key: cord-0894636-8s5g7h1c
authors: Priyadarshini, Jaya; Singh, Rajesh Kr; Mishra, Ruchi; Bag, Surajit
title: Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach
date: 2022-04-27
journal: Oper Manag Res
DOI: 10.1007/s12063-022-00259-7
sha: a7a6ec04fcfe3fb35dcecfa7c94b74c97d9149e9
doc_id: 894636
cord_uid: 8s5g7h1c

The outbreak of the Covid-19 pandemic has come across as an exogenous shock to the firms and their supply chains. It has led firms to rethink and rework their existing robust and resilient supply chains. The purpose of this study is to move beyond robustness and resilience and shift to an antifragile supply chain that sees disorder as an opportunity to learn and grow. In this study, various factors to attain an antifragile supply chain have been identified through literature review and experts’ opinions. Using TISM-MICMAC, structural relationships among these factors have been developed, and, then the factors have been classified as drivers or dependents. The study reveals the importance of having proactive top management as a major driving power to build an antifragile supply chain. Development of a strategy for collaboration and innovation, development of a skilled workforce for technology adoption, and resource allocation for digitalization are some other factors with strong driving power. The novelty of the study lies in its effort to drive the attention of researchers and practitioners towards thinking beyond robustness and resilience and shifting towards antifragility. The study will help firms in strategic decision-making for the adoption of additive manufacturing technology to develop antifragility in the supply chain and save itself from negative consequences in the face of disruption.

In the recent past, firms have seen an increasing number of epidemic outbreaks (Baral et al. 2021) . A whopping 1438 epidemics cases were reported by World Health Organization (WHO) from 2011 to 2018 (Hudecheck et al. 2020 ). The last decade alone has seen the outbreak of epidemics such as the Middle East respiratory syndrome / MERS-CoV in 2012, the Western African Ebola virus epidemic in 2013, and the Zika virus epidemic in 2015 among others. 1 The outbreak of Coronavirus towards the end of 2019 was a rare catastrophic event. Later, on March 11, 2020, WHO classified the COVID-19 outbreak as a pandemic. The Covid-19 disaster constitutes a special case of pandemic outbreaks in terms of duration and intensity. It can be classified as a low-frequency-high-impact (LFHI) event. LFHI events cause a ripple effect that cascades through all the operations involved in a supply chain causing considerable adverse effects (Ivanov et al. 2014; Dolgui et al. 2018; Hosseini et al. 2019; Kinra et al. 2020) .

This pandemic has shocked supply chains across nations. It has exposed the vulnerabilities in the supply chains (El Baz and Ruel 2021) . The supply chains that were otherwise considered a source of sustained competitive advantage (Barney 2012 ) turned out to be the Achilles heel of the companies negatively affecting the operations of more than 94% of Fortune's top 1000 companies (Fortune 2020). It has not only affected the supply side but demand ripples have also been observed (Guan et al. 2020; Sarkis 2021) both at global and local scales . While demand deficiencies arose due to real or anticipated income risks and economic uncertainty, the world also saw a huge rise in demand for necessary items such as medical supplies and equipments including masks, gloves, oxygen cylinders, ventilators, PPEs (personal protective equipment), and sanitizers (Balleer et al. 2020; Bag et al. 2021a, b; Dubey et al. 2021) . Similarly, with the closure of national and international borders, vehicle movement and international trade were interrupted. There was a shortage of labor and social distancing was supposed to be maintained (Gunessee and Subramanian 2020) . These led to disruption in the flow of materials, information, and funds, and hence the entire supply chain was disrupted substantially (Chopra et al. 2021) . The toilet paper shortage in the early days of the pandemic and the semiconductor shortage, a product with long lead times, for automakers are a few such examples. 2 The global supply chains struggled to deliver even the necessary items and their fragility and lack of operational agility became conspicuous . Supply chains which were otherwise designed to be global and lean faced increased vulnerabilities in the current epidemic context. Globalization, offshoring, and lean systems were heavily scrutinized and looked at with mistrust since they increased the firms' exposure to disorder (El Baz and Ruel 2021) .

The crisis has led to the researchers reflecting on the inadequacies of existing supply chain structures and risk management practices. The researchers have highlighted the need to build a fuller picture of the disruptions caused by the Covid-19 pandemic, its impact on the supply chain, and the response so far (Queiroz et al. 2020; Remko 2020) . They also call for a robust and resilient supply chain in the event of future disruptions Chowdhury et al. 2021) . Resilience can be defined as "the ability of a supply chain to return to normal operating performance, within an acceptable period, after being disturbed" (Christopher and Peck 2004) . The 68th session of the WHO Regional Committee for the Eastern Mediterranean also talks about building stronger systems and resilient communities. 3 By implementing several resilience-building strategies such as investing in flexibility, visibility, risk mitigation, or redundancy to name a few, these organizations strive to mitigate the immense pandemic crisis disruption in their supply chains (Chopra et al. 2021 ).

Digitalization of the supply chain has emerged as a resilience-building strategy in the recent past. Technological advancements and digitalization (Hald and Coslugeanu 2021) have played a substantial role in accelerating supply chain resilience. Additive manufacturing (AM) methods, such as 3-D printing technology can prove an effective way to digitalize manufacturing and supply chain operations. AM involves layer upon layer joining of the materials to make the final product from a 3D model data (ASTM F2792-12 2012 . This is the stark opposite of what traditional subtractive manufacturing does. AM remarkably surpasses the traditional manufacturing methods and has the potential to become the norm over the decades to come (Attaran 2017). The popularity of AM has grown considerably in the last decade and it is now being pursued by many researchers (Achillas et al. 2017) . With new technologies and new materials coming into play, there has been rampant growth in the application of AM across various manufacturing industries (Gardan 2016; Huang et al. 2021a, b) which includes sectors such as automotive (Muhammad et al. 2021) , aerospace (Kemsaram and Maley 2019) , medical (Choudhary et al. 2021; Patel and Gohil 2021) , and electronics (Ghobadian et al. 2020) . During the pandemic, AM technologies have been used to quickly meet the increased demand for medical equipment and kits such as ventilators, PPEs, hands-free door openers, and face shields (Iyengar et al. 2020; Novak and Loy 2020) . Thus, AM technologies can be leveraged to build a robust and resilient supply chain.

