Purpose: The 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/approach: A 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.Findings: Industry 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/implications: 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.Practical implications: The study is expected to benefit supply chain managers in understanding the challenges for implementing Industry 4.0 in their network.Originality/value: Simulation 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.

Supply chains (SCs) are exposed to multiple risks and vulnerable to disruption propagation (i.e. the ripple effect). Despite established literature, quantitative analysis of the ripple effect in SCs considering simultaneous, long-term disruptions (i.e. induced by the COVID-19 pandemic) remains limited. This study defines, applies and demonstrates the capability of system dynamics modelling to recognise and visualise the ripple effect subject to supply, demand, and logistics disruptions as well as a combined, simultaneous disruption of supply, demand and logistics. Simulation results for these four risk scenarios indicate that disruption propagation and its impacts vary based on risk type, combination of risks and the impacting node. The bi-directional, increasing effect is significant for disruptions of longer duration. Retailers and manufacturers are most fragile to multiple disruptions due to broader risk exposure points. In generalised terms, systems theory-based study provides insights into the complex behaviour of simultaneous risks and associated disruptions occurring at a node and across the SC. The outcomes derived can help practitioners visualise and recognise the dynamic nature of the ripple effect cascading across the SC network. In addition, some novel insights on the systemic nature and delayed impact of disruption propagations are uncovered and discussed.

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.

Food supply chains are receiving increased attention due to rapid depletion of natural resources, increasing quality standards and rising food safety and security concerns. Implementing sustainability practices in food supply chains is believed to overcome such emerging challenges. However, limited studies address sustainability implementation concerns, particularly in cold food supply chains. Thus, this study attempts to identify factors hindering sustainability implementation in cold food chain networks by considering a case of UK artisan cheese producers. Survey data is utilised to identify and prioritise barriers for implementing sustainability following 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 the implementation of sustainability practices in comparison to external barriers. Lack of consensus regarding the concept of sustainability by different stakeholders is observed to be an issue negatively affecting the level of integration in SMEs. Findings will help food and dairy SME's in gaining competitive advantage through the successful implementation of sustainability practices.

Supplier evaluation and selection is one of the most critical strategic decisions for developing a competitive and sustainable organization. Companies have to consider supplier related risks and threats in their purchasing decisions. In today’s competitive and risky business environment, it is very important to work with reliable suppliers. This study proposes a clustering based approach to group suppliers based on their risk profile. Suppliers of a company in the heavy-machinery sector are assessed based on 17 qualitative and quantitative risk types. The weights of the criteria are determined by using the Best-Worst method. Four factors are extracted by applying Factor Analysis to the supplier risk data. Then k-means clustering algorithm is applied to group core suppliers of the company based on the four risk factors. Three clusters are created with different risk exposure levels. The interpretation of the results provides insights for risk management actions and supplier development programs to mitigate supplier risk.