A digital twin describes the process and technology to have a complete digital representation of a physical product.The definition, use cases, and benefits of digital twins have been continually evolving.Just as one can easily update, modify, and publish a digital document, the concept of a digital twin for the manufacturing and operation of physical products is to allow quick and agile updating, modifying, and production of these products.A digital twin makes perfect sense for a company that produces a product completely in-house and then has a direct connection to the operational use of that product.Unfortunately, that is rarely the case, and the more common scenario is a supply chain to produce the product which is then sold into the market with limited connection to its operations.
A typical supply chain for a physical product might consist of raw materials, engineered materials, engineered components, and assembly before selling the product into the market.A pure digital twin model would have all of the data for the entire production of the product.In this scenario, the digital twin should contain specific details of each part of the process.The challenge is that some of the data needed might be considered proprietary by different supply chain participants.For example, engineered materials will have proprietary formulations of raw materials that will not be shared with others in the supply chain, the Original Equipment Manufacturer (OEM), or the customer of the finished product.Those creating engineered components will likely have proprietary manufacturing processes that they will not allow to be shared outside of their company.
Given the constraints on access to proprietary data, it is important to define what data is needed to achieve benefits of a digital twin approach while protecting proprietary data of the supply chain.Each member of the supply chain will likely want to acquire and track all of the data that pertains to their part of the process but then share only portions of that data with their customer.There may be scenarios where one part of the supply chain needs access to some proprietary data from another part of the supply chain, but that data is not needed by all parts of the supply chain.The issue then becomes how to share the needed data while protecting that data from others. Also, there can be competitive concerns. Take, for example, a customer doing business with competing suppliers. While the supplier is willing to share data with the customer, that data may have to be restricted within the customer’s company to ensure that it is not shared with the competing supplier. These tactical implementation issues can completely bog down a “digital twin” initiative within a company.Rather than delivering value to your customer, you end up in non-value-added discussions around protecting your data.
"Given the constraints on access to proprietary data, it is important to define what data is needed to achieve benefits of a digital twin approach while protecting proprietary data of the supply chain"
Another challenge is that digital twin data formats are often specified to be compatible with a digital design, manufacturing, and operations solution that an OEM might have chosen to implement.Having all of the data stored and tracked in a single system provides efficiency benefits for the OEM, but it can cause real problems for the supply chain.The OEM might dictate that a supplier provideits digital twin data with a specific software format.If the supplier is small or has chosen another software platform, this can create significant financial and operational challenges for the supplier.The commercial software systems usually have capabilities to restrict data to different individuals, but everyone needs to be using the software system for these to apply.
As a result, implementing a digital twin will require compromises within the supply chain.I recommend the following approach:
1. Define what data you need now and what data you need your supplier to store but not share.For example, an engineered material provider could provide the mechanical, thermal, and electrical properties of the material, when and where it was produced, and how it was transported to the customer.The supplier could also be required to store all of the raw material data they used and then provide it to the customer only if there were a failure or warranty claim in the field where this data is needed to address the failure.
2. Be sensitive to the proprietary data of your suppliers and implement procedures to protect that data.Your supply chains are proprietary to you and provide you value so you should treat their data with the same diligence as your own.Your supply chain can unravel quickly if you don’t protect your supplier’s data.
3. Where possible, push for open data formats that are not dependent on a particular software vendor.
4. Define the goals of your digital twin efforts and share these with your suppliers.If you treat your suppliers as your partners and articulate your goals, your suppliers will work with you to find solutions to the problem you are trying to address while protecting their proprietary data and utilizing the data capture systems they have put into place.The goal is to get the right data for your digital twin rather than all of the data.The models can fail to perform if there is too much data, so it is imperative to define the important data to achieve desired goals.Edicts from an OEM without discussion with the supplier can create an adversarial relationship that is not in the best interests of either party.
The digital twin idea is one that can provide tremendous value when implemented, but it can be a complex endeavor when used with a supply chain.Through open dialogue with your supply chain and clearly defining your goals and objectives over time, you can achieve incremental benefits of digital twin technology to provide value to your customers and shareholders.
