Optimizing seasonal grain intakes with non-linear programming: An application in the feed industry

Alperen Ekrem Çelikdin

doi:10.11121/ijocta.2022.1158

Authors

Alperen Ekrem Çelikdin Planning and Logistics Manager, Tarfaş Aksaray Integrated Facilities, Aksaray, Turkey https://orcid.org/0000-0002-2215-3812

DOI:

https://doi.org/10.11121/ijocta.2022.1158

Keywords:

Financial optimization, Non-linear programming, Purchase planning

Abstract

In the feed sector, 95% of the input costs arise from the supply of raw materials used in feed production. The selling price is determined by competition in free market conditions. Due to the use of similar technologies and the very small share of production costs in total costs, it is unlikely that a competitive advantage will be gained through innovations in production. Between 30% and 50% of grain products are used in feed ration analysis. Cereals can only be harvested at a certain time of the year. Due to this limited time frame, feed production enterprises have to balance their financial burdens with their operational needs while making their annual stocks. The study was carried out to cover all the relevant businesses of the company, which has feed factories in four regions of Turkey. Based on the season data of the year 2020-2021, the grain purchase planning for the year 2021-2022 was tried to be optimized with non-linear programming. While creating the mathematical model, grain prices, interest rates, production needs according to production planning, sales according to sales forecasts, factory stocking capacities, licensed warehouse rental, transportation, handling and transshipment costs were taken into account.

With this unique paper, in the cattle feed production sector, storage, transportation and handling costs will be minimized. Cost advantage will be provided with optimum purchase planning in the season. According to the grain pricing forecast and market data for the 2021-2022 season, model can provide a cost advantage of 0.7%. Model will also provide insight to the managers for additional storage space investments.

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Author Biography

Alperen Ekrem Çelikdin, Planning and Logistics Manager, Tarfaş Aksaray Integrated Facilities, Aksaray, Turkey

Alperen Ekrem ÇELİKDİN currently works as a planning and logistics professional in the dairy feed industry. He received his B.S. degree from Çankaya University Industrial Engineering department. He received his MSc and Ph.D. degrees from the Business Administration Department of Aksaray University. His research interests include, supply chain planning, vehicle routing, heuristic optimization and organizational studies.

References

Tay Bayramoglu, Y. D. D. A. & Koç Yurtkur, Y. D. D. A. (2016). Türkiye’de G?da ve Tar?msal Ürün Fiyatlar?n? Uluslararas? Belirleyicileri. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 15 (2) , 63-73.

Soysal, M., Bloemhof-Ruwaard, J., Meuwissen, M., & Van der Vorst, J. (2012). A Review on Quantitative Models for Sustainable Food Logistics Management. International Journal of Food System Dynamics. 3. 136-155.

Ge, H., Gray, R. & Nolan, J. (2015). Agricultural supply chain optimization and complexity: A comparison of analytic vs simulated solutions and policies. International Journal of Production Economics, 159(C), 208-220.

Hosseini, S.M., Motlagh, M.R.G., Samani, F. & Abbasi S. (2019). Strategic optimization of wheat supply chain network under uncertainty: a real case study. Springer Berlin Heidelberg.

Mirzapour A., Malekly, H. & Aryanezhad, M.B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics. 134. 28-42.

Sat?r, B. & Y?ld?r?m, G. (2020). A General Production and Financial Planning Model: Case of a Poultry Integration, Arabian Journal for Science and Engineering. 45, 6803–6820.

Mogale, D., Kumar, S. & Tiwari, M. (2018). An MINLP model to support the movement and storage decisions of the Indian food grain supply chain. Control Engineering Practice. 70. 98-113.

Razmi, J., Kazerooni, M. P., & Sangari, M. S. (2016). Designing an integrated multi-echelon, multi-product and multi-period supply chain network with seasonal raw materials. Economic Computation & Economic Cybernetics Studies & Research, 50(1).

Nourbakhsh, S. M., Bai, Y., Maia, G. D., Ouyang, Y., & Rodriguez, L. (2016). Grain supply chain network design and logistics planning for reducing post-harvest loss. Biosystems Engineering, 151, 105-115..

Dwivedi, A., Jha, A., Prajapati, D., Sreenu, N., & Pratap, S. (2020). Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem. Modern Supply Chain Research and Applications. 2 (3), 161-177

Teixeira, A., Costa e Silva, E., & Lopes, I. C. (2021). A mixed integer nonlinear multiperiod model for supply chain management of a company in the retail sector. RAIRO-Operations Research, 55(2), 997-1013.

Smith, S. A., Agrawal, N., & McIntyre, S. H. (1998). A discrete optimization model for seasonal merchandise planning. Journal of Retailing, 74(2), 193-221.

Nwanya, S. (2015). Material inventory optimization in bakery supply chain: Implications for food security in Nigeria. International Journal of Supply and Operations Management, 2(2), 683-699.

Bilgen, B., & Günther, H. O. (2010). Integrated production and distribution planning in the fast moving consumer goods industry: a block planning application. Or Spectrum, 32(4), 927-955.

Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131(1), 421-429.

Brown, G.G., Keegan, J., Vigus, B., & Wood, R.K. (2001). The Kellogg Company Optimizes Production, Inventory, and Distribution, Interfaces, 31, 1-15.

Wouda, F. H., van Beek, P., van der Vorst, J. G., & Tacke, H. (2002). An application of mixed-integer linear programming models on the redesign of the supply network of Nutricia Dairy & Drinks Group in Hungary. Or Spectrum, 24(4), 449-465.

Ioannou, G. (2005). Streamlining the supply chain of the Hellenic sugar industry. Journal of Food Engineering, 70(3), 323-332.

Jeynes, P. H. (1964). Minimum acceptable return. The Engineering Economist, 9(4), 9-25.

Sinha, M. K., & Poole, A. V. (1987, September). Selection of a discount rate or minimum acceptable IRR. In SPE Annual Technical Conference and Exhibition. OnePetro.