A new approach in identifying and evaluating quality risks in the pharmaceutical industry

  • Mohammad Hajimolaali Department of Drug and Food Control, Faculty of Pharmacy, Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
  • Abbas Kebriaeezadeh Department of Pharmacoeconomics and Pharmaceutical Management, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
  • Akbar Abdollahiasl Department of Pharmacoeconomics and Pharmaceutical Management, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
  • Hossein Safari Department of Industrial Management, Faculty of Management, University of Tehran, Nasr Bridge, North Kargar St., Tehran, Iran
  • Alireza Yektadoost Department of Pharmacoeconomics and Pharmaceutical Management, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
Keywords: Pharmaceutical Industry, Quality, Risk Identification, Risk Evaluation, FMEA, Fuzzy TOPSIS

Abstract

Background: Failure Mode and Effects Analysis (FMEA) is a highly structured and systematic technique for risk analysis, commonly used in all procedures of the pharmaceutical industry, from the design of the production facility and new product development to the product release. The important part of this method is the identification of risks and determining the risk priorities. Methods: This study has been carried out in two steps: in the first step, all possible quality related risks have identified through literature review and interviews with experts of the pharmaceutical industry, subsequently these experts validated recognized risks. In the next step, the valid risks analyzed and evaluated through the combination of FMEA and Fuzzy TOPSIS methods. Results: More than 100 main quality risks were identified in the pharmaceutical manufacturing companies. These risks originate from the redundant practices and processes of the industry. Consequently, twenty of the identified risks recognized as effective risks in the industry. Human errors in production, inadequate supervision on conduction of qualification of the production machineries, improper qualification in design and implementation of the heating, ventilation, and air-conditioning (HVAC) system, lack of standard procedures for handling of the non-conforming products, inadequate supervision on conduction of cleaning validation of the production facilities, and weakness in the documentation have been recognized as the most important risks in this study. Conclusion: Risks survey results can point to the prominence of the quality assurance unit and its vital but partially neglected role in the generic pharmaceutical industry.

References

References

(1) Jaberidoost M, Nikfar S, Abdollahiasl A, Dinarvand R. Pharmaceutical supply chain risks: a systematic review. DARU Journal of Pharmaceutical Sciences. 2013;21(1):69.

(2) Spurling GK, Mansfield PR, Montgomery BD, Lexchin J, Doust J, Othman N, et al. Information from pharmaceutical companies and the quality, quantity, and cost of physicians' prescribing: a systematic review. PLoS medicine. 2010;7(10):e1000352.

(3) Henderson R, Orsenigo L, Pisano GP. The pharmaceutical industry and the revolution in molecular biology: interactions among scientific, institutional, and organizational change. Sources of industrial leadership: studies of seven industries. 1999:267-311.

(4) Das A, Kadwey P, Mishra JK, Moorkoth S. Quality Risk Management (QRM) in Pharmaceutical Industry: Tools and Methodology. International Journal of Pharmaceutical Quality Assurance. 2014;5(3):13-21.

(5) Spink JW. Analysis of counterfeit risks and development of a counterfeit product risk model: Michigan State University; 2009.

(6) Scheme PIC-o. Guide to Good Manufacturing Practice for Medicinal Products Annexes. PE; 2009.

(7) Mehregan M, Safari H. Combination of fuzzy TOPSIS and fuzzy ranking for multi attribute decision making. Artificial Intelligence and Soft Computing–ICAISC 2006. 2006:260-7

(8) Safari H, Faraji Z, Majidian S. Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. Journal of Intelligent Manufacturing. 2016;27(2):475-86.

(9) Gilchrist W. Modelling failure modes and effects analysis. International Journal of Quality & Reliability Management. 1993;10(5).

(10) Franceschini F, Galetto M. A new approach for evaluation of risk priorities of failure modes in FMEA. International Journal of Production Research. 2001;39(13):2991-3002.

(11) Carbone TA, Tippett DD. Project risk management using the project risk FMEA. Engineering Management Journal. 2004;16(4):28-35.

(12) Kutlu AC, Ekmekçioğlu M. Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications. 2012;39(1):61-7.

(13) Petrovskiy EA, Buryukin FA, Bukhtiyarov VV, Savich IV, Gagina MV. The FMEA-Risk analysis of oil and gas process facilities with hazard assessment based on fuzzy logic. Modern Applied Science. 2015;9(5):25.

(14) Chen CT. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems. 2000 Aug 16;114(1):1-9.

(15) Luukka P. Fuzzy similarity in multicriteria decision-making problem applied to supplier evaluation and selection in supply chain management. Advances in Artificial Intelligence. 2011 Jan 1;2011:6.

(16) Chu T-C. Selecting plant location via a fuzzy TOPSIS approach. The International Journal of Advanced Manufacturing Technology. 2002;20(11):859-64.

Published
2018-12-08
How to Cite
1.
Hajimolaali M, Kebriaeezadeh A, Abdollahiasl A, Safari H, Yektadoost A. A new approach in identifying and evaluating quality risks in the pharmaceutical industry. JPPM. 3(1/2):17-0.
Section
Original Article(s)