Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9672826
Wen Song, S. Fujimura
Sensor Data prediction has been an interesting and practical topic in many domains. In the process industry, sensor data prediction can help us detect, diagnose and even predict possible failures to reduce unnecessary losses. Due to the complex relationship among multiple sensors, it is challenging to accurately predict the time series of multivariate sensors. In this research, we aim to solve the problem of predicting the time series of several related sensor data and proposed a novel structure for addressing with this provocative problem. More specifically, several proposed mixed length dilation layers and recurrent cells are used to capture mixed length of time dependencies. Experiments demonstrate that our proposed model indicates competitiveness in predicting comparing with other baseline methods.
{"title":"Sensor Data Prediction in Process Industry by Capturing Mixed Length of Time Dependencies","authors":"Wen Song, S. Fujimura","doi":"10.1109/IEEM50564.2021.9672826","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9672826","url":null,"abstract":"Sensor Data prediction has been an interesting and practical topic in many domains. In the process industry, sensor data prediction can help us detect, diagnose and even predict possible failures to reduce unnecessary losses. Due to the complex relationship among multiple sensors, it is challenging to accurately predict the time series of multivariate sensors. In this research, we aim to solve the problem of predicting the time series of several related sensor data and proposed a novel structure for addressing with this provocative problem. More specifically, several proposed mixed length dilation layers and recurrent cells are used to capture mixed length of time dependencies. Experiments demonstrate that our proposed model indicates competitiveness in predicting comparing with other baseline methods.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"15 1","pages":"1174-1178"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74095690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9672944
E. Neumann, B. Vogel‐Heuser, I. Bayar
automated Production Systems (aPS) are highly complex mechatronic systems with lifetimes of up to 50 years. The lack of advanced software engineering methods in industrial automation often causes historically grown legacy code with low quality. Modeling and refactoring of legacy control software are considered as inevitable to manage ever-increasing software complexity and face upcoming challenges in the context of Industry 4.0. However, promising goals such as mechatronic modularization, product line engineering, or variability management have up to now hardly made their way to control software in aPS. To address this gap, the main contribution of this paper is a concept of function-oriented software modeling for aPS. The approach provides three models to map control software in aPS to the implemented system functionalities on code level, by also considering extra-functional software tasks that cannot be directly mapped to the physical modules. The models are implemented using ME-TUS software, i.e., a renowned tool for modeling and optimizing mechatronic system architectures based on a function-oriented construction kit principle. The modeling approach is evaluated with industrial and academic experts with promising results.
{"title":"Representing Control Software Functionality as Part of a Modular, Mechatronic Construction Kit","authors":"E. Neumann, B. Vogel‐Heuser, I. Bayar","doi":"10.1109/IEEM50564.2021.9672944","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9672944","url":null,"abstract":"automated Production Systems (aPS) are highly complex mechatronic systems with lifetimes of up to 50 years. The lack of advanced software engineering methods in industrial automation often causes historically grown legacy code with low quality. Modeling and refactoring of legacy control software are considered as inevitable to manage ever-increasing software complexity and face upcoming challenges in the context of Industry 4.0. However, promising goals such as mechatronic modularization, product line engineering, or variability management have up to now hardly made their way to control software in aPS. To address this gap, the main contribution of this paper is a concept of function-oriented software modeling for aPS. The approach provides three models to map control software in aPS to the implemented system functionalities on code level, by also considering extra-functional software tasks that cannot be directly mapped to the physical modules. The models are implemented using ME-TUS software, i.e., a renowned tool for modeling and optimizing mechatronic system architectures based on a function-oriented construction kit principle. The modeling approach is evaluated with industrial and academic experts with promising results.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"313 ","pages":"644-648"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72551199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9672894
R. Li, M. Tee
Collaborative Customer Relationship Management (CCRM) has developed Automated Customer Support Services (ACSS), where it focuses on providing more efficient and immediate customer service. Through chatbots, virtual customers, internet routing, and automated responses, technology has evolved to aid the customer support sector through automations trained by Artificial Intelligence (AI), Machine Learning (ML), and other advancements in technology. However, ACSS is relatively new with various implementation frameworks in choosing ACSS platforms developed by CRM experts for organizations. The study aims to cover the research gaps of integrating the customer perspective in terms of behavioral trends, data security issues, engagement and responses, and proper maintenance and evaluation of the ACSS performance based on the customer relationships and experience, through the development of a new implementation framework for ACSS in an organization. Through a rating-questionnaire answered by CRM experts on three (3) different ACSS based on different frameworks and the developed one by the study, the findings show that the developed framework enhances customer relationships and experiences more than the existing frameworks, thereby validating the effectiveness of the implementation framework in the study.
