1 Introduction

World markets are expanding and so are the supply chains in an environment full of volatility, uncertainty, complexity and ambiguity. The supply chains are finding it hard to bring resiliency in spite of becoming colossal and sophisticated with the time (Ozdemir et al. 2022; Huo et al. 2019; Barbieri et al. 2020). The existing ecosystem have stressed the supply chains to devise resilient business models capable of managing disruption and maintain operational continuity with the required level of connectivity and recover from interruptions by controlling structure and functionality (Scholten et al. 2019). Covid-19 pandemic became the basis for demand and supply scares and majority of supply chain companies collapsed as they were not able to improvise their resiliency and revival (Remko 2020). The impact of this disruption was testified when out of one thousand fortune companies, 94% of them were facing the heat by finding it difficult to revive or bring resiliency (Sherman 2020). The Covid-19 eruption has lately lowered the dependence on international supply chain due to complete supply chain system collapse (Samson 2020). Various loop-holes were identified in global supply chain when either partial or full paralysis of the supply chain was witnessed (Ivanov 2020) making the organizations realize the importance of supply chain resilience to deal with abrupt changes (Barbieri et al. 2020; Bastas and Garza-Reyes 2022).

The concept of knowledge is critical both at the organisational levels. Additionally, businesses must integrate knowledge across the organisation (Zahra et al. 2020). The employees of the businesses have valuable tacit information that is challenging to copy or pass to competitors. However, internal transfer and integration of tacit knowledge inside the department/firm is required (Grant 1996). Different practices such as knowledge integration (Fugate et al. 2012; Kotlarsky et al. 2015); knowledge sharing (Spekman and Davis 2016); application of knowledge in cross-functional sourcing situations using analytics (Herden 2020) have been assessed to measure its impact. The shared knowledge has been discussed to enhance communication and integration of planning processes (Kearns and Sabherwal 2006). One of the most significant contributions is Pawlowsky's (2003) classification, which gives more scope to distinguish and identify the possible impact of knowledge management (KM) on supply chain activities. According to the knowledge-based view, knowledge exists in specialised forms within organisations, and the integration of individual expert knowledge is the essence of organisational capability. Extending the knowledge-based perspective to supply chains, Hult et al. (2004) showed how special capacities for producing and using knowledge to contribute to improved supply chain performance. Since then, several studies have shown a strong relationship between knowledge management and supply chain performance (Blome et al. 2014; Hult et al. 2007); KM strengthens the capacity of supply chain partner enterprises to generate and utilise wisdom, leading to performance improvement (Hult et al. 2007). To increase the sustainability of the entire supply chain, innovations in products and processes can be disseminated through the proper channels. KM enables businesses to acquire a competitive edge and enhance the overall operational effectiveness of the supply chain (e.g. Blome et al. 2014; Handfield et al. 1999; Hult et al. 2006; Wadhwa and Saxena 2007). Additionally, efficient KM promotes innovation in goods, services, and procedures as well as improvements in the relationships within the supply chain and supply chain flexibility (e.g. Bouncken et al. 2016; Malhotra et al. 2005; Wadhwa and Saxena 2007; Wood et al. 2016). Several KM mechanisms, including collaborative learning (Bessant et al. 2003), knowledge acquisition (Hult et al. 2004), knowledge sharing (Dyer and Nobeoka 2000; Lawson et al. 2009), and knowledge transfer (Kotabe et al. 2003), are likely to help businesses develop supply chain operational advantages, which will ultimately result in better supply chain business performances, according to a substantial body of literature.

There have been numerous studies done to determine the function of knowledge-based supply chains (KSC) (Majumdar et al. 2022). Acquiring and absorbing knowledge in supply chains through partners; knowledge creation through collaborative approach; knowledge protection measures while sharing knowledge are all the practices to adopt knowledge management in the supply chains. Several scholars asserted worthy opinions regarding resiliency and KSC and how they can create a robust system to make organizations prepared for any challenges that can hamper their performance. Das et al. (2021) elaborated how resiliency can be attained in the KSC; through a collaborative effort of all the stakeholders involved rather than any individual or a particular organization. Agostini et al. (2020) showed the significance of knowledge-based strategies to bring the progressive shift in the supply chains for achieving resiliency. Growingly researchers discern the progression of KSC for better preparedness towards achievement of goals and any disruption, encourage optimum utilization of resources and ameliorate resiliency (Martins et al. 2019). Resiliency in supply chain is about the organization capability to be multidimensional, which empowers them to recover quickly and bring stability to their business (Kamalahmadi and Parast 2016). In order to bring resiliency in supply chain the organization are required to be proactively prepared for unforeseen occurrences, disruption and regain the setbacks faced frequently (Ponomarov and Holcomb 2009).

