Assessing technological impact on vaccine supply chain performance
| DOI | https://doi.org/10.1108/IMDS-08-2021-0488 |
| Published date | 08 August 2022 |
| Date | 08 August 2022 |
| Pages | 1938-1955 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Pratik Rai,Sasadhar Bera,Pritee Ray |
Assessing technological impact on
vaccine supply chain performance
Pratik Rai, Sasadhar Bera and Pritee Ray
Indian Institute of Management Ranchi, Ranchi, India
Abstract
Purpose –The 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/approach –An 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.
Findings –This study demonstrates that technological intervention improves the internal process
dimension of a VSC for the re gions under consideration. The empirical study delive rs two groups of regions
showing improvement or de terioration in relative perf ormance ranking due to the techno logical intervention.
However, the efficien cy-based bilateral comparison may re veal an insignificant difference be tween the two
groups.
Practical implications –Decision-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 outcomebyfocusingontheareasofconcernforaspecific
region.
Originality/value –This 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.
Keywords Vaccine supply chain, Impact assessment, Technological intervention, Performance measurement,
MCDM, DEA
Paper type Research paper
1. Introduction
Immunization is one of the most cost-effi cient and socially effective public hea lth
interventions, significantly reducing child morbidity and mortality (Goodman et al., 2017).
Vaccines are central to any immunization program, and coordinating its flow from
manufacturing centers to the beneficiaries is achieved worldwide through a complex supply
chain that includes cold storage and transportation (Li et al., 2022). Managing vaccine
demand, distribution, wastage and stock information is crucial to the internal process
performance of any vaccine supply chain (VSC) (Chandra and Kumar, 2021;Kumar et al.,
2022;Li et al., 2022). However, several underdeveloped and developing countries cannot
attain the standards set by WHO and UNICEF for storing, handling, distribution, and stock
management of vaccines (WHO, 2018). It leads to critical bottlenecks like shortage of
inventory visibility for pharmaceuticals, improper supply planning, inadequate storage
facilities, etc. (Finkenstadt and Handfield, 2021). Integrating modern technology
infrastructure and training the conce rned officials for effective adaptation , internal
assessment and service innovation can resolve these issues (Gurnani et al., 2020;Kumar
et al., 2022;Yan, 2017). It is vital to assess the impact of these technological interventions
using relevant VSC performance indicators (PIs) to justify the efforts and investments made
while implementing them and simultaneously identify the PIs which show relatively minor
improvement for a given region.
IMDS
122,8
1938
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 12 August 2021
Revised 7 February 2022
25 June 2022
Accepted 18 July 2022
Industrial Management & Data
Systems
Vol. 122 No. 8, 2022
pp. 1938-1955
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-08-2021-0488
Several studies have reported technology-based interventions and their benefit in VSC
management in developing nations. Kumar et al. (2022) explored the Internet of Things (IoT)
application in vaccine distribution during COVID-19 in an Indian state. An indigenous eVIN
technology was developed and implemented in multiple Indian states for efficient vaccine
stock management and estimating the return on investment based on reduced costs
associated with vaccine wastage (Gurnani et al., 2022). Microneedle (or array) patch
technology adoption improved vaccine thermostability, reducing its wastage and enhancing
its availability in Benin, Mozambique, India and Australia (Bozorgi and Fahimnia, 2021;
Wedlock et al., 2019). The use of short message service (SMS) technology using mobile phones
and web-based applications established a visibility network to track the supply and stocks of
vaccines in Pakistan (Altaf et al., 2021) and Zambia (Lamanna and Byrne, 2019). The
effectiveness of a dose-based reporting tool, designed to reduce vaccine wastage at primary
healthcare facilities, was assessed by comparing the pre-and post-intervention situation in
Delhi, India (Rustagi et al., 2021). All these studies evaluated the impact of a given
technological intervention using a few PIs to capture the output in a particular specific aspect
of VSC. Most of them did not incorporate input parameters in their impact assessment. There
is a lack of studies that simultaneously co nsider input parameters for technology
implementation and pre- and post-intervention outputs for PIs in various regions to assess
improvement or deterioration concerning VSC objectives. This paper provides a holistic,
integrated quantitative approach to bridge the research gap.
We used multicriteria decision-making (MCDM) methods and bilateral data envelopment
analysis (DEA) in our proposed impact assessment model. The strength of MCDM lies in its
ability to rank decision-making units (DMUs) considering a set of criteria and the specific
weight of each criterion. Similarly, techniques like stochastic frontier analysis (SFA) and data
envelopment analysis (DEA) are used for measuring operational efficiency (Rahimpour et al.,
2020). The basic DEA models have limitations in identifying the ranking of DMUs reliably
when many of them get the maximum efficiency level of one. Hence, MCDM techniques are
more suitable as a ranking method. When comparing efficiencies of two sets of DMUs, Cooper
et al. (2007) suggested bilateral DEA that envelops each DMU of a set with the efficiency
frontier curve of the other set. This approach enabled a better efficiency comparison between
the two sets.
The impact assessment model considered the pre-and post-intervention values for VSC
PIsinmultipleregions.WeselecttheCRITICmethodtodeterminethePIs’weights. We then
apply the VIKOR MCDM technique to compute each administered region’s pre-and post-
intervention rank and determine the performance scores of the objectives. The difference
between pre- and post-ranks measured the improvement or deterioration in a relative scale
of a region, thereby dividing them into two groups. Finally, we conducted a performance
efficiency comparison using bilateral DEA to analyze the difference in efficiency between
the groups.
The proposed model provides significant insights on relative improvement in VSC
performance for each region when assessing the influence of technological intervention
across multiple regions. Also, the specific VSC objectives that are improving at a slower rate
for a region can be identified. The individual PIs belonging to the identified objective(s) can be
further investigated. Finally, bilateral DEA analysis provides an insight to the policymakers
on whether an appropriate resource allocation has been done.
The rest of the paper is organized as follows. Section 2 reviews the relevant literature to
identify research gaps and frame the objectives. Section 3 outlines the proposed research
methodology with a framework to assess the impact of a technological intervention on the
internal process dimensions of VSC. Section 4 provides an empirical examination of our
proposed approach using secondary data. Section 5 underlines the managerial and policy
implications and concludes with possible future avenues of the study.
Technological
impact on VSC
performance
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