While the cyclical approach aims to achieve steady improvement in a stable environment and the extreme approach addresses cases of unlikely triumphs and disasters, a third application provides a means of developing an appropriate reaction to possible or even probable scenarios before they happen.
These might include a shift in balance of one’s business objectives, such as new emphasis on a specific growth sector; a change in corporate strategy, such as an increased commitment to reduction in use of fossil fuels; or an external change such as the operating climate if a new market entrant’s growth path continues for the next three years. In all these cases, the digital twin can be used on a “what-if” basis to find the most promising course of action, not just in production terms, but in the way F&A, HR, and the supply chain address the situation. The response that emerges will form part of the organization’s digital transformation that bridges the front and back offices, creating a unified business operation.
There is another factor here. We are going through an AI-infused revolution, and the digital twin is part of it. Machine learning (ML) – a subset of AI – is applied to the mining data received from the company’s systems. While the mining delivers information on where the bottlenecks are, ML makes predictions for key metrics and service level agreements (SLA) that will improve over time. In this way, organizations can gain foresight over business operations, enabling considered and prepared responses.
When the digital twin is leveraged to simulate changes, organizations will be able to model the impact of AI on the business. Often radical process change is required to get the best out of the new wave of intelligent automation and AI solutions. (Indeed, we’ve written another white paper on that very topic. Read Taoufik Amri’s paper on “Towards operational excellence through orchestrating machines and humans with AI”) Simulating the impact gives more weight to such approaches.