Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management

This chapter considers the importance of decision support systems for supply chain risk management (SCRM). The first part provides an overview of the different operations research techniques and methodologies for decision making for managing risks, focusing on multiple-criteria decision analysis methods and mathematical programming. The second part is devoted to artificial intelligence (AI) techniques which have been applied in the SCRM domain to analyse data and make decisions regarding possible risks. These include Petri nets, multi-agent systems, automated reasoning and machine learning. The chapter concludes with a discussion of potential ways in which future decision support systems for SCRM can benefit from recent advances in AI research.

[1]  S. H. Ghodsypour,et al.  A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply Chain , 2009 .

[2]  Samir Dani,et al.  Supply Chain Risk Management: Present and Future Scope , 2012 .

[3]  Zhaohan Sheng,et al.  Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system , 2009, Expert Syst. Appl..

[4]  Amanda J. Schmitt,et al.  OR/MS models for supply chain disruptions: a review , 2014 .

[5]  Joaquín A. Pacheco,et al.  Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty , 2014 .

[6]  Kannan Govindan,et al.  Supply chain network design under uncertainty: A comprehensive review and future research directions , 2017, Eur. J. Oper. Res..

[7]  Kun Chang Lee,et al.  MACE-SCM: A multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties , 2007, Expert Syst. Appl..

[8]  R. Tavakkoli-Moghaddam,et al.  Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty , 2016 .

[9]  MengChu Zhou,et al.  Modelling risks in supply chains using Petri net approach , 2006 .

[10]  Xinlei Wang,et al.  Managing Supply Uncertainties Through Bayesian Information Update , 2010, IEEE Transactions on Automation Science and Engineering.

[11]  Mir Saman Pishvaee,et al.  A robust optimization approach to closed-loop supply chain network design under uncertainty , 2011 .

[12]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[13]  Antonio Espuña Camarasa,et al.  An agent-based approach for supply chain retrofitting under uncertainty , 2007, Comput. Chem. Eng..

[14]  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..

[15]  S. Chopra,et al.  Reducing the Risk of Supply Chain Disruptions , 2014 .

[16]  Stefan Nickel,et al.  A multi-stage stochastic supply network design problem with financial decisions and risk management , 2012 .

[17]  Abhijeet Ghadge,et al.  A Systems Approach for Modelling Supply Chain Risks , 2012 .

[18]  Seyed Ali Torabi,et al.  Resilient supplier selection and order allocation under operational and disruption risks , 2015 .

[19]  Andrew Lim,et al.  Reliable logistics networks design with facility disruptions , 2011 .

[20]  David L. Olson,et al.  Supply chain risk, simulation, and vendor selection , 2008 .

[21]  M. O'Sullivan,et al.  Agribusiness supply chain risk management: A review of quantitative decision models , 2017, Omega.

[22]  Sarah M. Ryan,et al.  Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition , 2016, Eur. J. Oper. Res..

[23]  Vipul Jain,et al.  Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS , 2013 .

[24]  Robert J. Vanderbei,et al.  Robust Optimization of Large-Scale Systems , 1995, Oper. Res..

[25]  Jennifer Blackhurst,et al.  Methodology for supply chain disruption analysis , 2007 .

[26]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[27]  Myles D. Garvey,et al.  An analytical framework for supply network risk propagation: A Bayesian network approach , 2015, Eur. J. Oper. Res..

[28]  Lian Qi,et al.  The Effect of Supply Disruptions on Supply Chain Design Decisions , 2010, Transp. Sci..

[29]  Tiaojun Xiao,et al.  Price and service competition of supply chains with risk-averse retailers under demand uncertainty , 2008 .

[30]  Richard Colbaugh,et al.  Improving supply chain security using big data , 2013, 2013 IEEE International Conference on Intelligence and Security Informatics.

[31]  Thomas J. Kull,et al.  The risk of second-tier supplier failures in serial supply chains: Implications for order policies and distributor autonomy , 2008, Eur. J. Oper. Res..

[32]  A. Land,et al.  An Automatic Method for Solving Discrete Programming Problems , 1960, 50 Years of Integer Programming.

[33]  A. Ruiz-Torres,et al.  The optimal number of suppliers considering the costs of individual supplier failures , 2007 .

[34]  Xin Yu,et al.  A case based reasoning approach on supplier selection in petroleum enterprises , 2011, Expert Syst. Appl..

[35]  Agostino G. Bruzzone,et al.  AI and simulation-based techniques for the assessment of supply chain logistic performance , 2003, 36th Annual Simulation Symposium, 2003..

[36]  Alan Matthews.  Brexit Impacts on Irish Agri‐food Exports to the UK , 2017 .

[37]  Maurizio Bevilacqua,et al.  Supply chain modelling and managing, using timed coloured Petri nets: a case study , 2012 .

[38]  C. A. Petri.  Communication with automata , 1966 .

[39]  M. Giannakis,et al.  A multi-agent based framework for supply chain risk management , 2011 .

[40]  Der-Horng Lee,et al.  Dynamic network design for reverse logistics operations under uncertainty , 2009 .

[41]  Keely L. Croxton,et al.  Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool , 2013 .

[42]  Alessandro Brun,et al.  Value and risk assessment of supply chain management improvement projects , 2006 .

[43]  J. F. Benders.  Partitioning procedures for solving mixed-variables programming problems , 1962 .

[44]  Ali Azadeh,et al.  A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: Simulation analysis , 2010, Expert Syst. Appl..

[45]  Harvey J. Everett.  Generalized Lagrange Multiplier Method for Solving Problems of Optimum Allocation of Resources , 1963 .

[46]  Seyed Hessameddin Zegordi,et al.  Developing a supply chain disruption analysis model: Application of colored Petri-nets , 2012, Expert Syst. Appl..

[47]  Tommaso Rossi,et al.  A formal method for analysing and assessing operational risk in supply chains , 2012 .

[48]  M. Quaddus,et al.  Supply chain risk management (SCRM): a case study on the automotive and electronic industries in Brazil , 2009 .

[49]  V. Tummala,et al.  Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company , 2008 .

[50]  Yankui Liu,et al.  Robust optimization of supply chain network design in fuzzy decision system , 2016, J. Intell. Manuf..

[51]  Putu Dana Karningsih,et al.  SCRIS: A knowledge‐based system tool for assisting manufacturing organizations in identifying supply chain risks , 2012 .

[52]  Ata Allah Taleizadeh,et al.  Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: A harmony search algorithm , 2011, Appl. Math. Comput..

[53]  Abhijeet Ghadge,et al.  Using Risk Sharing Contracts for Supply Chain Risk Mitigation: A Buyer-Supplier Power and Dependence Perspective , 2016, Comput. Ind. Eng..

[54]  Barbara Thönssen,et al.  Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions , 2012, KMIS.

[55]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[56]  P. Miranda,et al.  Valid inequalities for Lagrangian relaxation in an inventory location problem with stochastic capacity , 2008 .

[57]  Marc Goetschalckx,et al.  A stochastic programming approach for supply chain network design under uncertainty , 2004, Eur. J. Oper. Res..

[58]  Zuo-Jun Max Shen,et al.  Risk Mitigation Benefit from Backup Suppliers in the Presence of the Horizontal Fairness Concern , 2015, Decis. Sci..

[59]  William Ho,et al.  Supply chain risk management: a literature review , 2015 .

[60]  David L. Olson,et al.  Trade‐offs in Supply Chain System Risk Mitigation , 2014 .

[61]  S. Vachon,et al.  Mitigation, Avoidance, or Acceptance? Managing Supplier Sustainability Risk , 2016 .