Methodological approach for determining the size of the optimal raw material zone in the logistics system of dairy processing enterprise
Abstract
Purpose. The purpose of the article is the improvement of the methodological approach for determining the optimal raw material area in the logistics system of a milk processing enterprise using economic-mathematical tools.
Methodology / approach. The research is based on the applying such methods as analysis, synthesis, generalization, induction, deduction – to determine the content and subsystem of the logistic system of milk processing plant, factors of the size of the raw material area of the milk processing enterprise, formulating conclusions regarding the methodological approach to assessing its rational size; economic-mathematical modeling and cluster analysis – to determine the rational size of the raw material area of the milk processing enterprise; graphic – for a visual presentation of the cluster analysis of the raw material zone of the milk processing enterprise according to the Ward’s method. The research was carried out on the basis of statistical data of the Main Department of Statistics in Chernihiv Region, reports of agricultural enterprises of Chernihiv Region and data of the authors’ own observations for 2011–2021 (for the calculation of some indicators, data for 2020–2021 were used).
Results. The peculiarities, role and tasks of logistics in the dairy sub-complex were determined, a conceptual model of the logistics system for the milk processing plant was developed, which consists of functional and provisional subsystems and covers production, purchasing, transport, certification, storage and processing processes, minimizes logistical risks. In order to identify reserves of the optimization of the raw material area, a cluster analysis was conducted (the Ward’s method was chosen as the clustering method), based on data on the volume of milk purchases, distance from the factory and potential opportunities for expanding the raw material area. The economic-mathematical model was designed, which allows determining the optimal raw material area of the processing enterprise based on the criterion of minimum transport costs for the delivery of dairy raw materials, as well as determining the optimal structure of dairy production based on the criterion of minimum technological costs of processing raw materials in the production of various types of milk products, taking into account the volume of consumer demand. The economic-mathematical model was tested and used to determine the rational distance of milk producers from the milk processing enterprise at a distance of 46–56 km in the studied region.
Originality / scientific novelty. The methodical approach to determine the optimal raw material area in the logistics system of a milk processing enterprise has been improved by using economic-mathematical tools and applying cluster analysis according to the Ward’s method.
Practical value / implications. The results of the study can be used to calculate the optimal raw material area of milk processing enterprises, which will contribute to the sustainable development of the dairy sub-complex, of all its participants, from producers of raw materials (milk) to the final consumer.
References
Окландер М. А. Логістична система підприємства: моногр. Одеса: Астропринт, 2004. 312 с.
Szymonik A. Logistics and supply chain management. Lodz: Technical University of Lodz Press, 2012. 358 p. URL: https://www.researchgate.net/publication/297369572_Logistics_and_Supply_Chain_Management.
Крикавський Є. В., Чорнописька Н. В. Логістичні системи. Львів: Львівська політехніка, 2019. 288 с.
Чухрай Н., Патора Р. Інновації та логістика товарів: моногр. Львів: Львівська політехніка, 2001. 264 с.
APICS dictionary: the industry standard for more than 3500 terms and definitions; eds. J. F. Cox, J. H. Blackstone, 11th ed. Amer Production & Inventory, 2005. 126 p.
State of Supply Chain Sustainability 2022: report. CSCMP, 2022. 23 p. URL: https://cscmp.org/store/detail.aspx?id=SUS-22.
Аналіз впливу війни на агросектор України. Український клуб аграрного бізнесу, 2022. URL: https://ucab.ua/ua/pres_sluzhba/novosti/analiz_vplivu_viyni_na_agrosektor_ukraini.
Waters D. Logistics. An introduction to supply chain management. Palgrave Macmillan, 2003. 369 p.
Harrison A., Van Hoek R., Skipworth H. Logistics management and strategy: competing through the supply chain, 5th ed. Pearson, 2014. 427 p.
Christopher М. Logistics and supply chain management: creating value-adding networks, 4th ed. Pearson, 2011. 288 p.
National council of physical distribution management: Twenty-First Annual Conference Proceedings (New Orleans, Louisiana, 2–5 October 1983). New Orleans, 1984. 1009 p.
Langevin A., Riopel D. Logistics systems: design and optimization. New York: Springer, 2005. 388 p. URL: https://doi.org/10.1007/b106452.
Колодійчук В. Менеджмент логістичних систем підприємств АПК Agricultural and Resource Economics. 2016. Vol. 2. No. 4. Pp. 106–117. https://doi.org/10.51599/are.2016.02.04.10.