The modern-day supply chain performance is strongly dependent on how the organizations accumulate information, store and utilize the knowledge attained for better and sustainable decision making, that can bring resiliency (Han et al. 2020). KSC plays a vital role in fruition with the customer expectations, depending on the ultra-smooth coordination across the supply chain; it syncs the information, operations, and required resources with an objective to deliver the customers with augmented product as per their expectations (Khan et al. 2019) but faces several challenges such as efficient knowledge capturing, knowledge transfer or sharing, application, domain mapping etc. But in order to bring resiliency to the supply chain, it requires to embrace and evolve capabilities and minimizes the challenges (Pettit et al. 2013). Therefore, this study identifies the challenging factors and strategic measures for enhancing the resiliency in KSC. The following research questions are addressed.

RQ1. What are the challenging factors for KSC to achieve resiliency ?

RQ2: What are the significant strategic solutions to enhance resiliency in KSC?

While the past research examines the KSC (Pettit et al. 2013; Li et al. 2012; Börjeson et al. 2015), limited literature exist on challenging factors of KSC and strategic measures to achieve resiliency. The current study has proposed a framework to identify and assess the factors using Fuzzy-DEMATEL method application. The proposed framework has been assessed based on the judgment of decision makers. The key implications of the study are the pioneering work in exploring knowledge-based supply chains for achieving resiliency. The study offers following key contributions:

  • Exploring the challenging factors to KSC for achieving KSC

  • Ranking of strategies to enhance resiliency in the KSC

The subsequent sections of the paper are placed in this order: Section 2 discusses the literature review on existing factors of knowledge based supply chains to achieve resiliency, addressing the first research question. Section 3 explains the research methodology employed in the study. The results obtained from the research methods are discussed in Section 4. Section 5 presents findings discussion and implications. Section 6 concludes the study.

2 Literature review

Knowledge management (KM) has bridged the information supply and demand gaps for the learning processes, which improves organisational performance (Curado et al. 2011). Knowledge takes in implicit and overt guises (Polanyi 1962). On the one hand, explicit information is codified and transmittable in formal, systematic language and is thus recorded in libraries, archives, and databases, whereas tacit knowledge is firmly embedded in action, commitment, and involvement (Nonaka et al. 1994). Tactic and explicit knowledge are always in conversation with one another, moving through people, groups, and organisations before returning to individuals (Nonaka and Toyama 2005). The theory of knowledge generation put forth by Nonaka et al. in 1994 built on the idea that information is formed over an unending cycle and, as a result, develops organisational knowledge through the processes of: a) socialisation; b) externalisation; c) combination; and d) internalisation. Since organisations learn through experience how to allocate resources between exploitation (which is efficiency driven) and exploration (which is efficiency driven), finding the right balance can be challenging (Curado et al. 2011). High-qualified personnel are supported when using the exploration approach (developing innovations), whereas low-qualified employees are replaced when using the exploitation method (improving procedures) (Wilkesmann and Wilkesmann 2018). Technology, processes, and people are all involved in the acquisition, management, and transfer of knowledge, according to Cepeda Carrión et al. (2004). These are regarded as knowledge management's three pillars.

To extract value from the firm's knowledge assets, Alavi and Leidner (2001) outlined a framework of KM procedures that included knowledge production, storage/retrieval, transfer, and application. Knowledge management encompasses not only the technical skills of managers and employees, but also the use of social networks to develop, adapt, and carry out work flows, allowing knowledge to freely circulate within processes as well as to reach search engines to capture and apply external knowledge. According to Wilkesmann and Wilkesmann (2018), the use of digitisation technologies facilitates routines that support knowledge management practises in industrial applications. As a result, knowledge management and digitisation are frequently linked in literature (Ilvonen et al. 2018; Shamim et al. 2017; Wilkesmann and Wilkesmann 2018).

According to various studies (Dost et al. 2016; Olson 2018; Schoenherr et al. 2014), knowledge management is generally related to supply chain management because it offers the tools required to manage large amounts of data generated by supply chain operators and their customers. Management of the complete supply chain, including sourcing, logistics, production, and retail delivery to customers, is a requirement of supply chain management (Olson 2018). According to Gunasekaran and Ngai (2007), all of these responsibilities include managing knowledge, both from the technological perspective through information systems and the supply chain's quality through data management, whether it be tacit or explicit knowledge (Schoenherr et al. 2014). Since the domains of knowledge management in supply chain management are both intra- and inter-organisational, firms are able to respond to the needs of both internal and external stakeholder groups, which results in knowledge management contributing to supply chain performance (Dost et al. 2016; Schoenherr et al. 2014). In order to achieve greater value, the supply chain system should be able to acquire knowledge from anywhere in the supply chain, whether it is inter-company or intra-company (Thomas et al. 2017). Documents, proof, employee experiences, and learnings that can be combined with the organization's economic and non-economic goals can all be used to collect knowledge (Kassaneh et al. 2021). Domain mapping is necessary to organise captured knowledge because it makes it easier to concentrate on micro areas by categorising, planning, analysing, empowering, and envisioning organisations' knowledge for enhancing the planning process, production process, organisational activities and procedures, market comprehension, and strategic partnerships (Centobelli et al. 2017). The use of new knowledge to address supply chain difficulties is referred to as the application of knowledge. Application of knowledge is possible at all organisational levels, including those within the business and those participating in the supply chain, as well as for the technology utilised, established procedures, operations, supplier and customer relations, and services received or provided (Kassaneh et al. 2021).

