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Artificial Intelligence and Information System Resilience to Cope With Supply Chain Disruption

Article

Artificial Intelligence and Information System Resilience to Cope With Supply Chain Disruption

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Abstract

Artificial intelligence (AI) as a technology has the potential to interpret and evaluate alternatives where multi-dimensional data is involved in dynamic situations such as supply chain disruption. This study aims to explore the role of resilient information systems (RIS) in minimizing the risk magnitude in disruption situations in supply chain operations. The study is conducted in the qualitative mode through semi-structured interview schedule for professionals of supply chains. Thematic analysis has been used to create emerging categories. The findings of this work present critical gaps in current information systems and demonstrate how AI-oriented systems can facilitate the ecosystem of disrupted supply chains to save costs and drive efficiency on multiple parameters. The study also proposes a conceptual framework where organizational values and architectural components can be viewed jointly for quick and adequate business decisions in the complex and uncertain disruptions. The framework presents the relationships among AI, information systems and supply chain disruption. Installing appropriate AI-based data acquisition, processing and self-training capabilities along with information system infrastructure can help organizations lessen the impact of supply chain disruption while aligning the transportation network and ensuring geographically-suitable supply chains and cybersecurity. Finally, the implications for theory and practice with the limitations and scope for future research are described.

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... The results of this study identify serious inadequacies in present information systems and demonstrate how AI-driven solutions could benefit the ecosystem of disrupted supply chains in terms of cost savings and increased efficiency in a variety of areas. The framework shows how supply chain disruption, information systems, and artificial intelligence are related (Gupta et al., 2021). ...
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... In this regard, various studies have examined the effects of disruptions on the SCs; particularly, the COVID-19 pandemic has been extensively studied by scholars (Ivanov and Dolgui 2020b;Queiroz et al. 2020;Nikolopoulos et al. 2020;Chowdhury et al. 2021;Ivanov 2020;Gupta et al. 2021). However, existing research has yet to consider how small-medium enterprises (SMEs) can identify appropriate strategies during disruptions and assess the effectiveness of their strategies in the context of the firm's capabilities (Gruber, Kim, and Brinckmann 2015;Papadopoulos, Baltas, and Balta 2020). ...
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Traditional frameworks for risk assessment do not work well for cloud computing. While recent work has often focussed on the risks faced by firms adopting or selecting cloud services, there has been little research on how cloud providers might assess their own services. In this paper, we use an in-depth review of the extant literature to highlight the weaknesses of traditional risk assessment frameworks for this task. Using examples, we then describe a new risk assessment model (CSCCRA) and compare this against three established approaches. For each approach, we consider its goals, the risk assessment process, decisions, the scope of the assessment and the way in which risk is conceptualised. This evaluation points to the need for dynamic models specifically designed to evaluate cloud risk. Our suggestions for future research are aimed at improving the identification, assessment, and mitigation of inter-dependent cloud risks inherent in a defined supply chain.
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Cognitive computing holds considerable potential for holistic data interpretation in a dynamic business environment. It can act as an enabler of organizational ambidexterity. The present study explores the potential role cognitive computing can play in an organizational context with global partnerships. The study uses qualitative mode of enquiry and organizational information processing theory as the theoretical framework. Emergent categories were identified using thematic analyses. The key findings of the study highlight the critical gaps in traditional decision systems; cognitive computing as an enabler of ambidextrous orientation and facilitator of informational access for enhanced performance in case of global strategic partnerships.
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Supply chain resilience and data analytics capability have generated increased interest in academia and among practitioners. However, existing studies often treat these two streams of literature independently. Our study model reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and supply chain resilience as a means to reduce a ripple effect in supply chain or quickly recover after disruptions in the supply chain. We have grounded our theoretical model in the organisational information processing theory (OIPT). Four research hypotheses are tested using responses from 213 Indian manufacturing organisations collected via a pre-tested survey-based instrument. We further test our model using variance-based structural equation modelling, popularly known as PLS-SEM. All of the hypotheses were supported. The findings of our study offer a unique contribution to information systems (IS) and operations management (OM) literature. The findings further provide numerous directions to the supply chain managers. Finally, we note our study limitations and provide further research directions. Keywords: Data analytics; ripple effect; disruption; supply chain resilience; competitive advantage; structural equation modelling; organisational information processing theory
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Discusses the design and management of sustainable and resilient supply chains. Organizations are under varied and increasing pressure from a broad spectrum of stakeholders to incorporate sustainability measures into their supply chainmanagement practices. In this environment, the development and availability of analytical models and decision support tools can help organizations make more effective and informed decisions. To respond to this call, academic research on sustainable supply chain design and management has seen substantial development over the past two decades and. Most efforts to achieve supply chain sustainability have been predominantly directed at reducing the environmental burdens of the supply chain, commonly measured in terms of greenhouse gas emissions and resource consumption. The social sustainability aspect has focused more on the potential damage to human health, community, or society at large.
