Designing Supply Chain Performance Measurement Using SCOR and AHP in an Indonesian Packaged Drinking Water Company

Nurullaily Kartika; Bima Karismanda Diantoro

doi:10.61255/jeemba.v4i4.1323

Authors

Nurullaily Kartika Faculty of Economics and Business, Airlangga University, Indonesia
Bima Karismanda Diantoro Faculty of Economics and Business, Airlangga University, Indonesia

DOI:

https://doi.org/10.61255/jeemba.v4i4.1323

Keywords:

Analytical Hierarchy Process (AHP), Key Performance Indicator (KPI), Supply Chain, Performance Measurement, Supply Chain Operations Reference (SCOR)

Abstract

Purpose – This study aims to develop an appropriate Supply Chain performance measurement design for a packaged drinking water company (Company X) using the Supply Chain Operations Reference (SCOR) model.

Design/methodology/approach – The Supply Chain Operations Reference (SCOR) model was used to develop key performance indicators (KPIs) related to supply chain performance measurement, The identified Key Performance Indicators (KPIs) were validated and weighted by three experts directly involved in the company’s supply chain activities, namely the Head of Candal and Warehouse Unit, the Head of Production, Maintenance and Laboratory Unit, and the Quality Control and Laboratory Staff, while the Analytical Hierarchy Process (AHP) method was applied to determine the weight of each KPI.

Findings/Results – The study produced 35 KPIs consisting of 6 Plan process KPIs, 8 Source process KPIs, 9 Make process KPIs, 5 Deliver process KPIs, 6 Return process KPIs, and 1 Enable process KPI. The AHP weighting results indicate that the Return activity has the highest priority among the six supply chain activities, with a weight value of 0.362.

Originality/Value – The study provides a Supply Chain performance measurement design for Company X by integrating the SCOR model and AHP method and identifies the Return activity as the most important supply chain activity.

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References

Agostini, L., Nosella, A., Sarala, R., & Nkeng, C. (2025). Emerging trends around strategic flexibility: A systematic review supported by bibliometric techniques. Management Decision, 63(6), 1835–1881.

Alkhawaldeh, A. E. (2025). Measuring Supply Chain Performance. Asian Business Research Journal, 10(4), 20–25.

Ayyildiz, E., & Taskin Gumus, A. (2021). Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0. Complex & Intelligent Systems, 7(1), 559–576. https://doi.org/10.1007/s40747-020-00221-9

Bentahar, O., & Belhadi, A. (2025). Integrating project management and supply chain management for resilient and sustainable operations in a VUCA world. Supply Chain Forum: An International Journal, 26(1), 1–6.

Chen, F., Liu, Y. H., & Chen, X. Z. (2024). ESG performance and business risk—Empirical evidence from China’s listed companies. Innovation and Green Development, 3(3), 100142.

Frederico, G. F., Garza-Reyes, J. A., Kumar, A., & Kumar, V. (2020). Performance measurement for supply chains in the Industry 4.0 era: A balanced scorecard approach. International Journal of Productivity and Performance Management, 70(4), 789–807. https://doi.org/10.1108/IJPPM-08-2019-0400

Jouicha, Y., Cherrafi, A., Hamani, N., & Elfezazi, S. (2025). Performance Measurement Systems for Supply Chain 5.0: Gaps, Challenges, and Future Research Avenues. IFAC-PapersOnLine, 59(10), 2933–2938.

Kamble, S. S., & Gunasekaran, A. (2020). Big data-driven supply chain performance measurement system: A review and framework for implementation. International Journal of Production Research, 58(1), 65–86. https://doi.org/10.1080/00207543.2019.1630770

Khairul Akter, M. M., Haq, U. N., Islam, M. M., & Uddin, M. A. (2022). Textile-apparel manufacturing and material waste management in the circular economy: A conceptual model to achieve sustainable development goal (SDG) 12 for Bangladesh. Cleaner Environmental Systems, 4(July 2021), 100070. https://doi.org/10.1016/j.cesys.2022.100070

Khan, M. R., Alam, M. J., Tabassum, N., & Khan, N. A. (2022). A Systematic review of the Delphi–AHP method in analyzing challenges to public-sector project procurement and the supply chain: A developing country’s perspective. Sustainability, 14(21), 14215.

Liu, L., Wu, H., Hafeez, M., Albaity, M. S. A., & Ullah, S. (2022). Carbon neutrality through supply chain performance: Does green innovation matter in Asia? Economic Research-Ekonomska Istraživanja, 1–13. https://doi.org/10.1080/1331677X.2022.2149588

Mannaperuma, B., Ho, W., & Singh, P. J. (2025). Decoding the Interplay Between Supplier Network Flexibility and Financial Performance within the Buyer Network in Uncertain Business Environments. Global Journal of Flexible Systems Management, 26(Suppl 1), 233–252.