Several articles in the extant literature have studied the impact of catastrophes and pandemics on the supply chain (Fonseca and Azevedo 2020; Okorie et al. 2020; Sharma et al. 2020) , and some have suggested resilience and robustness as a means to tackle supply chain disruption. However, resilience talks only about the bouncing back of supply chains. It fails to recognize the need to learn and grow from such disruptions. Hence, the need of the hour is to build an antifragile supply chain. While a resilient supply chain aims at absorbing shocks, an antifragile supply chain sees these shocks as an opportunity to get stronger and better. An antifragile supply chain does not only respond to disruption but also thrives in it (Taleb 2012) . While resilient supply chains have been extensively discussed, very few articles discuss an antifragile supply chain as a response to the disruption caused by pandemics. Größler (2020) has explored the consequences of antifragility on supply chain behaviour and performance through simulation. Jaaron and Backhouse (2014) explored the impact of the application of systems approach in service organizations to build an antifragile system that flourishes in disruption. Nikookar et al. (2021) have built on Taleb's concept of antifragility and have introduced the concept in the purchasing and supply chain management discipline. They have briefly discussed some directions leading to an antifragile supply chain and have suggested the nexus between antifragility and Industry 4.0 technologies as a scope for future research. This study discusses Additive manufacturing applications to build an antifragile supply chain. AM reduces the lead time, offers huge flexibility, shortens the time to market (Engelseth et al. 2021) , and helps to achieve environmental sustainability (Afshari et al. 2019) . The decentralized supply chain promoted by AM helps to locate the manufacturing facility closer to the customer (Rinaldi et al. 2021a, b) . With operational and technological innovations, AM can also be used to achieve high-volume production output (Huang et al. 2021a, b) . The digital inventory that can be held in AM and the possibility of having multiple iterations before actually printing the product make it preferable over the conventional manufacturing technologies (Engelseth et al., 2021) . Thus, AM enhances the overall supply chain flexibility and performance (Delic and Eyers 2020) . It helps in achieving supply chain agility (Ohmori 2021) , and increases the supply chain responsiveness (Boer et al. 2020) which are important factors to thrive during a disruption. To the best of the authors' knowledge, this is by far the only study that exclusively talks about leveraging additive manufacturing capabilities as a strategy to build an antifragile supply chain. Consequently, this study seeks to achieve the following research objectives:

To identify the factors affecting the implementation of additive manufacturing technologies to build an antifragile supply chain during disruption. RO2 To develop a structural relationship framework for the identified factors that affect the implementation of additive manufacturing technologies to build an antifragile supply chain during disruption. RO3 To categorize and analyze these factors based on driving and dependence power.

For the first objective, a literature review is conducted to derive the factors affecting AM implementation from an academic perspective. Post this, the industry perspective is captured using experts' opinions. For the second and third objectives, total interpretive structural modeling (TISM) and Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis are used to establish the structural relationships among AM implementation factors and to categorize these factors based on driving and dependence power respectively. While several studies have been performed on additive manufacturing techniques, these are mostly qualitative, and very few studies have used multicriteria decision-making (MCDM) techniques to categorize and analyze the factors. This study uses TISM to build a structural framework that would help in better understanding the relationship between the factors. Few studies in the past, have studied the factors for AM implementation using ISM. Sonar et al. (2020) have used ISM to understand the factors that influence AM implementation in the Indian manufacturing sector. Dwivedi et al. (2017) have used fuzzy-ISM to analyze the barriers to implement AM in the Indian automotive sector. Shukla et al. (2018) have used ISM to understand the barriers to implementing AM for mass customization. Choudhary et al. (2021) analyzed the barriers to AM adoption in the medical sector. To the best of the authors' knowledge, this is by far the first study utilizing the approach of TISM to explore the structural relationship between the factors affecting the implementation of additive manufacturing technologies to build an antifragile supply chain during disruption.

The remaining part of the paper is structured as follows. In the Sect. 2, we discuss related literature and provide the theoretical foundation for the paper. Following this, the research methodology is described. In Sect. 3, we then present the TISM model for understanding the critical success factors for AM implementation to build an antifragile supply chain. In Sect. 4, the MICMAC analysis is presented and then in Sect. 5, we discuss the results of the research, managerial and practical implications of the study. Finally, in Sect. 6, we conclude by summarizing the results, discussing the limitations of our study, and providing some areas for future research.

In this study, first, a thorough review of the extant literature was done to find the critical success factors to build an antifragile supply chain (SC). For this purpose, literature on additive manufacturing and the ones discussing antifragility and resilience building in a supply chain were reviewed.

In the first phase, a pilot search was conducted to gain an idea of the ongoing research in the field. Scopus was used as the database to search for articles on the two topics. Scopus is one of the largest multidisciplinary databases with citation indexing. It has a broader and more robust cover-to-cover indexing policy than Web of Science and offers the best choice amongst the multidisciplinary databases with citation indexing (Norris and Oppenheim 2007 Rowley and Slack (2004) . For this purpose, the keywords such as 'Additive manufacturing', '3D printing', and 'supply chain' were included in the search string. The reason for including supply chain along with additive manufacturing was to get an output where the articles would be discussing additive manufacturing from the supply chain perspective because several studies in AM have also been done on manufacturing part in the engineering domain which was not to be a part of the scope of this study. The search syntax for the keywords has been discussed in Table 1 .

As discussed in the previous sections, antifragility is a novel concept and is unexplored. The search syntax for an antifragile supply chain also reveals the same. Thus, this provides us with a very good opportunity to explore the field further. In the next stage, the articles written in English were only considered and the subject area was limited to Business management. For literature on resilient supply chain, the study focussed mostly on publications from 2020 and after. This is because ever since the outbreak of the Covid-19 pandemic, there have been numerous studies done on building resilience into the supply chain. However, some studies discussing resilience-building strategies in the supply chain that were published before 2020 were also identified through cross-referencing and included in the study. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed for article selection and inclusion. The above Boolean search resulted in 378 articles and another 24 articles were included through snowballing and cross-reference. These articles were then uploaded in Zotero software where the duplicates were removed, leaving us with 363 documents. Further, an abstract search for these articles was done and another 211 documents were excluded. In the end, after the full-text screening, 105 documents were retained for this study. The PRISMA flow diagram for study selection and exclusion has been shown in Fig. 1. 

The ever-changing business environment, business scenarios, and such a high level of uncertainties during a disruption such as the Covid-19 pandemic cause sudden and huge changes in customer demand. The firms are cautiously looking forward to moving towards technological innovations and a resilient, robust, and antifragile supply chain. The firms must build dynamic capabilities to address such disruptions to the supply chain in a timely fashion to avoid negative consequences on firms' overall performance (Dubey et al. 2019) .

Introduced in the 1980s, Additive Manufacturing (AM), also known as 3D printing or rapid manufacturing has grown in popularity by benefitting manufacturers who want to produce less quantity of complex parts. The easy availability and accessibility of tools required in the additive manufacturing process is enabling firms to adopt this technology. With need basis printing and reduction in inventories, AM seems to be having a bright future. In this study, we have aimed to introduce AM as an implementation strategy to build an antifragile supply chain. An antifragile supply chain gains from disorder. They get stronger by being exposed to intentional randomness, similar to the human immune system (Nikookar et al. 2021 ). An article in Financial Times (Ft. com 2020) also discusses how firms need to move beyond robustness and resilience and aim for antifragility.

