PurposeThe study aims to develop an integrated quantitative approach and suggest a framework to assess the impact of a technological intervention on the internal process dimension of the vaccine supply chain (VSC) system for multiple administered regions.Design/methodology/approachAn evaluation index system is developed by selecting suitable performance indicators (PIs) that define the objectives of a VSC. Then multicriteria decision-making (MCDM) methods are applied to obtain pre and post-intervention relative ranks for the regions and performance scores of the objectives. A bilateral data envelopment analysis (DEA) compares significant efficiency differences between improvement and deterioration groups.FindingsThis study demonstrates that technological intervention improves the internal process dimension of a VSC for the regions under consideration. The empirical study delivers two groups of regions showing improvement or deterioration in relative performance ranking due to the technological intervention. However, the efficiency-based bilateral comparison may reveal an insignificant difference between the two groups.Practical implicationsDecision-makers associated with VSC will find the suggested model helpful in assessing the impact of technological intervention. They can easily identify specific objectives of VSC's internal process dimension, whether a particular region has observed an improvement or deterioration in its relative performance and maximize the outcome by focusing on the areas of concern for a specific region.Originality/valueThis study is the first to provide a quantitative approach that empirically determines relative performance improvement or deterioration of different regions for a set of identified VSC objectives in the context of the Indian states.
In order to tackle the Corona Virus Disease, it took a considerable amount of time for the governments to come up with effective and efficient vaccines. After the vaccines were developed, the next challenge was to supply the vaccines to various designated centers based on demographics, population distribution, and other factors. The whole system for vaccine supply played a vital role during the COVID-19 pandemic. We also saw a lot of haphazard and mismanagement in some places especially when the cases per day surged high, as people weren't prepared for such a situation. Now that we have got enough data, we can use it to optimize the vaccine supply across various Covid Vaccination Centers and be prepared for any such circumstances in the future. In this paper, we have proposed a two-step approach where considering the past supply and wastage data we performed a classification task that indicates whether doses are to get wasted at a given center. If yes, we then perform demand forecasting based on the number of administered doses so that the wastage can be reduced, and supply can be optimized.
This study proposes a quantitative evaluation model for comparative analysis of vaccine supply chain (VSC) performance while considering multiple regions input real time data that is both subjective as well as objective in nature. Performance indicators (PIs), capable of taking subjective as well as objective inputs, are constructed to capture the VSC development status and categorized under different objectives. A combination of CRITIC and VIKOR method are used to define weights of the performance criteria and rank the alternatives regions respectively, while analysing objective and subjective weights. Whereas, the spherical fuzzy extension of CRITIC and VIKOR method was used to interpret the subjective performance information provided the VSC managers at the respective region. The ranks obtained are then compared for each region to identify the regions observing conflicting narratives based on objective and subjective information. Also, further investigation of VSC in such regions can highlight reasons responsible for conflicting narratives.
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