key: cord-0895393-ad86z00t authors: Dolgui, Alexandre; Ivanov, Dmitry title: Exploring supply chain structural dynamics: New disruptive technologies and disruption risks date: 2020-07-26 journal: Int J Prod Econ DOI: 10.1016/j.ijpe.2020.107886 sha: 22799aa40a11624fd22ec9ab1d1c1a42bde4af6a doc_id: 895393 cord_uid: ad86z00t We discuss recent developments in exploring supply chain structural dynamics. We focus on both positive (i.e., new disruptive technologies) and negative (i.e., disruption risks) triggers of the structural dynamics in complex supply chain networks. We discuss papers in the special issue which focus on supply chain structural dynamics using different methods, collating and presenting recent research in the field. In particular, the ripple effect, blockchain, network resilience, data analytics, and service platforms have been identified as the leading research directions. The management and optimization of structural dynamics plays a crucial role in determining a firm's competitiveness in the markets. This research stream has also been extended by coining two new theoretical lens, i.e., supply chain viability (Ivanov 2020b, Ivanov and and supply chain reconfigurability . Moreover, the Covid-19 virus outbreaks and the global pandemic are likely to result in wide ranging structural changes to global supply networks, making the topic of this Special Issue very timely.This Special Issue has sought to attract new research in supply chain structural dynamics, considering disruptions in supply chains from both positive and negative perspectives. Examples of positive disruptive technologies and paradigms include: • Blockchain and supply chain structural dynamics • Industry 4.0 and supply chain structural dynamics • Sharing and circular economy and supply chain structural dynamics • Firm's organizational transformation (e.g., mergers&acquisitions, industrial symbioses) and supply chain structural dynamics • Digital technology innovation (e.g., cloud manufacturing platforms or supply chain visibility) and supply chain structural dynamics • Sustainability and supply chain structural dynamics. However, disruption risks cause negative changes in supply chain structural dynamics: • Capacity disruptions and supply chain structural dynamics • Disruption propagation (i.e., the ripple effect) and supply chain structural dynamics • Resilience and supply chain structural dynamics • Recovery and supply chain structural dynamics • Proactive control and supply chain structural dynamics. A firm's competitive advantages strongly depends on the adoption of new disruptive technologies, such as Industry 4.0, Blockchain, Internet of Things, development of supply chain sustainability, and increasing resilience in light of more and more frequent and severe disruption risks. As such, new research is needed to advance our understanding of the place, role, and impacts of new technologies in the further development of digital and resilient supply chains with efficient and sustainable resource utilization (Dubey et al. 2019a ,b, Ivanov et al. 2019 , Babich and Hilary 2020 . Notably, supply chain structural dynamics is encountered in multiple structures such organizational, functional, information, financial, technical and product structures, i.e., it can be looked at from different perspectives and studied by methods covered in different disciplines. In other words, we encounter multi-structural supply chain dynamics (Ivanov et al. 2010) . Besides, structural dynamics appears at strategic, tactical and operative decision-making levels. We summarize different dimensions of supply chain multi-structural dynamics in Table 1 . At the strategic level, supply chain structural dynamics is triggered by long-term disruptions which may be both positive (i.e., new technological developments) and negative (i.e., long-term market disruption due to a political or financial crisis). Examples include re-design of facilities and supply networks in the organizational structure (Silbermayr and Minner 2014 , Yildiz et al. 2016 , Yu et al. 2019 ), re-allocation of functions in the functional structure (e.g., a logistics company takes over some retail activities), information infrastructure re-design (e.g., formation of cloud manufacturing platform in the supply chain) (Liu et al. 2019 , Rossit et al. 2019 ), re-design of supply chain financial flows, product re-design, and re-design of technical equipment (e.g., introduction of autonomous mobile robots). At the tactical level, supply chain structural dynamics is triggered by medium-term disruptions, e.g., as a reaction to a natural disaster or a cyber-attack. Examples include resilience and Ripple effect analysis in the organizational structure (Ivanov et al. 2014 , Ambulkar et al. 2015 , Garvey et al. 2015 , Hosseini et al. 2019 , Li et al. 2019 , Tan et al. 2019 , Xu et al. 2020 , supply chain plan reconfiguration in the functional structure (Elluru et al. 2019 , Pavlov et al. 2019 , Sawik 2020), information system reconfiguration, adjustment of payment and contracting mechanisms (Shen et al. 2019) , product postponement and some modernization