In the next section we have discussed the 19 factors leading to an antifragile supply chain identified through literature review:

i. Development of skilled workforce for technology adoption Afshari et al. (2019) highlight the need for highly skilled workers for AM, who would facilitate the design process, and who could operate the AM machines. They also discuss how skilled workers can 

Scopus -AM TITLE-ABS-KEY ("Supply chain" AND ("Additive manufacturing" OR "3D printing" OR "3DP")) AND (LIMIT-TO (SUBJAREA, "BUSI")) AND (LIMIT-TO (LANGUAGE, "English")) 173

Scopus -Resilient supply chain TITLE-ABS-KEY ("supply chain" AND ("resilient" OR "resilience" OR "robust") AND ("Covid" OR "covid-19" OR "pandemic")) AND (LIMIT-TO (SUBJAREA, "BUSI")) AND (LIMIT-TO (PUBYEAR 2022) OR LIMIT-TO (PUBYEAR 2021) OR LIMIT-TO (PUBYEAR 2020)) AND (LIMIT-TO (LANGUAGE, "English"))

Scopus -Antifragile supply chain TITLE-ABS-KEY (("antifragile" OR "Anti fragile" OR "anti-fragile" OR "antifragility") AND ("supply chain" OR "operations")) AND (LIMIT-TO (LANGUAGE, "English")) AND (LIMIT-TO (SUBJAREA, "BUSI") OR LIMIT-TO (SUBJAREA, "DECI")) affect profitability while implementing novel paradigms, such as AM. discuss how the lack of skills and education prevents the firms from adopting AM technology. Technical and managerial skills are two important aspects of human resources and these skills need to be developed over time (Gupta and George 2016) . It is important to plan for the resource supply chain strategy which involves selecting the critical workforce for the firm, one which will have the capability to handle disruptions in the supply chain. The firm should be able to handle those resources under any circumstances ). ii. Cost-effective design and manufacturing Researchers have suggested 3-D printing techniques, as a solution to help companies design and manufacture products quickly (Novak and Loy 2020) . In the recent past, owing to the advancement in technologies, the price of 3D printers has gone down making it even more feasible for firms to implement 3D printing (Attaran 2017). Moreover, a decentralized supply chain facilitated by AM offers a better solution in terms of holding stock, and in general, on supply chain costs as compared to traditional manufacturing (Nagarajan et al. 2018) . AM also shortens the supply chain which leads to a shorter cash-to-cash cycle. With AM machines there are no costly setups, and so production of small batches becomes economical (Pour et al. 2016 ). On-demand manufacturing eliminates the need for stockpiling inventories. iii. Reduction in manufacturing lead time Additive manufacturing supports mass customization at low cost and without a lengthy delivery time (Pereira et al. 2019) . AM, by its additive capabilities, reduces the number of parts that need to be produced, procured, or assembled. This considerably brings down the assembly time leading to shorter manufacturing lead times (Rogers et al. 2016; ). The faster production process coupled with a simple supply chain brings down the supply chain lead time (SCLT). An SCLT reduction of up to 60% has been estimated by switching from conventional to additive manufacturing (Rinaldi et al. 2021a, b) . iv. Environmental sustainability AM has the potential to substantially reduce material consumption and wastage during manufacturing thus reducing the emission of CO 2 . It minimizes the need for logistics, thus, CO 2 emissions associated with transport activities are reduced (Rinaldi et al. 2021a, b) . It also facilitates an extended product life which is achieved through technical approaches such as repair, remanufacture, and refurbishment (Ford and Despeisse 2016; Boer et al. 2020) . The strength and durability of the product are enhanced. There are 3D printed concrete-based houses which can withstand 8.0 Richter scale earthquakes (Ghaffar et al. 2018 ). Kumar et al. (2020) discuss how AM technologies can be utilized to build sustainability in the supply chain. Mukherjee et al. (2021) argue that to develop supply chain capabilities, it is important to support sustainable practices. v. Development of strategy for collaboration and innovation A synchronized system would lead to intelligent workflows which will improve efficiencies across the supply chain. With this, the firms will be able to quickly intuit the changes in the environment and respond with the help of intelligent workflows by processing any delivery changes or responding to a new customer request with minimum hassle and error (Oyekan et al. 2017; Zhong et al. 2017 ). To perform efficiently, a supply chain depends on close collaboration across various supply chain partners (Bag et al. 2021c ). Close collaboration with SC partners is also required for efficiently mitigating the risk factors in a supply chain before a disruptive event occurs or after the event has unfolded (Fan and Stevenson 2018) . SC collaboration will help the firms in achieving end-to-end visibility, understanding associated complexities, and reducing the vulnerabilities in the supply chain. Also, technological and operational innovation could help firms in achieving competitive advantage and catering to rapid demand changes (Huang et al. 2021a, b) . vi. Manufacturing repurposing Manufacturing repurposing has been identified as critical to manufacturing life-saving products. Repurposing is a quick response solution to cater to the manufacture of essential items such as PPEs, diagnostic equipment, and clinical care equipment. 4 However, it is considered a temporary strategy and can be challenging and expensive to implement. It would require the employees to be trained to handle such temporary repurposing thus making employee skills and knowhows a crucial enabler (Okorie et al. 2020) . vii. Effective additive manufacturing processes Additive manufacturing can easily be considered a revolution in the field of manufacturing technology (Oettmeier and Hofmann 2016) . It opens new avenues for business firms that intend to improve their manufacturing efficiency. Some studies consider AM a powerful tool to simplify the supply chain in many ways. The popularity of AM has grown considerably in the last decade and it is now being pursued by many researchers who study the advantages it offers as compared to traditional manufacturing (Achillas et al. 2017) . With new technologies and new materials coming into play, there has been rampant growth in the application of AM across various manufacturing industries (Gardan 2016) which includes sectors such as automotive, aerospace, medical, and electronics (Ghobadian et al. 2020) . viii. Supply chain risk management SC risk management practices are aimed at reducing the supply chain vulnerabilities and mitigating the impacts of the disruptions (Ho et al. 2015) . This involves four interconnected processes: risk identification, risk assessment, risk mitigation, and risk control (Fan and Stevenson 2018) . AM technologies have been known to lead to supply chain integration and risk management through its digital nature, where the design can be easily transferred to a different location and printed in case of disruptions faced at one manufacturing location (Durach et al. 2017) . The design and volume changes can be easily accommodated which helps in moving the order decoupling point closer to the customer (Tuck et al. 2007 ). This provides agility in case of supply chain disruption. A diversified supply chain reduces the firms' dependence on a few suppliers, provides supply chain flexibility and creates agility that helps reconfigure the supply chain whenever required. ix. Effective inventory management Additive manufacturing efficiently changes the supply chain configuration, thus achieving a reduction in safety inventory which helps in cutting down the inventory holding costs significantly (Cestana et al. 2019) . In additive manufacturing, there are no held-up costs and risk of scrapping the unsold finished goods inventories. Instead, firms only hold digital 3D data in stock (Berman 2012) . The on-demand manufacturing capability of AM eliminates the need for stockpiling inventories (Hedenstierna et al. 2019 ). Direct digital manufacturing might currently lag traditional manufacturing methods but with technological and operational innovations, it will very soon question the established practices of inventory management (Holmström et al. 2016 ).

x. Proactive top management Top management commitment is an important factor for the implementation of AM to build a resilient supply chain (Sonar et al. 2020) . A proactive decision-maker would plan for rapid global crisis detection and response systems which will eventually lead to enhanced competitiveness for the firm (Singh et al. 2007; Dwivedi et al. 2017 ). Ivanov and Das (2020) have also highlighted the need for proactive top management who would be responsible for mapping the supply chain and synchronization of strategic processes such as creating flexible redundancies in the supply chain. These activities would enhance supply chain collaboration (Bag et al. 2021d ) and make the SC networks less sensitive to external uncertainties. Devi et al. (2020) highlight the importance of proactive decision-making by top management who have the sole responsibility to plan, strategize and implement digitalization of the supply chain. xi. Supply chain redesigns

