Pub Date : 2023-12-01DOI: 10.1109/EMR.2023.3337415
Yossiri Adulyasak;Maxime C. Cohen;Warut Khern-Am-Nuai;Michael Krause
The COVID-19 pandemic has severely disrupted the retail landscape and has accelerated the adoption of innovative technologies. A striking example relates to the proliferation of online grocery orders and the technology deployed to facilitate such logistics. In fact, for many retailers, this disruption was a wake-up call after which they started recognizing the power of data analytics and artificial intelligence (AI). In this article, we discuss the opportunities that AI can offer to retailers in the new normal retail landscape. Some of the techniques described have been applied at scale to adapt previously deployed AI models, whereas in other instances, fresh solutions needed to be developed to help retailers cope with recent disruptions, such as unexpected panic buying, retraining predictive models, and leveraging online–offline synergies.
{"title":"Retail Analytics in the New Normal: The Influence of Artificial Intelligence and the Covid-19 Pandemic","authors":"Yossiri Adulyasak;Maxime C. Cohen;Warut Khern-Am-Nuai;Michael Krause","doi":"10.1109/EMR.2023.3337415","DOIUrl":"10.1109/EMR.2023.3337415","url":null,"abstract":"The COVID-19 pandemic has severely disrupted the retail landscape and has accelerated the adoption of innovative technologies. A striking example relates to the proliferation of online grocery orders and the technology deployed to facilitate such logistics. In fact, for many retailers, this disruption was a wake-up call after which they started recognizing the power of data analytics and artificial intelligence (AI). In this article, we discuss the opportunities that AI can offer to retailers in the new normal retail landscape. Some of the techniques described have been applied at scale to adapt previously deployed AI models, whereas in other instances, fresh solutions needed to be developed to help retailers cope with recent disruptions, such as unexpected panic buying, retraining predictive models, and leveraging online–offline synergies.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"268-280"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229707","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 : 2023-11-30DOI: 10.1109/EMR.2023.3336871
Mohammadali Farjoo
In our globalized and digitalized post-COVID world, knowledge and technology transfer offices and commercialization companies (collectively hereafter TTOs) strive to increase and diversify their research impact portfolio. To achieve this target, TTOs should clearly redefine “research impact” and communicate it effectively, engage strategically based on an intersubjective understanding and mutual benefits with their stakeholders to be able to identify the market's unmet needs, and proactively foster an innovation pipeline aligned with their socioeconomical innovation ecosystem. Drawing on the author's lived experience, this article explains the key characteristics of an impactful TTO and suggests approaches to improve its performance. In addition, this article discusses the role of such a TTO in an ever-evolving research commercialization ecosystem. Also, it proposes a practical model for a TTO to maximize its opportunity to create impact.
{"title":"Be an Impactful, Proactive Technology Transfer Office in a Globalized and Digitalized World: A Lived Experience","authors":"Mohammadali Farjoo","doi":"10.1109/EMR.2023.3336871","DOIUrl":"https://doi.org/10.1109/EMR.2023.3336871","url":null,"abstract":"In our globalized and digitalized post-COVID world, knowledge and technology transfer offices and commercialization companies (collectively hereafter TTOs) strive to increase and diversify their research impact portfolio. To achieve this target, TTOs should clearly redefine “research impact” and communicate it effectively, engage strategically based on an intersubjective understanding and mutual benefits with their stakeholders to be able to identify the market's unmet needs, and proactively foster an innovation pipeline aligned with their socioeconomical innovation ecosystem. Drawing on the author's lived experience, this article explains the key characteristics of an impactful TTO and suggests approaches to improve its performance. In addition, this article discusses the role of such a TTO in an ever-evolving research commercialization ecosystem. Also, it proposes a practical model for a TTO to maximize its opportunity to create impact.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"9-14"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619643","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 : 2023-11-28DOI: 10.1109/EMR.2023.3336834
Robert G. Cooper
Artificial Intelligence (AI) is poised to revolutionize all aspects of business, particularly new-product development (NPD). Currently, our approach to NPD has remained largely unchanged for decades, yielding stubbornly poor results: only 30% of NPdevelopment projects become commercial successes . However, the AI revolution is set to alter this landscape significantly! Leading early adopter firms demonstrate that AI not only finds many applications in NPD but also offers substantial payoffs, such as 50% reductions in development times. This article provides an outline of the diverse and powerful applications of AI in NPD, offering numerous examples from leading companies. Examples include GE's use of digital models and twins to quickly test product designs in turbine development; BASFs use of AI to identify new molecules for use in customer formulations; and AI to generate new-product ideas, identify new-product opportunities, and even create new-product concepts. Our exploratory journey begins at the idea stage and traverses the entire new-product process to the postlaunch period. While AI might still resemble science fiction to many, that future is no longer fiction—it is here now. AI has arrived in full force! With an adoption window of about 13 years, the time is now to embrace AI in NPD in your business. AI will become a major milestone in NPD, perhaps the most important, within the decade.