The scholars stipulate that knowledge sharing amongst the firms within the supply chain is vital in order to achieve the desired objectives, though it doesn’t provide any surety to improve the performance every time, but in several cases, results have been positive (Chen et al. 2018). Knowledge sharing benefits the organization structure and can improve the flow of communication in both the directions, whether upward or downward (Aydogan 2012). Knowledge sharing can be motivated within the supply chain, if the employees are encouraged to develop the mindset towards knowledge creation and knowledge sharing (Whitehead et al. 2019; Cheung et al. 2012). The organizations having global plans can really benefit from knowledge sharing, as it can help them in developing dynamic competencies (Lindblom and Tikkanen 2010). Knowledge retention and protection stresses on safeguarding the novel knowledge retained for improvising supply chain and protecting it from outside diffusion (Li and Kang 2019). Knowledge attained is obviously of high importance, but its protection is equally important for the organizations just because of its tacit nature and high human involvement (Ilvonen et al. 2018). Utilizing knowledge strategy as an obvious part of the overall supply chain strategy can empower the organization to be preemptively prepared for the unanticipated events that may emerge at any point of time to surprise the overall mechanism (Ruel et al. 2019). Assessment of all the knowledge practices is a regular exercise for KSC, it is a part of overall strategy to identify the risk involved by adopting proactive approach, to order the task and risk as per importance, rather than being risk responsive (Centobelli et al. 2019). Regular assessment of tangible and intangible knowledge can benefit the supply chain with prudent and issue specific strategic solutions (Baryannis et al. 2019).

KSC can plan for collaborative knowledge management as it eases the functioning of the partners in supply chain to be more effective and receptive with the common achievement of strategic goals (Li et al. 2012). It helps the supply chain partners to share knowledge amongst themselves and make themselves more competitive by adopting holistic supply chain approach (Li and Hu 2012; Ekinci et al. 2022). These strategic decisions are always directed from top management and they are one of the primary enablers for sustainable KSC. The top management support for strategic decisions regarding the business are vital, especially for the sector and business type, nature of operations, the geographical location, business practices and the organizational culture (Kannan 2021). The top management is responsible for the identifying and applying the economic and environmental dimensions that are in sync with the social aspect so as to bring sustainability to the business (Khan et al. 2022). No organization can be proactive towards the challenges that it may face, unless the top management develops such an organizational culture (Koster et al. 2017).

Many firms are progressively adopting the green practices with an objective to reduce the impact of their products, services, processes, activities on the global environment but still the implementation is in initial phase. Sustainable procurement is focused on “procuring the future”, it is a process through which the supply chain fulfills its need for product and services, the activities and processes in a manner that it generates value for the organization, society, economy and mitigates its effect of the environment (Kannan 2021). Innovation can enable an organization to gain competitive edge over its competitors, it is the organizations openness to adopt and apply novel ideas to its resources, processes, standard operating procedures, policies, products and services (De Guimaraes et al. 2016). Any KSC can attain edge over its competitors by showcasing its capability to innovate and apply it to reduce its costs, binging novelty to its products and service, and delivering value to every stakeholder (Chen 2018). Sustainable marketing is an approach of a knowledge-based supply chain to plan, embrace and administer the facets related to the product, price, promotion and place with an objective to augment the customer expectations, achieving organizational goals and ensuring the competitive edge (Kim et al. 2020). Sustainable marketing is committed towards continuously analyzing the consumer expectations, especially during the times of disruption, climatic changes, environment deterioration and following out of economic disparities across the world (Sheth and Parvatiyar 2021). Organization culture being an integral part of any organization is composed of organizational philosophy, its beliefs, ethics and supposition that is practiced to achieve their goals (Khazanchi et al. 2007; Nazam et al. 2020). It is how an organization practices and shares it ideas and values that applied to the processes to enhance organizational performance (Chunsheng et al. 2020).

