Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework

Artificial Intelligence (AI) offers a promising solution for building and promoting more resilient supply chains. However, the literature is highly dispersed regarding the application of AI in supp...

[1]  Dara G. Schniederjans,et al.  Supply chain digitisation trends: An integration of knowledge management , 2020 .

[2]  Duncan McFarlane,et al.  Extracting supply chain maps from news articles using deep neural networks , 2020, Int. J. Prod. Res..

[3]  Manoj Kumar Tiwari,et al.  Bayesian network modelling for supply chain risk propagation , 2018, Int. J. Prod. Res..

[4]  Ramez Kian,et al.  A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach , 2020, Omega.

[5]  D. Kumar,et al.  Multicriterion decision making in irrigation planning , 1999 .

[6]  Edmundas Kazimieras Zavadskas,et al.  Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS) , 2015, Informatica.

[7]  P. S. Scott,et al.  Building routines for non-routine events: supply chain resilience learning mechanisms and their antecedents , 2019, Supply Chain Management: An International Journal.

[8]  S. Wamba,et al.  Are we preparing for a good AI society? A bibliometric review and research agenda , 2020, Technological Forecasting and Social Change.

[9]  Angappa Gunasekaran,et al.  Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view , 2018, Production Planning & Control.

[10]  Amine Belhadi,et al.  Prioritizing the solutions of lean implementation in SMEs to overcome its barriers , 2017 .

[11]  Dmitry Ivanov,et al.  Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review , 2020, Expert Systems with Applications.

[12]  Rita Gamberini,et al.  Machine learning for multi-criteria inventory classification applied to intermittent demand , 2018, Production Planning & Control.

[13]  Mohammad Hossein Jarrahi,et al.  Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making , 2018, Business Horizons.

[14]  M. Christopher,et al.  Building the Resilient Supply Chain , 2004 .

[15]  Bo Wang,et al.  Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry , 2019, Int. J. Inf. Manag..

[16]  Davood Golmohammadi,et al.  Neural network application for fuzzy multi-criteria decision making problems , 2011 .

[17]  Mahour Mellat Parast,et al.  The impact of entrepreneurship orientation on project performance: A machine learning approach , 2020, International Journal of Production Economics.

[18]  R. Rajesh,et al.  A grey-layered ANP based decision support model for analyzing strategies of resilience in electronic supply chains , 2020, Eng. Appl. Artif. Intell..

[19]  Amr Arisha,et al.  Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review , 2017 .

[20]  Sébastien Thomassey,et al.  Sales forecasts in clothing industry: The key success factor of the supply chain management , 2010 .

[21]  Qiang Zhang,et al.  Combining MPC and integer operators for capacity adjustment in job-shop systems with RMTs , 2018, Int. J. Prod. Res..

[22]  Sachin S. Kamble,et al.  The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa , 2020 .

[23]  J. Busby,et al.  Supply chain resilience in a developing country context: a case study on the interconnectedness of threats, strategies and outcomes , 2017 .

[24]  Sachin S. Kamble,et al.  Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries , 2020, Technological Forecasting and Social Change.

[25]  Soheil Ganjefar,et al.  Variable structure fuzzy wavelet neural network controller for complex nonlinear systems , 2018, Appl. Soft Comput..

[26]  Enzo Morosini Frazzon,et al.  A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing , 2019, Int. J. Inf. Manag..

[27]  Bernd Hellingrath,et al.  Applications of Artificial Intelligence in Supply Chain Management and Logistics: Focusing Onto Recognition for Supply Chain Execution , 2019, The Art of Structuring.

[28]  Jurgita Antucheviciene,et al.  A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations , 2017, Comput. Ind. Eng..

[29]  Mohammad Mahdi Paydar,et al.  A hybrid genetic algorithm for integrating virtual cellular manufacturing with supply chain management considering new product development , 2020, Comput. Ind. Eng..

[30]  Masoud Mehdizadeh,et al.  Integrating ABC analysis and rough set theory to control the inventories of distributor in the supply chain of auto spare parts , 2020, Comput. Ind. Eng..

[31]  M. Parast,et al.  A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research , 2016 .

[32]  Mihalis Giannakis,et al.  A multi-agent based system with big data processing for enhanced supply chain agility , 2016, J. Enterp. Inf. Manag..

[33]  Brian Fynes,et al.  Mitigation Processes – Antecedents for Building supply chain resilience , 2014 .