{"title":"The Artificial Intelligence Revolution in New-Product Development","authors":"Robert G. Cooper","doi":"10.1109/EMR.2023.3336834","DOIUrl":"https://doi.org/10.1109/EMR.2023.3336834","url":null,"abstract":"Artificial Intelligence (AI) is poised to revolutionize all aspects of business, particularly new-product development (NPD). Currently, our approach to NPD has remained largely unchanged for decades, yielding stubbornly poor results: \u0000<roman>only 30% of NP</roman>\u0000 \u0000<italic>development projects become commercial successes</i>\u0000. However, the AI revolution is set to alter this landscape significantly! Leading early adopter firms demonstrate that AI not only finds many applications in NPD but also offers substantial payoffs, such as 50% reductions in development times. This article provides an outline of the diverse and powerful applications of AI in NPD, offering numerous examples from leading companies. Examples include GE's use of digital models and twins to quickly test product designs in turbine development; BASFs use of AI to identify new molecules for use in customer formulations; and AI to generate new-product ideas, identify new-product opportunities, and even create new-product concepts. Our exploratory journey begins at the idea stage and traverses the entire new-product process to the postlaunch period. While AI might still resemble science fiction to many, that future is no longer fiction—it is here now. AI has arrived in full force! With an adoption window of about 13 years, the time is now to embrace AI in NPD in your business. AI will become a major milestone in NPD, perhaps the most important, within the decade.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"195-211"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619561","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 : 2023-11-22DOI: 10.1109/EMR.2023.3327742
Asghar Afshar Jahanshahi;Milagros Isabel Rivas Mendoza;Iliana E. Aguilar-Rodríguez
This research aims to identify critical success factors and challenges faced by nontechnical female entrepreneurs during the COVID-19 pandemic. Additionally, it aims to explore the strategic financial, marketing, and safety responses taken by these entrepreneurs to sustain their businesses. Data are collected through interviews and surveys from 106 female entrepreneurs in Peru and 100 female entrepreneurs in Ecuador. Key findings reveal the importance of physical and mental health, as well as excellent customer service, for the success of nontechnology-based businesses. Both countries express concerns about the risk of infecting family members. Resilience strategies include cost reduction and issuing bonds. Our research contributes to the understanding of nontech female entrepreneurs and their businesses in Peru and Ecuador during the ongoing COVID-19 crisis. It highlights important factors for success and survival and addresses the significant challenges faced, as well as the strategic responses employed by these entrepreneurs within the Latin American context.
