A group risk assessment approach for the selection of pharmaceutical product shipping lanes

Abstract This paper provides a risk assessment framework to select shipping lanes for pharmaceutical products. The main categories of risks are determined through an algorithm based on yes/no decisions. Then, according to the risk categories, a Failure Mode and Effects Analysis (FMEA) table is proposed for risk assessment of pharmaceutical product shipments and logistics. The evaluations are based on Intuitionistic Fuzzy Numbers (IFNs) to be able to account for the uncertainty in the experts’ judgments. By using an intuitionistic fuzzy hybrid TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, the evaluated risks of each shipment lane can be scored and prioritized. The proposed TOPSIS-based FMEA approach in the intuitionistic fuzzy environment provides an opportunity to aggregate the risk assessments of different experts in a practically efficient way. Different from the earlier literature, we address risk identification and risk assessment under uncertainty as the two key challenges in group decision making. Our method further provides a framework that integrates the categorization and evaluation of risks with subsequent decision making. A case study of shipping lane selection in the context of air cargo distribution of pharmaceutical products demonstrates a potential implementation of the proposed approach.

[1]  Anna Nagurney,et al.  Pharmaceutical Supply Chain Networks with Outsourcing Under Price and Quality Competition , 2013, Int. Trans. Oper. Res..

[2]  Mir Saman Pishvaee,et al.  An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain , 2014 .

[3]  Hui Zhao,et al.  The Unexpected Impact of Information Sharing on US Pharmaceutical Supply Chains , 2011, Interfaces.

[4]  Habib Chabchoub,et al.  A combined approach for supply chain risk management: description and application to a real hospital pharmaceutical case study , 2014 .

[5]  Mary E. Helander,et al.  Supply Chain Scenario Modeler: A Holistic Executive Decision Support Solution , 2014, Interfaces.

[6]  Mehmet Soysal,et al.  A Simulation Based Restricted Dynamic Programming approach for the Green Time Dependent Vehicle Routing Problem , 2017, Comput. Oper. Res..

[7]  Hui Zhao,et al.  Fee-for-Service Contracts in Pharmaceutical Distribution Supply Chains: Design, Analysis, and Management , 2012, Manuf. Serv. Oper. Manag..

[8]  A. Nagurney,et al.  A Supply Chain Network Game Theory Model with Product Differentiation, Outsourcing of Production and Distribution, and Quality and Price Competition , 2015 .

[9]  Zeshui Xu,et al.  An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation , 2018, Inf. Sci..

[10]  Peter Kipruto Chemweno,et al.  Development of a risk assessment selection methodology for asset maintenance decision making: An analytic network process (ANP) approach , 2015 .

[11]  Jian-Xin You,et al.  Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach , 2015, Soft Comput..

[12]  T. Papadopoulos,et al.  Supply chain sustainability: A risk management approach , 2016 .

[13]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[14]  Ehsan Shekarian.  A novel application of the VIKOR method for investigating the effect of education on housing choice , 2015 .

[15]  Hu-Chen Liu,et al.  An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty , 2016 .

[16]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[17]  Chee Peng Lim,et al.  Clustering and visualization of failure modes using an evolving tree , 2015, Expert Syst. Appl..

[18]  Kyungmee O. Kim,et al.  General model for the risk priority number in failure mode and effects analysis , 2018, Reliab. Eng. Syst. Saf..

[19]  Jonathan E. Helm,et al.  Distribution of Medication Considering Information, Transshipment, and Clustering: Malaria in Malawi , 2018 .

[20]  Athakorn Kengpol,et al.  The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study , 2016 .

[21]  Adam J. Fleischhacker,et al.  Positioning Inventory in Clinical Trial Supply Chains , 2015 .

[22]  Gülçin Büyüközkan,et al.  A new combined IF-DEMATEL and IF-ANP approach for CRM partner evaluation , 2017 .

[23]  Ettore Settanni,et al.  Pharmaceutical supply chain models: A synthesis from a systems view of operations research , 2017 .

[24]  Luiz Cesar Ribeiro Carpinetti,et al.  A group decision approach for supplier categorization based on hesitant fuzzy and ELECTRE TRI , 2018, International Journal of Production Economics.

[25]  Rommert Dekker,et al.  Literature review: The vaccine supply chain , 2017, Eur. J. Oper. Res..

[26]  Nan Yang,et al.  PBM Competition in Pharmaceutical Supply Chain: Formulary Design and Drug Pricing , 2015, Manuf. Serv. Oper. Manag..

[27]  Ping Li,et al.  Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator , 2014, Int. J. Syst. Sci..

[28]  Ming Liu,et al.  A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating , 2016, J. Oper. Res. Soc..

[29]  Diyar Akay,et al.  A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method , 2009, Expert Syst. Appl..