The supply chain redesign can be considered in two aspects: multi-sourcing and reinforced e-commerce capabilities. Multi sourcing involves procuring the raw materials from multiple suppliers instead of relying on a single supplier. This would result in building redundancy across suppliers. 5 Resorting to e-commerce and moving from Business to Business (B2B) to Business to Consumer (B2C) are other ways of SC redesign that enable building resilience. Many firms, with physical stores closed, have transitioned to e-commerce (Barriball et al. 2020 ). xii. Efficient information sharing process A resilient supply chain requires firms to collect data on their critical processes, flows, and partners so that the necessary optimization steps can be taken when required (El Baz and Ruel 2021). Another means to prevent supply chain disruption is to time the closing and opening of the facilities at different SC echelons. This can be done only when necessary technologies have been installed for real-time information sharing which enhances supply chain visibility (Routroy et al. 2018) . The technologies enable realtime tracking of the goods and with digitalization, this huge volume of information can be analyzed and used for process improvements (Zhong et al. 2017) . For this purpose, RFID and GPS systems could be utilized. Also, this would help in pre-emptive breakdown and maintenance of the machinery (Sanders et al. 2016) . Converting data into meaningful information will help the firms in predicting disruptions and preparing necessary actions and this information sharing leads to better collaboration, thus enhancing the overall supply chain performance (Baah et al. 2021) . Dubey et al. (2019) have discussed how information-sharing positively impacts supply chain visibility, which in turn impacts supply chain resilience. xiii. Distributed manufacturing system Decentralization of manufacturing capacity and production in smaller batches are important aspects for building future supply chains (Fonseca and Azevedo 2020) . The establishment of geographicallydispersed manufacturing facilities with the necessary logistical supports is considered effective as a proactive readiness strategy (Shokrani et al. 2020) . For this purpose, the firms will also need to redesign their logistics systems and switch to faster delivery modes (Chowdhury et al. 2021) . AM facilitates a shorter and simpler supply chain. It reduces the number of raw material suppliers required by dint of its additive properties. It supports decentralized manufacturing where the technology could potentially reduce the need for logistics as designs could be transferred digitally (Luomaranta and Martinsuo 2020) . Flexibility to produce different products in a single run, tooling freedom, as well as the possibility of making products without having molds, are some other benefits of AM implementation (Pour et al. 2016) . It also supports easy manufacturing of products that fall under the Mass Complexity category, where parts exhibit complex geometries (Pereira et al. 2019) . Smaller batch size production is possible due to AM capabilities which decreases the dependency on forecasting (Bogue 2013; Bogers et al. 2016) . xiv. Resource allocation for digitalization Gupta and George (2016) have proposed seven resources critical to an organization to build digital capabilities. They discuss tangible resources such as data, technology, time, and investments, human resources such as managerial and technical skills, and intangible resources such as data-driven culture and the intensity of organizational learning. Integrating the technologies with the supply chain improves the overall operational efficiency of the supply chain and eliminates wastes and non-value-added tasks (Raji et al. 2021) . Technology deployment will also enable the firms in monitoring their supply partners' actions . Bag et al. (2021c) in their study, elucidate how tangible resources and workforce skills drive technological enablement. xv. Antifragile supply chain

The COVID-19 crisis has revealed fragility and exposed the global supply chains' vulnerability and low resilience (Delic et al. 2019) . Operational and supply chain resilience has three aspects, namely preparedness, response, and recovery. Preparedness corresponds to taking a proactive measure for unforeseen disruptions. Responsiveness corresponds to acting quickly along with the other supply chain partners to minimize the immediate impacts. For recovery, the aim is to return to the original or even better form after the disruption (Chowdhury and Quaddus 2016 ). An antifragile supply chain goes beyond a robust and resilient supply chain and thrives in disorder. To gain antifragility, a supply chain needs to develop optionality, create redundancy, weaken the link between the nodes, encourage eustress, adopt a barbell strategy, allow systems to fail fast, learn from the failures, and conduct trial and error, effectuation, and swarming. These help the supply chain in detecting vulnerabilities early, learning quickly, and gaining from disorder (Nikookar et al. 2021 ). Antifragility provides a complete alternative to deal with future uncertainties (Derbyshire and Wright 2014) . xvi. Rapid market responsiveness AM offers flexibility which enables manufacturers to create an optimal design for lean production (Chekurov et al. 2018; Devi et al. 2020) . With AM, customer demand can be met quickly, and this leads to improvement in the quality of care and aftermarket services. While modification of design costs significantly and also causes time delays in a traditional manufacturing setup, additive manufacturing facilitates the production of multiple versions of a single product through digital 3D mode, that too in a cost-effective manner (Rogers et al. 2016; Jimo et al. 2019) . xvii. Integrated manufacturing processes/operations Integration of manufacturing processes is a major requirement for achieving the global manufacturing paradigm (Valilai and Houshmand 2015) . AM encourages a distributed manufacturing system thus making data management a crucial task. This requires the integration of autogenous, scattered, and unorganized data sources into a single data source. Also, to reduce the build time, improve geometry quality and reduce the geometrical errors in the production process, global integration through AM paradigm becomes imperative (Jin et al. 2013) . The digital nature of AM promotes the integration of various processes and functions in a supply chain where improving data flow becomes very important (Delic et al. 2019) . By adopting the integrated and coordinated approach, additive manufacturing enhances firm competitiveness and helps achieve sustainability targets (Gebler et al. 2014; Dircksen and Feldmann 2020) . xviii. Effective knowledge management process Proactive top-level management would ensure accurate mapping of the supply chain beyond the first or second supply tiers (Ivanov and Rozhkov 2020) . Nissan has developed a supply chain resilience program that incorporates supply chain mapping and visibility. This would protect its supply chain in case of disruptions by helping the firms to formulate node or supplier-specific strategies (Ivanov and Dolgui 2021) . The firms need to assess how much they are dependent on other countries and what critical technologies, critical resources, and manufacturing capacity they want to retain. After the pandemic, several countries have started re-evaluating their supply chains and reducing their dependency on other countries to be resilient. Even if a firm decides to outsource prod-ucts from overseas, it will still need to strike a balance between domestic production and international trade to reduce vulnerability (Deaton and Deaton 2020). Flexible manufacturing system technologies such as additive manufacturing and robotics can localize production capabilities (Sarkis 2021) . The collaborative environment and the sharing of knowledge provided by AM aim to expand the strategic scope, maximizing the supply chain surplus ( Shah et al. 2017; Equbal et al. 2021) . xix. Design freedom and customisability

The need of the hour is to design products that incorporate component switching options. This would enable the firm to procure the raw materials or components from other suppliers where the disruption has not yet occurred . With AM, any design changes can be easily made digitally and multiple iterations could be tried without any cost penalties (Engelseth et al. 2021) . It also substantially reduces the product repair time and provides an opportunity to modify the repaired components to the latest design. Furthermore, with the innovation of smaller 3D printing machines, it is now possible for consumers to print their parts for fixing their purchased products (Attaran 2017). AM also enables customers to co-design products that can perfectly fit their demands and ambitions (Ghaffar et al. 2018 ).

