scholarly journals Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Erfan Babaee Tirkolaee ◽  
Saeid Sadeghi ◽  
Farzaneh Mansoori Mooseloo ◽  
Hadi Rezaei Vandchali ◽  
Samira Aeini
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Inventory Management ◽  
Future Research ◽  
Comprehensive Overview ◽  
Chain Management ◽  
Efficiency And Effectiveness ◽  
Applications Of Machine Learning ◽  
Supply Chain Risks

In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

scholarly journals The human factor in SCM

2017 ◽  
Vol 47 (4) ◽  
pp. 238-262 ◽  
Author(s):  
Timm Schorsch ◽  
Carl Marcus Wallenburg ◽  
Andreas Wieland
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Research Field ◽  
Future Research ◽  
Comprehensive Overview ◽  
Social Phenomena ◽  
Chain Management ◽  
Content Type ◽  
Research Opportunities ◽  
Meta Theory

Purpose The purpose of this paper is to advance supply chain management by describing the current state of behavioral supply chain management (BSCM) research and paving the way for future contributions by developing a meta-theory for this important field. Design/methodology/approach The results are generated by applying the systematic literature review methodology and an iterative theory-building approach involving a panel of academics. Findings This review provides a comprehensive overview of the BSCM research landscape. Additionally, a meta-theory of BSCM is presented that encompasses all central elements of the research field and introduces the concept of emergence to the field of BSCM. Furthermore, five promising future research opportunities are formulated. Research limitations/implications The critical discussions and the formulated research opportunities will help scholars in positioning their research to enhance its contribution. Practical implications Results from this research indicate that supply chain decisions benefit from explicit consideration for cognitive and social phenomena. Originality/value This review is the first to provide a comprehensive overview of the field of BSCM research and facilitates BSCM in advancing further.

A systematic review of machine learning in logistics and supply chain management: current trends and future directions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadreza Akbari ◽  
Thu Nguyen Anh Do
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Current Literature ◽  
Geographic Location ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
A Chain ◽  
Design And Methods

PurposeThis paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.Design/methodology/approachA systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.FindingsOver the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.Research limitations/implicationsThis review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.Originality/valueThis paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

scholarly journals Blockchain technology: implications for operations and supply chain management

2019 ◽  
Vol 24 (4) ◽  
pp. 469-483 ◽  
Author(s):  
Rosanna Cole ◽  
Mark Stevenson ◽  
James Aitken
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Inventory Management ◽  
Future Research ◽  
Sustainable Supply Chain ◽  
Chain Management ◽  
Content Type ◽  
Blockchain Technology ◽  
New Research

PurposeThis paper aims to encourage the study of blockchain technology from an operations and supply chain management (OSCM) perspective, identifying potential areas of application, and to provide an agenda for future research.Design/methodology/approachAn explanation and analysis of blockchain technology is provided to identify implications for the field of OSCM.FindingsThe hype around the opportunities that digital ledger technologies offer is high. For OSCM, a myriad of ways in which blockchain could transform practice are identified, including enhancing product safety and security; improving quality management; reducing illegal counterfeiting; improving sustainable supply chain management; advancing inventory management and replenishment; reducing the need for intermediaries; impacting new product design and development; and reducing the cost of supply chain transactions. The immature state of practice and research surrounding blockchain means there is an opportunity for OSCM researchers to study the technology in its early stages and shape its adoption.Research limitations/implicationsThe paper provides a platform for new research that addresses gaps in knowledge and advances the field of OSCM. A research agenda is developed around six key themes.Practical implicationsThere are many opportunities for organisations to obtain an advantage by making use of blockchain technology ahead of the competition, enabling them to enhance their market position. But it is important that managers examine the characteristics of their products, services and supply chains to determine whether they need or would benefit sufficiently from the adoption of blockchain. Moreover, it is important that organisations build human capital expertise that allows them to develop, implement and exploit applications of this technology to maximum reward.Originality/valueThis is one of the first papers in a leading international OSCM journal to analyse blockchain technology, thereby complementing a recent article on digital supply chains that omitted blockchain.

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

The Decision Process of Contracting in Supply Chain Management

2020 ◽  
Author(s):  
Jens K. Roehrich ◽  
Beverly B. Tyler ◽  
Jas Kalra ◽  
Brian Squire
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Decision Process ◽  
Interorganizational Relationships ◽  
Future Research ◽  
Theoretical Frameworks ◽  
Chain Management ◽  
Research And Practice ◽  
Future Developments ◽  
The Relationship

Contracts are a formal mode of governing interorganizational relationships. They specify the terms and conditions of the agreement between two parties, interpret and adapt the relevant legal and industrial norms, serve as framing devices, and establish the rules and norms underpinning the relationship. The objective of this chapter is to synthesize the extant literature on interorganizational contracting to guide future research and practice. This chapter focuses on the three phases of contracting: (1) designing the contracting portfolio; (2) negotiating initial contracts; and (3) managing the relationship using contracts. The chapter explores the key decisions in each phase and the criteria involved in making these decisions. In doing so, it draws on existing research and theoretical frameworks that have contributed to the development of the contracting literature. The chapter also identifies some important and interesting directions for future contracting research and offers suggestions regarding how selected theoretical lenses might guide these endeavors. The principal conclusion is that while the existing research has primarily focused on the structural issues guiding contracting design, a more processual, social, and behavioral focus is required in future developments of the contracting literature.