Mardesci, H., Santosa, Nazir, N., & Hadiguna, R. A. (2021). Determination of Value-Added and Contributing Organization in the Development of Coconut Water-Based Agro Industry. In N. N., I. I., D. F., R. R., & H. R. (Eds), IOP Conference Series: Earth and Environmental Science (Vol. 709, Issue 1). IOP Publishing Ltd.

Mrad, M., Belgaroui, R., Boujelbene, Y., & Abelkawy, N. A. (2026). Bridging Digitalization and Sustainability in Supply Chain Performance Measurement: An MLP-Based Predictive Model. Logistics, 10(2), 42.

Nguyen, T. T. H. (2024). Measuring Supply Chain Performance Using the SCOR Model. Operations Research Forum, 5(2), 37. https://doi.org/10.1007/s43069-024-00314-y

Nicoletti, B. (2023). SCOR Model. In B. Nicoletti (Ed.), Supply Network 5.0: How to Improve Human Automation in the Supply Chain (pp. 19–41). Springer International Publishing. https://doi.org/10.1007/978-3-031-22032-6_2

Pourreza, S., Faezipour, M., & Faezipour, M. (2022). Eye-SCOR: A Supply Chain Operations Reference-Based Framework for Smart Eye Status Monitoring Using System Dynamics Modeling. Sustainability, 14(14), 8876. https://doi.org/10.3390/su14148876

Prasetyaningsih, E., Muhamad, C. R., & Amolina, S. (2020). Assessing of supply chain performance by adopting Supply Chain Operation Reference (SCOR) model. IOP Conference Series: Materials Science and Engineering, 830(3), 032083. https://doi.org/10.1088/1757-899X/830/3/032083

Psarommatis, F., Danishvar, M., Mousavi, A., & Kiritsis, D. (2024). Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing. IEEE Transactions on Engineering Management, 71, 702–714. https://doi.org/10.1109/TEM.2021.3133619

Rasool, F., Greco, M., & Grimaldi, M. (2021). Digital supply chain performance metrics: A literature review. Measuring Business Excellence, 26(1), 23–38. https://doi.org/10.1108/MBE-11-2020-0147

Rodríguez Mañay, L. O., Guaita-Pradas, I., & Marques-Perez, I. (2022). Measuring the Supply Chain Performance of the Floricultural Sector Using the SCOR Model and a Multicriteria Decision-Making Method. Horticulturae, 8(2), 168. https://doi.org/10.3390/horticulturae8020168

Ryandono, M. N. H., Widiastuti, T., Filianti, D., Robani, A., Al Mustofa, M. U., Susilowati, F. D., Wijayanti, I., Dewi, E. P., & Atiya, N. (2025). Overcoming barriers to optimizing cash waqf linked sukuk: A DEMATEL-ANP approach. Social Sciences & Humanities Open, 11, 101588.

Setyadi, A., Rimawan, E., Kristanto, I., & Rohmah, P. E. (2022). A proposed conceptual framework of supply chain operations reference (SCOR) model in Indonesian industries: A literature review. SINERGI, 26(3), 385–396. https://doi.org/10.22441/sinergi.2022.3.014

Singh, R. K. (2025). Impact of leadership, TQM and supply chain capabilities on sustainable supply chain performance: Moderating role of institutional pressure. The TQM Journal, 37(4), 953–976.

Stefana, E., Cocca, P., Fantori, F., Marciano, F., & Marini, A. (2022). Resource Overall Equipment Cost Loss indicator to assess equipment performance and product cost. International Journal of Productivity and Performance Management, 73(11), 20–45. https://doi.org/10.1108/IJPPM-10-2021-0615

Teymourifar, A. (2026a). A critical review of the SCOR Digital Standard (SCOR-DS): Conceptual implications for supply chain performance measurement. Frontiers in Sustainability, 7. https://doi.org/10.3389/frsus.2026.1769304

Teymourifar, A. (2026b). A critical review of the SCOR Digital Standard (SCOR-DS): Conceptual implications for supply chain performance measurement. Frontiers in Sustainability, 7, 1769304.

Wulandari, R., Ridwan, A. Y., & Muttaqin, S. (2023). Halal Supply Chain Performance Measurement Model in Food Industry Using SCOR Model, AHP Method and OMAX. In Akhyar, S. Huzni, & M. Iqbal (Eds), Proceedings of the 3rd International Conference on Experimental and Computational Mechanics in Engineering (pp. 187–197). Springer Nature. https://doi.org/10.1007/978-981-19-3629-6_20

Zhang, L., Wang, M., Zhou, X., Wu, X., Cao, Y., Xu, Y., Cui, L., & Shen, Z. (2023). Dual graph multitask framework for imbalanced delivery time estimation. International Conference on Database Systems for Advanced Applications, 606–618.