Once the factors were identified, using expert opinion initial reachability matrix was built and, in the end, total interpretative structural modeling (TISM) was used to highlight the interdependence between these factors. Furthermore, crossimpact matrix multiplication (MICMAC) was used to determine each factor's driving and dependence power.

Interpretive Structural Modeling (ISM) was first developed by J. Warfield in 1974 (Warfield 1974 and is an adaptation of paired comparison approach (Haleem et al. 2012). ISM methodology helps in addressing complex issues and aims to provide order and direction to the complex relationships among the elements in a system. The application of ISM helps the top-level management reassess their perceived priorities of the factors and understand the linkages between the factors (Singh 2015) . Singh and Gupta (2020) used ISM to develop a structural relationship among sustainable maintenance system factors from a strategic perspective. Even though ISM serves to provide solutions to critical management issues through the development of hierarchies among the elements, it has several limitations. First, the correct interpretation of how the direct linkages between the elements operate is not explained by ISM. Second, it fails to explain the transitive links and causality of the linkage between building blocks of the ISM (Sushil 2012) . Thus, TISM was developed which provides an interpretation of both nodes and links. TISM is the extension of ISM and can provide a better explanatory framework with important transitive links (Behl et al. 2018) . While ISM helps to understand the 'what' and 'how' of a research study, TISM helps in answering the 'why' (Hasan et al. 2019a, b) . TISM is an innovative technique used by many researchers across various fields. Yadav and Sushil (2014) used TISM to model the performance factors in the Indian Telecomm. Service Industry. Dubey et al. (2015) employed TISM to study the association among various enablers for sustainable manufacturing. Singh and Sushil (2013) have used TISM in the airline industry, Bag (2016) employed TISM in the green procurement sector and Mahajan et al.

(2016) used TISM to analyze the interrelations among challenges of management education in India. Since this study intends to model the critical success factors for AM implementation to build an antifragile supply chain, TISM was chosen as the method to accomplish the objective. Table 2 discusses some of the recent papers that have successfully applied TISM in their studies. TISM flowchart illustrating all the steps in the TISM-MICAMC process has been shown in Fig. 2. 

After identification of a total of 19 critical success factors from the literature, the second phase was to focus on the selection of experts who would be willing to participate in the study. Through an internet search, companies that have applied AM technologies were identified and contacted for participation. Also, a few academicians (subject matter experts) were identified and contacted. We received participation confirmation from 7 experts across industry and academia. Murry and Hammons (1995) and Novakowski and Wellar (2008) have suggested that a sample size between 5 and 15 could be taken to get quality results in cases of high heterogeneity of the panel of experts. Thus, a sample size of 7 experts used in this study is adequate. Details of the experts who participated in the study have been mentioned in Table 3 .

Delphi method was used to finalize the factors and build the structural self-interaction matrix (SSIM). The Delphi method was proposed by Dalkey and Helmer (1963) and aims at getting consensus from a panel of experts while providing anonymity to the participants. To determine the critical success factors, a questionnaire was developed and shared with the experts asking for their opinion on the importance of those 19 factors identified from literature on a Likert scale of 1 to 5 (1 being strongly disagreed and 5 being strongly agreed). The experts were also asked to add to the list, any factor which they found missing. The views and opinions of the experts were then compiled and, in the end, resulted in 16 critical success factors being a part of our study. For this purpose, the factors for which the mean score was greater than 3 were only considered for the study. Thus, after the experts' opinion, the factors 'Supply chain redesign' 'Manufacturing repurposing' and 'Environmental sustainability' were dropped. Then the response was collected from the participants for the SSIM based on the Delphi approach where the output from the previous round was compiled and sent to the participants for review. The list of the critical success factors for AM implementation to build an antifragile supply chain has been mentioned in Table 4 .

Different steps of TISM that were followed to analyze results are mentioned in the following sections.

After finalizing the 16 critical success factors, the next step was to establish a contextual relationship among the factors. This was done based on the pair of factors that were identified. Thus, a pairwise comparison matrix (SSIM) for the 16 critical success factors was developed with the help of expert opinion. To establish a relationship between any two factors, four symbols have been used which denote the direction of the relationship between the factors i and j (where i < j). The symbols are as given below:

• V -Factor i will lead to parameter j • A -Factor j will lead to parameter i • X -Factors i and j will lead to each other • O -Factors i and j are unrelated In the SSIM (Table 5) , we can see the use of the symbols V, A, X, and O. Here, factor 1 leads to factor 15 (V). Factor 14 leads to factor 3 (A). Factors 6 and 14 lead to each other (X). Factors 2 and 6 are unrelated (O). The structural selfinteraction matrix (SSIM) has been displayed in Table 5 .

In the next step, the SSIM was converted into a binary matrix, also known as the initial reachability matrix. This was done by substituting V, A, X, and O with 1 and 0 as per the case. The substitution of 1 s and 0 s were done based on the following rules (Singh et al. 2018 ): Mir et al. 2020 • If the (i, j) cell in the SSIM is V, the (i, j) entry in the reachability matrix becomes 1 and the (j, i) entry becomes 0. • If the (i, j) entry in the SSIM is A, the (i, j) entry in the reachability matrix becomes 0 and the (j, i) entry becomes 1. • If the (i, j) entry in the SSIM is X, the (i, j) entry in the reachability matrix becomes 1 and the (j, i) entry also becomes 1.

• If the (i, j) entry in the SSIM is O, the (i, j) entry in the reachability matrix becomes 0 and the (j, i) entry also becomes 0

The above rules have been followed to form the initial reachability matrix as shown in Table 6 .

After the formation of the initial feasibility matrix, the next step is to check for transitivity. Transitivity can be explained with the following example: if element A relates to element B and element B relates to element C, then transitivity implies that element A should relate to element C. It also helps in maintaining conceptual consistency. After incorporating the transitivity, the final reachability matrix is prepared as shown in Table 7 . The * marks represent transitivity. We have also calculated the driving power and dependence for each factor in Table 7 . The driving power is the total number of variables (including itself), that the factor might help to achieve. On the other hand, dependence is the total number of variables (including itself), that might help in achieving the factor. These driving power and dependencies will be utilized at a later stage while we classify the factors into four groups of autonomous, dependent, linkage, and drivers.

The final reachability matrix is then divided into different levels for each factor based on reachability and antecedent sets through a series of iterations called level partitions. The reachability set for each factor comprises of the element itself and other elements which it might help in achieving, whereas the antecedent set for each factor comprises of the element itself and other elements, which might help in achieving it. Then, the intersection of these sets is derived for all factors. The factors for which the reachability and intersection sets are the same are the Senior product engineer Consumer products 12 Table 4 Critical success factors for AM implementation to build an antifragile SC Effective knowledge management process Shah et al. 2017; Deaton and Deaton 2020; Ivanov and Rozhkov 2020; Equbal et al. 2021; Ivanov and Dolgui 2021; Sarkis 2021 16 Design freedom and Customisability Attaran 2017; Ghaffar et al. 2018; Ivanov and Das 2020; Engelseth et al. 2021 top-level factors in the hierarchy. The top-level factor of the hierarchy would not help achieve any other factor above it. Once the top-level factor is identified, it is separated from the other factors. The process is then repeated to find the next level. This process is continued until the level of all the factors is determined. In Table 8 , we can see that there are a total of 12 levels across which these 16 factors will be arranged.