Sustainable supply chain management: continuing evolution and future directions

2019 ◽  
Vol 50 (1) ◽  
pp. 122-146 ◽  
Author(s):  
Craig R. Carter ◽  
Marc R. Hatton ◽  
Chao Wu ◽  
Xiangjing Chen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Theory Development ◽  
Future Research ◽  
Sustainable Supply Chain ◽  
Unit Of Analysis ◽  
Chain Management ◽  
Content Type ◽  
Empirical Validity ◽  
Sustainable Supply Chain Management

Purpose The purpose of this paper is to update the work of Carter and Easton (2011), by conducting a systematic review of the sustainable supply chain management (SSCM) literature in the primary logistics and supply chain management journals, during the 2010–2018 timeframe. Design/methodology/approach The authors use a systematic literature review (SLR) methodology which follows the methodology employed by Carter and Easton (2011). An evaluation of this methodology, using the Modified AMSTAR criteria, demonstrates a high level of empirical validity. Findings The field of SSCM continues to evolve with changes in substantive focus, theoretical lenses, unit of analysis, methodology and type of analysis. However, there are still abundant future research opportunities, including investigating under-researched topics such as diversity and human rights/working conditions, employing the group as the unit of analysis and better addressing empirical validity and social desirability bias. Research limitations/implications The findings result in prescriptions and a broad agenda to guide future research in the SSCM arena. The final section of the paper provides additional avenues for future research surrounding theory development and decision making. Originality/value This SLR provides a rigorous, methodologically valid review of the continuing evolution of empirical SSCM research over a 28-year time period.

scholarly journals Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

2020 ◽  
Vol 13 (1) ◽  
pp. 56
Author(s):  
Tino Herden
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Success Factors ◽  
Research Collaboration ◽  
Future Research ◽  
Structured Interviews ◽  
Chain Management ◽  
Domain Specific ◽  
The Status ◽  
The Impact

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains.Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin.Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed.Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.

Healthcare supply chain management: literature review and some issues

2018 ◽  
Vol 15 (3) ◽  
pp. 265-287 ◽  
Author(s):  
Bhavana Mathur ◽  
Sumit Gupta ◽  
Makhan Lal Meena ◽  
G.S. Dangayach
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Literature Review ◽  
Organizational Performance ◽  
Critical Role ◽  
Sample Survey ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
Potential Benefits

PurposeThe purpose of this paper is to examine the causal linkages among supply chain practices, effectiveness of supply chain performance (SCP) and organizational performance (OP) in Indian healthcare industries.Design/methodology/approachThis paper is helpful in developing a framework for linking a healthcare supply chain practice to its OP, and thus identifies how such a linkage can be connected to the effectiveness of SCP. Such effort also enables the authors to derive a set of recommended supply chain practices for SC performance.FindingsFrom the literature review, this paper finds that, in the context of Indian healthcare industries, efficient SC performance may play a critical role for overall OP improvement, as there is a close interrelationship between supply chain management (SCM) practices and SCP that may have a more significant effect on OP improvement.Research limitations/implicationsThe principle limitation of the paper is that it is performed only in a particular industry and with a questionnaire survey which could be extended in future for other industries also. Another limitation of the paper is that it is focused only on the SCP of medical device and equipment supply chain which is a small portion of the whole healthcare supply chain, and therefore requires further research covering various other domains of healthcare supply chain. Another limitation of the study is that the sample survey has been taken from only one respondent per company at one point of time which may create biasness in the results. Thus, future research should collect data through multiple members from the organization.Practical implicationsThis study contributes to know the effect of SCM practices on healthcare SCP and provides a practical and useful tool to evaluate the extent of effectiveness of SCP and finally their impact on the healthcare OP. Finally, this study provides conceptual and descriptive literature regarding SCM practices that leads to improvement in healthcare performance.Social implicationsThis study adds to the knowledge on healthcare SCM performance by exploring the relationship between supply chain practices, healthcare SCP and healthcare OP and by developing and testing a research framework thus help in improving patient satisfaction.Originality/valueThis study attempts to show how the potential benefits of supply chain practices can no longer be ignored in healthcare supply chain.

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