Digital based supply chain plays a vital role in providing competitive edge and gaining sustainability. It enables organization to integrate the innovative technology with the knowledge-based supply chain with motive to provide superior customer experience (Preindl et al. 2020). KSC should be able to ascertain the distinct and usual attributes of a nimble and resilient supply chain with a purpose to invest in the resources where they are needed the most and can improvise the agility (Gligor et al. 2019). It is very important to allocate the resources wisely, as any misallocation of resources may result in wastage of resources and loss of investment and efforts leading to ineffectiveness and non-accomplishment of organizational goals (Zhang et al. 2021). Cost optimization is vital strategy that KSC may use by optimizing the quality of their supply chain services and ensuring the sustainability (Xue and Ge 2018). E-procurement benefits organizations in reducing several forms of costs, for instance; transaction cost, inventory cost, reducing the time to respond, reducing the purchase order cycle time and purchase requisitions and improving the cooperation within and outside the organization, clarity on process and procedures, efficacy and proficiency (Kumar and Ganguly 2020). KSC needs a well-integrated and long-lasting multimodal process and handling system that can assist in improvising and preserving the quality of appropriate, dependable and competent multimodal logistics especially in case where supply chain is in transition phase from unimodal to multimodal logistics (Paraskevadakis et al. 2016; Paraskevadakis et al. 2020). Based on the discussion, the challenging factors are presented in Table 1.

Table 1 Challenging factors to achieve resiliency in knowledge-based supply chains
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3 Research methodology

The current study has proposed the framework shown in Figure 1. In the initial phase, the factors are identified through literature review and further are validated by the experts. The experts were asked for providing pairwise comparisons using linguistics scales shown in Table 2. The responses were analysed through Fuzzy DEMATEL application. The proposed framework is categorized into two phases. The first phase consists of literature review to extract the previous literature for exploring the factors that affect KSC. The current study has utilized Fuzzy DEMATEL for observing the cause-and-effect relationship between the factors (Luthra et al. 2019; Sharma et al. 2022). The evaluation of the factors was done using Fuzzy DEMATEL, the most appropriate method for determining the interrelationship among the variables. Moreover, this method has the potential to overcome the biases and vagueness that exist in human judgments. The advantage of Fuzzy DEMATEL is that it can deal with limited information and can be used with a small sample of experts (Khan et al. 2019; Parmar and Desai 2020). Khan et al. (2019) have applied Fuzzy DEMATEL with five or less than five experts. This study has undertaken 20 experts who were involved in different decision-making positions. The results help in exploring the cause-and-effect group factors. Based on the results obtained from Fuzzy DEMATEL application, the same experts were asked to suggest the strategic solutions for managing the factors and enhance the resiliency in the KSC. The results of the study help the decision makers to understand the key strategies that can reduce the impact of challenges and enhance the resiliency in the KSC. The second phase includes the strategic solutions based on the results of the study that can enhance the resiliency in the KSC. The ranking of the strategic solutions is obtained by Best Worst Method (BWM). BWM is a reliable MCDM method that is most preferred to determine pairwise comparisons between criteria and calculate their optimal weights Rezaei et al. (2018). This method has an advantage over the other pairwise comparison methods due to its handling inconsistencies capability. This method is successfully applied in several areas including manufacturing, sustainability, waste management, selection of supply chain partners, green performance etc. The Scopus database is extracted to find the most relevant articles in the area of KSC.

Fig. 1
figure 1

Proposed research framework

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Table 2 Expert details
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The proposed research framework is shown in Fig. 1.

The stages that were used in the methodology for the research are all explained in the next subsections.

3.1 Data collection

A questionnaire was used to receive the response of the experts. A total of 20 experts were undertaken from the supply chains of several industries of FMCG, automobiles, electronics, IT etc. designated at managerial positions with an experience of approximately 8-10 years and more. Table 2 presents the details of the 20 experts undertaken and were aware of the knowledge management supply chains and the challenges faced in the current scenario. The research was carried in two phases. During first phase, the challenges were identified and validated and assessed by F-DEMATEL application. This method was helpful to explore the aggregate weightage of the challenging factors to recognise the most challenging. During the second phase the strategic solutions were proposed to enhance the resiliency in the KSC. Further, validated by the experts and decision makers. The methods undertaken in the study are discussed in the subsequent sections

3.2 Fuzzy DEMATEL

Fuzzy DEMATEL is the most appropriate method for determining the interrelationship among the variables. The current study has utilized Fuzzy DEMATEL for observing the inter-relationship among the factors. Moreover, Fuzzy DEMATEL has the potential to overcome the biases and vagueness in human judgments (Farooque et al. 2020). The steps followed in Fuzzy DEMATEL application is as follows:

  • Step 1: Developing a fuzzy direct relation matrix

    The experts were asked to evaluate the impact of factor i on factor j using a linguistic scale shown in Table 2. Triangular fuzzy numbers (TFNs) were used for capturing the fuzziness in the judgments (Seçme et al. 2009).