{"title":"Unveiling Resilience: Strategic Responses, Success Factors, and Challenges Faced by Latin American Female Entrepreneurs Amid the COVID-19 Pandemic","authors":"Asghar Afshar Jahanshahi;Milagros Isabel Rivas Mendoza;Iliana E. Aguilar-Rodríguez","doi":"10.1109/EMR.2023.3327742","DOIUrl":"https://doi.org/10.1109/EMR.2023.3327742","url":null,"abstract":"This research aims to identify critical success factors and challenges faced by nontechnical female entrepreneurs during the COVID-19 pandemic. Additionally, it aims to explore the strategic financial, marketing, and safety responses taken by these entrepreneurs to sustain their businesses. Data are collected through interviews and surveys from 106 female entrepreneurs in Peru and 100 female entrepreneurs in Ecuador. Key findings reveal the importance of physical and mental health, as well as excellent customer service, for the success of nontechnology-based businesses. Both countries express concerns about the risk of infecting family members. Resilience strategies include cost reduction and issuing bonds. Our research contributes to the understanding of nontech female entrepreneurs and their businesses in Peru and Ecuador during the ongoing COVID-19 crisis. It highlights important factors for success and survival and addresses the significant challenges faced, as well as the strategic responses employed by these entrepreneurs within the Latin American context.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"92-107"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619641","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 : 2023-11-16DOI: 10.1109/EMR.2023.3333794
Jose Joskowicz;Daniel Slomovitz
The integration of artificial intelligence (AI) into the workplace requires commitment not only from the leadership of company directors but, equally important, from the engineers responsible for its implementation. This article presents a survey on perceptions concerning the utilization of AI tools in engineering environments. It was focused on engineers and students in the areas of electricity, electronics, and computing. The questionnaire covered demographic information, AI knowledge level, preferred tools, primary applications, perceived impact, and attitudes toward labor substitution. With the endorsement of the IEEE Uruguay Section and IEEE Region 9, the survey was distributed via email to a diverse group of potential participants, in several countries, including all IEEE members in Region 9. There were 375 replies to the survey, from 20 different countries in the Americas.
{"title":"Engineers' Perspectives on the Use of Generative Artificial Intelligence Tools in the Workplace","authors":"Jose Joskowicz;Daniel Slomovitz","doi":"10.1109/EMR.2023.3333794","DOIUrl":"https://doi.org/10.1109/EMR.2023.3333794","url":null,"abstract":"The integration of artificial intelligence (AI) into the workplace requires commitment not only from the leadership of company directors but, equally important, from the engineers responsible for its implementation. This article presents a survey on perceptions concerning the utilization of AI tools in engineering environments. It was focused on engineers and students in the areas of electricity, electronics, and computing. The questionnaire covered demographic information, AI knowledge level, preferred tools, primary applications, perceived impact, and attitudes toward labor substitution. With the endorsement of the IEEE Uruguay Section and IEEE Region 9, the survey was distributed via email to a diverse group of potential participants, in several countries, including all IEEE members in Region 9. There were 375 replies to the survey, from 20 different countries in the Americas.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"258-267"},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619642","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 : 2023-11-13DOI: 10.1109/EMR.2023.3332005
Rosiane Serrano;Daniel Pacheco Lacerda;Maria Isabel Wolf Motta Morandi;Ricardo Augusto Cassel;Carlos Alberto Diehl
This article is aimed at proposing a model for the soccer value ecosystem, identifying the actors present and the existing relationships. Ecosystems are interconnected communities that rely on one another for their survival, and soccer is no exception. In this case, the term ecosystem refers to a group of companies and individuals that interact and depend on each other's activities to create value for the members of the system. The success of soccer is directly or indirectly linked to the relationships among the participants, fans, and other stakeholders. The methodology employed in this article focuses on qualitative modeling, allowing comprehensive exploration and analysis of the subject. Initially, the article introduces the theme, followed by the presentation of the theoretical framework concerning the value ecosystem. The initial version of the soccer value ecosystem was subjected to analysis by participants of a soccer club who discussed each value dimension and suggested changes, such as the inclusion of services and actors. By depicting soccer as a value ecosystem, our objective was to emphasize a network of productive and economic activities that go beyond the game itself. Moreover, this perspective highlights the importance of collaborating with other productive sectors, showcasing their interdependence and the potential for joint actions in the overall value creation process. To capture the complexity of these relationships, the traditional value chain incorporating new actors and elements was reimagined.
