It is exciting to know that you can create a digital replica of your business, assets, and even ecosystem! This is a digital twin which is officially defined as a virtual representation of real-world entities and processes. The market is experiencing exponential growth in the digital twin space, and just like any other technology that emerged in the past, vendors are competing to put forward the best products, and consumers are looking for the best product that fits their need. However, when it comes to digital twins, there are major blind spots that are causing failures and lack of utilization which we will briefly cover here.
Types of Digital Twins
There are asset, process, and system digital twins and the type of digital twin you decide to work with will determine the next steps you take in procurement and implementation. Asset digital twins are sometimes called status twins that are widely used across industries and available heavily on the market with IoT technology. Process digital twins are also widely used mainly to predict process optimization outcomes, improve workflows, identify system faults, and automate operations. Asset and process digital twins can be procured as products that will provide status or optimize a specific asset or process. However, system digital twin, on the other hand, is a complex implementation that requires expertise in various technologies as it is not one product, and it provides a higher level of intelligence. System digital twin includes control capabilities where the model is designed so that users may change its parameters. It allows them to see how a system will operate in different conditions (what-if scenarios).
Think of a system digital twin as an ideal orchestrator tool to customize your enterprise architecture and maximize the utilization of your fragmented siloed data, creating an organizational digital twin that enables you to forecast, test decisions, and automate business responses.
The Why & The How
What will lead you to successful digital twin procurement & implementation?
Systems digital twin is the context here for decision-makers in enterprise organizations, the focus should be on the (Why)- use cases and the (How) data sources not on (What) digital twin product will help my business. No product on the market can provide us value if we don’t know why we are using it, and how it will be used. Vendors cannot answer these questions for business as they are intimate business questions that differ from one business to another and are tied to the competitive business and their data strategy. What powers the digital twin is the data, and what yields the maximum result is knowing why it will be used in the organization.
Digital Ecosystem & Interoperability
After finding the Why & the How, now start thinking of the technology composition required knowing that the system digital twin is not one product. Digital twin architecture is essential to be designed to scale from day one. Since we know the why, a clear view of the vision, and the what, the data and systems we have, then the digital twin architecture get procured and implemented to accommodate the end goal to enable its scalability and evolution. Think of a digital twin as a child who needs to grow over time as your organization acquires more data sources and grows its business. Therefore, your infrastructure, security, intelligence, and data management framework need to be in place to offer scalability. To build a digital twin architecture, you will find that you are sourcing multiple technologies from different vendors and the focus here is on interoperability between data sources and choosing products in your digital twin stack that serve your business and data acquisition goals. Again, here in the procurement stage, if you don’t understand your use cases, data sources and the technical capabilities you need, then it is like looking for a needle in a haystack.
Why should you look at it differently?
Digital twins fail because they are approached as a traditional implementation. We are working with advanced technology -i.e. AI, and Metaverse –in a traditional mindset and this is the major challenge. To record success, we need to do two things and do them well:
Understand our business, its capabilities, and its value proposition. Once we know where we stand, we start small; identify a bite-size use case, then build a model with scalability in mind. Focus on architecture and build your ecosystem from there to support your growth and finally track your progress.
What should you avoid?
Avoid vendor lock-in. Data is the new oil, and today’s business growth opportunities lie in the story your data captures. In addition to the opportunities, data creates additional revenue streams and personalizes customer experience. Thinking of system digital twins will allow you to adopt technologies faster and embed resilience in your organization by creating the building blocks for infrastructure, intelligence, and data management.
Avoid building an ecosystem that locks you with products and lean on system integrators and service companies to help you build your capabilities, create interoperable systems, and provide you with ongoing support as your journey evolves especially if commercialization is within your strategic plans. Finally, and most importantly start with a data strategy and don’t end with it.
