Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach

Priyadarshini, Jaya; Singh, Rajesh Kumar; Mishra, Ruchi; Bag, Surajit

doi:10.1007/s12063-022-00259-7

Cited by 35 publications

(17 citation statements)

References 136 publications

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“…Future researchers are also encouraged to use m-TISM as a simpler and more convenient way of constructing structural explanatory models to validate their results and findings if there are fewer variables, the field of study is well established and the relevant literature is extensive. [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93]…”

Section: Limitations and Future Workmentioning

confidence: 99%

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

2022

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Objectives This study used the Technology-Organization-Environment (TOE) framework to identify the factors involved in the decisions made by integrated medical and healthcare organizations to adopt artificial intelligence (AI) elderly care service resources. Method This study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis. Results The ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources. Conclusion The combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.

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Section: Limitations and Future Workmentioning

confidence: 99%

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

2022

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“…A fifth more recent theme revolves around the concept of "antifragility", the ability of a system to gain from volatility and disorder (Taleb, 2012;Taleb and Douady, 2013), and argues that sourcing firms may benefit from supply disruptions they experience and achieve even higher performance after such experiences (cf. Nikookar et al, 2021;Priyadarshini et al, 2022). According to Nikookar et al (2021), for example, by actively seeking to build an antifragile supply chain (e.g.…”

Section: Ijopm 433mentioning

confidence: 99%

The effects of tie strength and data integration with supply base on supply disruption ambiguity and its impact on inventory turnover

Pandey

Chatterjee²,

Rungtusanatham³

2022

IJOPM

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PurposeIn this paper, the authors introduce supply disruption ambiguity as the inability of a sourcing firm to attach probability point estimates to the occurrence of and to the magnitude of loss from supply disruptions. The authors drew on the “ambiguity in decision-making” literature to define this concept formally, connected it to relevant supply disruption information deficit, positioned it relative to supply chain risk assessment and hypothesized and tested its negative associations with both supply base ties and inventory turnover.Design/methodology/approachThe authors analysed survey data from 171 North American manufacturers and archival data for a subset (88 publicly listed) of these manufacturers via Ordinary Least Squares (OLS) estimation after ensuring that methodological concerns with survey research have been addressed. They used appropriate controls and employed the heteroskedasticity-based instrumental variable (HBIV) approach to ensure that inferences from our results are not unduly influenced by endogeneity.FindingsStrong supply base ties decrease supply disruption ambiguity, which, in turn, increases inventory turnover. Moreover, strong supply base ties and data integration with the supply base have indirect and positive effects on inventory turnover. As sourcing firms strengthen ties and integrate data exchange with their supply base, their inventory turnover improves from access to information relevant to detect and diagnose supply disruptions effectively.Originality/valueResearch on supply disruption management has paid more attention to the “disruption recovery” stage than to the “disruption discovery” stage. In this paper, the authors add novel insights regarding the recognition and diagnosis aspects of the “disruption discovery” stage. These novel insights reveal how and why sourcing firms reduce their overall ambiguity associated with detecting and assessing losses from supply disruptions through establishing strong ties with their supply base and how and why reducing such ambiguity improves inventory turnover performance.

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“…However, scholarly research is at a nascent stage of development (e.g., Größler, 2020; Nikookar et al, 2021). In particular, little is known about the capabilities required to build supply chain antifragility and facilitate growth after disruption (Nikookar et al, 2021; Priyadarshini et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Building an antifragile supply chain: A capability blueprint for resilience and post‐disruption growth

Nikookar,

Stevenson,

Varsei

2024

J Supply Chain Manag

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Considerable research has focused on how supply chains can better handle disruptions. Consequently, concepts such as supply chain robustness and engineering resilience have emerged, with the dominant emphasis being that disruptions are a wholly bad thing to be avoided or resisted. However, recent discourse in the supply chain disruption management literature, such as the social–ecological interpretation of supply chain resilience, suggests that disruptions can be positioned more positively as potential catalysts for growth. Yet little is known about the capabilities required for a supply chain to grow following disruption. The emerging concept of supply chain antifragility focuses specifically on growth, providing an arrowhead for investigating what enables firms to grow following disruption. Utilizing a metaphorical transfer method, this research translates the capabilities of individuals—those who grow psychologically and emotionally after experiencing trauma—into supply chain capabilities that enhance antifragility. Five key capabilities for building antifragility in supply chains are identified: supply chain mindfulness, supply chain transformative learning, supply chain plasticity, supply chain bricolage, and supply chain collaboration. Furthermore, a hierarchy of capabilities is revealed that points to a sequential approach to capitalizing on the potential growth opportunities presented by supply chain disruptions. The findings are sense‐checked through focus groups with practitioners, informing the development of five propositions. This research contributes to theory development on handling supply chain disruptions by providing a capability blueprint for post‐disruption growth that complements the literature on social–ecological supply chain resilience. Finally, this research highlights the value of metaphorical transfer as an innovative approach for understanding contemporary supply chain phenomena and advancing novel theoretical frameworks.

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Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach

Cited by 35 publications

References 136 publications

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

The effects of tie strength and data integration with supply base on supply disruption ambiguity and its impact on inventory turnover

Building an antifragile supply chain: A capability blueprint for resilience and post‐disruption growth

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