    Table 3 exhibits the fuzzy linguistic scale (Venkatesh et al. 2017; Wu and Lee 2007) to convert impact scores to triangular fuzzy numbers.

    The fuzzy direct relation matrix Z = \({[{Z}_{ij}]}_{n\times n}\) is obtained through Eqs. (1-3)

    $$x{l}_{ij}^{k}=({l}_{ij}^{k}-min{l}_{ij}^{k})/{\Delta }_{min}^{max}$$
    (1)
    $$x{m}_{ij}^{k}=({m}_{ij}^{k}-min{l}_{ij}^{k})/{\Delta }_{min}^{max}$$
    (2)
    $$x{r}_{ij}^{k}=({r}_{ij}^{k}-min{l}_{ij}^{k})/{\Delta }_{min}^{max}$$
    (3)

    where \({\Delta }_{min}^{max}=max{r}_{ij}^{k}-min{l}_{ij }^{k}\)

  • Step 2: Developing the normalised direct relation matrix using Eq. (4)

    $$m=\mathit{min}[\frac{1}{\mathit{max}{\sum }_{j=1}^{n}|aij|},\frac{1}{\mathit{max}{\sum }_{j=1}^{n}|aij|}]$$
    (4)

    Integrating crisp value through

    $${Z}_{ij}=\frac{1}{p}\left({z}_{ij}^{1}{+Z}_{ij}^{2}{+Z}_{ij}^{p}\right)$$
    (5)

  • Step 3: Establishment of the total relation matrix using Eq. (6)

    $$T=N{\left(I-N\right)}^{-1}$$
    (6)

  • Step 4: Computing the scores of sums of rows (D) and the sum of columns (R) using Eqs. (7) and (8)

    $$D= {\left[{\sum }_{j=1}^{n}{{t}_{i}}_{j}\right]}_{nX1}$$
    (7)
    $$R= {\left[{\sum }_{i=1}^{n}{{t}_{i}}_{j}\right]}_{1Xn}$$
    (8)

  • Step 5: Developing the cause-and-effect relationship

    (D+R) exhibits the prominence of a factor, indicating its total effects in terms of influenced and influential power. (D−R) describes the causal-effect relationship between the factors. In case, the value of (D−R) value is more than zero it is categorized into casual group factors whereas, the factor is categorised in effect group if (D−R) value is less than zero. The Fuzzy DEMATEL was applied using equations 1-8 and cause and effect relationship was identified and network relationship map has been developed to show the influence of the dominant challenging factors.

Table 3 Linguistic labels
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3.3 Best worst method (BWM) application

BWM is a pairwise comparison-based technique proposed by Rezaei et al. (2018). The most popular MCDM technique for determining pairwise comparisons between criteria and calculating their ideal weights is this one (Moktadir et al. 2019; Nasr et al. 2021; Orji et al. 2020). Because it can handle inconsistencies, this method has an advantage over the other pairwise comparison techniques. This approach uses linear mathematical modelling to get the best weights for a problem with several criteria. In this procedure, each expert uses a BWM questionnaire that has been prepared using a linguistic scale with scores ranging from 1 to 9 to evaluate pairwise comparisons between the best criterion and other criteria. Every expert has been asked to comment on the importance of a specific determinant that is seen as the best criterion in comparison to the other criteria/determinants. When a best criterion is deemed to be "Equally essential to" other criteria by an expert, the score is "1," whereas the score is "9" when the pairwise comparison is "Extremely important to".

  • Step 1: Establishing a set of decision criteria

  • Step 2: Based on expert judgement, determining the Best (B) the most important and the Worst (W) the least important

  • Step 3: Using a 9-point scale, decide which decision criterion (B) you prefer out of all the available options. Equation provides the Best-to-Others (BO) vector result through eq. (9).

    $${A}_{B} =\left\{{a}_{B1}, {a}_{B2},\dots \dots {a}_{Bj}\dots {a}_{Bn}\right\})$$
    (9)

    where \({a}_{Bj}\) represents the preferences of the best criterion B over the criterion j.

  • Step 4: Using a 9-point scale to determine which decision criteria are preferred over the worst criteria (W), resulting in Others-to-Worst (OW), as shown in eq. (10).

    $${A}_{W}=\left\{{a}_{1W},{a}_{2W},\dots \dots {a}_{jW},..{a}_{nW} \right\})$$
    (10)

    where \({a}_{jW}\) represents the preference of the criterion j over the worst criterion W.

