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Cost Optimisation of Supply Chains in the Food Industry: Cost Function Modelling

Author

Listed:
  • Ion Popa

    (Bucharest University of Economic Studies and Academy of Romanian Scientists,)

  • Sorina-Geanina Stanescu

    (Valahia University of Targoviste and Institute of Multidisciplinary Research)

  • Ani?oara Duica

    (Valahia University of Targoviste, Romania)

  • Elisabeta Ilona Molnar

    (Partium Christian University of Oradea, Romania)

  • Mircea Constantin Duica

    (Valahia University of Targoviste, Romania)

Abstract

The food industry faces complex challenges in managing supply chains, significantly affecting operational performance and costs. This paper explores the critical factors influencing the efficiency of food supply chains, such as product perishability, seasonality, climate change, and high logistics costs. The study uses an applied approach based on modelling a cost function that integrates the main components of supply chain expenses — procurement, transportation, warehousing, production and distribution — and how they are affected by industry-specific challenges. The proposed cost function allows for assessing the impact of these variables on total costs and identifying critical areas for optimisation. The results obtained demonstrate that monitoring logistical conditions, adjusting stocks based on seasonal forecasts and optimising transport routes are essential measures to reduce losses and increase the competitiveness of companies in the food industry. The study's applied impact consists of providing a practical cost optimisation tool applicable to manufacturers and distributors. The conclusions emphasise the importance of an integrated approach to risk and cost management in the food industry, providing recommendations for sustainable practices and strategies to increase long-term competitiveness.

Suggested Citation

  • Ion Popa & Sorina-Geanina Stanescu & Ani?oara Duica & Elisabeta Ilona Molnar & Mircea Constantin Duica, 2025. "Cost Optimisation of Supply Chains in the Food Industry: Cost Function Modelling," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 27(69), pages 293-293, April.
  • Handle: RePEc:aes:amfeco:v:27:y:2025:i:69:p:293
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    References listed on IDEAS

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    1. Jiaying Liu & Bin Liu, 2023. "Commodity Pricing and Replenishment Decision Strategy Based on the Seasonal ARIMA Model," Mathematics, MDPI, vol. 11(24), pages 1-14, December.
    2. Hu, Jing & Hu, Qiying & Xia, Yusen, 2019. "Who should invest in cost reduction in supply chains?," International Journal of Production Economics, Elsevier, vol. 207(C), pages 1-18.
    3. Monideepa Tarafdar & Sufian Qrunfleh, 2017. "Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 925-938, February.
    4. Dmitry Ivanov, 2024. "Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains," Annals of Operations Research, Springer, vol. 335(3), pages 1627-1644, April.
    5. Koc, T. & Bozdag, E., 2017. "Measuring the degree of novelty of innovation based on Porter's value chain approach," European Journal of Operational Research, Elsevier, vol. 257(2), pages 559-567.
    6. Yusuf, Y. Y. & Gunasekaran, A. & Adeleye, E. O. & Sivayoganathan, K., 2004. "Agile supply chain capabilities: Determinants of competitive objectives," European Journal of Operational Research, Elsevier, vol. 159(2), pages 379-392, December.
    7. Ntabe, E.N. & LeBel, L. & Munson, A.D. & Santa-Eulalia, L.A., 2015. "A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues," International Journal of Production Economics, Elsevier, vol. 169(C), pages 310-332.
    8. Amit Sachan & B.S. Sahay & Dinesh Sharma, 2005. "Developing Indian grain supply chain cost model: a system dynamics approach," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 54(3), pages 187-205, April.
    9. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    10. Pettersson, Annelie I. & Segerstedt, Anders, 2013. "Measuring supply chain cost," International Journal of Production Economics, Elsevier, vol. 143(2), pages 357-363.
    11. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    12. Shih-Hsien Tseng & Hui-Ming Wee & Samuel Reong & Chun-I Wu, 2019. "Considering JIT in Assigning Task for Return Vehicle in Green Supply Chain," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
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    More about this item

    Keywords

    supply chain; cost; efficiency; food logistics; cost function;
    All these keywords.

    JEL classification:

    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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