A structural model, which we also call a digraph is developed based on the final reachability matrix. In the model, critical success factors are arranged and represented by nodes, and the relationship linkages between two factors as obtained in the final reachability matrix are also depicted. The level of each factor achieved during level partitioning helps in assigning the levels for each element. The final TISM model formed from the levels of each factor is shown in Fig. 3 

Once the TISM model is developed, MICMAC analysis is performed to place the factors into one of the four quadrants. MICMAC analysis was first used by Duperrin and Godet in 1973. It is based on a system of multiplication of (Govindan et al. 2012) . MICMAC aims to group the factors based on their dependence and driving powers. It helps in understanding whether the factor drives the other factors or is dependent on other factors. A direct relationship matrix reveals the variables with maximum direct impact but fails to identify the hidden variables which at times might have high influence. MICMAC analysis helps in understanding the diffusion of these impacts (Saxena et al. 1990 ). The necessary information can be derived from the final reachability matrix, where the row total gives the driving power, and the column total gives the dependence for each factor. A graph is then plotted with X-axis representing the dependence power and Y-axis representing the driving power. The four quadrants of the graph are as mentioned below:

• Autonomous: Factors with low dependency and low driving power are placed in the first quadrant. • Dependent: Factors with high dependency and low driving power are placed in the second quadrant. • Linkage: Factors with high dependency and high driving power are placed in the third quadrant. 1 1 1 1 1 1 1 1 1 1 1 1 1 1* 16  9 EISP 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 12 10 DMS 0 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 7 11 RAD 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1* 14 12

AFSC 0 ,2,3,5,6,7,9,10,11,12,13,14,15,16 1,4,8,11 1,11 10 2 2,7,12,13 1,2,3,4,5,6,7,8,9,10,11,14,15,16 2,7 3 3 2,3,7,12,13,16 1,3,4,5,6,8,9,10,11,14,15,16 3,16 4 4 1,2,3,4,5,6,7,9,10,11,12,13,14,15,16 4,8 4 11 5 2,3,5,7,10,12,13,16 1,4,5,6,8,9,11,14,15 5 6 6 2,3,5,6,7,10,12,13,14,16 1,4,6,8,9,11,14,15 6,14 7 7 2,7,12,13 1,2,3,4,5,6,7,8,9,10,11,14,15,16 2,7 3 8 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16 8 8 12 9 2,3,5,6,7,9,10,12,13,14,15,16 1,4,8,9,11 9 9 10 2,3,7,10,12,13,16 1,4,5,6,8,9,10,11,14,15 10 5 11 1,2,3,5,6,7,9,10,11,12,13,14,15,16 1,4,8,11 1,11 10 12 12 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16 12 1 13 12,13 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16 13 2 14 2,3,5,6,7,10,12,13,14,16 1,4,6,8,9,11,14,15 6,14 7 15 2,3,5,6,7,10,12,13,14,15,16 1,4,8,9,11,15 15 8 16 2,3,7,12,13,16 1,3,4,5,6,8,9,10,11,14,15,16 3,16 4 These quadrants have been shown in Fig. 4 along with the factors belonging to each quadrant.

The Covid-19 driven disruption has made resilience a buzzword for business firms. A resilient firm can be considered an adaptive system that withstands disruption and recovers from it in quick time. In contrast, an antifragile supply chain does not adapt but evolves. AM has gained huge attention in recent times as a technological intervention to save the supply chain from disruption. We see many applications of AM across various sectors such as aerospace, automotive components, medical supplies, jewelry, and pharmaceuticals. This study reveals its importance in building an antifragile supply chain.

The first objective of the study was to identify the factors affecting the implementation of additive manufacturing technologies to build an antifragile supply chain during disruption. To begin with, nineteen critical success factors to build an antifragile supply chain were identified through literature.

Seven experts were identified across various fields who substantiated the findings through the Delphi method and that resulted in a total of sixteen factors to work with. The second objective was to develop a structural relationship framework for the factors identified. For this purpose, the experts were contacted again to collect their response through the Delphi method, and accordingly, the reachability matrix was developed. This led to the development of a TISM based model which clearly explains the interrelations between these factors. The TISM model reveals the importance of having proactive top management whose quick decision-making and strategic planning skills will help in developing strategies for collaboration and innovation across the supply chain, which is an important factor, especially during a disruption. This would provide firms with access to new knowledge and help them learn and innovate for enhanced performance. These strategies will also help the firm in the selection of critical workforce who are skilled and assist the firm in technology adoption. The skilled workforce helps in implementing novel paradigms which leads in efficient information sharing. The firms also build strategies to plan for resource allocation and implementation of digitalization of the supply chain. These technologies enable real-time information sharing in the supply chain. The efficient information sharing process leads to an effective knowledge management process by facilitating Fig. 4 Driving power and dependence diagram the firms in expanding their strategic scope and maximizing the supply chain surplus. This knowledge management helps in the integration of various processes and functions in a supply chain and these interconnected processes lead to risk identification, assessment, mitigation, and control. Integrated and coordinated approaches and effective risk management strategies help the firm in AM adoption and its effective implementation. As the designs can be digitally stored and transferred, AM facilitates a decentralized manufacturing system. This moves the manufacturing closer to the customer and digital designs allow multiple iterations of the product before printing as the changes are made to the 3D design digitally. This provides design freedom and customisability. Also, since AM reduces the number of parts required to be produced, procured, or assembled, the manufacturing lead time is reduced. This helps the firms to design and manufacture new products cost-effectively. Since the stock can be digitally held, it reduces the need to have a huge inventory. These quick design changes, reduced procurement needs, distributed systems, and digital designs save a lot of time and help the firms in rapid market responsiveness. During a disruption, these help the firms in acting quickly and minimizing the effects of disruption which ultimately leads to building an antifragile supply chain.

The third objective was to categorize and analyze these factors based on driving and dependence power. The MIC-MAC analysis helps in classifying the factors into the following clusters:

• Autonomous factors: These factors have weak driving and dependence power indicating that they are disconnected from the system and do not have much influence on developing the antifragile supply chain. In this study, there are no factors categorized as autonomous meaning that all 16 factors identified are important for the system. • Dependent factors: These factors have high dependence and low driving power. These factors are important for an organization and can be seen at the top of the TISM model. They represent the desired objectives for an organization. In this study, effective additive manufacturing processes, distributed manufacturing system, effective knowledge management process, reduction in manufacturing lead time, design freedom and customisability, cost-effective design and manufacturing, effective inventory management, rapid market responsiveness, and antifragile supply chain are the dependent factors. The success of these factors is strongly dependent on other factors. • Linkage factors: The results reveal that there are no linkage factors that have strong driving power as well as strong dependence. These variables are unsteady and any action on these variables will influence other variables as well as the feedback on them. Thus, it can be inferred that among all the 16 factors chosen for this study, no factor is unstable, and the factors do not require handling with care while dealing with them. • Driver factors: These factors help organizations in achieving their desired objectives and are classified as independent factors or drivers. They have high driving power and low dependence and are investigated from a strategic perspective by the top management. In this study, supply chain risk management, integrated manufacturing processes, efficient information sharing process, development of a skilled workforce for technology adoption, resource allocation for digitalization, development of a strategy for collaboration and innovation, and proactive top management are the drivers.