{"title":"Developing an Implementation Framework for Automated Customer Support Service in Collaborative Customer Relationship Management Systems","authors":"R. Li, M. Tee","doi":"10.1109/IEEM50564.2021.9672894","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9672894","url":null,"abstract":"Collaborative Customer Relationship Management (CCRM) has developed Automated Customer Support Services (ACSS), where it focuses on providing more efficient and immediate customer service. Through chatbots, virtual customers, internet routing, and automated responses, technology has evolved to aid the customer support sector through automations trained by Artificial Intelligence (AI), Machine Learning (ML), and other advancements in technology. However, ACSS is relatively new with various implementation frameworks in choosing ACSS platforms developed by CRM experts for organizations. The study aims to cover the research gaps of integrating the customer perspective in terms of behavioral trends, data security issues, engagement and responses, and proper maintenance and evaluation of the ACSS performance based on the customer relationships and experience, through the development of a new implementation framework for ACSS in an organization. Through a rating-questionnaire answered by CRM experts on three (3) different ACSS based on different frameworks and the developed one by the study, the findings show that the developed framework enhances customer relationships and experiences more than the existing frameworks, thereby validating the effectiveness of the implementation framework in the study.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"1 1","pages":"1092-1096"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79836397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9672937
B. Vogel‐Heuser, T. Herrmann, M. Zou
In multi-team engineering projects with multi-team collaborations and frequent changes, decision-makers can hardly keep an overview of cost. Standard decision support in industry focuses either solely on workflow cost or the performance of multi-team labor, leaving out a holistic view. This paper proposes an approach to analyze cost at multi-team work interfaces using the Business Process Modeling Notation (BPMN). Such interfaces are crucial for the success of an interdisciplinary engineering effort, representing frequent interactions of different teams (multi-team systems) during one working tasks like the interaction of mechanical and electrical engineering teams choosing sensors fulfilling both the mechanical and electrical requirements. The approach is evaluated using an industrial use case with two different multi-team interface alternatives: a human expert versus an IT-based assistance system. To support decision-making whether the IT-based solution with its software license costs is beneficial the interface cost of both alternatives are assessed exemplarily.
{"title":"Cost Analysis of Collaboration Interfaces in an Interdisciplinary Engineering Workflow: A Model Based Approach Using BPMN+I","authors":"B. Vogel‐Heuser, T. Herrmann, M. Zou","doi":"10.1109/IEEM50564.2021.9672937","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9672937","url":null,"abstract":"In multi-team engineering projects with multi-team collaborations and frequent changes, decision-makers can hardly keep an overview of cost. Standard decision support in industry focuses either solely on workflow cost or the performance of multi-team labor, leaving out a holistic view. This paper proposes an approach to analyze cost at multi-team work interfaces using the Business Process Modeling Notation (BPMN). Such interfaces are crucial for the success of an interdisciplinary engineering effort, representing frequent interactions of different teams (multi-team systems) during one working tasks like the interaction of mechanical and electrical engineering teams choosing sensors fulfilling both the mechanical and electrical requirements. The approach is evaluated using an industrial use case with two different multi-team interface alternatives: a human expert versus an IT-based assistance system. To support decision-making whether the IT-based solution with its software license costs is beneficial the interface cost of both alternatives are assessed exemplarily.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"79 1","pages":"1571-1575"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81498004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9673031
S. Karmakar, J. Sunder, B. John
During the pandemic, the supply chains got highly disrupted and faced a new challenge to sustain service. We have proposed a mathematical model for managing supply chains in a post pandemic situation, also coined as “new normal”. We are trying to design a model and simulate different scenarios while optimizing the network to thrive and fulfill customer demand. The model has been supported with scenario analysis and illustrative examples a packaged drinking water supply chain. The objective is to minimize the supply chain operating cost with respect to the changes in capacity due to pandemic. The fill-rate has also been recorded as a performance matrix for the chain. Particle Swarm Optimization (PSO) has been used to optimize the objective function. This research will help supply chain practitioners and researchers to design networks and carry out study in risk management for pandemic or other similar outbreaks situations.