{"title":"Soccer Value Ecosystem: Proposal Based on Brazilian Soccer","authors":"Rosiane Serrano;Daniel Pacheco Lacerda;Maria Isabel Wolf Motta Morandi;Ricardo Augusto Cassel;Carlos Alberto Diehl","doi":"10.1109/EMR.2023.3332005","DOIUrl":"10.1109/EMR.2023.3332005","url":null,"abstract":"This article is aimed at proposing a model for the soccer value ecosystem, identifying the actors present and the existing relationships. Ecosystems are interconnected communities that rely on one another for their survival, and soccer is no exception. In this case, the term ecosystem refers to a group of companies and individuals that interact and depend on each other's activities to create value for the members of the system. The success of soccer is directly or indirectly linked to the relationships among the participants, fans, and other stakeholders. The methodology employed in this article focuses on qualitative modeling, allowing comprehensive exploration and analysis of the subject. Initially, the article introduces the theme, followed by the presentation of the theoretical framework concerning the value ecosystem. The initial version of the soccer value ecosystem was subjected to analysis by participants of a soccer club who discussed each value dimension and suggested changes, such as the inclusion of services and actors. By depicting soccer as a value ecosystem, our objective was to emphasize a network of productive and economic activities that go beyond the game itself. Moreover, this perspective highlights the importance of collaborating with other productive sectors, showcasing their interdependence and the potential for joint actions in the overall value creation process. To capture the complexity of these relationships, the traditional value chain incorporating new actors and elements was reimagined.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"177-194"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659366","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 : 2023-11-10DOI: 10.1109/EMR.2023.3331247
Randy A. Williams;Gazi Murat Duman;Elif Kongar;Dan Tenney
Business intelligence (BI) systems are a suite of technologies enabling rapid decision making in modern business environments of rapidly changing market dynamics and exploding data volumes. BI implementation is intended to enable enterprises to become data driven, delivering actionable insights based on the factual synthesis of up-to-the-minute information. Contrary to its vastly increasing importance and investment, research suggests a majority of BI implementations fail to achieve successful results . Relevant studies fall short of explaining failures across various deployment sizes and their overall impact. In an effort to address the gap, this article attempts to assess the drivers of failed BI implementation across scenarios using the expert opinion of practitioners. Using the technology, organization, and process framework, the analysis provides a ranking of failure drivers under three deployment scenarios: enterprise wide, departmental or business unit level, and small team or individual-sized deployments. Practitioners cannot assume that a one-size-fits-all model for explaining BI implementation failure is appropriate. To create a more holistic evaluation framework, the analytical hierarchy process is adopted to provide the evaluation of significance for each perspective and criterion under alternate scenarios. The findings will enable decision makers to make more informed investment decisions, providing significant savings while contributing to literature via a customizable data-driven model.
商业智能（BI）系统是一整套技术，能够在市场动态瞬息万变、数据量爆炸式增长的现代商业环境中实现快速决策。实施商业智能的目的是使企业成为数据驱动型企业，根据最新信息的事实综合提供可行的见解。尽管商业智能的重要性和投资日益增加，但研究表明，大多数商业智能实施都未能取得成功。相关研究未能解释各种部署规模的失败及其总体影响。为了弥补这一不足，本文试图利用从业人员的专家意见，评估各种情况下 BI 实施失败的驱动因素。利用技术、组织和流程框架，分析提供了三种部署情景下失败驱动因素的排名：企业范围、部门或业务单位级别以及小型团队或个人规模的部署。实践者不能假定 "一刀切 "的模式适用于解释 BI 实施失败。为了创建一个更全面的评估框架，我们采用了层次分析法，在不同情况下对每个角度和标准的重要性进行评估。研究结果将使决策者能够做出更明智的投资决策，节省大量资金，同时通过可定制的数据驱动模型为文献做出贡献。
{"title":"Understanding Business Intelligence Implementation Failure From Technology, Organization, and Process Perspectives","authors":"Randy A. Williams;Gazi Murat Duman;Elif Kongar;Dan Tenney","doi":"10.1109/EMR.2023.3331247","DOIUrl":"10.1109/EMR.2023.3331247","url":null,"abstract":"Business intelligence (BI) systems are a suite of technologies enabling rapid decision making in modern business environments of rapidly changing market dynamics and exploding data volumes. BI implementation is intended to enable enterprises to become data driven, delivering actionable insights based on the factual synthesis of up-to-the-minute information. Contrary to its vastly increasing importance and investment, research suggests a majority of BI implementations fail to achieve successful results . Relevant studies fall short of explaining failures across various deployment sizes and their overall impact. In an effort to address the gap, this article attempts to assess the drivers of failed BI implementation across scenarios using the expert opinion of practitioners. Using the technology, organization, and process framework, the analysis provides a ranking of failure drivers under three deployment scenarios: enterprise wide, departmental or business unit level, and small team or individual-sized deployments. Practitioners cannot assume that a one-size-fits-all model for explaining BI implementation failure is appropriate. To create a more holistic evaluation framework, the analytical hierarchy process is adopted to provide the evaluation of significance for each perspective and criterion under alternate scenarios. The findings will enable decision makers to make more informed investment decisions, providing significant savings while contributing to literature via a customizable data-driven model.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"151-176"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135612018","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 : 2023-11-09DOI: 10.1109/EMR.2023.3331151
