Tramarico, C. L. (2022). Multi-criteria analysis of big data and big data analytics on supply chain management. International Journal of Integrated Supply Management, 15(3), 280–303. https://doi.org/10.1504/IJISM.2022.124420
Souza, R . F., Wanke, P., & Correa, H. (2020). Demand forecasting in the beauty industry using fuzzy inference systems. Journal of Modelling in Management, 15(4), 1389–1417. https://doi.org/10.1108/JM2-03-2019-0050
Stadtler , H., Kilger, C., & Meyr, H. (Eds.). (2015). Supply chain management and advanced planning: Concepts, models, software, and case studies (5th ed.). Springer. https://doi.org/10.1007/978-3-642-55309-7
Stojanov ić, M., & Regodić, D. (2017). The significance of the integrated multicriteria ABC-XYZ method for the inventory management process. Acta Polytechnica Hungarica, 14(5), 29–48. https://doi.org/10.12700/APH.14.5.2017.5.3
Testoni, P. S., Tramarico, C. L., Rodríguez, E. C. A., & Marins, F. A. S. (2024). Analytic hierarchy process applied in the prioritization of third-party logistics providers in banking services. Production, 34, e20230108. https://doi.org/10.1590/0103-6513.20230108
Teunter, R. H., Babai, M. Z., & Syntetos, A. A. (2010). ABC classification: Service levels and inventory costs. Production and Operations Management, 19(3), 343–352. https://doi.org/10.1111/j.1937-5956.2009.01098.x
Tramaric o, C. L. (2021). Systematic mapping analysis on sustainable supply chain management. In Proceedings of the 2nd South American International Conference on Industrial Engineering and Operations Management (pp. 279–289).IEOM Society International. https://doi.org/10.46254/SA02.20210136
Tramaric o, C. L. (2025). Circular supply chain: Addressing critical success factors through multi-criteria analysis. In J. C.Gonçalves dos Reis, F. G. Mendonça Freires, M. Vieira Junior, R. Garcia Barbastefano, & Â. M. Oliveira Sant’Anna (Eds.), Industrial engineering and operations management: IJCIEOM 2024 (Springer Proceedings in Mathematics &Statistics, Vol. 483, pp. 357–368). Springer. https://doi.org/10.1007/978-3-031-80785-5_28
Tramaric o, C. L., Belmar, P. I. P., & Salomon, V. A. P. (2025a). Enhancing circular supply chain implementation: Multicriteria analysis with the Analytic Hierarchy Process. In A. Ujwary-Gil & A. Florek-Paszkowska (Eds.), AI, analytics and strategic decision-making (pp. 330–352). Routledge. https://doi.org/10.4324/9781003507840-16
Tramaric o, C. L., Paredes, J. A. L., & Salomon, V. A. P. (2026). Process and strategic criteria assessment in platform-based supply chains: A framework for identifying operational vulnerabilities. Systems, 14(1), 75. https://doi.org/10.3390/systems14010075
Tramaric o, C., Petrillo, A., Andrade, H., & Salomon, V. (2025b). Advancing circular supplier selection: Multi-criteria perspectives on risk and sustainability. Sustainability, 17(15), 6814. https://doi.org/10.3390/su17156814
Trubchen ko, T. G., Kiseleva, E. S., Loshchilova, M. A., Dreval, A. N., Ryzhakina, T. G., & Shaftelskaya, N. V. (2020). Application of ABC and XYZ analysis to inventory optimization at a commercial enterprise. SHS Web of Conferences,80, 01007. https://doi.org/10.1051/shsconf/20208001007
van Stee nbergen, R. M., & Mes, M. R. K. (2020). Forecasting demand profiles of new products. Decision Support Systems,139, 113401. https://doi.org/10.1016/j.dss.2020.113401
Venkates h, P., & Sowmiya, M. P. (2024). ABC-XYZ classification and forecasting for inventory optimization. International Journal of Research Publication and Reviews, 5(12), 5348–5357. https://doi.org/10.55248/gengpi.5.1224.0227
Vereecke , A., Vanderheyden, K., Baecke, P., & van Steendam, T. (2018). Mind the gap—Assessing maturity of demand planning, a cornerstone of S&OP. International Journal of Operations & Production Management, 38(8), 1618–1639.https://doi.org/10.1108/IJOPM-11-2016-0698
Wang, Y. , & Minner, S. (2025). Data-driven multi-location inventory placement in digital commerce. Computers & Industrial Engineering, 200, 110842. https://doi.org/10.1016/j.cie.2024.110842
Yan, X., Chao, X., & Lu, Y. (2024). Optimal control policies for dynamic inventory systems with service level dependent demand. European Journal of Operational Research, 314(3), 935–949. https://doi.org/10.1016/j.ejor.2023.11.005
Zenkova, Z., Andrievskaya, A., & Tarima, S. (2022). Accounting for deficit and a known demand quantile in ABC-XYZ analysis. In 2022 IEEE 6th International Conference on Logistics Operations Management (GOL) (pp. 1–7). IEEE.https://doi.org/10.1109/GOL53975.2022.9820013
Zheng, X ., & Chen, Y. (2024). Optimization of inventory cost control for SMEs in supply chain transformation: A case study and discussion. E&M Economics and Management, 27(1), 87–107. https://doi.org/10.15240/tul/001/2024-5-002
Zietsman , H. J., & van Vuuren, J. H. (2023). A generic decision support framework for inventory procurement planning in distribution centres. Computers & Industrial Engineering, 177, 109057. https://doi.org/10.1016/j.cie.2023.109057




