DYNAMIC DISRUPTIONS AND THE PHARMACUETICAL SUPPLY CHAIN- A SYSTEMATIC REVIEW OF LITERATURE
Author(s)
Yaroson EV
Univeristy of Bradford, BRADFORD, UK
Objective of the Study Supply chain disruptions have been documented to deter the financial as well as operational performance of the supply chain. More detrimental are the impacts of dynamic disruptions where their mode of operations, causes and impacts differ with every situation. The aim of this study therefore, is to examine the various forms of dynamic disruptions inherent to the pharmaceutical supply chain using systematic literature review. Methodology: The study adopts a systematic review of literature on dynamic disruptions in the pharmaceutical supply chain. In seeking to achieve the research aim, the study collects data from google scholar, EBSCOhost and Scopus. 380 peer reviewed papers were identified and content analysis were used to arrive and the study’s findings . Research Implications: This study aids in defining dynamic disruptions in the pharmaceutical supply chain as well as provide a wide array of current realities, causes, impacts and managing mechanisms leading to the development of a conceptual framework for. Findings: The study identified drug shortages, theft and counterfeit medicines as dynamic disruptions within the pharmaceutical supply chain using five key features. Several realities, impact and managing mechanisms were also highlighted within extant literature. However, defining drug shortages is extremely difficult and as such developing resilience strategies for this form of disruption remains a herculean task. This findings are particularly relevant to both managers and academics as it provides a bedrock for tackling dynamic disruptions in the pharmaceutical supply chain. Key words: Counterfeiting, Drug Shortages, Pharmaceutical Supply Chain
Conference/Value in Health Info
2018-11, ISPOR Europe 2018, Barcelona, Spain
Value in Health, Vol. 21, S3 (October 2018)
Code
PCP66
Topic
Methodological & Statistical Research
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
Multiple Diseases