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The main objective of the study is to understand how big data analytics capability (BDAC) as an organizational culture can enhance trust and collaborative performance between civil and military organizations engaged in disaster relief operations. The theoretical framework is grounded in organizational information processing theory (OIPT). We have conceptualized an original theoretical model to show, using the competing value model (CVM), how BDAC, under a moderating influence of organizational culture, affects swift trust (ST) and collaborative performance (CP). We used WarpPLS 6.0 to test the proposed research hypotheses using multi-respondent data gathered through an email questionnaire sent to managers working in 373 organizations, including the military forces of different countries, government aid agencies, UN specialized agencies, international non-government organizations (NGOs), service providers, and contractors. The results offer four important implications. First, BDAC has a positive, significant effect on ST and CP. Second, flexible orientation (FO) and controlled orientation (CO) have no significant influence on building ST. Third, FO has a positive and significant moderating effect on the path joining BDAC and CP. Finally, CO has negative and significant moderating effect on the path joining BDAC and CP. The control variables: temporal orientation (TO) and interdependency (I) have significant effects on ST and CP. These results extend OIPT to create a better understanding of the application of information processing capabilities to build swift trust and improve collaborative performance. Furthermore, managers can derive multiple insights from this theoretically-grounded study to understand how BDAC can be exploited to gain insights in contexts of different management styles and cultures. We have also outlined the study limitations and provided numerous future research directions.
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The digitalization phenomenon is leveraging new relationship models through the entire supply chain network. In this outlook, blockchain is a cutting-edge technology that is already transforming and remodeling the relationships between all members of logistics and supply chain systems. Yet, while studies on blockchain have gained a relative pace over the recent years, the literature on this topic does not report sufficient research cases on blockchain adoption behavior at the individual level. The present study, therefore, aims to bridge this gap, notably by helping understand the individual blockchain adoption behavior in the logistics and supply chain field in India and the USA. Drawing on the emerging literature on blockchain, supply chain and network theory, as well as on technology acceptance models (TAMs), we have developed a model based on a slightly-altered version of the classical unified theory of acceptance and use of technology (UTAUT). The model being developed was then estimated using the Partial least squares structural equation modeling (PLS-SEM). As the model was eventually supported, the results obtained revealed the existence of distinct adoption behaviors between India-based and USA-based professionals. In parallel, the findings appear as a useful contribution to and a sign of progress for the literature on IT adoption, SCM, and blockchain.
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Purpose The purpose of this paper is to identify the contributions of information systems (IS) for the evolutionary process of corporate environmental management by highlighting implications for big data research. Design/methodology/approach The authors conducted two case studies with Brazilian enterprises certified by ISO 14001, by conducting interviews, document analysis and direct observation. Implications for a research agenda on big data are also presented. Findings As results, the authors present the identification of the main contributions of IS for the evolution of environmental management in the studied cases. The authors found that advanced stage regarding IS may be considered a factor that implies a more effective environmental management. Originality/value The main contribution of this research consists of the presentation of a framework that identifies the support of IS for corporate environmental practices. By confirming the relation between IS and maturity levels of environmental management, the authors highlight that application of big data has the potential of boosting the relation between IS and corporate environmental management.
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The system of systems (SoS) theory is an important topic in modern systems engineering management. Previous studies focus mainly on using it to explore large-scale physical systems. Relatively little is known about its applications in business operations. In this paper, through the SoS theory, we explore sustainable fashion supply chain management via a multimethodological approach. To be specific, we first investigate and indicate that the fashion supply chain is a well-qualified SoS. We then propose the critical SoS principles for building a sustainable fashion supply chain. We analytically reveal and investigate the values of these principles by deriving the “expected value of SoS principles” (EVSOS). In particular, we highlight how the number of market observations as well as the fabric and product leftovers related loss and gain affect EVSOS. We further build a two-stage framework as well as an action matrix for achieving sustainable fashion supply chain management. Finally, a public-data based real case study on Sweden giant fashion enterprise H&M is conducted to illustrate real world applications of the proposed framework.
