Purpose: The purpose of this paper is to explore how multinational corporations (MNCs) orchestrate internal and external resources to help their multi-tier supply chains learn sustainability-related knowledge.Design/methodology/approach: An exploratory multiple case study approach was adopted and three MNCs’ sustainable initiatives in China were examined. The data were primarily collected through 43 semi-structured interviews with managers of focal companies and their multi-tier suppliers.Findings: The authors found that in order to facilitate their supply chains to learn sustainability, MNCs tend to orchestrate in breadth by internally setting up new functional departments and externally working with third parties, and orchestrate in depth working directly with their extreme upstream suppliers adopting varied governance mechanisms on lower-tier suppliers along the project lifecycle. The resource orchestration in breadth and depth and along the project lifecycle results in changes of supply chain structure.Practical implications: The proposed conceptual model provides an overall framework for companies to design and implement their multi-tier sustainable initiatives. Companies could learn from the suggested learning stages and the best practices of case companies.Originality/value: The authors extend and enrich resource orchestration perspective (ROP), which is internally focused, to a supply chain level, and answer a theoretical question of how MNCs orchestrate their internal and external resources to help their supply chains to learn sustainability. The extension of ROP refutes the resource dependence theory, which adopts a passive approach of relying on external suppliers and proposes that MNCs should proactively work with internal and external stakeholders to learn sustainability.

Blockchain – also known as distributed ledger – technology is set to revolutionise data and business process management and transactions. Blockchain adoption, pioneered initially as a financial tec...

This paper aims to apply a hybrid method of supplier selection to a well-known Turkish company operating in the appliance industry. The data envelopment analytic hierarchy process (DEAHP) methodology developed by Ramanathan, R., (Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process. Computers and Operations Research, 2006 Ramanathan, R . 2006. Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process. Comput. Oper. Res., 33: 1289–1307. [Crossref], [Web of Science ®] [Google Scholar] , 33, 1289–1307) was chosen as the survey method. In this method, the data envelopment analysis (DEA) approach is embedded into analytic hierarchy process (AHP) methodology. This research concluded that the DEAHP method outperforms the AHP method for supplier selection despite the findings that the AHP model suggested supplier 1 to be the best supplier, contradicting the suggestion made by the DEAHP model and the real action taken by BEKO in selecting supplier 2. These findings imply that DEAHP criteria reflect closer to the real optimum of the decision made. Drawing on a real case our study has supported Ramanathan's ( 2006 Ramanathan, R . 2006. Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process. Comput. Oper. Res., 33: 1289–1307. [Crossref], [Web of Science ®] [Google Scholar] ) work confirming the view that the DEAHP method provides a better decision than the AHP method for supplier selection. Because the DEAHP model is relatively more cumbersome to apply, its application will be more appropriate for high-value components where stringent purchasing criteria are required. In contrast, AHP would remain to be an appropriate approach for relatively lower value components (C class). The novelty of this research lies in the application of a hybrid approach to a real industry case–the DEAHP method for supplier selection, where little has been done on this subject. This study has dealt with one of the most important subjects in supply chain management providing a better decision for supplier selection using appropriate quantitative approaches.

Purpose: In response to calls for conceptual frameworks and generic theory building toward the advancement of sustainability in supply chain resource utilization and management, the purpose of this paper is to advance a circular framework for supply chain resource sustainability (SCRS), and a decision-support methodology for assessing SCRS against the backdrop of five foundational premises (FPs) deduced from the literature on resource sustainability.Design/methodology/approach: Taking a conceptual theory-building approach, the paper advances a set of SCRS decision-support criteria for each of the theoretical premises advanced, and applies the theory of constraints to illustrate the conceptual and practical applications of the framework in SCRS decision making.Findings: This study uses recent conceptualizations of supply chains as “complex adaptive systems” to provide a robust and novel frame and a set of decision rules with which to assess the interconnectedness of environmental, economic, and social capital of supply chain resources from pre-production to post-production.Research limitations/implications: The paper contributes to theory building in sustainability research, and the SCRS decision framework developed could be applied in tandem with existing quantitative hybrid life-cycle and input-output approaches to facilitate targeted resource sustainability assessments, with implications for research and practice.Originality/value: The novel SCRS framework proposed serves as a template for evaluating SCRS and provides a decision-support methodology for assessing SCRS against the five theorized FPs.

Based upon an increasing academic and business interest in greening the industrial supply chains, this paper establishes the need for a state-of-the-art decision support system (DSS) for carbon emissions accounting and management, mainly across the product supply chains by identifying methodological shortcomings in existing tools, and proposing a supply chain (SC) framework which provide businesses with a holistic understanding of their supply chains and ensuring partners within supply chain collaborative networks have a shared understanding of their emissions. It describes the design and development of a DSS now known as supply chain environmental analysis tool (SCEnAT) in detail, putting its unique and innovative features into a comparative perspective vis-à-vis existing tools and software of different types. The methodological framework used to design and develop SCEnAT integrates different individual techniques/methods of supply chain (SC) mapping, SC carbon accounting, SC interventions and SC interventions evaluation on a range of key performance indicators (KPIs). These individual methods have been used and applied innovatively to the challenge of designing SCEnAT within the desired framework. Finally, we demonstrate the application of SCEnAT, especially the advantage of using a robust carbon accounting methodology, to a SC case study. The SCEnAT framework pushes the theoretical boundary by addressing the problems of intra-organisational approach in decision making for lowering carbon along the supply chain; with an open innovation, cutting edge, hybridised framework that considers the supply chain as a whole in co-decision making for lowering carbon along the supply chain with the most robust methodology of hybrid life cycle analysis (LCA) that considers direct and indirect emissions and interventional performance evaluation for low carbon technology investment and business case building in order to adapt and mitigate climate change problems. This research has implications for future sustainability research in SC, decisions science, management theory, practice and policy.

With smart sensors and embedded drivers, today’s automotive industry has taken a giant leap in emerging technologies like Machine learning, Artificial intelligence, and the Internet of things and started to build data-driven decision-making strategies to compete in global smart manufacturing. This paper proposes a novel design framework that uses Federated learning-Artificial intelligence (FAI) for decision-making and Smart Contract (SC) policies for process execution and control in a completely automated smart automobile manufacturing industry. The proposed design introduces a novel element called Trust Threshold Limit (TTL) that helps moderate the excess usage of embedded equipment, tools, energy, and cost functions, limiting wastages in the manufacturing processes. This research highlights the use cases of AI in decentralised Blockchain with smart contracts, the company’s trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. The developed model supported by real-time cases incorporated cost functions, delivery time and energy evaluations. Results spotlight the use of FAI in decision accuracy for the developed smart contract-based Automobile Assembly Model (AAM), thereby qualitatively limiting the threshold level of cost, energy and other control functions in procurement assembly and manufacturing. Customisation and graphical user interface with cloud integration are some challenges of this model.