of technical equipment. Unlike the strategic structural dynamics, which result in long-term, irreversible changes in the supply chain, structural dynamics at the tactical level changes the supply chain for some period of time to overcome a medium-term disruption (Ivanov et al. 2018) . After the recovery, the supply chain structures may return to normal ones. At the operative level, supply chain structural dynamics is triggered by some operational oscillations. This kind of structural dynamics is less obvious than at the strategic and tactical levels. Most of the operative oscillations can be recovered without any significant structural changes (Paul et al. 2018 , Lücker et al. 2019 ). However, some operative recovery actions can involve structural dynamics also. For example, the use of a backup supplier can help to mitigate some temporary material shortages, which results in changes in the supply structures, even if for a short period of time (Sawik 2013) . Within the functional structure, stability analysis and Bullwhip-effect control may be used to understand the impact of and react to some inventory and lead-time oscillations (Wang and Disney 2012 , Spiegler et al. 2017 , Demirel et al. 2019 . Product substitution and some temporary re-assignments of production lines (Zennaro et al. 2019 , Battaïa et al. 2020 ), e.g., as a reaction to demand fluctuations might be used in the product and technical structures, respectively (Lu et al. 2011 , Gupta et al. 2020 . Unlike the strategic and tactical structural dynamics, the structural dynamics at the operative level changes the supply chain for a shorter period of time to overcome some short-term oscillations. After recovery, the supply chain structures will typically return to a previous state. Notably, there are many situations when structural dynamics analysis involves the interconnection of different decision-making levels. For example, strategic decisions on supply chain re-design may be driven by the tactical and operative dynamics across the structures (Klibi and Martel 2012 , Brintrup et al. 2015 , Wamba et al. 2015 , Altay et al. 2018 , Bier et al. 2019 . Moreover, dynamics can be dispersed across different structures. Examples of such mutual interrelations include, but are not limited to, integrated dynamics at the interfaces of organizational and financial structures Yu 2020, Gupta et al. 2020) , organizational and product structures (Lu et al. 2011) , technical and information structures , and functional and organizational structures (Sawik 2017) . This Special Issue has sought to focus on supply chain structural dynamics from a multimethodological perspective, collating and presenting recent research in the field. We received 39 submissions and selected 10 papers for publication subject to a rigorous peer-review process. Papers in this Special Issue represent a variety of methodologies and research paradigms, including mainstream supply chain and operations management research, network theory, graph theory, control, dynamical systems theory, game theory, optimization and simulation, surveys, and case-studies. The paper "Exploring Supply Chain Network Resilience in the Presence of the Ripple Effect" by Li and Zobel (2020) elaborates on the ripple effect, i.e., disruption propagation and the subsequent structural network dynamics in the context of multi-dimensional quantitative resilience assessment. The authors present a comprehensive analysis of how network structure and node risk capacity influence different aspects of supply chain network resilience. In particular, the results show that the influence of network type on resilience tends to be more significant in the short-term than it is in the longer-term, given the ripple effect. Moreover, resilience can be improved more effectively by enhancing node risk capacity than by adjusting network structure. Dixit et al. (2020) present a paper "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure" that analyses dependencies of supply chain resilience on structural parameters and their dynamics. Using an empirically-grounded dataset, the authors compute resilience as a compounding function of density, centrality, connectivity, and network size. Using a conditional-value at risk (CVaR) approach, the authors show that firms with the lowest supply network density and centrality and the highest connectivity and network size exhibit the highest resilience. Garvey and Carnovale (2020) analyse inventory control under conditions of the ripple effect and structural dynamics in their paper "The Rippled Newsvendor: A New Inventory Framework for Modelling Supply Chain Risk Severity In The Presence of Risk Propagation". The authors propose a new version of the single-period newsvendor model -the "Rippled Newsvendor"with supply chain severity as the primary objective while taking into account network structure dynamics due to disruption propagations. They utilize a Bayesian Network approach whereby the conditional probability distributions are functions of the inventory ordering decisions. The results help to show the behavior of the objective function