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Digital Engineering – A new approach to product innovation and lifecycle management


“57% of manufacturing companies are not able to adequately create, capture, share, and reuse knowledge across functions,” reveals a recent digital engineering survey conducted by Capgemini Research Institute. But with all the buzz around digital transformation in discrete manufacturing, what this report really highlights is where these organizations stand when it comes to becoming a truly digital engineering enterprise.

In effect, many customers that we are speaking with aren’t sure how to move away from the existing legacy system to the new engineering system. This survey also reveals that maintaining digital continuity is one of the top three trends affecting businesses of manufacturing firms. As a result, the transformation of product innovation and engineering services will take center stage for most discrete manufacturers in the foreseeable future. For this transformation to happen, a new digital engineering system will need to be put in place. This new system should streamline data flow and reduce discontinuity in PLM processes of manufacturing organizations.

Let’s take sports for example. To take home the trophy, each player must be able to share any information they have with the rest of the team. Imagine if the coach discovers a critical flaw in the opposition’s strategy during the game but can’t pass the data on to the players, or a player figures out a weakness in the defense but isn’t able to let the captain know. What good is that information if it is not delivered to the right teammate at the right time? I am sure this was not the case with “les bleus” who just brought home the most coveted trophy in world football. The flow of data across the system is crucial to win the game. It helps modify strategies in real time and do a course correction to achieve the desired results. Things are not that different when it comes to product engineering. The key here is continuity. It helps organizations manage traceability which is able to explain root causes and engineering alternatives with the help of configuration management.

A digital and data-driven approach to product innovation and engineering

In order to innovate and optimize product development, it is critical that a consistent stream of data flows through the entire product lifecycle, starting from engineering to manufacturing, to services. But the legacy systems currently in place are almost 20 years old and most functions within them run in silos. This hampers digital continuity, which is critical when it comes to product innovation. Moreover, these systems are composed of several subsystems, each carrying out additional tasks. Several of these tasks and processes work independently of one another, thus the flow of information is not consistent. The data is there, but there is lack of data association among different manufacturing units and that affects the system’s ability to use the data and produce clear objectives. Digital continuity also requires to use the right associative toolchain in order to visualize the data from across the design phase, to engineering phase until the final manufacturing phase.

This will only be possible with a consistent flow of data throughout the product engineering lifecycle. The new PLM technology that transforms the existing information system can offer these capabilities. However, such a transformation isn’t easy. It means that organizations will need to change how they work. They will have to modify their existing engineering system and move to a new one.

Companies have realized that they need good partners to embark on this transformation journey. Undertaking this transformation is not only about optimizing your information system for engineering. It will also help the business as a whole and make it more efficient. A good partner is able to handle the different phases of the product development. Not only from the technology point but also from the change management standpoint. For Capgemini, the first step is to rationalize large domains by the number of systems and subsystems that are in place by using a strategy of data filtration. We work on making the data more intelligent.

We work with our clients to create a complete transition map. This transition begins with analyzing and minimizing the transformation of data by domain. We then centralize the data into one coherent system. It is not only a question of implementing the technology but also about the execution model which will have an impact on the organization’s processes and values that will help make the business more efficient. We work with our customers to decipher many things, such as what is really behind the digital twin and digital continuity for the engineering part.

Once the transformation is complete, your employees and partners will have one version of the truth irrespective of where the data is gathered and stored. The focus will be on making sure that the data that flows through the information system is complete, available, and therefore usable.

Our latest digital engineering survey – The new growth engine for discrete manufacturers – reveals that only 21% of manufacturers are at an advanced stage of transforming product innovation and engineering, with close to a third still running pilots. Connect with us to understand why these numbers are too low and transforming product innovation and engineering is actually critical to your discrete manufacturing business.