Afef Saihi;Mohamed Ben-Daya;Rami As'ad
In today's competitive industrial environment, the implementation of maintenance digital technologies has become a necessity for organizations to enhance operational efficiency and ensure the longevity of their assets. However, many organizations struggle with the successful integration of these technologies due to the lack of a structured approach to implementation. This article proposes a comprehensive seven-stage framework for maintenance digital transformation (MDT) implementation based on a thorough review of the literature, industry best practices, and expert empirical validation. The proposed framework covers the entire MDT implementation process, from planning and technology selection to continuous improvement and sustaining success. It offers a systematic and practical approach to help organizations navigate through the complexities associated with MDT. The main contribution of this conceptual framework lies in its holistic approach, which encompasses various enablers such as leadership support and technology and data management, and its provision of a structured process for implementation. While the framework provides a practical guide for organizations and serves as a useful tool for maintenance professionals seeking to leverage MDT to achieve operational excellence and competitive advantage, there are still some limitations to its development. Future research is warranted to further refine and apply the framework in a real industrial setting.
{"title":"Advancing Maintenance Digital Transformation: A Conceptual Framework to Guide Its Effective Implementation","authors":"Afef Saihi;Mohamed Ben-Daya;Rami As'ad","doi":"10.1109/EMR.2023.3331151","DOIUrl":"10.1109/EMR.2023.3331151","url":null,"abstract":"In today's competitive industrial environment, the implementation of maintenance digital technologies has become a necessity for organizations to enhance operational efficiency and ensure the longevity of their assets. However, many organizations struggle with the successful integration of these technologies due to the lack of a structured approach to implementation. This article proposes a comprehensive seven-stage framework for maintenance digital transformation (MDT) implementation based on a thorough review of the literature, industry best practices, and expert empirical validation. The proposed framework covers the entire MDT implementation process, from planning and technology selection to continuous improvement and sustaining success. It offers a systematic and practical approach to help organizations navigate through the complexities associated with MDT. The main contribution of this conceptual framework lies in its holistic approach, which encompasses various enablers such as leadership support and technology and data management, and its provision of a structured process for implementation. While the framework provides a practical guide for organizations and serves as a useful tool for maintenance professionals seeking to leverage MDT to achieve operational excellence and competitive advantage, there are still some limitations to its development. Future research is warranted to further refine and apply the framework in a real industrial setting.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"121-150"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135561943","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 : 2023-10-30DOI: 10.1109/EMR.2023.3328780
Antonio Piepoli;Francesco Arcidiacono;Luigi Jesus Basile;Roberta Pellegrino;Florian Schupp;Tobias Zuehlke
Several automotive industry players are investing in the implementation of Industry 4.0 (I4.0). However, the adoption of I4.0 technologies in automotive supply chains (SCs) is still not uniform, which limits the value creation of advanced technologies. In particular, suppliers, which play a crucial role in the automotive SC, often struggle in their I4.0 projects. Since their adoption of I4.0 technologies can significantly impact the overall performance and value creation within the SC, it is crucial to gain insights into actions and strategies that can support suppliers’ I4.0 implementations. Building on a multiple case study approach, this article aims at providing guidance to prospective I4.0 adopters by investigating the organizational-level actions that support I4.0 projects, the financial capital and KPIs typically used by suppliers, and the resulting impact of I4.0 technology implementation on business performance. Evidence shows that the planning stage of I4.0 initiatives, from the organizational-level actions to the investment strategy, is a complex and strategic stage for ensuring success. Equally important are the external collaborations, which ensure that firms are in line with technological innovation and market needs. Finally, results show that the implementation of more advanced I4.0 technologies is correlated with a more robust improvement in business performance.
