@article {Dogru2020, title = {AI in operations management: applications, challenges and opportunities}, journal = {Journal of Data, Information and Management}, volume = {2}, number = {2}, year = {2020}, pages = {67{\textendash}74}, publisher = {Journal of Data, Information and Management}, abstract = {We have witnessed unparalleled progress in artificial intelligence (AI) and machine learning (ML) applications in the last two decades. The AI technologies have accelerated advancements in robotics and automation, which have significant implications on almost every aspect of businesses, and especially supply chain operations. Supply chains have widely adopted smart technologies that enable real-time automated data collection, analysis, and prediction. In this study, we review recent applications of AI in operations management (OM) and supply chain management (SCM). Specifically, we consider the innovations in healthcare, manufacturing, and retail operations, since collectively, these three areas represent a majority of the AI innovations in business as well as growing problem areas. We discuss primary challenges and opportunities for utilizing AI in those industries. We also discuss trending research topics with significant value potential in these areas.}, keywords = {AI, artificial intelligence, Artificial Intelligence (AI), automation, machine learning, machine learning (ML), ml, om, operations management, Operations Management (OM), Robotics, scm, supply chain management, Supply Chain Management (SCM)}, issn = {2524-6356}, doi = {10.1007/s42488-020-00023-1}, author = {Dogru, Ali K and Keskin, Burcu B} } @conference {Das2020, title = {Learning occupational task-shares dynamics for the future of work}, booktitle = {AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society}, year = {2020}, pages = {36{\textendash}42}, abstract = {The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations{\textquoteright} underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.}, keywords = {AI, automation, future of work, Occupational Task Demands}, isbn = {9781450371100}, doi = {10.1145/3375627.3375826}, author = {Das, Subhro and Steffen, Sebastian and Clarke, Wyatt and Reddy, Prabhat and Brynjolfsson, Erik and Fleming, Martin} } @article {2018, title = {Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making}, journal = {Business Horizons}, volume = {61}, year = {2018}, publisher = {Elsevier}, edition = {586}, chapter = {577}, abstract = {Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making process typically characterized by uncertainty, complexity, and equivocality. WIth a greater computational information processing capacity and an analytical approach, AI can extend humans{\textquoteright} cognition when addressing complexity, whereas human can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contribution.}, keywords = {Analytical and intuitive decision making, artificial intelligence, Human augmentation, Human-machine symbiosis, Organization decision making}, doi = {10.1016/j.bushor.2018.03.007}, author = {Mohammad Hossein Jarrahi} } @article {2018, title = {Occupational Classifications With A Machine Learning Approach}, year = {2018}, keywords = {administrative data, machine learning, occupational classifications, transaction data, UMETRICS}, url = {https://www.iza.org/publications/dp/11738/occupational-classifications-a-machine-learning-approach}, author = {Akina Ikudo and Julia Lane and Joseph Staudt and Bruce A. Weinberg} } @article {2018, title = { Robots worldwide: The impact of automation on employment and trade}, number = {36}, year = {2018}, month = {10/2018}, keywords = {economics of automation, Employment, off-shoring, re-shoring, robot, technology}, url = {https://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_648063.pdf}, author = {Francesco Carnonero and Ekkehard Ernst and Enzo Weber} } @article {1994, title = {Design concepts of computer-aided integrated manufacturing systems: Work-psychological concepts and empirical findings}, journal = {International Journal of Industrial Ergonomics}, volume = {17}, year = {1994}, publisher = {Elsevier}, chapter = {11-19}, abstract = {The research project "GRIPS" is investigating the design of computer-aided integrated manufacturing systems from a work psychological perspective. The goal is to develop and empirically support adequate design concepts. The project consists of three phases. Evidence from a broad questionnaire survey indicates that most CIM implementations fail to meet expectations associated therewith. Based on the assumption that only the joint optimization of social and technical system results in humane working conditions and economic efficiency, implementations and use of CIM systems has been investigated in 60 companies in Switzerland. THe conceptual framework distinguishes technically-oriented and work-oriented design concepts on four levels; the enterprise, the organizational unit, the group and the individual. Work-oriented manufacturing systems - as opposed to technically-oriented ones - are characterized by decentralization, functional integration, work in self-regulated groups and complete and challenging tasks. The findings support the hypothesis that work-oriented design concepts are related to higher efficiency and better achievement of goals and pursued with the use of new technologies. In the third phase 12 companies have been selected for detailed case studies: The companies are comparable concerning product range and manufacturing conditions but different on the level of work-orientation.}, keywords = {Computer-Integrated-Manufacturing CIM, Organizational design, Production design concepts, Socio-technical system approach, Work psychology, Work-orientation}, author = {C. Kirsh and O. Strohm and E. Ulich} }