Antifragile supply chain, which is at the top of the hierarchy in the TISM model revealing its low driver power and high dependence. Thus, it has a more competitive orientation. Proactive top management, on the other hand, is at the bottom of the hierarchy revealing its low dependence and high driver power. Thus, it has a more strategic orientation. Hence, a competitive advantage can be achieved by continuously enhancing the capabilities of the drivers. Also, the study has no autonomous and linkage factors. All the factors are either drivers or dependants stating that all are steady factors and important for the model. They either drive the other factors or are dependent on other factors for their success.

This study makes substantial contributions to the literature. The literature review revealed that most of the studies focus on building a resilient and/or robust supply chain. These studies discuss how to make the supply chain bounce back quickly in the event of a disruption or an exogenous shock such as the Covid-19 pandemic. However, only a handful of studies could be identified which talk about building an antifragile supply chain that thrives in uncertainty and chaos. This indicates that the focus of researchers is towards tackling the disruptions and saving the supply chain from any adversaries. On the contrary, the shift from robustness and resilience to antifragility requires the firms to deliberately cause some level of disruption to the supply chain, so that it embraces disruption and gains from it rather than avoiding it altogether. The few studies that have discussed antifragility focus on the need for antifragility and how it impacts the supply chain. None of the studies in the literature have focused on the factors that lead to antifragility. In this regard, this study adopts a very novel approach in determining the factors that could lead to an antifragile supply chain and how additive manufacturing capabilities can be utilized to lead to antifragility. The study draws the attention of the researchers towards a concept less explored. The conceptual framework is built using the TISM approach and discusses a firm's strategies and decisions in a turbulent environment. The study figures out the relevant factors towards an antifragile supply chain, determines the hierarchies and interlinks and classifies them based on driving and dependence power.

The recent worldwide disruption to operations and supply chains has got the firms rethinking their strategies. The need of the hour is to develop dynamic capabilities which would prove beneficial in these uncertain times so that the firms can combat and learn from disruption to avoid any negative consequences on the firms' overall performance. Currently, resilience and robustness are the go-to strategies for a firm to save itself in the face of adversities. This study brings to the forefront, the concept of antifragility which points out the need to embrace disruption and learn from them. The study helps the firms develop a mind shift towards building a supply chain that sees disorder as an opportunity to learn and grow. Finally, the nexus between antifragility and digitalization develops a fertile ground for the adoption of additive manufacturing technologies. This study reveals how AM can be used as a competitive advantage by the firms to combat any negative consequences on its overall performance in the face of disruption. The structural framework, its hierarchies, and interlinks can be utilized by decision-makers as a powerful tool to understand the cause and effect. Also, the MICMAC analysis helps the firms understand what factors are driving the changes and which are dependent on others. Efforts of the decision-makers can now be directed towards achieving an antifragile supply chain by understanding the links and prioritizing their actions towards the goal. These actions will help the firms prepare better in case of future disruptions to their supply chains.

In the post-Covid-19 era, supply chain resilience has gained the attention of researchers and practitioners. Supply chain resilience is the ability of a supply chain to return to its predisrupted mode in an acceptable period. An antifragile supply chain strives to embrace the disorder and gain from it rather than avoid it. Here, we can find its analogy with any vaccination, the aim of which is to intentionally expose our immune system to the virus to build a stronger immunity towards it. Similarly, an antifragile supply chain helps firms to convert the challenges into opportunities. During the recent outbreak, additive manufacturing evolved to become a ubiquitous technology and the world saw its potential applications in the printing of medical supplies. Quite a few studies have discussed how additive manufacturing technology can be utilized to combat the Covid-19 challenges. Several industries and business sectors, such as the medical sector, used AM to manufacture customized and cost-efficient products in a timely fashion. Recent improvements in AM technology enable co-designing and mass-personalization. Operational and technological innovations have also been done to push the boundaries of customization and provide high volume at a low cost. AM helps the firms in overcoming the traditional manufacturing constraints, including but not limited to inventory buffers and tooling requirements. AM's potential to rapidly produce unlimited range of products can prove to be a winning factor during a disruption for any firm.

Existing literature talks about resilient and robust supply chains but factors leading to an antifragile supply chain have not yet been discussed. In this study, through literature review, initially, nineteen factors are identified for an antifragile supply chain. Delphi method is used to collect experts' opinions to corroborate the findings which led to the final sixteen factors for this study. Using TISM-MICMAC, the hierarchy and interaction of these factors are analyzed and the factors are then clubbed as drivers and dependants. The results reveal the importance of proactive top management to implement AM, which ultimately leads to an antifragile supply chain. The TISM-MICMAC approach used in this study provides a macro picture of the critical factors to build an antifragile supply chain. Findings will help managers in decision-making to prepare for future disruptions in the supply chain. However, this study has some limitations. It gives only the interrelationships between the factors and fails to assign relative importance to the factors. Hence, for further validation of findings, Structural Equation Modeling (SEM) and m-TISM could be applied as future scope of the study.

Alternative production strategies based on the comparison of additive and traditional manufacturing technologies

Investigating the effects of learning and forgetting on the feasibility of adopting additive manufacturing in supply chains

Modelling the barriers of Health 4.0-the fourth healthcare industrial revolution in India by TISM

Effect of information sharing in supply chains: understanding the roles of supply chain visibility, agility, collaboration on supply chain performance

Analysis of factors impacting survivability of sustainable supply chain during COVID-19 pandemic: an empirical study in the context of SMEs

How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic

Roles of Innovation Leadership on Using Big Data Analytics to Establish Resilient Healthcare Supply Chains to Combat the COVID-19 Pandemic: A Multimethodological Study

Key Success Factors for Supply Chain Sustainability in COVID-19 Pandemic: An ISM Approach. IFIP Advances in Information and Communication Technology 633 IFIP

Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities

Demand or Supply? Price Adjustment during the Covid-19 Pandemic

Big data analytics based enablers of supply chain capabilities and firm competitiveness: a fuzzy-TISM approach

Purchasing, supply chain management and sustained competitive advantage: the relevance of resource-based theory

Jump-starting resilient and reimagined operations

Sustainability of the Indian auto rickshaw sector: Identification of enablers and their interrelationship using TISM

3-D printing: The new industrial revolution

Additive manufacturing in military and humanitarian missions: Advantages and challenges in the spare parts supply chain

Additive manufacturing for consumercentric business models: Implications for supply chains in consumer goods manufacturing

3D printing: The dawn of a new era in manufacturing?