[34]  Susana Carla Farias Pereira,et al.  Supply chain resilience: the whole is not the sum of the parts , 2019, International Journal of Operations & Production Management.

[35]  Jennifer Blackhurst,et al.  Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss? , 2020, Int. J. Prod. Res..

[36]  Charu Chandra,et al.  Supply chain resilience: model development and empirical analysis , 2017, Int. J. Prod. Res..

[37]  Hêriş Golpîra,et al.  Optimal integration of the facility location problem into the multi-project multi-supplier multi-resource Construction Supply Chain network design under the vendor managed inventory strategy , 2020, Expert Syst. Appl..

[38]  Hao Wang,et al.  A risk evaluation method to prioritize failure modes based on failure data and a combination of fuzzy sets theory and grey theory , 2019, Eng. Appl. Artif. Intell..

[39]  Angappa Gunasekaran,et al.  Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations , 2020, International Journal of Production Economics.

[40]  Xin Song,et al.  A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization , 2019, Math. Comput. Simul..

[41]  Borja Ponte,et al.  Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments , 2018, Int. J. Prod. Res..

[42]  Omar Khadeer Hussain,et al.  PERCEPTUS: Predictive complex event processing and reasoning for IoT-enabled supply chain , 2019, Knowl. Based Syst..

[43]  Tianyang Zhang,et al.  Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0 , 2019, Int. J. Prod. Res..

[44]  Angappa Gunasekaran,et al.  A systematic literature review on machine learning applications for sustainable agriculture supply chain performance , 2020, Comput. Oper. Res..

[45]  Sajjad Shokouhyar,et al.  Implementing a fuzzy expert system for ensuring information technology supply chain , 2019, Expert Syst. J. Knowl. Eng..

[46]  Hokey Min,et al.  Artificial intelligence in supply chain management: theory and applications , 2010 .

[47]  Jing Li,et al.  Sustainability evaluation via variable precision rough set approach: A photovoltaic module supplier case study , 2018, Journal of Cleaner Production.

[48]  S. Z. Mohd Hashim,et al.  Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis , 2019, Comput. Biol. Medicine.

[49]  Francisco Rodrigues Lima Junior,et al.  An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics , 2020, Comput. Ind. Eng..

[50]  Hamed Fazlollahtabar,et al.  Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry , 2020, Comput. Ind. Eng..

[51]  Alain Bernard,et al.  Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory , 2016, Expert Syst. Appl..

[52]  Sachin S. Kamble,et al.  Infectious Waste Management Strategy during COVID-19 Pandemic in Africa: an Integrated Decision-Making Framework for Selecting Sustainable Technologies , 2020, Environmental Management.

[53]  Chen-Fu Chien,et al.  Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor , 2020, Int. J. Prod. Res..

[54]  Tsan-Ming Choi,et al.  Big Data Analytics in Operations Management , 2018 .

[55]  Melih Inal,et al.  Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems , 2014, Appl. Soft Comput..

[56]  Mark Goh,et al.  Fuzzy belief propagation in constrained Bayesian networks with application to maintenance decisions , 2020 .

[57]  H. Y. Lam,et al.  A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment , 2019, Int. J. Prod. Res..

[58]  Mustapha Nourelfath,et al.  A hybrid scenario cluster decomposition algorithm for supply chain tactical planning under uncertainty , 2016, Eur. J. Oper. Res..

[59]  Surajit Bag,et al.  Role of artificial intelligence in operations environment: a review and bibliometric analysis , 2020, The TQM Journal.

[60]  Eleonora Bottani,et al.  Modelling wholesale distribution operations: an artificial intelligence framework , 2019, Ind. Manag. Data Syst..

[61]  P. Ball,et al.  Local food supply chain resilience to constitutional change: the Brexit effect , 2019, International Journal of Operations & Production Management.

[62]  Jamal Salahaldeen Majeed Alneamy,et al.  Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis , 2019, Comput. Biol. Medicine.

[63]  Alexandre Dolgui,et al.  The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics , 2018, Int. J. Prod. Res..

[64]  Manoj Kumar Tiwari,et al.  Optimising integrated inventory policy for perishable items in a multi-stage supply chain , 2018, Int. J. Prod. Res..

[65]  G. Antoniou,et al.  Supply chain risk management and artificial intelligence: state of the art and future research directions , 2018, Int. J. Prod. Res..