as well as to gain insight into the potential optimal ordering policies of this new model. Dubey et al. (2020) focus their paper "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations" on the role of entrepreneurial orientation on the adoption of big data analytics powered by artificial intelligence and operational performance. The authors use the dynamic capabilities view of firms and contingency theory to develop and test their model. The results provide novel insights on the dynamic use of firm capabilities utilizing new disruptive technologies. Choi et al. (2020) propose in their paper "Information Disclosure Structure in Supply Chains with Rental Service Platforms in the Blockchain Technology Era" a stylized duopoly model to analyze the product-information-disclosure Nash game between two rental service platforms whose products-to-rent are substitutable. The authors derive the equilibrium level of product information disclosure and identify conditions under which the platforms choose to disclose or not to disclose information. The results show that for products with higher profit margin, both platforms are more likely to disclose information. The findings also indicate that there exists a critical threshold on the information-sensitive consumers, which helps each platform decide whether or not to disclose product information. The impacts of product information disclosure on the consumer surplus and seller benefits are explored, and the roles played by the blockchain technology are discussed. Fosso analyse technology adoption and supply chain performance in the context of blockchain in their paper "Dynamics Between Blockchain Adoption Determinants and Supply Chain Performance: An Empirical Investigation". The authors indicate numerous directions in how blockchain can disrupt the existing supply chain practices. Their findings reveal that knowledge sharing and trading partner pressure play an important role in blockchain adoption. Moreover, the authors show that supply chain performance is significantly influenced by blockchain transparency. The authors utilize an agent-based simulation methodology to examine the potential of blockchain technology to build resilient supply chains. They identify possible risk-related applications of blockchain-coordinated supply chains and examine their impacts on different resilience strategies. The experiments conducted under a range of scenarios demonstrate that the propagation of disruptions (i.e., the ripple effect), network recovery time and total costs can be substantially reduced due to time-efficient collaboration. De Giovanni (2020) analyses a supply chain managed through either a traditional online platform or a blockchain in his paper "Blockchain and Smart Contracts in Supply Chain Management: A Game Theoretic model". In the case of an online platform, the firms face business risks due to delivery and service and also pay high transaction costs. In a blockchain platform, firms can avoid risks and save the transaction costs. However, the blockchain requires initial implementation investments and variable costs can increase. The author identifies the conditions and the stochastic cases in which the blockchain is not worth implementing. Further, he investigates the suitability of a smart wholesale price contract and a smart revenue sharing contract to better coordinate firms' relationships and negotiations. The results show that the use of smart contracts makes blockchain applications more operationally convenient and economically appealing. Fattahi et al. (2020) propose a new measure to quantify supply chain resilience by using stochastic programming in their paper "Stochastic optimization of disruption-driven supply chain network design with a new resilience metric". The specific feature of this new measure is the expected value of supply chain cost increase due to a possible disruption during the recovery period. The new resilience measure combines recovery time and costs. Harpreet and Prakash Singh (2020) consider a novel problem setting when supplier selection and order allocation should take into account both potential disruption risks and disruptive technologies in their paper "Multi-Stage Hybrid Model for Supplier Selection and Order Allocation Considering Disruption Risks and Disruptive Technologies". In their model, suppliers are evaluated based on a set of criteria suitable in an Industry 4.0 environment using Data Envelopment Analysis (DEA) and are further prioritized using the Fuzzy Analytical Hierarchical Process and Technique for Order of Preference by Similarity to Ideal Solution (FAHP-TOPSIS). The risk associated with each supplier is computed. For optimization purposes, the authors develop a mixed integer programme (MIP) to optimize multi-period, multi-item order allocation so that costs and risk of disruption are minimized simultaneously. Agility and Resilience as antecedents of Supply Chain Performance under moderating effects of Organizational Culture within Humanitarian Setting: A Dynamic Capability View Firm's resilience to supply chain disruptions: Scale development and