Recent technological developments and advances in Artificial Intelligence (AI) have enabled sophisticated capabilities to be a part of Digital Twin (DT), virtually making it possible to introduce automation into all aspects of work processes. Given these possibilities that DT can offer, practitioners are facing increasingly difficult decisions regarding what capabilities to select while deploying a DT in practice. The lack of research in this field has not helped either. It has resulted in the rebranding and reuse of emerging technological capabilities like prediction, simulation, AI, and Machine Learning (ML) as necessary constituents of DT. Inappropriate selection of capabilities in a DT can result in missed opportunities, strategic misalignments, inflated expectations, and risk of it being rejected as just hype by the practitioners. To alleviate this challenge, this paper proposes the digitalization framework, designed and developed by following a Design Science Research (DSR) methodology over a period of 18 months. The framework can help practitioners select an appropriate level of sophistication in a DT by weighing the pros and cons for each level, deciding evaluation criteria for the digital twin system, and assessing the implications of the selected DT on the organizational processes and strategies, and value creation. Three real-life case studies illustrate the application and usefulness of the framework.
翻译:最近的技术发展和人工智能(AI)的进展使尖端能力成为数字双元的一部分,实际上使得有可能将自动化引入工作过程的所有方面。鉴于DT能够提供的这些可能性,执业者在实际部署DT时,在选择何种能力时面临越来越困难的决定;这一领域缺乏研究也没有帮助;导致将预测、模拟、AI和机器学习(ML)等新兴技术能力作为DT的必要组成部分进行重塑和再利用。在DT中,不适当地选择能力可能导致错失机会、战略错配、期望过高以及它有可能被执业者仅仅当作合金加以拒绝。为缓解这一挑战,本文件提出了在18个月内根据设计科学研究(DSR)方法设计和开发的数字化框架。该框架有助于执业者通过权衡每一层次的利弊,决定数字双系统的评价标准,以及评估选定的DT对组织过程和战略的影响以及三个实际生命案例研究的应用和价值框架。