Before building a complex digital twin, Takeda analyzed historical manufacturing data. A simple end-to-end model identified that changing set points for six parameters—within existing validated ranges—could boost yield by 35%, demonstrating massive value in data that companies already possess.

Instead of criticizing the current system, frame a data transformation project as a way to eliminate critical blind spots. Present leadership with specific, unanswerable questions that the new model can solve, linking visibility to tangible outcomes like higher performance and lower acquisition costs.

Instead of aiming for a massive, all-encompassing digital twin, identify a critical business bottleneck first. Build a focused, end-to-end offline model to prove its value. Only after demonstrating a clear return on investment should you scale it into a real-time, fully integrated system. This 'moonshot before Mars' approach minimizes risk and builds momentum.

The term 'digital twin' is often misused. It represents the final stage of a three-step evolution: 1) a Digital Model (offline simulation), 2) a Digital Shadow (receives real-time data), and 3) a Digital Twin. The critical distinction of a true twin is its ability to feed recommendations back to influence the physical process, creating a closed loop.

Many assume vast amounts of data are necessary for a digital twin. In reality, process validation data combined with a handful of manufacturing trends is often sufficient. The focus should be on data quality and its relevance to a specific business decision, not sheer quantity. This approach makes powerful modeling accessible much earlier.

To overcome leadership resistance to an internal tool, Walmart's PM built prototypes populated with actual production data. This tangible "what if" scenario demonstrated exactly what executives would see and the value they would get, proving far more effective than standard mockups for securing buy-in.

To sell large transformation projects, present the ambitious "North Star" goal but break it into sequential stages. Critically, Stage 1 must deliver tangible business value on its own. This approach wins over skeptics by providing an early return on investment, securing the momentum and buy-in needed for subsequent stages.

Instead of vague promises of 'efficiency,' frame software investments around specific, pre-identified workflows that already generate positive ROI. Pitch the tool by quantifying how it will improve the profitability of that existing motion. This provides a clear ROI story that resonates with finance leaders.

To secure budget and prove value, leaders must frame automation not by its outputs (e.g., containment rates) but by its impact on business fundamentals. By connecting automation results back to the root cause of the initial problem, teams can demonstrate tangible ROI in terms of growth, efficiency, or risk reduction—the language CFOs understand.