Reducing resupply time with additive manufacturing in spare part supply chain

Modeling the lean barriers for successful lean implementation: TISM approach

The perceived value of additively manufactured digital spare parts in industry: An empirical investigation

Achieving supply chain efficiency and resilience by using multi-level commons

Barriers in adoption of additive manufacturing in medical sector supply chain

Digital supply chain to unlock new agility: a TISM approach

Supply chain readiness, response and recovery for resilience

COVID-19 pandemic related supply chain studies: A systematic review

Building the Resilient Supply Chain

Tourism and its economic impact: A literature review using bibliometric tools

An experimental application of the Delphi method to the use of experts

Food security and Canada's agricultural system challenged by COVID-19

The effect of additive manufacturing adoption on supply chain flexibility and performance: An empirical analysis from the automotive industry

Additive manufacturing: empirical evidence for supply chain integration and performance from the automotive industry

Preparing for the future: Development of an "antifragile" methodology that complements scenario planning by omitting causation

Unlocking value for a circular economy through 3D printing: A research agenda

Skills and education for additive manufacturing: A review of emerging issues

Interpretive framework by analysing the enablers for implementation of Industry 4.0: an ISM approach

Holistic evaluation of the impacts of additive manufacturing on sustainability, distribution costs, and time in global supply chains

Ripple effect in the supply chain: an analysis and recent literature

How frugal innovation shape global sustainable supply chains during the pandemic crisis: lessons from the COVID-19. Supply Chain Manag

Antecedents of Resilient Supply Chains: An Empirical Study

Building theory of sustainable manufacturing using total interpretive structural modelling

The impact of additive manufacturing on supply chains

Analysis of barriers to implement additive manufacturing technology in the Indian automotive sector

Can supply chain risk management practices mitigate the disruption impacts on supply chains' resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era

Additive manufacturing: Currently a disruptive supply chain innovation? Operations and Supply Chain Manag

The usefulness of additive manufacturing (AM) in COVID-19. Annals of 3D Printed Medicine 2

A review of supply chain risk management: definition, theory, and research agenda

COVID-19: Outcomes for Global Supply Chains

Additive manufacturing and sustainability: an exploratory study of the advantages and challenges

Fortune (2020) 94% of the Fortune 1000 are seeing coronavirus supply chain disruptions: Report. Fortune

Companies should shift from 'just in time' to 'just in case

Additive manufacturing technologies: State of the art and trends

A global sustainability perspective on 3D printing technologies

Additive manufacturing technology and its implementation in construction as an eco-innovative solution

Examining legitimatisation of additive manufacturing in the interplay between innovation, lean manufacturing and sustainability

Analysis of third party reverse logistics provider using interpretive structural modeling

A managerial operationalization of antifragility and its consequences in supply chains

Global supply-chain effects of COVID-19 control measures

Ambiguity and its coping mechanisms in supply chains lessons from the Covid-19 pandemic and natural disasters

Toward the development of a big data analytics capability

Big data in humanitarian supply chain management: a review and further research directions

Analysis of critical success factors of world-class manufacturing practices: An application of interpretative structural modelling and interpretative ranking process

Requirements for large-scale adoption of rapid manufacturing technologies

Modified total interpretive structural modelling (TISM) of asymmetric motives and its drivers in Indian bilateral CBJV

Economies of collaboration in build-tomodel operations

Supply chain risk management: A literature review

The direct digital manufacturing (r)evolution: definition of a research agenda

Review of quantitative methods for supply chain resilience analysis

Dynamically scheduling and maintaining a flexible server

Breaking the mould: achieving high-volume production output with additive manufacturing

How companies can respond to the Coronavirus. MIT Sloan Manag Rev Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review

Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: A research note

OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications

Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company

The Ripple effect in supply chains: Trade-off "efficiency-flexibility-resilience" in disruption management

Challenges and solutions in meeting up the urgent requirement of ventilators for COVID-19 patients

Building antifragility in service organisations: Going beyond resilience

Traditional vs additive manufacturing supply chain configurations: A comparative case study

An adaptive process planning approach of rapid prototyping and manufacturing

Procurement 4.0 to the rescue: catalysing its adoption by modelling the challenges

An aerospace business case on additive layer manufacturing technologies in aerospace and defense supply chain

Ripple effect quantification by supplier risk exposure assessment

Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges

Supply chain innovations for additive manufacturing

Impact of disruptions in agri-food supply chain due to COVID-19 pandemic: contextualised resilience framework to achieve operational excellence

Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories

An analysis of growth-accelerating factors for the Indian automotive industry using modified TISM

Critical success factors for integrating artificial intelligence and robotics. Digital Policy

The journal coverage of Web of Science and Scopus: a comparative analysis

Potential of additive manufacturing for upstream automotive supply chains

Application of blockchain technology for sustainability development in agricultural supply chain: justification framework

Delphi: A Versatile Methodology for Conducting Qualitative Research

A Sustainability Assessment Framework for Dynamic Cloud-based Distributed Manufacturing

Gaining from disorder: Making the case for antifragility in purchasing and supply chain management

Comparing alternatives to the Web of Science for coverage of the social sciences' literature

A critical review of initial 3D printed products responding to COVID-19 health and supply chain challenges

Using the Delphi technique in normative planning research: Methodological design considerations

Impact of additive manufacturing technology adoption on supply chain management processes and components

The impact of location of 3d printers and robots on the supply chain

Manufacturing in the Time of COVID-19: An Assessment of Barriers and Enablers

Remote real-time collaboration through synchronous exchange of digitised human-workpiece interactions

Role of additive manufacturing in medical application COVID-19 scenario: India case study

A comparison of traditional manufacturing vs additive manufacturing, the best method for the job

Additive Manufacturing Impacts on Productions and Logistics Systems. IFAC-PapersOnLine

Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review

Technology management for innovation in organizations: an argumentation-based modified TISM approach

Industry 4.0 technologies as enablers of lean and agile supply chain strategies: an exploratory investigation

Research opportunities for a more resilient post-COVID-19 supply chain -closing the gap between research findings and industry practice

Technology selection in green supply chains -the effects of additive and traditional manufacturing

The impact of Additive Manufacturing on Supply Chain design: A simulation study

3D printing services: classification, supply chain implications and research agenda

Analysis of manufacturing supply chain agility performance using Taguchi loss functions and design of experiment

Conducting a literature review

Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing

Supply chain sustainability: learning from the COVID-19 pandemic

Impact of indirect relationships in classification of variables-a micmac analysis for energy conservation

Contribution of Additive Manufacturing Systems to Supply Chain

Covid-19′s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data

Barriers to blockchain adoption in healthcare industry: an Indian perspective

Exploration of alternative supply chains and distributed manufacturing in response to COVID-19; a case study of medical face shields

Mater Des

Application of additive manufacturing for mass customisation: understanding the interaction of critical barriers

Modeling enablers of TQM to improve airline performance

Interpretive structural modelling of factors for improving competitiveness of SMEs

Modelling of critical factors for responsiveness in supply chain

Framework for sustainable maintenance system: ISM-fuzzy MICMAC and TOPSIS approach

Analysing the interaction of factors for resilient humanitarian supply chain

Investigating additive manufacturing implementation factors using integrated ISM-MIC-MAC approach

Interpreting the interpretive structural model

Rapid manufacturing: Impact on supply chain methodologies and practice

Depicting additive manufacturing from a global perspective; Using Cloud manufacturing paradigm for integration and collaboration

Developing Interconnection Matrices in Structural Modeling

Total interpretive structural modelling (TISM) of strategic performance management for Indian telecom service providers

Risk analysis of the agri-food supply chain: A multimethod approach

Intelligent Manufacturing in the Context of Industry 4.0: A

Acknowledgements Authors would like to express sincere thanks to