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We consider a supply chain consisting of a supplier and a buyer. The buyer faces demand as a function of the selling price. There is risk of supply disruption, but the supplier can rebuild his capacity if he has invested for the capacity restoration before a disruption. To motivate the supplier to invest, the buyer can use one of two incentive contracts, namely direct and indirect. With a direct contract, the buyer provides a financial subsidy to share the supplier’s capacity restoration cost when disruption occurs. With an indirect contract, the buyer adjusts the wholesale price in the case of disruption to stimulate the capacity restoration. Both contracts allow the buyer to adjust the order quantity in the case of disruption. We analyze decisions of each supply chain member under the two contracts with different commitment strategies (the ex ante commitment (EA) strategy under which the contract is signed before disruption and the ex post commitment (EP) strategy under which the contract is signed after disruption), and study how the supply disruption affects the buyer’s selling price to end customers. We show that, from the perspective of either the supplier or the buyer, the incentive contract under the EA strategy is preferred by leading to a greater profit. Further, through numerical experiments, we recognize conditions for the fixed investment cost, probability of disruption, and regular wholesale price, under which each incentive contract encourages the supplier to invest for the capacity restoration, and brings greater profit to each supply chain member.
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Using newspaper job ad text from 1960 to 2000, we measure job tasks and the adoption of individual information and communication technologies (ICTs). Most new technologies are associated with an increase in nonroutine analytic tasks, and a decrease in nonroutine interactive, routine cognitive, and routine manual tasks. We embed these interactions in a quantitative model of worker sorting across occupations and technology adoption. Through the lens of the model, the arrival of ICTs broadly shifts workers away from routine tasks, which increases the college premium. A notable exception is the Microsoft Office suite, which has the opposite set of effects.
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Environmental change and unexpected crises are major threats to industrial development. Industries must build technological resilience to reduce the impact of shocks. Resilience has become a crucial concept for addressing vulnerabilities and developing flexible methods of adapting to crises. This study examined the concept of technological resilience, particularly at the industry level, as well as the factors that may influence technological resilience. Moreover, the relative capability of industries to maintain their production of technological knowledge during disruptive events was examined by analyzing the growth and crises of patenting. This methodology yielded novel insights into technological resilience with multidimensional perspectives. Collaboration reinforces technological resilience by reducing crisis probability and intensity. In addition, high-quality and diversified technological knowledge enhances technological resilience by reducing the probability, intensity, or duration of crises.
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This special issue explores big data analytics and applications for logistics and supply chain management by examining novel methods, practices, and opportunities. The articles present and analyse a variety of opportunities to improve big data analytics and applications for logistics and supply chain management, such as those through exploring technology-driven tracking strategies, financial performance relations with data driven supply chains, and implementation issues and supply chain capability maturity with big data. This editorial note summarizes the discussions on the big data attributes, on effective practices for implementation, and on evaluation and implementation methods.
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The rapid development of Artificial Intelligence/deep learning technology and its implementation into routine clinical imaging will cause a major transformation to the practice of radiology. Strategic positioning will ensure the successful transition of radiologists into their new roles as augmented clinicians. This paper describes an overall vision on how to achieve a smooth transition through the practice of augmented radiology where radiologists-in-the-loop ensure the safe implementation of Artificial Intelligence systems.
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Smart grids provide great opportunities for design and implementation of sustainable and efficient electricity supply chains that are more resilient to disruptions. The problem with electricity supply chain network design (ESCND) using smart grid has not been broadly explored by researchers. This paper aims to approach this problem using a multiobjective robust optimization method. The three objectives are economic (profit maximization), environmental (greenhouse gas emissions minimization), and resilience (network resilience maximization). Efficiency maximization—power loss minimization—has also been taken into account using its corresponding cost element in the economic objective function. The proposed approach accounts for different unique smart grid components such as demand side management programs, microgrid structure, two-way distribution lines, and supplier–consumer nodes, while incorporating different interrelated decisions including facility location, capacity expansion, load allocation, and pricing. To solve the resultant model, a hybrid multiobjective robust optimization technique is proposed based on cutting plane and AUGMECON2 algorithms. The proposed model and solution approach are then applied to an actual case study and the produced results are thoroughly analyzed. We find that while economic and environmental objectives can be strictly conflicting, the implementation of smart grids can result in concurrent boost in both environmental performance and network resilience.
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With the increasing trend of global warming, the frequent occurrences of natural disasters have brought serious challenges to the sustainable development of the society. Therefore, emergency decision making (EDM) for natural disasters plays an increasingly significant role in improving the capability to respond disasters. In this paper, we first elaborate the concept and characteristics of EDM for natural disasters and briefly expound emergency decision contents in different stages of natural disasters. Then, an overview is provided for the EDM theory and methods of natural disasters from the methodological perspective. After that, we give a detailed illustration of the construction of emergency decision support system. Finally, we summarize the main conclusions of the paper and point out the prospect of future researches.
