PurposeThe study aims to analyse the impact of Industry 4.0 implementation on supply chains and develop an implementation framework by considering potential drivers and barriers for the Industry 4.0 paradigm.Design/methodology/approachA critical literature review is performed to explore the key drivers and barriers for Industry 4.0 implementation under four business dimensions: strategic, organisational, technological and legal and ethical. A system dynamics model is later developed to understand the impact of Industry 4.0 implementation on supply chain parameters, by including both the identified driving forces and barriers for this technological transformation. The results of the simulation model are utilised to develop a conceptual model for a successful implementation and acceleration of Industry 4.0 in supply chains.FindingsIndustry 4.0 is predicted to bring new challenges and opportunities for future supply chains. The study discussed several implementation challenges and proposed a framework for an effective adaption and transition of the Industry 4.0 concept into supply chains.Research limitations/implicationsThe results of the simulation model are utilised to develop a conceptual model for a successful implementation and acceleration of Industry 4.0 in supply chains.Practical implicationsThe study is expected to benefit supply chain managers in understanding the challenges for implementing Industry 4.0 in their network.Originality/valueSimulation analysis provides examination of Industry 4.0 adoption in terms of its impact on supply chain performance and allows incorporation of both the drivers and barriers of this technological transformation into the analysis. Besides providing an empirical basis for this relationship, a new conceptual framework is proposed for Industry 4.0 implementation in supply chains.

Food supply chains are receiving increased attention due to the rapid depletion of natural resources, increasing quality standards and rising food safety and security concerns regarding contamination and fraud. Implementing sustainability practices in food supply chains is believed to overcome emerging challenges at both regional and global levels. However, limited studies address sustainability implementation concerns particularly in cold food supply chains.This study aims to contribute to this evident research gap by addressing the major factors hindering sustainability implementation in these networks by considering a case of UK artisan supply chain. Survey data from the UK artisan cheese producers are utilized to identify and prioritise barriers for implementing sustainability following a fuzzy analytic hierarchy process and sensitivity analysis. The analysis identified several key barriers including initial investment cost, firm size and unawareness of government regulations. The internal barriers significantly dominate implementation of sustainability practices in comparison to the external barriers.Lack of consensus regarding the concept of sustainability by different stakeholders was observed to be an issue negatively affecting the level of integration in SMEs. The findings will be highly useful for food and dairy SME's to gain competitive advantage through the successful implementation of sustainability practices.

Increased risk exposure levels, technological developments and the growing information overload in supply chain networks drive organizations to embrace data-driven approaches in Supply Chain Risk Management (SCRM). Data Mining (DM) employs multiple analytical techniques for intelligent and timely decision making; however, its potential is not entirely explored for SCRM. The paper aims to develop a DM-based framework for the identification, assessment and mitigation of different type of risks in supply chains. A holistic approach integrates DM and risk management activities in a unique framework for effective risk management. The framework is validated with a case study based on a series of semi-structured interviews, discussions and a focus group study. The study showcases how DM supports in discovering hidden and useful information from unstructured risk data for making intelligent risk management decisions.

Climate change is among the top global risks due to its growing adverse impact on businesses.However, few empirical studies address this imminent risk from a supply chain perspective.Due to a lack of established approaches for capturing complex interaction between climate change risk and supply chain performance, a three-phase mixed methodology approach was attempted. A cognitive map first captured the inter-relationships based on a mental model established by a group of experts. Later, a survey gathered from industry practitioners assessing causal relationships identified key climate change factors and most influenced supply chain performance dimensions. Finally, a system dynamics model supported by multiple case scenarios assessed the implications of climate change on supply chain performance. The results indicated a significant reduction in the availability of natural resources/raw material and capacity, leading to increase in stock-outs, inventory costs and bottlenecks disrupting procurement, manufacturing and logistics functions. Supply chain performance captured through efficiency and effectiveness shows a negative trend with increasing climate change consequences. The systems approach followed in this paper contributes by providing a quantitative model for assessing the impact of climate change risk on supply chain performance.