A Proposed Supply Chain Risk Management Framework (original) (raw)
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A Knowledge Based Approach for Handling Supply Chain Risk Management
SSRN Electronic Journal, 2012
This paper discusses the concept of supply chain risk management (SCRM) in relation to the emerging challenges brought by globalisation and information and communication technologies (ICT) and the ability of SCRM frameworks to adapt to these latest requirements. As SCRM can be responsible for loss or gain of profit, the ultimate goal of enterprises is to have resilient supply chains with automated decision making that can deal with potential disruptions. In response to these, taking advantage of ICT developments such as knowledge and data discovery techniques and automated risk management frameworks have become a vital aspect for assuring business success. Having this context, this research has the following aims: 1) to perform literature review on identifying and categorising several types of supply chain risks in order to analyze their management strategies, 2) to perform a literature review on knowledge management frameworks and 3) to propose a knowledge management and a risk management framework that would be, at a further stage of this research, integrated in an agent based decision support system for supply chain risk management.
Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management
Springer Series in Supply Chain Management, 2018
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.
A data mining-based framework for supply chain risk management
Computers & Industrial Engineering, 2019
Increased risk exposure levels, technological developments and the growing information overload in supply chain networks drive organizations to embrace data-driven approaches in Supply Chain Risk Management (SCRM). Data Mining (DM) employs multiple analytical techniques for intelligent and timely decision making; however, its potential is not entirely explored for SCRM. The paper aims to develop a DM-based framework for the identification, assessment and mitigation of different type of risks in supply chains. A holistic approach integrates DM and risk management activities in a unique framework for effective risk management. The framework is validated with a case study based on a series of semi-structured interviews, discussions and a focus group study. The study showcases how DM supports in discovering hidden and useful information from unstructured risk data for making intelligent risk management decisions.
A multi-agent based framework for supply chain risk management
Journal of Purchasing and Supply Management, 2010
The high level of complexity of supply chains and the inherent risks that exist in both the demand and supply of resources-especially in economic downturns-are recognized as major limiting factors in achieving high levels of supply chain performance. The use of modern information technology (IT) decision support systems is fast becoming an indispensable tool for designing and managing complex supply chain systems today. This paper develops a framework for the design of a multi-agent based decision support system for the management disruptions and mitigation of risks in manufacturing supply chains.
Intelligent Agents and Risk Based Model for Supply Chain Management
Technological and Economic Development of Economy, 2012
This paper presents a software agent based framework's architecture for boosting performance in supply chain management applications. The framework is based on agent interaction and semantic web service composition. The purpose of such a platform is to develop flexible business applications for SCM transactions modeling, in collaborative and distributed economic systems. The interaction between agents is limited by a cybernetic model that takes into account several constraints one of the main being bankruptcy risk potential of the peer partner company.
Challenges in Applying Artificial Intelligence for Supply Chain Risk Management
Journal of economics and business administration, 2020
Purpose: To define the scope and nature of challenges in applying artificial intelligence (AI) for supply chain risk management (SCRM). Design/Methodology/Approach: Initial theoretical conceptualisation and respective approach were set by following the risk management maturity framework. The scope of explored challenges was defined by two data categories (supply chain risk events’ and risk events’ indicators) that are essential for AI tools to predict risk events’ probability based on a set of risk prediction indicators. The nature of challenges is associated with the ways and forms of data collection, management, and application. The qualitative primary data research strategy was employed to explore selected case company practices associated with conceptually defined categories of scope and nature of challenges in applying AI for SCRM. Findings: The article concludes with a conceptual typology of challenges in applying AI for SCRM defined by their scope and nature along with the se...
Presenting a multi agent system for estimating risk in supply chain management
International Journal of Services and Operations Management, 2017
Nowadays, supply chains play an inevitable role in prompt handling of varying customer's needs. Furthermore, with increasing emphasis on vulnerabilities in supply chains, effective mathematical tools for analysing and understanding appropriate supply chain risk evaluation are now attracting more attention. Administration of a successful supply chain depends on how efficiently of the network design is measured and how source risks effect it. This research has two objectives. The first one is to design a multi agent supply chain network that addresses an uncertain environment threatened by different risk sources in order to capture the real world conditions; the second one is to present a methodology for estimating risk in the proposed network. Moreover tree of scenarios are constructed and risk assessment model considering domino effect is built in order to carry out the overall quantitative risk assessment. Then, probability theories are applied in the quantitative method. In conclusion, the key benefits and experience gained from this study and further research opportunity are emphasised.
Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, 2018
Supply chain management paradigms are becoming increasingly common management perspectives all over the world due to violent global competition of trade organizations and rapid changes in technology. In recent years, thanks to the communication improvements, customers have become more conscious about purchasing goods or services. Furthermore, organizations have to be customer oriented and more flexible against the dynamism of supply chain environment which increases uncertainties in supply chain parameters. Although a considerable amount of risk factors appearing in supply chain operations, this study concentrates on detecting key supply chain risks which could cause abnormalities and occur from rapid changes in customer demand, unpredictable price fluctuations, defect variations and delivery delays and provides the correction of these problems automatically. Thus, a system dynamics model is established for determining risks. This combined approach would be helpful for integrated su...
European Research on Management and Business Economics, 2020
Supply chain disruptions have serious consequences for society and this has made supply chain risk management (SCRM) an attractive area for researchers and managers. In this paper, we use an objective literature mapping approach to identify, classify, and analyze decision-making models and support systems for SCRM, providing an agenda for future research. Through bibliometric networks of articles published in the Scopus database, we analyze the most influential decision-making models and support systems for SCRM, evaluate the main areas of current research, and provide insights for future research in this field. The main results are the following: we found that the identity of the area is structured in three groups of risk decision support models: (i) quantitative multicriteria decision models, (ii) stochastic decision-making models, and (iii) computational simulation/optimization models. We mapped six current research clusters: (i) conceptual and qualitative risk models, (ii) upstream supply chain risk models, (iii) downstream supply chain risk models, (iv) supply chain sustainability risk models, (v) stochastic and multicriteria decision risk models, and (vi) emerging techniques risk models. We identified seven future research clusters, with insights from further studies for: (i) tools to operate SCRM data, (ii) validation of risk models, (iii) computational improvement for data analysis, (iv) multi-level and multi-period supply chains, (v) agrifood risks, (vi) energy risks and (vii) sustainability risks. Finally, the future research agenda should prioritize SCRM's holistic vision, the relationship between Big Data, Industry 4.0 and SCRM, as well as emerging social and environmental risks.
Data mining and operations research techniques in Supply Chain Risk Management: A bibliometric study
Data mining and operations research techniques in Supply Chain Risk Management: A bibliometric study, 2020
Goal: This paper aims to carry a bibliometric study to map how data mining and operations research techniques are being applied to Supply Chain Risk Management. Design/Methodology/Approach: We conducted a bibliometric analysis implemented in R language (bibliometrix package) using Systematic Literature Review approach to conduct the search. Results: As the main results we highlight the gap we found in the literature considering Data Mining techniques in Supply Chain Risk Management and we set a full panorama of this stream of research. Limitations of the Investigation: We used Scopus database which allows recovering peer-reviewed texts from dozens of strong databases, nevertheless, we can not guarantee that all relevant documents were recovered. In addition, we considered only full published papers published in English language. Practical Implications: Managers and companies that are related in a supply chain must gradually redesign processes to include Data Mining techniques to support SCRM processes and activities along the SC. Originality / Value: The paper showed the updated panorama of Data Mining implementation regarding SCRM. We did not find any similar studies, which shows our unique contribution.