{"title":"Multi-agent architecture for waste minimisation in beef supply chain","authors":"Ai Nguyen, Akshit Singh, S. Kumari, S. Choudhary","doi":"10.1080/09537287.2021.1979679","DOIUrl":null,"url":null,"abstract":"Abstract Food waste is an alarming issue pertaining to the rising global hunger, huge environmental footprint, and high monetary value. In developing and developed nations, it occurs primarily due to inefficiencies upstream and downstream of the supply chain respectively. A common factor in both developed and developing nations is product flow within the supply chain from farms to retailers. This study aims to identify the root causes of waste generated across the product flow of the beef supply chain from farm to retailer. A workshop involving twenty practitioners of the beef industry was conducted and the collected information was transcribed and coded to generate a current reality tree, which assisted in identifying root causes of waste in the entire beef supply chain. A multi-agent architecture framework spanning the entire beef supply chain from farm to retailer is proposed, which is composed of autonomous agents capable of bringing all segments of the beef industry on a single platform and collaboratively assist them in mitigating root causes of waste. The proposed framework will aid the practitioners in the beef industry to reduce waste, improve their operational efficiency thereby raising food security, economic development whilst curbing their carbon footprint.","PeriodicalId":20627,"journal":{"name":"Production Planning & Control","volume":"75 1","pages":"1082 - 1096"},"PeriodicalIF":6.1000,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Production Planning & Control","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/09537287.2021.1979679","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 3
Abstract
Abstract Food waste is an alarming issue pertaining to the rising global hunger, huge environmental footprint, and high monetary value. In developing and developed nations, it occurs primarily due to inefficiencies upstream and downstream of the supply chain respectively. A common factor in both developed and developing nations is product flow within the supply chain from farms to retailers. This study aims to identify the root causes of waste generated across the product flow of the beef supply chain from farm to retailer. A workshop involving twenty practitioners of the beef industry was conducted and the collected information was transcribed and coded to generate a current reality tree, which assisted in identifying root causes of waste in the entire beef supply chain. A multi-agent architecture framework spanning the entire beef supply chain from farm to retailer is proposed, which is composed of autonomous agents capable of bringing all segments of the beef industry on a single platform and collaboratively assist them in mitigating root causes of waste. The proposed framework will aid the practitioners in the beef industry to reduce waste, improve their operational efficiency thereby raising food security, economic development whilst curbing their carbon footprint.
期刊介绍:
Production Planning & Control is an international journal that focuses on research papers concerning operations management across industries. It emphasizes research originating from industrial needs that can provide guidance to managers and future researchers. Papers accepted by "Production Planning & Control" should address emerging industrial needs, clearly outlining the nature of the industrial problem. Any suitable research methods may be employed, and each paper should justify the method used. Case studies illustrating international significance are encouraged. Authors are encouraged to relate their work to existing knowledge in the field, particularly regarding its implications for management practice and future research agendas.