  • Step 5: The mathematical model 1 is used to compute the weights of the criteria (w1* , w2* ,....wn* ) (11)

    Model 1

    $${\mathrm{min}\;\underset{j}{\mathrm{ma}}x\{|\mathrm{W}}_{\mathrm{B}}-{\mathrm{a}}_{\mathrm{Bj}}{\mathrm{W}}_{\mathrm{j}}|,\left|{\mathrm{W}}_{\mathrm{j}}-{\mathrm{a}}_{\mathrm{jW}}{\mathrm{W}}_{\mathrm{W}}\right|\}$$
    (12)

    s.t:

    \(\frac{\Sigma }{j}{\mathrm{W}}_{\mathrm{j}}=1, {\mathrm{W}}_{\mathrm{j}}\ge 0\) for all j

    To determine the weigts of the criteria (w1* , w2* ,....wn* ), model 1 can be converted into model 2,

    Min \(\xi\)s.t.

    $$\left|\frac{{w}_{B}}{{w}_{j}}-{a}_{Bj}\right|\le \xi\;\;\;for\;all\;j$$
    (13)
    $$\left|\frac{{w}_{j}}{{w}_{W}}-{a}_{j}\right|\le \xi\;\;for\;all\;j$$
    (14)

    The consistency ratio (CR) of BWM is represented by \(\xi\)* and the corresponding consistency index (CI) values are computed by Eq. (3).

    $$CR =\frac{{\xi }^{*}}{CI}$$
    (15)

    The smaller the \({\xi }^{*}\), the smaller is the CR value, and the more consistent the vectors are.

3.4 Case location

To further explain the suggested methodology, a case study of FMCG manufacturer "X" in northern India is conducted.

The case firm is the leading producer in the FMCG supply chains in India and hence the resiliency in the post pandemic time is essential to be explored. This case location has global and local supply chains and includes engaging suppliers and communicating with them in real time. Due to Covid-19 impact, the countries have adopted the complete lockdown to minimize the effect. But due to lockdown, their business has fallen down. Their manufacturing has stopped for few months, and the supplier were dissatisfied too. Due to imbalance in the supply and demand the question of survivability in the long run has been risen. Also, the increasing demand of the FMCG products due to pandemic has created a need of resiliency in the supply chains. The case location has been active in adopting the technologies as per the need, changed the capacity planning, shared the resources, shared the expertise but still resiliency is debatable. The data has been collected from the firm’s managers through the structured questionnaire.

4 Results from fuzzy DEMATEL

The normalised tables developed on the responses from the experts are shown in Annexure A1. The fuzzy direct relation matrix is obtained through Eq.1-3 shown in section 3.2. The initial Direct-Relation Matrix (Z) was developed based on the responses from the experts using the linguistic scale shown in Table 2.

The pairwise matrix was developed for each expert. With the implementation of equation 3-5 normalised tables were developed. The values obtained for the fuzzy values (l, m, u) are shown in Table 4. After the compilation of each expert responses, the total relationship matrix was formed and the values obtained are shown in Table 4.

Table 4 Total Relation matrix
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With the total relationship matrix, the value of D+R and D-R were calculated to the influence of the factors on each other. Based on the D+R and D-R the causal and effect group factors were categorized. The impact results are shown in Table 5.

Table 5 Cause and Effect results
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5 Discussion

The results from the Fuzzy DEMATEL application shows that factors Knowledge sharing (FR2), knowledge domain mapping (FR4), knowledge strategy planning (FR6), collaborative knowledge management (FR7) Top management support (FR 8), Green perspective (Green sustainability) (FR 9), competitive edge in innovation (FR 13), Multimodal logistics (FR14), organization culture (FR15), Agility (FR17), Last mile delivery (FR20), Green transportation (FR21), Use of Alternative Fuel vehicles (FR24), Digital technologies-based supply chains (FR25) are cause group factors based on their D-R values (greater than zero). These are the factors which have influence on the other factors.

According to the findings of the study, it is evident that digital technologies-based supply chain is the most significant challenge for the KSC which answers the first research question. This aligns with the previous research conducted by Preindl et al. (2020) which states that digital supply chain plays a vital role in providing sustainability; it enables organization to integrate the technologies with the KSC with a motive to improvises the operations and practices of the supply chains (Preindl et al. 2020). Digital based supply chains help the firms in adopting cutting-edge technologies will increase the supply chain's integration, enhancing customer service and the organization's sustainability. Also, agility (FR17) is one of the most significant challenging factors. This is line with the previous research conducted by Gligor et al. (2019). According to the study KSC should be able to ascertain the distinct and usual attributes of a nimble and resilient supply chain that can improvise the agility. Last mile delivery (FR20), Green perspective (Green sustainability), Cost Optimization (FR22) are the most influential challenging factor than others. The supply chain can benefit with the technologies like artificial intelligence, augmented reality, big data, machine learning and, blockchain (Ageron et al. 2020). KSC should be able to ascertain the distinct and usual attributes of a nimble and resilient supply chain with a purpose to invest in the resources where they are needed the most and can improvise the agility (Gligor et al. 2019). As the environment is uncertain It is really important to have an agile procurement strategy that are mightily influenced by the latest technology enabled with predictive analytics, latest metrics supported by big data with access to real time information (Nicoletti 2018). Green perspectives enable organizations to mitigate their environmental impacts, enhance optimum utilization of resources and in the long run provides sustainability (Kong et al. 2021). Many KSC brush aside the assessment of the knowledge captured and even apply it to the organization processes that may not really fruitful of the organization. It is essential to possessing knowledge managers with expertise of strategies or action-based missions and regular assessments is of high importance so that they provide long term benefits to the organisation and helps in achieving resiliency (Ward and Wooler 2010). The changing and increasing need of the customers expectation, there is huge pressure of the last mile as it will drive the customer retention process, companies need to optimize the delivery processes and routes to stay competitive. But due to current situation, last mile delivery is losing its grip on the market and its competitiveness (Modgil et al. 2021). Therefore, the focus has to be on the KSC with efficient last mile delivery for the customers.

