Big data analytics in Australian pharmaceutical supply chain

Abstract

Purpose

Drawing on information processing view (IPV) theory, the objective of 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.

Citation

Ziaee, M., Shee, H.K. and Sohal, A. (2023), "Big data analytics in Australian pharmaceutical supply chain", Industrial Management & Data Systems, Vol. 123 No. 5, pp. 1310-1335. https://doi.org/10.1108/IMDS-05-2022-0309