A Study of Supply Chain Management System of Maize in Khon Kaen

Abstract

           This research aimed to study the supply chain management system for maize in Khon Kaen province. The research was qualitative research collected from structure interview using 32 key informants, 30 maize farmers, 1 agricultural extension worker, and 1 animal feed processing entrepreneur, which was selected from a purposive sampling. The methods used for data analysis are Fishbone diagram, SCOR Model, and SWOT analysis.
            The research results from the in-depth interview findings are as follows: The upstream supply chain of maize in Khon Kaen consists of maize growers, millers, and local aggregators who main produce the maize and improve quality. Then send to a feed mill or a corn starch factory in the midstream supply chain to process maize into end products. After that, in the downstream supply chain the maize will be distribute to consumers. The supply chain analysis results under the dimension of each function of SCOR model are as follows: 1) Planning was a short-term decision or some farmers have monopoly on the company to manage all the plans. 2) In the sourcing process, it is found that farmers tend to procure as much as they wanted and when they needed without the planning. 3) In the production process, it is found the cultivation of maize by farmers in each area depends on rainfall, experience in planting and maintaining. 4) In the delivery process, there are both sell themselves and the company to pick it up at the farm. 5) In the return process, it is no return. The results from fishbone diagram found main problem that prevents maize being sold at low price is humidity. As for the analysis of strengths and weaknesses in supply chains, it was found that farmers were highly experienced. There are groups to build networks and bargaining power with a government and market support. However, they are high production cost and low access to technology.

Article history: Received 20 April 2022
                            Revised 12 June 2022
                            Accepted 14 June 2022
                            SIMILARITY INDEX = 0.00 %