Decision modeling for evaluating risks in pharmaceutical supply chains
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AbstractPurpose - Managing risks is becoming a highly focused activity in the health service sector. In particular, due to the complex nature of processes in the pharmaceutical industry, several risks have been associated to its supply chains. This paper therefore aims at identifying and analyzing the risks occurring in the supply chains of the pharmaceutical industry and proposing a decision model, based on the Analytical Hierarchy Process (AHP) method, for evaluating risks in pharmaceutical supply chains. Design/methodology/approach – The proposed model was developed based on the Delphi method and AHP techniques. The Delphi method helped to select the relevant risks associated to pharmaceutical supply chains. Sixteen sub-risks within four main risks were identified through an extensive review of the literature and by conducting a further investigation with experts from five pharmaceutical companies in Bangladesh. AHP contributed to the analysis of the risks and determination of their priorities. Findings – The results of the study indicated that supply related risks such as fluctuation in imports arrival, lack of information sharing, key supplier failure and non-availability of materials should be prioritised over operational, financial and demand related risks. Originality/value – This work is one of the initial contributions in the literature that focused on identifying and evaluating PSC risks in the context of Bangladesh. This research work can assist practitioners and industrial managers in the pharmaceutical industry in taking proactive action to minimize its supply chain risks. To the end, we performed a sensitivity analysis test, which gives an understanding of the stability of ranking of risks.
CitationMoktadir, M.A., Ali, S.M., Kumar Mangla, S., Sharmy, T.A., Luthra, S., Mishra, N., Garza-Reyes, J.A. (2017), “Decision modeling for evaluating risks in pharmaceutical supply chains”, Industrial Management & Data Systems
JournalIndustrial Management & Data Systems