Big data analytics in Australian pharmaceutical supply chain
| DOI | https://doi.org/10.1108/IMDS-05-2022-0309 |
| Published date | 28 February 2023 |
| Date | 28 February 2023 |
| Pages | 1310-1335 |
| 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 | Maryam Ziaee,Himanshu Kumar Shee,Amrik Sohal |
Big data analytics in Australian
pharmaceutical supply chain
Maryam Ziaee and Himanshu Kumar Shee
Victoria University Business School, Melbourne, Australia, and
Amrik Sohal
Department of Management, Faculty of Business and Economics,
Monash University, Melbourne, Australia
Abstract
Purpose –Drawing on information processing view (IPV) theory, the objectiveof this study is to explore big
data analytics (BDA) in pharmaceutical supply chain (PSC) for better business intelligence. Supply chain
operations reference (SCOR) model is used to identify and discuss the likely benefits of BDA adoption in five
processes: plan, source, make, deliver and return.
Design/methodology/approach –Semi-structured interviews with managers in a triad comprising
pharmaceutical manufacturers, wholesalers/distributors and public hospital pharmacies were undertaken.
NVivo software was used for thematic data analysis.
Findings –The findings revealed that BDA capability would be more practical and helpful in planning,
delivery and return processes within PSC. Sourcing and making processes are perceived to be less beneficial.
Practical implications –The study informs managers about the strategic role of BDA capabilities in SCOR
processes for improved business intelligence.
Originality/value –Adoption of BDA in SCOR processes within PSC is a step towards resolving the
challenges of drug shortages, counterfeiting and inventory optimisation through timely decision. Despite its
innumerable benefits of BDA, Australian PSC is far behind in BDA investment. The study advances the IPV
theory by illustrating and strengthening the fact that data sharing and analytics can generate real-time
business intelligence helping in better health care support through BDA-enabled PSC.
Keywords Big data analytics, SCOR model, Pharmaceutical supply chain, Qualitative, Australia
Paper type Research paper
1. Introduction
A healthcare system is contingent upon an efficient and effective pharmaceutical supply
chain (PSC) which ensures the provision and supply of life saving drugs, equipment and
healthcare products with minimal delays, shortages and error (Mehralian et al., 2015).
Referring to Nguyen et al. (2022) and Silva and Mattos (2019), we define PSC as a socio-
technical system comprising suppliers, manufacturers, wholesalers and pharmacies who are
involved in producing, storing, distributing and dispensing medicinal drugs to end
consumers (patients) by timely sharing of information about right drugs for right patients.
Silva and Mattos (2019) assert that PSCs are often affected by medicine counterfeiting and
theft, thereby not only compromising profits and reputations of all partners, but it puts the
public health and consumer safety at risk. Drug shortages in Australia are common due to
limited, non-alternative or discontinuation of medicinal drugs posing serious threats to
healthcare which calls for an urgent improvement in medicine supply (SHPA-b, 2017).
Nguyen et al. (2022) state that “drug shortages are a major concern for hospitals worldwide,
—frequent overstocks and high inventory turns are reported”(p. 1). We believe this is partly
due to the unavailability of timely information about critical drug requirement, neither there
is a way of notifying the hospitals and pharmacies ahead of time (SHPA-c, 2017). But there are
IMDS
123,5
1310
The authors thankfully acknowledge their sincere thanks to the editor in chief and anonymous
reviewers for offering valuable feedback that improved the paper quality.
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 16 May 2022
Revised 22 August 2022
25 August 2022
7 September 2022
Accepted 1 November 2022
Industrial Management & Data
Systems
Vol. 123 No. 5, 2023
pp. 1310-1335
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-05-2022-0309
technological issues that remain unresolved in managing the information flow in PSC
(Yousefi and Alibabaei, 2015).
The PSC partners are generally engaged in drugs, information and funds flow (Saha and
Jha, 2018). Ideally, the right medicinal drugs must reach the right patients, at right time, in
good condition and right price to save lives (Mehralian et al., 2015). But there are challenges
such as inadequate coordination and lack of information visibility (e.g. in-transit inventory,
delay) (Privett and Gonsalvez, 2014;Saha and Jha, 2018) and non-availability of right
optimisation tools to track these medicines (Elmuti et al., 2013;Wang et al., 2016b). As data
insights amid uncertainties and supply complexities are likely to help PSC partners in
mobilising the right drugs and equipment to right pharmacy, big data analytics (BDA) has
emerged as a plausible solution helping in timely decision making (Arunachalam et al., 2018;
Wang et al., 2016a,b;Zhong et al., 2016).
Arunachalam et al. (2018) have put BDA as the latest form of business intelligence (BI 3.0).
Wamba et al. (2015) have provided variety of BDA definitions that one can refer. But in context of
this study, we define BDA (also known as SCA in Arunachalam et al., 2018), as an organisational
capability that enables high volume, variety and veracity (quality) of supply chain data
(structured and unstructured) to be generated, captured and analysed for value creation resulting
in timely business intelligence. Although the healthcare sector provides one of the largest and
fastest-growing datasets which include clinical, pharmaceutical and supply chain data (Kambatla
et al., 2014), BDA adoption in PSC is relatively scarce (Privett and Gonsalvez, 2014). Research on
BDA in the healthcare sector so far has focused mainly on BDA applications in clinical research to
reduce hospitals’costs,improvepatientscarequality and support clinical decisions (Ebenezer
and Durga, 2015;Maheshwari et al., 2021;Raghupathi and Raghupathi, 2014). Also, BDA in the
pharmaceutical industry is used for identification and efficacy assessment of new drugs,
improvement in clinical trials and treatment methods, enhanced patient satisfaction,
development of new therapies and disease prevention (Tormay, 2015). Thus, BDA
applications in the pharmaceutical industry are mostly narrowed down to intra-organisation
operations and the optimal data-centric decision-making among partners in a PSC context is
underexplored. Unless all partners coordinate, share and analyse data, it is hard to get an insight
of the current issues in an uncertain and complex business environment. BDA is perceived to
decrease uncertainties for each partner by the speed of decision-making in any disruption.
Further, PSC is seen to have inadequate capability to deal with higher order analytical tools
(Wang et al., 2016a,b). Inamdar et al. (2020) find BDA having the lowest applications in the
healthcare sector (so as the PSC) indicating a need for more research to explore the challenges at
the ground level. As real-time supply chain process integration has been an inherent bottleneck
for organisations (Shee et al., 2018), supply chain operations reference (SCOR) model
(SupplyChainCouncil, 2017) is used in this study to explore how BDA adoption within SCOR
processes can integrate information flow as the driver of success (Kamble and Gunasekaran,
2020) amidst volatile, uncertain, complex and ambiguous (VUCA) business environment. SCOR
model, developed and endorsed by the Supply Chain Council, divides supply chain activities into
five major processes: plan, source, make, deliver and return (APICS, 2021). As stated by the
Association for Supply Chain Management (ASCM, 2021), the planning process refers to
collecting information and resources, anticipating demand and potential gaps and identifying
appropriate resources to remedy the gaps. The sourcing process involves ordering and receiving
the required goods and services from suppliers. The making process refers to the activities
involved in converting raw materials into products and/or services. The delivery process is
attributed to activities involved in the fulfilment and shipment of customers’orders. The return
process refersto the “reverse flow of goods”includingactivities involvedin the decisionto return
some products and the arrangement of dispatching the returned goods to suppliers. SCOR
framework is considered as a strategic decision-making tool that helps in visibility and improved
efficiency with measurable and actionable outcomes (Ntabe et al., 2015).
PSC big data
analytics
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