empirical examination Sustainable and Energy Efficient Reconfigurable Manufacturing Systems Distributed ledgers and operations: What operations management researchers should know about blockchain technology Methods for mitigating disruptions in complex supply chain structures: A systematic literature review Resilient supply chains for extreme situations: Outlining a new field of study Supply networks as complex systems: A network sciencebased characterization Information Disclosure Structure in Supply Chains with Rental Service Platforms in the Blockchain Technology Era Epidemics control and logistics operations: A review Identifying dynamical instabilities in supply networks using generalized modeling Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure Ripple effect in the supply chain: An analysis and recent literature Stochastic optimization of disruption-driven supply chain network design with a new resilience metric Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics Industry 4.0 technologies: Implementation pat-terns in manufacturing companies An analytical framework for supply network risk propagation: A Bayesian network approach The Rippled Newsvendor: A New Inventory Framework for Modelling Supply Chain Risk Severity In The Presence of Risk Propagation Blockchain and Smart Contracts in Supply Chain Management: A Game Theoretic model Introduction to dynamics of manufacturing supply networks Competitive pricing of substitute products under supply disruption Review of quantitative methods for supply chain resilience analysis The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics Low-Certainty-Need (LCN) Supply Chains: A new perspective in managing disruption risks and resilience The Ripple effect in supply chains: trade-off 'efficiencyflexibility-resilience' in disruption management A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19 / SARS-CoV-2) case Viable Supply Chain Model: Integrating agility, resilience and sustainability perspectives -lessons from and thinking beyond the COVID-19 pandemic Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. A Position Paper Motivated by COVID-19 Outbreak Researchers' Perspectives on Industry 4.0: Multi-Disciplinary Analysis and Opportunities for Operations Management Multi-Stage Hybrid Model for Supplier Selection and Order Allocation Considering Disruption Risks and Disruptive Technologies Modeling approaches for the design of resilient supply networks under disruptions Network Characteristics and Supply Chain Resilience under Conditions of Risk Propagation Exploring Supply Chain Network Resilience in the Presence of the Ripple Effect Scheduling in cloud manufacturing: state-ofthe-art and research challenges Analysis of Resilience Strategies and Ripple Effect in Blockchain-Coordinated Supply Chains: An Agent-based Simulation Study Product Substitution and Dual Sourcing Under Random Supply Failures Roles of inventory and reserve capacity in mitigating supply chain disruption risk Supply chain evolutiontheory, concepts and science A quantitative and simulation model for managing sudden supply delay with fuzzy demand and safety stock Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics A Review of the Existing and Emerging Topics in Supply Chain Risk Management Literature Impacts of Epidemic Outbreaks on Supply Chains: Mapping a Research Agenda Amid the COVID-19 Pandemic through a Structured Literature Review Integrated selection of suppliers and scheduling of customer orders in the presence of supply chain disruption risks A portfolio approach to supply chain disruption management Supply Chain Disruption Management A review on supply chain contracting with information considerations: information updating and information asymmetry A multiple sourcing inventory model under disruption risk Scheduling in Industry 4.0 and cloud manufacturing systems Investigating sustained oscillations in nonlinear production and inventory control models Structural-aware simulation analysis of supply chain resilience The strategic role of logistics in the Industry 4.0 era Identifying Risk Issues and Research Advancements in Supply Chain Risk Management How 'big data' can make big impact: Findings from a systematic review and a longitudinal case study Stability analysis of constrained inventory systems Flexible strategies under supply disruption: the interplay between contingent sourcing and responsive pricing Reliable Supply Chain Network Design Disruption risks in supply chain management: a literature review based on bibliometric analysis Dynamism, disruption orientation, and resilience in the supply chain and the impacts on financial performance: A dynamic capabilities perspective Big size highly customised product manufacturing systems: a literature review and future research agenda Finally, we would like to thank Editor-in-Chief Prof. Stefan Minner and European Editor Prof. Bartholomew MacCarthy for their invaluable support for this Special Issue. We also thank all the reviewers who volunteered their expertise and enabled a thorough evaluation and improvement of the submissions to this Special Issue.