Трушкіна Н. Удосконалення організаційно-економічного механізму управління логістичною діяльністю підприємства. Agricultural and Resource Economics. 2019. Vol. 5. No. 4. Pp. 156–172. https://doi.org/10.51599/are.2019.05.04.09.
Сумець О. М. Теоретико-методологічні засади логістичної діяльності підприємств агропродовольчого комплексу: моногр. Харків: Мадрид, 2015. 543 с.
Данчук В. Д. Сватко В. В. Оптимізації пошуку шляхів по графу в динамічній задачі комівояжера методом модифікованого маршрутного алгоритму. Системні дослідження та інформаційні технології. 2012. № 2. С. 78–86. URL: https://ela.kpi.ua/bitstream/123456789/9179/1/07_Danch.pdf.
Chowdhury M. M. H., Quaddus M. Supply chain resilience: conceptualization and scale development using dynamic capability theory. International Journal of Production Economics. 2017. Vol. 188. Pp. 185–204. https://doi.org/10.1016/j.ijpe.2017.03.020.
Ward Jr J. H. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association. 1963. Vol. 58. Is. 301. Pp. 236–244. https://doi.org/10.1080/01621459.1963.10500845.
De Amorim R. C. Feature relevance in Ward’s Hierarchical Clustering using the Lp norm. Journal of Classification. 2015. Vol. 32. Is. 1. Pp. 46–62. https://doi.org/10.1007/s00357-015-9167-1.
Onegina V., Megits N., Kravchenko O., Kravchenko Yu. Price transmission in milk supply chain in Ukraine. Agricultural and Resource Economics. 2022. Vol. 8. No. 1. Pp. 152–170. https://doi.org/10.51599/are.2022.08.01.08.
Weissgerber C., Hess S. Farmers’ preferences for adopting on-farm concentration of raw milk: results from a discrete choice experiment in Germany. Journal of Dairy Science. 2022. Vol. 105. Is. 4. Pp. 3176–3191. https://doi.org/10.3168/jds.2021-20528.
Haji M., Kerbache L., Muhammad M., Al-Ansari T. Roles of technology in improving perishable food supply chains. Logistics. 2020. Vol. 4. Is. 4. 33 https://doi.org/10.3390/logistics4040033.
Burke N., Zacharski K. A., Southern M., Hogan P. et al. The dairy industry: process, monitoring, standards, and quality. Descriptive Food Science; eds. V. Díaz, R. M. García-Gimeno. IntechOpen Limited, 2018. 182 p. https://doi.org/10.5772/intechopen.80398.
Saberi S., Kouhizadeh M., Sarkis J., Shen L. J. Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research. 2019. Vol. 57. Is. 7. Pp. 2117–2135. https://doi.org/10.1080/00207543.2018.1533261.
Sethanan K., Pitakaso R., Differential evolution algorithms for scheduling raw milk transportation. Computers and Electronics in Agriculture. 2016. Vol. 121. Pp. 245–259. https://doi.org/10.1016/j.compag.2015.12.021.
Krstić M., Agnusdei G. P., Miglietta P. P., Tadić S. Logistics 4.0 toward circular economy in the agri-food sector. Sustainable Futures. 2022. Vol. 4. 100097. https://doi.org/10.1016/j.sftr.2022.100097.
Brintrup A., Pak J., Ratiney D., Pearce T. et al. Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. International Journal of Production Research. 2020. Vol. 58. Is. 11. Pp. 3330–3341. https://doi.org/10.1080/00207543.2019.1685705.
Kazancoglu Y., Ozkan-Ozen Y. D., Ozbiltekin M. Minimizing losses in milk supply chain with sustainability: an example from an emerging economy. Resources, Conservation and Recycling. 2018. Vol. 139. Pp. 270–279. https://doi.org/10.1016/j.resconrec.2018.08.020.
References
Oklander, M. A. (2004). Lohistychna systema pidpryiemstva [The logistics system of the enterprise]. Odesa, Astroprynt.
Szymonik, A. (2012). Logistics and supply chain management. Lodz, Technical University of Lodz Press. Available at: https://www.researchgate.net/publication/297369572_Logistics_and_Supply_Chain_Management.
Krykavskyi, Ye. V., & Chornopyska, N. V. (2019). Lohistychni systemy [Logistics systems]. Lviv, Lviv Polytechnic Publishing.
Chukhrai, N., & Patora, R. (2001). Innovatsii ta lohistyka tovariv [Innovations and logistics of goods]. Lviv, Lviv Polytechnic Publishing.
Cox, J. F., & Blackstone, J. H. (Eds.). (2005). APICS dictionary: the industry standard for more than 3500 terms and definitions, 11th ed. Amer Production & Inventory.