{"title":"Optimizing a Supply Chain Network Using Metaheuristic for Pre and Post Pandemic Scenario","authors":"S. Karmakar, J. Sunder, B. John","doi":"10.1109/IEEM50564.2021.9673031","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9673031","url":null,"abstract":"During the pandemic, the supply chains got highly disrupted and faced a new challenge to sustain service. We have proposed a mathematical model for managing supply chains in a post pandemic situation, also coined as “new normal”. We are trying to design a model and simulate different scenarios while optimizing the network to thrive and fulfill customer demand. The model has been supported with scenario analysis and illustrative examples a packaged drinking water supply chain. The objective is to minimize the supply chain operating cost with respect to the changes in capacity due to pandemic. The fill-rate has also been recorded as a performance matrix for the chain. Particle Swarm Optimization (PSO) has been used to optimize the objective function. This research will help supply chain practitioners and researchers to design networks and carry out study in risk management for pandemic or other similar outbreaks situations.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"17 1","pages":"41-45"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84213576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9673039
A. Telukdarie, M. Munsamy, M. Gaula
The ability to predict skills of the future is fundamental in this rapidly changing global environment. The ability to predict and invest in the appropriate skills could prove to be a key differentiator for country specific growth. Global competitiveness, employment, technological investments, and social-economic dependencies are drivers to capacity development to predict future skills. The current approaches include a combination of expanding on current skill demand, adoption of economic indicators or survey-based forecasting. The challenge is the inclusion of future trends, specifically the ability to forecast new skills or combinations of existing skills. This research explores a novel method in adopting research publications to predict new/ undetermined skills requirements. This study downloads 700 000 papers, develops a multilayer data analysis protocol, screens and provides insights into the adoption of research publications analysis for the prediction of future skills.
{"title":"Big Data Analysis for Predicting Future Skills","authors":"A. Telukdarie, M. Munsamy, M. Gaula","doi":"10.1109/IEEM50564.2021.9673039","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9673039","url":null,"abstract":"The ability to predict skills of the future is fundamental in this rapidly changing global environment. The ability to predict and invest in the appropriate skills could prove to be a key differentiator for country specific growth. Global competitiveness, employment, technological investments, and social-economic dependencies are drivers to capacity development to predict future skills. The current approaches include a combination of expanding on current skill demand, adoption of economic indicators or survey-based forecasting. The challenge is the inclusion of future trends, specifically the ability to forecast new skills or combinations of existing skills. This research explores a novel method in adopting research publications to predict new/ undetermined skills requirements. This study downloads 700 000 papers, develops a multilayer data analysis protocol, screens and provides insights into the adoption of research publications analysis for the prediction of future skills.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"101 1","pages":"411-415"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84879560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9673047
J. Baschin, D. Schneider, T. Huth, T. Vietor
Agile methods can support the work in engineering design projects in order to react quickly to new boundary conditions such as changing product requirements. However, the use of agile methods is not established in mechanical engineering yet. It is useful to support the project manager in selecting a suitable agile method for the project's needs, because there is a large variety of agile methods in the literature and industry. Therefore, this paper presents a concept for the project-oriented selection of agile methods. The concept was applied to four different design projects in four companies. The results of the application are also shown in this paper.
{"title":"Project-oriented Selection of Agile Methods for the Design of Physical Products","authors":"J. Baschin, D. Schneider, T. Huth, T. Vietor","doi":"10.1109/IEEM50564.2021.9673047","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9673047","url":null,"abstract":"Agile methods can support the work in engineering design projects in order to react quickly to new boundary conditions such as changing product requirements. However, the use of agile methods is not established in mechanical engineering yet. It is useful to support the project manager in selecting a suitable agile method for the project's needs, because there is a large variety of agile methods in the literature and industry. Therefore, this paper presents a concept for the project-oriented selection of agile methods. The concept was applied to four different design projects in four companies. The results of the application are also shown in this paper.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"64 1","pages":"999-1003"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76601100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9673054
Marian Mistler, N. Schlüter, M. Löwer, V. Rafalczyk
The traceability of requirements is an enormous challenge for business networks (BN). To achieve a fast processing of customer complaints in BN, we present a methodology for a model-based traceability of requirements from com-plaints in BN using the e-DeCoDe approach. The methodology shows how the causes of errors in the product development of BN can be identified based on requirements from complaints. In addition, the methodology shows how the organization of the company is affected in the context of the product development and can contribute to finding solutions for the fulfilment of requirements. The development of such a model-based methodology holds a multitude of possibilities for finding the causes of errors and solutions, as well as the potential to automate the complaint process for the entire BN. This potential is currently not sufficiently exploited by companies because there is a lack of a consistent model-based methodology that considers the entire context of the BN with systems thinking. In order to face this problem, the developed methodology links two German Research Foundation projects for the first time through e-DeCoDe modeling.
