Artificial Intelligence (AI) and Data Sharing in Manufacturing, Production and Operations Management Research
The term Artificial Intelligence (AI) is being used as an umbrella term referring to the digital technologies performing activities, tasks and decisions normally performed by human intelligence (Pomerol, 1997). Recently, the use of AI techniques has been brought to the forefront of attention due to the wide range of organisational operations that could be transformed through multidisciplinary AI approaches based on data sharing, gathering and analytics (Baryannis et al., 2019; Spanaki et al. 2018).
The applications of AI, big data analytics and intelligent processes and practices (through the use of IoT, technology, machine learning techniques, cyber-physical systems, blockchain etc.) in Manufacturing, Production and Operations could pose multiple challenges and managerial implications. The vast range of challenges could span from difficulties in the use and adoption of these applications, identifying the required skills and capabilities for the employees, to a wide variety of productivity and performance problems. There are multiple opportunities but also respective challenges for the supported supply management tasks, therefore the research should support the operations by promoting AI approaches for smart and intelligent operations in multiple industrial sectors, while predicting weaknesses and risks (Sivarajah et al.,2019; Giannakis & Papadopoulos, 2016).
There is emerging anecdotal evidence that the use of AI and data analytics for manufacturing, production can fundamentally reshape the existing operational practices and tasks (Sivarajah et al., 2019; Papadopoulos et al., 2017; Dubey et al., 2019). Various organizations have already applied AI and data analytics for humanitarian operations addressing, healthcare and hunger challenges through early-stage medical diagnosis, identifying agrifood supply chain risk, optimized food distribution, effective crisis response by quickly and accurately forecasting natural disasters (Google AI, 2019;) and also effective food waste management (Irani et al., 2018; Despoudi et al., 2018) . However, scaling up AI usage could have some significant bottlenecks, such as misuse of AI algorithms, privacy breach and data accessibility (Spanaki et al., 2019). The use of AI just like other technological developments comes with its own challenges and risks in a commercial environment that can lead it to being misused, lead to user distrust and raise privacy and ethical concerns.
The special issue aims to promote the research around the area of AI and data sharing in manufacturing, production and operations research. The papers will develop concepts, methods, models and solutions that fit within the scope of the International Journal of Production Research.
Topics of interest
Some of the indicative topics include but are not limited to the following:
- Decision-making in Operations and Production Management through applications of AI.
- AI and robotisation of processes (e.g. Human-in-the-loop, cyber-physical systems)
- Opportunities and challenges of the AI adoption in Manufacturing and Production
- AI in Operations and Supply Chain Management: concepts, theories and applications
- AI and data analytics - the digital disruption of operating models
- AI approaches for innovation in the procurement, practices and services
- The implications of AI in sustainability, resilience and risk management
Important dates
Paper submission deadline: 28th September 2020
Complete first round of review: 28th November 2020
Selected authors submit revision: 28th February 2021
Complete second round of review (with accept/reject decision): 28th May 2021
Special Issue ready for submission to Journal: 28th October 2021
Submission guidelines
Papers submitted to the special issue will be subject to the Journal review process and submission guidelines. For further guidance on how to submit your manuscript to this Special Issue, visit our Instructions for Authors page.
Guest editors
Professor Thanos Papadopoulos, University of Kent, UK
Dr Uthayasankar Sivarajah, University of Bradford, UK
Dr Konstantina Spanaki, Loughborough University, UK
Dr Stella Despoudi, Aston Business School, UK
Professor Angappa Gunasekaran, California State University, USA
制造、生产和运营管理研究中的人工智能（AI）和数据共享
人工智能（AI）一词被用作一个总括术语，指的是执行人类智能通常执行的活动、任务和决策的数字技术（Pomerol，1997）。最近，由于可以通过基于数据共享、收集和分析的多学科人工智能方法转变的广泛组织运营，人工智能技术的使用已经成为人们关注的焦点（Baryannis等人，2019；Spanaki等人。2018）。
人工智能、大数据分析和智能流程和实践（通过使用物联网、技术、机器学习技术、网络物理系统、区块链等）在制造、生产和运营中的应用可能造成多重挑战和管理影响。各种各样的挑战可能从使用和采用这些应用程序的困难、确定员工所需的技能和能力到各种各样的生产力和绩效问题。被支持的供应管理任务有多种机会，但也有各自的挑战，因此，研究应通过在多个工业部门推广智慧和智能运营的人工智能方法来支持运营，同时预测弱点和风险（Sivarajah等人，2019；Giannakis&Papadopoulos，2016）。
有新的轶事证据表明，在制造、生产中使用人工智能和数据分析可以从根本上重塑现有的运营实践和任务（Sivarajah等人，2019；Papadopoulos等人，2017；Dubey等人，2019）。各种组织已经将人工智能和数据分析通过早期医疗诊断、识别农产品供应链风险、优化食品配送、通过快速准确地预测自然灾害有效应对危机（谷歌人工智能，2019；)以及有效的食物废物管理（Irani等人，2018；Despoudi等人，2018）应用于人道主义行动，医疗和饥饿挑战。然而，扩大人工智能的使用可能会遇到一些严重的瓶颈，例如人工智能算法的滥用、隐私泄露和数据可访问性（Spanaki等人，2019）。与其他技术发展一样，人工智能的使用在商业环境中也有其自身的挑战和风险，这可能导致人工智能被滥用，导致用户不信任，并引发隐私和道德问题。
本期专刊旨在推动制造业、生产和运营中人工智能和数据共享领域的研究。这些论文将提出概念，方法，模型和解决方案，以适应《国际生产研究杂志》的范围。
感兴趣的主题
一些指示性主题包括但不限于以下内容：
• 运用人工智能进行生产经营决策
• 流程的人工智能和自动化（例如人工介入、信息物理系统）
• 人工智能在制造业和生产中的机遇和挑战
• 运营与供应链管理中的人工智能：概念、理论与应用
• 人工智能和数据分析——运营模式的数字化颠覆
• 采购、实践和服务创新的人工智能方法
• 人工智能在可持续性、弹性和风险管理中的意义
重要日期
论文提交截止日期：2020年9月28日
完成第一轮审查：2020年11月28日
选定的作者提交修订版：2021年2月28日
完成第二轮评审（含接受/拒绝决定）：2021年5月28日
特刊准备提交：2021年10月28日
提交指南
提交给特刊的论文将遵循期刊审核流程和提交指南。有关如何向本期特刊投稿的进一步指导，请访问我们的作者指南页面。
特邀编辑
Professor Thanos Papadopoulos肯特大学, 英国
Dr Uthayasankar Sivarajah, 布拉德福德大学, 英国
Dr Konstantina Spanaki, 拉夫堡大学, 英国
Dr Stella Despoudi, 阿斯顿商学院, 英国
Professor Angappa Gunasekaran, 加利福尼亚州立大学, 美国