Automated Supply Chain Formation – A Theoretical Framework

The purpose of this paper is to review the different concepts and approaches regarding automated supply chain formation (SCF) in order to create a theoretical framework and identify gaps in existing research in SCF regarding the complexity of practical implementation in the context of Industry 4.0. The research is conducted through analyzing three perspectives regard-ing the complexity of the SCF process: 1) the existence of a central authority, 2) the mecha-nisms employed for communication between entities in the supply chain, 3) one/multi-unit dimension for the traded goods. A theoretical framework was created and the following gaps and issues were identified in the existing research literature: 1) Parameters used in order to pair-wise suppliers/consumers are limited. 2) The resulted supply chains are assessed mainly using a profit optimization function for the end-consumer. 3) The possible risks associated with participating entities in the supply chain are not considered

Expected Utility and Risk Management in Complex Projects

Much research has been conducted to the study of methods for managing ISD projects. This resulted in a large amount of literature on a variety of often normative ISD methods. The increasing projects\u27 complexity provide new challenges regarding management and development. In complex projects scenarios where the outcome is composed of several deployed components, guaranteeing specific contract requirements for the prime contractor of the project is a real challenge. The focus of this paper is to find appropriate methods to facilitate end-to-end contract parameters in complex projects environments by automated supply chain formation and to establish and enforce contract parameters between each pair of component consumer/provider. Communication between agents along the supply chain is done by message exchange and provides propagation of constraints between subcontractors in the supply chain. Our findings reveal that the proposed method is able to address the emerging issues arising in complex projects

An Agent-Based Decision Support System for Supply Chain Management in the Petroleum Industry

International audienceThe dynamic economic environment is driving the evolution of traditional supply chains toward a connected, smart, and highly efficient supply chain ecosystem. Algorithms become powerfull tools that enable machines to make autonomous decisions in the digitized supply chain of the future. The integration of software agents with decision support systems provides automated means for decision making. The present paper proposes an agent-based decision support system for supply chain management in the petroleum industry. This industry has a strategic position as it is the base for other essential activities of the economy of any country. The petroleum industry is faced with volatile feedstock costs, cyclical product prices and seasonal final products demand. The current paper considers the position of a refinery as it is at the middle of the integrated petroleum supply chain, between the upstream and downstream. It procures crude oil from upstream assessing the price, quality, timing, and distance to the refinery in order to decide the optimal acquisition. Additionally, the refiner has to carefully monitor the price risk and manage the inventory. The manufacturing activities of the refiner requires thoroughly planning and scheduling the production levels and supply chains for all the derivates and feedstocks for petrochemical industry using tools for decision making in order to estimate market opportunities and threats under volatile market conditions. In order to provide a reliable and practical decision making model, we proposed the integration of supply chain formation algorithm and a mechanism for decision support under uncertainty using maximum expected utility

Modelling decision making in digital supply chains: insights from the petroleum industry

International audiencePurpose :This paper aims to overcome some of the limitations of previous works regarding automated supply chain formation (SCF). Hence, it proposes an algorithm for automated SCF using multiple contract parameters. Moreover, it proposes a decision-making mechanism that provides means for incorporating risk in the decision-making process. To better emphasize the features of the proposed decision-making mechanism, the paper provides some insights from the petroleum industry. This industry has a strategic position, as it is the base for other essential activities of the economy of any country. The petroleum industry is faced with volatile feed-stock costs, cyclical product prices and seasonal final products demand.Design/methodology/approach :The authors have modeled the supply chain in terms of a cluster graph where the nodes are represented by clusters over the contract parameters that suppliers/consumers are interested in. The suppliers/consumers own utility functions and agree on multiple contract parameters by message exchange, directly with other participant agents, representing their potential buyer or seller. The agreed values of the negotiated issues are reflected in a contract which has a certain utility value for every agent. They consider uncertainties in crude oil prices and demand in petrochemical products and model the decision mechanism for a refinery by using an influence diagram.Findings :By integrating the automated SCF algorithm and a mechanism for decision support under uncertainty, the authors propose a reliable and practical decision-making model with a practical application not only in the petroleum industry but also in any other complex industry involving a multi-tier supply chain.Research limitations/implications :The limitation of this approach reveals in situations where the parameters can take values over continuous domains. In these cases, storing the preferences for every agent might need a considerable amount of memory depending on the size of the continuous domain; hence, the proposed approach might encounter efficiency issues.Practical implications :The current paper makes a step forward to the implementation of digital supply chains in the context of Industry 4.0. The proposed algorithm and decision-making mechanism become powerful tools that will enable machines to make autonomous decisions in the digital supply chain of the future.Originality/value :The current work proposes a decentralized mechanism for automated SCF. As opposed to the previous decentralized approaches, this approach translates the SCF optimization problem not as a profit maximization problem but as a utility maximization. Hence, it incorporates multiple parameters and uses utility functions to find the optimal supply chain. The current approach is closer to real life scenarios than the previous approaches that were using only cost as a mean for pairwise agents because it uses utility functions for entities in the supply chain to make decision. Moreover, this approach overcomes the limitations of previous approaches by providing means to incorporate risk in the decision-making mechanism