{"title":"Methodology for a Model-based Traceability of Requirements from Complaints in Business Networks Using e-DeCoDe","authors":"Marian Mistler, N. Schlüter, M. Löwer, V. Rafalczyk","doi":"10.1109/IEEM50564.2021.9673054","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9673054","url":null,"abstract":"The traceability of requirements is an enormous challenge for business networks (BN). To achieve a fast processing of customer complaints in BN, we present a methodology for a model-based traceability of requirements from com-plaints in BN using the e-DeCoDe approach. The methodology shows how the causes of errors in the product development of BN can be identified based on requirements from complaints. In addition, the methodology shows how the organization of the company is affected in the context of the product development and can contribute to finding solutions for the fulfilment of requirements. The development of such a model-based methodology holds a multitude of possibilities for finding the causes of errors and solutions, as well as the potential to automate the complaint process for the entire BN. This potential is currently not sufficiently exploited by companies because there is a lack of a consistent model-based methodology that considers the entire context of the BN with systems thinking. In order to face this problem, the developed methodology links two German Research Foundation projects for the first time through e-DeCoDe modeling.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"410 1","pages":"1255-1259"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76716918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9673005
Y. Prasetyo, M. Santiago
This study discussed the factors influencing people's well-being and living in known Smart Cities worldwide. The conceptual framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Structural Equation Modeling (SEM) was used for its statistical analysis. The study shows that the facilitating condition significantly influences people's behavioral intention working in smart cities across the globe. This paper indicates that the respondents' behavioral intention to continue their employment, report to their duties every day, and work effectively and efficiently is significantly affected by how the amenities of their working and living environment can do to them.
{"title":"Factors Affecting the Well-being of People Working in Known Smart Cities: UTAUT2 Approach","authors":"Y. Prasetyo, M. Santiago","doi":"10.1109/IEEM50564.2021.9673005","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9673005","url":null,"abstract":"This study discussed the factors influencing people's well-being and living in known Smart Cities worldwide. The conceptual framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Structural Equation Modeling (SEM) was used for its statistical analysis. The study shows that the facilitating condition significantly influences people's behavioral intention working in smart cities across the globe. This paper indicates that the respondents' behavioral intention to continue their employment, report to their duties every day, and work effectively and efficiently is significantly affected by how the amenities of their working and living environment can do to them.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"719 1","pages":"1270-1274"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76914221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-13DOI: 10.1109/IEEM50564.2021.9672606
M. Subramaniyam, S. A. Halim-Lim, S. F. Mohamad, Anjar Priyono
The global interest in the digital supply chain (DSC) has progressively increased due to the recent industrial revolution and pandemic demand for digital technologies. As a result, the implementation of DSC is highly critical; however, the implementation guidelines are still scarce and vague, especially on the standard implementation components such as critical success factors (CSFs) and barriers in the DSC implementation. The current study shows a systematic literature review of 72 articles on DSC implementation in the food supply chain (FSC) through technology, operation, and managerial dimension. The result shows the top three CSFs are the market pressure of an organisation, the knowledge, and training about the technology implementation and existing infrastructures. In contrast, expensive initial costs, a lack of skills, and a lack of inspiration are common barriers to implementing DSC in the food industry. This research will facilitate academics and food industry practitioners better understand how to adopt digital supply chains before investing in and planning supply chain (SC) digitalisation.
{"title":"Digital Supply Chain in the Food Industry: Critical Success Factors and Barriers","authors":"M. Subramaniyam, S. A. Halim-Lim, S. F. Mohamad, Anjar Priyono","doi":"10.1109/IEEM50564.2021.9672606","DOIUrl":"https://doi.org/10.1109/IEEM50564.2021.9672606","url":null,"abstract":"The global interest in the digital supply chain (DSC) has progressively increased due to the recent industrial revolution and pandemic demand for digital technologies. As a result, the implementation of DSC is highly critical; however, the implementation guidelines are still scarce and vague, especially on the standard implementation components such as critical success factors (CSFs) and barriers in the DSC implementation. The current study shows a systematic literature review of 72 articles on DSC implementation in the food supply chain (FSC) through technology, operation, and managerial dimension. The result shows the top three CSFs are the market pressure of an organisation, the knowledge, and training about the technology implementation and existing infrastructures. In contrast, expensive initial costs, a lack of skills, and a lack of inspiration are common barriers to implementing DSC in the food industry. This research will facilitate academics and food industry practitioners better understand how to adopt digital supply chains before investing in and planning supply chain (SC) digitalisation.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"16 1","pages":"404-410"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82370478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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