Ziaee, Maryam (2019) Big Data Analytics Adoption in Pharmaceutical Supply Chain Management and its Impact on SCOR Processes: A Qualitative Study of the Australian Pharmaceutical Industry. PhD thesis, Victoria University.

Abstract

Big Data Analytics (BDA) in supply chain management has recently drawn the attention of academics and practitioners. Big data refers to a massive amount of data from different sources, in different formats, generated at high speed through voluminous of transactions in business environments as well as within supply chain networks. Traditional statistical tools and techniques find it difficult to analyse this massive data. Modern applications, in the form of BDA, assist organisations to capture, store, and analyse data specifically in the field of supply chain. Although there is an increasing trend among academics to investigate the potentials of BDA in supply chain, there is a paucity of research on BDA in the pharmaceutical supply chain context. In this research, the Australian pharmaceutical supply chain was selected as the case study. This industry is highly significant since the right medicine must reach the right patients, at the right time, in right quantity, in good condition, and at the right price to save lives. However, drug shortages remain a substantial problem for hospitals across Australia with implications on patient care, staff resourcing, and expenditure. Furthermore, a massive volume and variety of data is generated at fast speed from multiple sources (inter-organisational and intra-organisational data) in pharmaceutical supply chain, which needs to be captured and analysed to benefit operational decisions at every stage of supply chain processes. As the pharmaceutical industry lags behind other industries in using BDA, it raises the question of whether the adoption of BDA can improve transparency among pharmaceutical supply chain by enabling the partners to make informed decisions across their operational activities. This study, therefore, aims to explore the determinants of BDA adoption in Australian pharmaceutical supply chain. It also examines the potential impacts of BDA adoption in various processes using the Supply Chain Operations Reference model (SCOR model: plan, source, make, deliver, and return). The current study draws upon the Technology-Organisation-Environment (TOE) framework which is the most commonly used research framework that underpins the technology adoption studies. An exploratory qualitative approach was adopted to analyse data collected through interviews. Twenty semi-structured interviews with top managers in 15 organisations comprising of five pharmaceutical manufacturers, five wholesalers/distributors, and five public hospital pharmacies were undertaken to investigate their views on BDA adoption. Therefore, the supply chain is considered as the unit of analysis in this study. The interviews were transcribed and imported into NVivo software for thematic and cross-case analysis. The thematic results identified several technological, organisational, and environmental factors that could motivate the Australian pharmaceutical supply chain entities to adopt BDA. The findings revealed that BDA would be more practical and helpful in the planning process, followed by delivery and return. However, no significant benefits of BDA were perceived for the sourcing and making processes of medicines. This study contributes to the theory and practice. As business-related data gains momentum in business intelligence, this research explores the BDA potential in improving the supply chain processes of the pharmaceutical supply chain rather than focusing only on a single entity. Some earlier studies demonstrate BDA adoption in context of clinical healthcare; however, this study is the first of its kind that explores the determinants and benefits of BDA adoption in the Australian pharmaceutical supply chain comprising of hospital pharmacies, wholesalers/distributors, and manufacturers. Furthermore, this study also offers practical and managerial implications by providing top managers with a picture of the technological, organisational and environmental factors that may influence their BDA adoption decision. Moreover, this research enhances managers’ insight into the potentials of BDA at every stage of supply chain processes such as plan, source, make, deliver, and return and helps to improve decision-making in their supply chain operations. The findings will turn the rhetoric of data-driven decision into a reality where the managers may opt for analytics for improved decision-making in the supply chain processes.

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