The other effect group factors are Knowledge capturing /acquisition (FR1), knowledge application (FR3), mentoring & coaching for knowledge retention (FR5), knowledge leakage / knowledge protection (FR10), knowledge assessment (FR11), Sustainable procurement practices (C12), Personalization (FR16), Scientific & effective decision making / Big Data analytics and Demand forecasting (FR18), Balance between stability and vulnerability (FR19), Cost Optimization (FR22), E-Procurement (FR23).

Knowledge capturing has become a challenge to KSC due to certain reasons including lack of inexperienced human resources, no-proper standards to collect information, lack of infrastructure or resourced required to capture knowledge (Roy 2018). In the current scenario when KSCs are struggling it is essential to re-align the knowledge capturing process for accumulating knowledge exploring how supply chain operations should incorporate economic and non-economic goals (Roy 2018). Also, due to the causal factors such as Industry 4.0 implementation barriers, lack of agility and others there is less effective decision making. This also restricts the organization with the accuracy, transparency and intelligence in the decision making that can offer competitive advantage in the longer run (Hofmann and Rutschmann 2018). Due to the causal factors, there is absence of direction and learning within the KSC that can affect the firm performance; it brings adverse effects. The absence of mentorship for knowledge retention is vital and without which the desired objectives may not be attained. There is need to take care of the knowledge protection and leakage as they are directly linked to the major risk that any KSC may face, any loss of confidential knowledge be it within or outside the organization can be catastrophic (Li and Kang 2019).

5.1 Strategic measures

The factors and their impact were discussed with the experts. The experts were requested to assess the strategic measures to tackle the identified factors. With the help of Best Worst Method (BWM), the strategies were prioritized and the most significant measure was identified, addressing the second research question. The steps of BWM are mentioned in section 3.2. This method has an advantage over the other pairwise comparisons methods due to its handling inconsistencies capability. The ranking of strategies based on BWM results is presented in the Table 6.

Table 6 Ranking of Strategies
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The BWM results have shown that implementation of industry 4.0 technologies is the best strategic solution that can bring resiliency in the KSC. Industry 4.0 technologies (Strategic option, S1) have the potential for linking the real assets and digital technology into a cyber-physical system. This is line with the previous research conducted by Kamble and Gunasekaran (2020). The supply chain can benefit with the technologies like big data, machine learning, artificial intelligence, augmented reality and blockchain (Ageron et al. 2020; Kazancoglu et al. 2023). KSC must be capable of providing personalized or customized solutions to the customers, starting with the interaction till the final product delivery. It can help the organization to identify considerable markets and meeting their expectations (Modgil et al. 2021). It is really important to have an agile procurement strategy that are mightily influenced by the latest technology enabled with predictive analytics, latest metrics supported by big data with access to real time information (Nicoletti 2018). In order to meet the customer expectations, gain the competitive edge and sustain in the volatile conditions, supply chain organizations need to use scientific and effective decision making through data analytics that can enable them with accurate, clear and insightful information that can be converted into business intelligence Hofmann and Rutschmann (2018). The second significant strategy is Social sustainable supply chain (Strategic option, S6) that focuses on benevolent and societal performance to reduce the hazards due to disruption at any level. This aligns with the previous research (Kamble et al. 2020). This will help the firms to retain the works force. Sustainable strategies require path dependent and embedded skills, as demonstrated by Aragon-Correra and Sharma (2003). These strategies are socially complicated and dependent on specific and observable processes. Such capacities need the acquisition of skills for navigating knowledge domains that are complicated, unpredictable, and ever-evolving (Aragon-Correra and Sharma 2003; Hart and Sharma 2004). Turning to the supply chain, prior research suggests that collaboration and integration between supply chain participants increase the likelihood that a socially responsible supply chain vision would develop (Carter and Jennings 2002; Gallear et al. 2012). Improved alignment of supply chain partners' economic strategies and social/ethical standards can improve the social performance of supply chains (Gallear, et al. 2012). Redesigning of supply chain can efficaciously establish a resilient and sustainable knowledge-based supply chain and reinforce the transformation around circular economy, creating an opportunity for evolving the latest technology (Nandi et al. 2021). The other strategies are in order of S3>S2>S4>S11>S7>S10 S8>S5>S9>S12.