State of Supply Chain Sustainability 2022 (2022). Report. CSCMP. Available at: https://cscmp.org/store/detail.aspx?id=SUS-22.
UCAB (2022). Analysis of war influence on agrosector of Ukraine. Available at: https://ucab.ua/ua/pres_sluzhba/novosti/analiz_vplivu_viyni_na_agrosektor_ukraini.
Waters, D. (2003). Logistics. An introduction to supply chain management. Palgrave Macmillan.
Harrison, A., Van Hoek, R., & Skipworth, H. (2014). Logistics management and strategy: competing through the supply chain, 5th ed. Pearson.
Christopher, M. (2011). Logistics and supply chain management: creating value-adding networks, 4th ed. Pearson.
National council of physical distribution management (1984). Twenty-First Annual Conference Proceedings (New Orleans, Louisiana, October 2–5, 1983). New Orleans.
Langevin, A., & Riopel, D. (2005). Logistics systems: design and optimization. New York, Springer. Available at: https://doi.org/10.1007/b106452.
Kolodiychuk, V. (2016). Management of logistics systems of agribusiness enterprises. Agricultural and Resource Economics, 2(4), 106–117. https://doi.org/10.51599/are.2016.02.04.10.
Trushkina, N. (2019). Improvement of the organizational and economic mechanism of management of logistics activities of enterprises. Agricultural and Resource Economics, 5(4), 156–172. https://doi.org/10.51599/are.2019.05.04.09.
Sumets, O. M. (2015). Teoretyko-metodolohichni zasady lohistychnoi diialnosti pidpryiemstv ahroprodovolchoho kompleksu [Theoretical and methodological principles of logistics activities of enterprises of the agro-food complex]. Kharkiv, Madryd.
Danchuk, V. D., & Svatko, V. V. (2012). Optimization of the search for paths on the graph in the dynamic problem of the traveling salesman by the method of the modified route algorithm. Systemni doslidzhennia ta informatsiini tekhnolohii, 2, 78–86. Available at: https://ela.kpi.ua/bitstream/123456789/9179/1/07_Danch.pdf.
Chowdhury, M. M. H., & Quaddus, M. (2017). Supply chain resilience: conceptualization and scale development using dynamic capability theory. International Journal of Production Economics, 188, 185–204. https://doi.org/10.1016/j.ijpe.2017.03.020.
Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845.
De Amorim, R. C. (2015). Feature relevance in ward’s hierarchical clustering using the Lp norm. Journal of Classification, 32(1), 46–62. https://doi.org/10.1007/s00357-015-9167-1.
Onegina, V., Megits, N., Kravchenko, O., & Kravchenko, Yu. (2022). Price transmission in milk supply chain in Ukraine. Agricultural and Resource Economics, 8(1), 152–170. https://doi.org/10.51599/are.2022.08.01.08.
Weissgerber, C., & Hess, S. (2022). Farmers’ preferences for adopting on-farm concentration of raw milk: results from a discrete choice experiment in Germany. Journal of Dairy Science, 105(4), 3176–3191. https://doi.org/10.3168/jds.2021-20528.
Haji, M., Kerbache, L., Muhammad, M., & Al-Ansari, T. (2020). Roles of technology in improving perishable food supply chains. Logistics, 4(4), 33. https://doi.org/10.3390/logistics4040033.
Burke, N., Zacharski, K. A., Southern, M., Hogan, P., Ryan, M., & Adley, C. C. (2018). The dairy industry: process, monitoring, standards, and quality. In V. Díaz, R. M. García-Gimeno (Eds.), Descriptive Food Science. https://doi.org/10.5772/intechopen.80398.
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. J. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261.
Sethanan, K., & Pitakaso, R. (2016). Differential evolution algorithms for scheduling raw milk transportation. Computers and Electronics in Agriculture, 121, 245–259. https://doi.org/10.1016/j.compag.2015.12.021.
Krstić, M., Agnusdei, G. P., Miglietta, P. P., & Tadić, S. (2022). Logistics 4.0 toward circular economy in the agri-food sector. Sustainable Futures, 4, 100097. https://doi.org/10.1016/j.sftr.2022.100097.
Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. International Journal of Production Research, 58(11), 3330–3341. https://doi.org/10.1080/00207543.2019.1685705.
Kazancoglu, Y., Ozkan-Ozen, Y. D., & Ozbiltekin, M. (2018). Minimizing losses in milk supply chain with sustainability: an example from an emerging economy. Resources, Conservation and Recycling, 139, 270–279. https://doi.org/10.1016/j.resconrec.2018.08.020.
