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Table 7 Table 9 Table 8

Table 7 Table 9 Table 8

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Fig. 1. DSC Readiness Evaluation Concept.
Fig. 1. DSC Readiness Evaluation Concept.
Table 1
Table 1
Fig. 2. Analytical Hierarchy Process.
Fig. 2. Analytical Hierarchy Process.
Fig. 5. Trapezoidal membership function [71]. Fig. 4. Membership function for temperature [71].
Fig. 5. Trapezoidal membership function [71]. Fig. 4. Membership function for temperature [71].
Variables of Eq. (2).
Variables of Eq. (2).
Table 4 many ways in which we can combine the results of individual rules. Table 4 contains possible accumulation methods that are used to combine the results of individual rules. The maximum algorithm is generally used for accumulation [71].
Table 4 many ways in which we can combine the results of individual rules. Table 4 contains possible accumulation methods that are used to combine the results of individual rules. The maximum algorithm is generally used for accumulation [71].
Table 3 Accumulation methods [71].
Table 3 Accumulation methods [71].
Fig. 6. Defuzzification of the fuzzy logic [71]. Table 2
Fig. 6. Defuzzification of the fuzzy logic [71]. Table 2
Demographics summary of experts. Table 5
Demographics summary of experts. Table 5
the importance weights of the DSC readiness factor. The results of DSC readiness factors are shown in Table 8. 4.2. Knowledge base system development
the importance weights of the DSC readiness factor. The results of DSC readiness factors are shown in Table 8. 4.2. Knowledge base system development
DSC readiness factors importance weights.
DSC readiness factors importance weights.
Identified and validated DSC readiness factors. “Added by experts. Table 6
Identified and validated DSC readiness factors. “Added by experts. Table 6
DSC readiness factor value of case company. Table 11
DSC readiness factor value of case company. Table 11
Fig. 8. Membership function for DSC readiness factors and output (score).
Fig. 8. Membership function for DSC readiness factors and output (score).
organizational readiness or their current position in transforming from traditional SCs to DSCs. Finally, the focus of the study and its evaluation of readiness in an Indian manufacturing organizational context adds additional perspective. It extends previous studies that only focus on DSCs adoption evaluation and show the level of readiness of organizations for transitioning to DSCs. Our proposed KBS will help decision-makers and managers of Indian manufacturing companies in evaluating the current capabilities of adopting digitalization in their supply chain oper- ations. This will also help them plan and focus more on paying More attention to the under-performed readiness factor. Our proposed KBS allows managers and decision-makers to perform sensitivity analysis. This will help them to have visibility of their decisions related to readiness in adopting digitalization and their impact before implementing it. In addition to that, this will also allow them to take necessary corrective actions effectively and efficiently. Due to limited resources, most of the managers and decision makers pay attention to few readiness factors without considering the importance of each readiness factor. This ignored readiness factor may or may not have a significant impact on overall DSC readiness.
organizational readiness or their current position in transforming from traditional SCs to DSCs. Finally, the focus of the study and its evaluation of readiness in an Indian manufacturing organizational context adds additional perspective. It extends previous studies that only focus on DSCs adoption evaluation and show the level of readiness of organizations for transitioning to DSCs. Our proposed KBS will help decision-makers and managers of Indian manufacturing companies in evaluating the current capabilities of adopting digitalization in their supply chain oper- ations. This will also help them plan and focus more on paying More attention to the under-performed readiness factor. Our proposed KBS allows managers and decision-makers to perform sensitivity analysis. This will help them to have visibility of their decisions related to readiness in adopting digitalization and their impact before implementing it. In addition to that, this will also allow them to take necessary corrective actions effectively and efficiently. Due to limited resources, most of the managers and decision makers pay attention to few readiness factors without considering the importance of each readiness factor. This ignored readiness factor may or may not have a significant impact on overall DSC readiness.
to firstly focus more on the top-ranking factor. Thus, the results obtained from our proposed KBS make it easier for decision- makers and managers to adopt digitalization in their SC to focus more on training policy and IT network infrastructure. By focusing on these top two DSC readiness factors, an organization can easily migrate to digitalization. Sensitivity analysis also helps managers and decision-makers to see the impact of their decision on overall DSC readiness performance (score) by changing readiness factor values. Our proposed KBS helps an organization know about their readiness factors score for moving in towards DSC and identifying bottlenecks in achieving their targets. Developed KBS will also be helpful for organizations to understand their SC complexity with respect to impact on performance through which proactive decision-making will take place. DSC integration thus becomes imperative to meet these demands. Organizations need to be ready to digitalize their SCs by adopting various innovations and technologies like blockchain, artificial intelligence, and smart production systems for better traceability of their products throughout the SC. Although they are essential and beneficial for the digitalization of the SC, these innovative technologies are often complex and expensive to adopt. Develop- ing countries especially face specific challenges for the adoption of these innovative technologies for digitalization. The case orga- nization is trying to transform its traditional SC to the digital one to benefit from technological advancements over its competitors and thus perform better in an increasingly competitive market.
to firstly focus more on the top-ranking factor. Thus, the results obtained from our proposed KBS make it easier for decision- makers and managers to adopt digitalization in their SC to focus more on training policy and IT network infrastructure. By focusing on these top two DSC readiness factors, an organization can easily migrate to digitalization. Sensitivity analysis also helps managers and decision-makers to see the impact of their decision on overall DSC readiness performance (score) by changing readiness factor values. Our proposed KBS helps an organization know about their readiness factors score for moving in towards DSC and identifying bottlenecks in achieving their targets. Developed KBS will also be helpful for organizations to understand their SC complexity with respect to impact on performance through which proactive decision-making will take place. DSC integration thus becomes imperative to meet these demands. Organizations need to be ready to digitalize their SCs by adopting various innovations and technologies like blockchain, artificial intelligence, and smart production systems for better traceability of their products throughout the SC. Although they are essential and beneficial for the digitalization of the SC, these innovative technologies are often complex and expensive to adopt. Develop- ing countries especially face specific challenges for the adoption of these innovative technologies for digitalization. The case orga- nization is trying to transform its traditional SC to the digital one to benefit from technological advancements over its competitors and thus perform better in an increasingly competitive market.
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