[30]  Thomas L. Saaty,et al.  On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process , 2007, Math. Comput. Model..

[31]  Selcuk Cebi,et al.  A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system , 2018 .

[32]  Ching-Lai Hwang,et al.  Methods for Multiple Attribute Decision Making , 1981 .

[33]  Hui Li,et al.  Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory , 2015, Int. J. Comput. Integr. Manuf..

[34]  Hing Kai Chan,et al.  When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process? , 2019, Decis. Support Syst..

[35]  Zhaojun Li,et al.  New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method , 2017, Reliab. Eng. Syst. Saf..

[36]  Janusz Kacprzyk,et al.  Atanassov's Intuitionistic Fuzzy Sets as a Promising Tool for Extended Fuzzy Decision Making Models , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[37]  John Quigley,et al.  Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies , 2017, Eur. J. Oper. Res..

[38]  Zeshui Xu,et al.  An overview of methods for determining OWA weights , 2005, Int. J. Intell. Syst..

[39]  Yi-Kuei Lin,et al.  Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS , 2012, Eur. J. Oper. Res..

[40]  Kèyù Zhü,et al.  Fuzzy analytic hierarchy process: Fallacy of the popular methods , 2014, Eur. J. Oper. Res..

[41]  Amir H. Masoumi,et al.  A Supply Chain Generalized Network Oligopoly Model for Pharmaceuticals under Brand Differentiation and Perishability , 2012 .

[42]  Jaafar M. H. Elmirghani,et al.  Telematics system for the intelligent transport and distribution of medicines , 2013 .

[43]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[44]  Xu Chen,et al.  Effects of price cap regulation on the pharmaceutical supply chain , 2018, Journal of Business Research.

[45]  David B. Dunson,et al.  Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics , 2014, Oper. Res..

[46]  Jian-Bo Yang,et al.  Failure mode and effects analysis using a group-based evidential reasoning approach , 2009, Comput. Oper. Res..

[47]  Changhyun Kwon,et al.  Integrated supply chain management for perishable products: Dynamics and oligopolistic competition perspectives with application to pharmaceuticals , 2016 .

[48]  Zeshui Xu,et al.  Some geometric aggregation operators based on intuitionistic fuzzy sets , 2006, Int. J. Gen. Syst..

[49]  Ahmed Saif,et al.  Cold supply chain design with environmental considerations: A simulation-optimization approach , 2016, Eur. J. Oper. Res..

[50]  James R. Bradley,et al.  Failure Modes and Effects Analysis: An Evaluation of Group versus Individual Performance , 2013 .

[51]  Hu-Chen Liu,et al.  Failure Mode and Effect Analysis Using Cloud Model Theory and PROMETHEE Method , 2017, IEEE Transactions on Reliability.

[52]  T. Saaty.  There is no mathematical validity for using fuzzy number crunching in the analytic hierarchy process , 2006 .

[53]  Ming-Tsung Wu,et al.  A modified failure mode and effects analysis method for supplier selection problems in the supply chain risk environment: A case study , 2013, Comput. Ind. Eng..

[54]  Ezutah Udoncy Olugu,et al.  An Integrated Fuzzy VIKOR Method for Performance Management in Healthcare , 2017 .

[55]  Hu-Chen Liu,et al.  An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment , 2017, International Journal of Production Economics.

[56]  Ualison Rébula de Oliveira,et al.  Failure mapping for occupational safety management in the film and television industry , 2018, International Journal of Production Economics.

[57]  Chee Peng Lim,et al.  A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry , 2015, Neural Computing and Applications.

[58]  Ann Vereecke,et al.  Reservation and allocation policies for influenza vaccines , 2012, Eur. J. Oper. Res..

[59]  Natalie Privett,et al.  The top ten global health supply chain issues: Perspectives from the field , 2014 .

[60]  Kai Meng Tay,et al.  Application of self-organizing map to failure modes and effects analysis methodology , 2017, Neurocomputing.

[61]  Arash Alizadeh,et al.  HSE risk prioritization using robust DEA-FMEA approach with undesirable outputs: A study of automotive parts industry in Iran , 2018 .

[62]  Bimal Nepal,et al.  Bayesian belief network-based framework for sourcing risk analysis during supplier selection , 2015 .

[63]  Tsu-Ming Yeh,et al.  Fuzzy-based risk priority number in FMEA for semiconductor wafer processes , 2014 .

[64]  Ali Gharbi,et al.  Joint production and subcontracting planning of unreliable multi-facility multi-product production systems , 2015 .

[65]  Christelle Guéret,et al.  Joint-optimization of inventory policies on a multi-product multi-echelon pharmaceutical system with batching and ordering constraints , 2013, Eur. J. Oper. Res..