5.2 Implications

The learned information can also be applied proactively to strengthen the organization's efforts to become more resilient. Despite the fact that the organisations in the study appeared to be aware that their suppliers and sub-suppliers did not always abide by their expectations and constraints, in practise the information was frequently applied reactively when addressing the problems within the organisation. The challenging factors found in the literature, such as collaborative learning, knowledge acquisition, and knowledge transfer, will allow businesses to develop operational advantages in the supply chain, which will ultimately result in superior business performance in the supply chain and increase the resiliency in the supply chains. The conversion of traditional SCs to KSC is essential in order to reduce the negative effects of disruptions. Resilience can be attained using the principles of a KSC that is based on data, information, knowledge, intelligence, and upcoming technologies like artificial intelligence, machine learning, and Industry 4.0.

The study is useful to the industry and decision makers to understand the need of digital technologies for enhancing the resiliency of the supply chains. It is very important to allocate the resources wisely, as any misallocation of resources may result in wastage of resources and loss of investment and efforts leading to ineffectiveness and non-accomplishment of organizational goals. Maintaining balance between stability and vulnerability is a difficult task, but with the adoption of scientific approach a knowledge-based supply chain organization can invest wisely in required areas to bring stability and minimize the risk and vulnerability. With growing customer expectations for last mile delivery, companies are consistently optimizing delivery processes and routes to stay competitive. With the technology integrated supply chain, the real time data enables the firm to deliver at higher pace. Automatic identification system (AIS) is an important function benefitting KSC in multiple ways, for instance; mapping the shipping activities, monitoring the effects on the environment, matchless high-resolution, real-time features improving the precision of ship trajectory extraction and prediction, traffic monitoring and control, understanding the problem from the bottom-up rather than top-down view; shipping volume, port performance. The proposed framework helps the industry and decision makers in evaluating their supply chain performance and helps in the critical areas for improvement for enhancing resiliency to make them ready for any emergency situations. This study may be undertaken as a tool for benchmarking KSC against competitors.

6 Conclusions, limitations and future research directions

Enhancing the resiliency in knowledge-based supply chain has enticed firm interest of the researchers and practitioners and likely impact on making organizations competitive in longer run. Prevailing disruption imposed with the spread of Covid-19 has forced the companies as well as countries to take numerous strategic actions in order to avoid recession like situation. Covid-19 pandemic has become the basis for demand and supply shocks and a reason for collapsed supply chains due to the lack of resiliency and revival. The objectives of the study are to explore and assess the factors and mapping strategic options to enhance resiliency in KSC. The factors identified were related to the need to disruption, global supply chains issue, infrastructural issues etc. Moreover, a robust model is developed to assess the existing factors using Fuzzy DEMATEL method application. The factors were identified, validated and assessed by Fuzzy DEMATEL method as this method was appropriate to categorize cause and effect group factors. Further, the strategies are assessed by the BWM application that helps the firms to enhance resiliency in the KSC in post pandemic situation. The results of this study show digital technologies-based supply chain and agility are the most significant causal factors that must be considered to enhance the resiliency in the KSC. Also, the strategies such as application of industry 4.0 technologies is the best strategic solution that can bring resiliency in the KSC. Industry 4.0 technologies (Strategic option, S1) have the potential for linking the real assets and digital technology into a cyber-physical system followed by the Social sustainable supply chain (Strategic option, S6) that focuses on benevolent and societal performance to reduce the hazards due to disruption at any level. The best strategies need to be considered by the decision makers to make the KSC more resilient in the current scenario.

This study suggests strategic measures to enhance resilience in the KSC. It will encourage the organizations to develop resilient KSC for emergency situations. This research study has few limitations. This study is limited to one case organization; hence conclusions are not general but specific to the case organization. The identification and finalization of factors is very challenging and thus the changing and disruptive environment will bring more factors to be considered in near future. Therefore, the identified 25 may increase in future. The study has undertaken experts from one country and thus the research can be extended in the other countries those have similar contextual conditions. The strategies applied for gathering information and for building knowledge depends upon the contextual factors. Hence, the strategies may differ to the other countries. The study has investigated cause-and-effect group analysis using Fuzzy DEMATEL which may be further assessed through empirical analysis.