A digital twin is a virtual model and detailed representation of a system (e.g., product design) or entity (e.g., factory) that can be used to understand performance, improve processes and create revenue opportunities from services A digital copy or replica of the real thing is a means to an end, for example, to optimize the operations of a plant or validate the expected behaviour of product design. Companies that own or operate products and facilities face an uphill battle when implementing their digital twin strategy due to virtual copies that don’t match reality; this ignition guide will help enterprise architecture and technology innovation leaders plan and build a digital twin pilot in an intentional, lean and business-outcome-oriented way.
This guide is tailor-made for short-cycle projects that take between a few weeks to a month to execute and may not require extensive planning to get started or monitoring after launch. Starting with a MVP – minimum viable product, significantly lowers the risks of commercial mistakes, such as investing a large sum of money in the wrong idea or running over budget. An MVP also significantly lowers the risks of technical mistakes, such as collecting too much data, using too complicated an approach to analytics, and applying the digital twins approach to a broadly scoped problem. Starting small helps business and IT leaders iterate on the model, test whether the chosen use case will generate the intended business outcome, gauge people and technical issues, and plan for future scalability.
Resource Requirements
- Business sponsorship: Business sponsors who are able and willing to provide project funding.
- Understanding of operational aspects: Any personnel who have an end- to-end understanding of all operational and commercial aspects of the asset, process or person being modelled. Ability to source the right data: Knowledge of batch (historical) or streaming data located both within and outside the enterprise.
- Utilization of software components: Open-or vendor-sourced algorithms and models that have reached a level of maturity that makes them suitable as lightweight solutions for diverse applications.
- Use of consumable dashboards: Dashboards that enable decision makers to visualize and interact with data, alongside tools and training that teach them how to make more informed data-driven business decisions.
Process Flow
Step 1: Identify the Use Case
- Work with business sponsors to create a list of potential business problems to solve with a digital twin.
- Use a broad set of tools and templates to drive conversations with suitable business sponsors to create a list of potential business problems that can be solved using a digital twin.
- Inform suitable business sponsors that, at this stage, the priority is to explore potential business problems to solve using a digital twin.
- Determine which identified business problems are most feasible for digital twin modelling.
Step 2: Identify Data and Resource Needs
- Identify what needs to be modelled.
- Identify necessary data assets and the people who have access to it and an understanding of it.
- Ensure that the data is appropriately cleaned, processed and integrated after it is gathered.
- Identify model needs – Create a list of all the technology infrastructure elements and the competencies that would be required to create the digital twin model. The models must include representations of key features, critical variables, and a description of those features and variables as algorithms.
- Although analytics are optional elements of a digital twin, we recommend you create descriptive analytics for your MVP. Descriptive analytics allow organizations to understand changes that have occurred to an asset or a process over a set period.
- Examine resource gaps and determine how they should be filled.
Step 3: Make an Incremental Business Case
- Document a business case roadmap that outlines business outcomes incrementally and justifies the minimal resource requirements to achieve each of them. Make sure business outcomes are clearly defined before the project begins, and don’t make stakeholders wait too long before they start to see value.
- Proactively anticipate and respond to stakeholder motivations, concerns and pushbacks about the digital twin and its use case.
Step 4: Build and Launch the Model
- Build the digital twin based on batch (historical) data that you identified with operational team guidance. This model must include representations of key features, critical variables and descriptions of the underlying asset, process or person. Then, update the model based on streaming data and team feedback to improve accuracy and applicability of the digital twin model and its insights.
- Launch the digital twin model by demonstrating how the information from the digital twin can drive actions to generate business value. Before you launch it, work with the personnel you identified to ensure that the information you get from the digital twin is ready for business actionability.
Step 5: Facilitate Business Decision Making from Insights Generated
- Build a simple and effective user interface to facilitate intuitive consumption of digital twin insights, ensuring that business leaders can make effective decisions from the digital twin capability. Additionally, prepare to expand the scope of the digital twin to allow a larger group of business stakeholders to benefit from it in future.
- Build dashboards with descriptive analytics and control mechanisms to enable decision makers to visualize and interact with the digital twin insights and make informed decisions.
- Prepare to scale the MVP for new audiences and more use cases. Ensure that business leaders are aware of the barriers to scaling up the digital twin.
Kanoo Elite, with its years of experience with Digital Twins, offers clients an unmatched value proposition to address their transformation and innovation needs. Kanoo Elite will be an assured assistant alongside you, from initial concept through industrialization, to invent your digital twin product and services of tomorrow. For over 30 years, the company has provided expertise in aerospace, automotive, defence, energy, finance, life sciences, railway and telecommunications, and our portfolio provides Owner Operators and EPCs – with a full suite of digital engineering solutions that span every phase of our capital project, from process simulation and design to execution and operator training which will prepare you to model and build the perfect Digital Twin MVP.
