Preventing Analysis Paralysis in Digital Transformation

Because digital transformation depends so heavily on data — connecting it, sharing it, analyzing it, and more — companies will likely need to add data-oriented talent to set the stage for success.

This will look different for every company. One area to look at first is technology leadership, a C-level decision-maker who can hire additional talent, make important data-related decisions confidently, and select technologies or outside providers to help with the overall effort. You may also need to bring in technologists and IT support staff, data specialists, master data management experts, or those with domain experience in analytics.

Again, the idea is not to hire a huge team that doesn’t have anything to do yet. Rather, you need to identify the gaps in your current talent and start hiring the people who can inform your digital transformation efforts, speak from experience, and raise their hand if they see analysis paralysis starting to set in.

The operative word here is “some.” Trying to migrate all your data to the cloud at once is an assignment nearly guaranteed to cause problems. Instead, start with one set of data that you can move to the cloud without really changing its role in the workflow.

For example, you may try a cloud collaboration tool that lets designers and engineers share data for 3D models in order to communicate changes, comments, and mark-ups directly and iterate more efficiently. Or start with a cloud-based data storage solution that centralizes design files so new projects can quickly locate and reuse appropriate data instead of starting from scratch. Or a product lifecycle tool that stores project management data in the cloud and automates certain steps in the product development workflow to keep jobs moving. Any of these can be done in phases, such as for a single project or product type.

You may have other ideas. Ask yourself: what if my two most important teams always had access to a specific set of critical data, no matter where they were, or when they needed it, or what device they were using? Identifying just one of these could be the ideal starter project for your digital transformation.

Generative design is a form of artificial intelligence (AI) that uses the power of the cloud to accelerate design ideation. It uses algorithms and machine learning to explore a multitude of potential solutions to a product design challenge, and it can optimize these solutions in advance for performance, cost, or manufacturability.

Many people associate generative design with odd or organic-looking objects that can only be 3D printed. But generative design can easily be limited (or “constrained”) to produce ideas that can be manufactured on the equipment you already have in your manufacturing department.

Pairing generative design with a 2.5-axis constraint, for example, typically generates ideas that look much more familiar to designers and engineers. Yet these ideas still push the envelope for creativity, unlocking the potential to think about product design challenges in new ways. Many companies are using generative design to incrementally improve the functionality or cost of proven components or generate, sort, and prioritize ideas for product designers to pursue.

The beauty of generative design in the cloud is that you aren’t using your own compute power and you can get many hundreds more workable ideas in significantly less time. Choosing a specific product design challenge to tackle — whether it’s lightweighting, part consolidation, or performance optimization — may be a great first step for your digital transformation.

Taking full advantage of connected digital design tools is a key aspect of digital transformation. One to consider for starters is a pilot project that connects mechanical design with simulation.

Giving the mechanical engineering department direct access to manufacturing simulation software can bring speed and confidence to the process and allow the same team to produce more effective designs in less time. Comprehensive finite element analysis (FEA), for example, can be used to pre predict product performance through linear, nonlinear, thermal, and dynamic analyses. With this information at their fingertips — again, in a connected environment — teams can optimize designs more rapidly as well as validate product behavior before manufacturing.

Both of these capabilities can improve product quality and reduce the risk of errors, resulting in better products available sooner. In addition, proving the value of simulation can be a great way to get some quick and measurable wins.

These kinds of quick wins are crucial for avoiding analysis paralysis. Getting out of the planning phase and into the doing phase gives companies valuable experience you can build on for the next project, and the next, and the next. Before you know it, your digital transformation will be fully underway.