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: In recent years, there has been a proliferation of interest in resilience in the supply chain field. Even though literature has acknowledged the antecedents of resilient supply chains, such as supply chain visibility, cooperation, and information sharing, their confluence in creating resilient supply chains where other behavioural issues are prevailing (i.e. trust and behavioural uncertainty) has not been studied. To address this gap, we conceptualized a theoretical framework firmly grounded in the resource based view (RBV) and the relational view that is tested for 250 manufacturing firms using hierarchical moderated regression analysis. The study offers a nuanced understanding of supply chain resilience and implications of supply chain visibility, cooperation, trust and behavioural uncertainty. Implications and suggestions for further research are provided.
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Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed self-learning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students’ self-learning processes. Secondly, it aims to improve computer engineering students’ self-learning during the courses. As general, the software system introduced here evaluates students’ intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex Optimization Algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. © 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:142–156, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21787.
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Purpose The purpose of this paper is to develop a taxonomy of how companies implement Supply Chain Risk Management (SCRM) in terms of two fundamental approaches: the first emerging from internal actions and operations within companies, and the other involving inter-organizational actions undertaken with external supply chain partners. This taxonomy aims to predict firms’ performance with regard to the frequency of supply chain disruption. Design/methodology/approach A cluster analysis of survey data from 908 firms representing 69 countries together with an analysis of variance. Findings The authors’ analysis demonstrates a clear structure of four different patterns of how companies manage supply chain risks: passive, internal, collaborative, and integral. The authors found that firms pursuing an inter-organizational orientation (collaborative and integral) face the lowest levels of supply chain disruption. On the contrary, strategies which simply concentrate on having greater control of internal operations are not vigorous enough to stop the cascade effect of a disruption at the supply chain level. Furthermore, the excellent performance of integral SCRM strategies also suggests that collaboration between buyers and suppliers ensures the efficacy of internal business continuity plans and security procedures. Practical implications Managers should play an active role in making sure that supply chain management and risk management disciplines evolve together. Obviously, when an exogenous event results in a supply chain disruption, a firm will try to put its operations under control through internal capabilities. But SCRM strategies designed proactively in advance with relevant partners are even more beneficial. Originality/value First, previous studies have limited the analysis of SCRM mainly to its reactive internal initiatives within a firm. This paper takes the SCRM literature beyond the internal focus by considering both internal and inter-organizational efforts and, more importantly, developing a single configurational model to analyze modes of interaction. Second, there is little empirical evidence showing the current situation of SCRM. Research in SCRM has been more qualitative than empirical, especially in global coverage. The research tackles this gap and, based on a broader scope of the samples the empirical findings show a higher level of generalizability.
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To mitigate and respond to supply chain risks, previous research usually views supply chain risk management as the management of various activities concerning risk identification, assessment, mitigation, and responses. While supply chain risk information plays a crucial role in the implementation and decisions of many of these activities, the importance of a firm's information processing capability to its supply chain risk management effort has received very little attention in the literature. Using information processing theory as the theoretical lens, we argue that a firm's capability in processing supply chain risk information, which comprises supply chain risk information sharing and supply chain risk information analysis, can improve operational performance, and this capability's effectiveness in improving performance is contingent on product-specific uncertainty characteristics (i.e., product complexity and product customization) and environment-related uncertainty characteristics (i.e., technology turbulence and market turbulence). We test the proposed theoretical model using data collected from 350 manufacturing firms in China. The results support that supply chain risk information processing capability comprises two constituent elements and has a positive effect on operational performance. The results also suggest that except for product complexity, all the other posited product-specific and environment-related uncertainty characteristics positively moderate the relationship between supply chain risk information capability and operational performance. We contribute to the literature by developing a theory-driven empirical model that integrates the core concepts of supply chain risk management and information processing theory to generate research findings with theoretical and managerial implications.
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The purpose of this paper is to propose and test a theoretical framework to explain resilience in supply chain networks for sustainability using unstructured Big Data, based upon 36,422 items gathered in the form of tweets, news, Facebook, WordPress, Instagram, Google+, and YouTube, and structured data, via responses from 205 managers involved in disaster relief activities in the aftermath of Nepal earthquake in 2015. The paper uses Big Data analysis, followed by a survey which was analyzed using content analysis and confirmatory factor analysis (CFA). The results of the analysis suggest that swift trust, information sharing and public-private partnership are critical enablers of resilience in supply chain networks. The current study used cross-sectional data. However the hypotheses of the study can be tested using longitudinal data to attempt to establish causality. The article advances the literature on resilience in disaster supply chain networks for sustainability in that (i) it suggests the use of Big Data analysis to propose and test particular frameworks in the context of resilient supply chains that enable sustainability; (ii) it argues that swift trust, public private partnerships, and quality information sharing link to resilience in supply chain networks; and (iii) it uses the context of Nepal, at the moment of the disaster relief activities to provide contemporaneous perceptions of the phenomenon as it takes place.