Distribution Network Design for Central Kitchen Expansion: A Decision-Making Case Study of Toko Bakmi Kita
DOI:
https://doi.org/10.25170/metris.v27i02.8179Keywords:
Distribution network design, Central kitchen; K-Means clustering, Logistics cost modelling, Analytic HierarchyAbstract
Multi-outlet food-service companies face growing logistics complexity as they expand, yet few studies integrate logistics cost analysis, capacity planning, and multi-criteria decision-making into distribution network design for small and medium-sized food-service enterprises in Indonesia. This study aims to determine the appropriate distribution network design to support Toko Bakmi Kita's expansion from one to ten outlets by 2029. As the existing Central Kitchen (CK) in Serang is projected to exceed its capacity, this research evaluates four alternative distribution network models by integrating financial and non-financial criteria. The study employs an explanatory single-case study design that integrates capacity analysis, logistics cost optimization, spatial analysis, and multi-criteria decision-making (AHP). The ten-outlet network requires 382.21 m² of CK production area and 140.00 m² of CK storage area. From a cost perspective, Scenario 3 (Optimal CK–Direct) is the most efficient, with a total logistics cost of approximately Rp2.08 billion per month and Rp181,464.28 per kilogram, a 2.05% saving over the baseline; demand variation has the greatest effect on cost. However, AHP identifies Scenario 2 (Existing CK–Hub) as the preferred overall alternative (priority 0.395048), because Service and Product Quality, Supply Reliability, and Supplier Proximity together account for more than 80% of the decision weight. The study presents an integrated decision-support framework for distribution network design in a small- to medium-sized Indonesian food-service company. By combining a cost-efficiency benchmark with multi-criteria evaluation, the study offers practical guidance for expansion decisions in Indonesian food-service SMEs and contributes an integrated, replicable framework to the limited literature in this context.
References
Apicbase. (2023). What is a central production kitchen and how does it work?. (https://get.apicbase.com/central-production-kitchen/). Di akses tanggal 1 Agustus 2026
Badan Pusat Statistik. (2025). Rata-rata pengeluaran per kapita seminggu menurut kelompok makanan minuman jadi, 2019–2024. BPS-Statistics Indonesia. (https://www.bps.go.id/). Di akses tanggal 1 Agustus 2026
Ballou, R. H. (2004). Business logistics/supply chain management: Planning, organizing, and controlling the supply chain (5th ed.). Pearson.
Chopra, S., & Meindl, P. (2016). Supply chain management: Strategy, planning, and operation (6th ed.). Pearson.
Devin, & Widaningrum, D. L. (2021). A GIS-based method for central kitchen location selection problem. IOP Conference Series: Earth and Environmental Science, 794(1), 012091.
Fujii, N., Kaihara, T., Uemura, M., Nonaka, T., & Shimmura, T. (2013). Facility layout planning of central kitchen in food service industry: Application to the real-scale problem. In V. Prabhu, M. Taisch, & D. Kiritsis (Eds.), Advances in production management systems: Sustainable production and service supply chains (pp. 33–40). USA:State College.
Guidani, B., Accorsi, R., Manzini, R., & Ronzoni, M. (2022). Post-pandemic strategic planning of food catering production and distribution networks: A regional case study. Transportation Research Procedia, 67, 46–55.
Heizer, J., Render, B., & Munson, C. (2017). Operations management: Sustainability and supply chain management (12th ed.). Pearson.
Heizer, J., Render, B., & Munson, C. (2020). Operations management: Sustainability and supply chain management (13th ed.). Pearson.
Jacobs, F. R., & Chase, R. B. (2018). Operations and supply chain management (15th ed.). McGraw-Hill Education.
Kurniawan, A. C., Redi, A. A. N. P., Amarajaya, D., Hernawati, T. R., & Adipramana, I. D. G. Y. (2024). Optimal location selection for mega-kitchens in the network design of free lunch programs in Nusa Tenggara Barat. Jurnal Serambi Engineering, 9(4), 10577–10590.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (pp. 281–297). University of California Press.
Parnell, G. S., Bresnick, T. A., Tani, S. N., & Johnson, E. R. (Eds.). (2013). Handbook of decision analysis. Wiley.
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global sensitivity analysis: The primer. Wiley.
Deng, A. (2023). Supply chain innovation in the catering industry: An assessment of the cost-effectiveness and sustainability of the central kitchen model. Advances in Economics, Management and Political Sciences, 65, 188–194.
Rasulov, R. (2024). Optimization of direct supply chains in the restaurant industry: Addressing key challenges through technological innovation. Economics of Development, 23(3), 93–103.
Liu, Y. (2023). A comprehensive analysis of the Chinese chain restaurant market: Success factors, challenges, and strategies for improvement. BCP Business & Management, 49, 700–706.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.
