AccScience Publishing / IJOCTA / Volume 12 / Issue 2 / DOI: 10.11121/ijocta.2022.1158
RESEARCH ARTICLE

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

Alperen Ekrem Çelikdin1*
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1 Planning and Logistics Manager, Tarfaş Aksaray Integrated Facilities, Aksaray, Turkey
IJOCTA 2022, 12(2), 79–89; https://doi.org/10.11121/ijocta.2022.1158
Submitted: 11 September 2019 | Accepted: 31 May 2022 | Published: 12 June 2022
© 2022 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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 advantagewill 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 uniquepaper,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 tothe managers for additional storage space investments.

Keywords
Financial optimizationN
on-linear programming
Purchase planning
Conflict of interest
The authors declare they have no competing interests.
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