{"title":"The Interplay Between Industry 4.0 Technologies and Business Performance: Evidence From a Multiple Case Study in the Automotive Sector","authors":"Antonio Piepoli;Francesco Arcidiacono;Luigi Jesus Basile;Roberta Pellegrino;Florian Schupp;Tobias Zuehlke","doi":"10.1109/EMR.2023.3328780","DOIUrl":"10.1109/EMR.2023.3328780","url":null,"abstract":"Several automotive industry players are investing in the implementation of Industry 4.0 (I4.0). However, the adoption of I4.0 technologies in automotive supply chains (SCs) is still not uniform, which limits the value creation of advanced technologies. In particular, suppliers, which play a crucial role in the automotive SC, often struggle in their I4.0 projects. Since their adoption of I4.0 technologies can significantly impact the overall performance and value creation within the SC, it is crucial to gain insights into actions and strategies that can support suppliers’ I4.0 implementations. Building on a multiple case study approach, this article aims at providing guidance to prospective I4.0 adopters by investigating the organizational-level actions that support I4.0 projects, the financial capital and KPIs typically used by suppliers, and the resulting impact of I4.0 technology implementation on business performance. Evidence shows that the planning stage of I4.0 initiatives, from the organizational-level actions to the investment strategy, is a complex and strategic stage for ensuring success. Equally important are the external collaborations, which ensure that firms are in line with technological innovation and market needs. Finally, results show that the implementation of more advanced I4.0 technologies is correlated with a more robust improvement in business performance.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"108-120"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135260925","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}
Industrial mining operations and associated activities cause adverse and sometimes irrevocable harm to ecosystems. Uncontrolled quarrying impacts the local geology and vegetation, thus it is important to examine local tree varieties, also known as alpha diversity, and species turnover over landscape gradients, known as beta diversity. This has prompted a review of three important issues: 1) How has mining affected the region's alpha and beta diversity? 2) How can floral changes along altitudinal gradients impact the composition of tree communities? and 3) Factors able to forecast changes in the environmental sustainability? A case study analysis was conducted to address these questions with the estimation of the significance value index for pioneer and successional tree species, utilizing certain phytosociological characteristics. The findings of the study indicated a limited range of species variety and carbon storage across three distinct elevation gradients, specifically those exceeding 450 m, those at precisely 450 m, and below 450 m. The results of this study provide a theoretical basis for the implementation of management strategies aiming to achieve sustainable practices in industrial operations. Additionally, it will contribute to multidisciplinary environmental sustainability science by employing scientific ecosystem analysis as a framework.
{"title":"Effect of Industrial Activities Vis-à-Vis Ecological Imbalance–Sustainability Perspective","authors":"Meenakshi Singh;Piyush Choudhary;Nikunj Kumar Jain;Bhavya Mishra;Atul Srivastava","doi":"10.1109/EMR.2023.3326817","DOIUrl":"10.1109/EMR.2023.3326817","url":null,"abstract":"Industrial mining operations and associated activities cause adverse and sometimes irrevocable harm to ecosystems. Uncontrolled quarrying impacts the local geology and vegetation, thus it is important to examine local tree varieties, also known as alpha diversity, and species turnover over landscape gradients, known as beta diversity. This has prompted a review of three important issues: 1) How has mining affected the region's alpha and beta diversity? 2) How can floral changes along altitudinal gradients impact the composition of tree communities? and 3) Factors able to forecast changes in the environmental sustainability? A case study analysis was conducted to address these questions with the estimation of the significance value index for pioneer and successional tree species, utilizing certain phytosociological characteristics. The findings of the study indicated a limited range of species variety and carbon storage across three distinct elevation gradients, specifically those exceeding 450 m, those at precisely 450 m, and below 450 m. The results of this study provide a theoretical basis for the implementation of management strategies aiming to achieve sustainable practices in industrial operations. Additionally, it will contribute to multidisciplinary environmental sustainability science by employing scientific ecosystem analysis as a framework.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"76-91"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135156217","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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