Sustainable operations of industrial symbiosis: an enterprise input-output model integrated by agent-based simulation

Industrial symbiosis (IS) is a key for implementing circular economy. Through IS, wastes produced by one company are used as inputs by other companies. The operations of IS suffers from uncertainty barriers since wastes are not produced upon demand but emerge as secondary outputs. Such an uncertainty, triggered by waste supply-demand quantity mismatch, influences IS business dynamics. Accordingly, companies have difficulty to foresee potential costs and benefits of implementing IS. The paper adopts an enterprise input-output model providing a cost–benefit analysis of IS integrated to an agent-based model to simulate how companies share the total economic benefits stemming from IS. The proposed model allows to explore the space of cooperation, defined as the operationally favourable conditions to operate IS in an economically win-win manner. This approach, as a decision-support tool, allows the user to understand whether the IS relationship is created and how should the cost-sharing policy be. The proposed model is applied to a numerical example. Findings show that cost-sharing strategies are dramatically affected by waste supply-demand mismatch and by the relationship between saved and additional costs to run IS. Apart from methodological and theoretical contributions, the paper proposes managerial and practical implications for business strategy development in IS

EDI and intelligent agents integration to manage food chains

Electronic Data Interchange (EDI) is a type of inter-organizational information system, which permits the automatic and structured communication of data between organizations. Although EDI is used for internal communication, its main application is in facilitating closer collaboration between organizational entities, e.g. suppliers, credit institutions, and transportation carriers. This study illustrates how agent technology can be used to solve real food supply chain inefficiencies and optimise the logistics network. For instance, we explain how agribusiness companies can use agent technology in association with EDI to collect data from retailers, group them into meaningful categories, and then perform different functions. As a result, the distribution chain can be managed more efficiently. Intelligent agents also make available timely data to inventory management resulting in reducing stocks and tied capital. Intelligent agents are adoptive to changes so they are valuable in a dynamic environment where new products or partners have entered into the supply chain. This flexibility gives agent technology a relative advantage which, for pioneer companies, can be a competitive advantage. The study concludes with recommendations and directions for further research