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.

本文提出了一个数字化框架，通过权衡每个层次的利弊，制定数字孪生系统的评价标准，评估所选数字孪生系统对组织流程、策略和价值创造的影响，来帮助实践者选择数字孪生系统中的适当复杂度，同时提出数字孪生系统能提供诸如预测、模拟、人工智能和机器学习等新兴能力的误用选择可能带来的风险。三个实际案例证明了该框架的应用和实用性。