Bringing a new medicine to patients depends on efficient tech transfer – the handover of recipe and process data between sites. Previously, this process was slowed by 12 disconnected information silos, lacking standardization, which led to delays and inefficiencies. The team implemented Basecamp 2.0, a digital platform that standardizes and harmonizes recipes, workflows and process knowledge across the drug development life cycle. It enables faster, more reliable transfers tailored to site-specific needs. This reduced tech transfer timelines from 12 to six months and has been scaled across Roche’s Drug Substance Network. Roche now leads the industry in this space and is sharing the model with peers and consortia.USE CASE Intelligent authoring – automated recipe management: Roche, Basel, Switzerland Previously, 30 configuration engineers manually configured more than 6,000 customized chiller orders per year by evaluating millions of component combinations. The response time was long, and configuration was not the most cost-effective. Midea’s Chongqing site used a multi-physics simulation to build a configuration data platform consisting of more than 100 high- precision physical models. The platform creates all alternative chillers virtually and calculates 46 core parameters (such as power and pressure drop) for each virtual chiller by solving 420 non- linear equations derived from 15 types of chiller operating scenarios. By evaluating the simulated performances for millions of chillers, a mixed integer non-linear programming (MINLP)-based optimization model generates the most cost- effective configuration and automates its quotation in one click.USE CASE One-click intelligent configuration and quotation based on multi-physics simulation: Midea, Chongqing, China The survey indicated that today, leading companies are enhancing efficiency and responsiveness by using automation, digitalization and real-time visibility in their supply chains, with many focusing on streamlining operations, digitalizing processes and investing in demand-sensing tools for proactive decision-making. Near real-time visibility and advanced production control systems are further optimizing workflows and coordination. By 2030, the survey disclosed that leading companies will likely shift towards modernizing legacy systems, implementing network-wide asset visibility and harnessing big data and advanced analytics for predictive insights. Companies are also creating and taking part in technology hubs that drive innovation as well as deploying real-time ERP systems for rapid disruption response. AI-driven decision- making, integrated with supply chain digital twins, will enable advanced scenario planning and optimization, creating a highly adaptive, data-driven and resilient supply chain ecosystem (Figure 2).3.3 Technology adoption From Shock to Strategy: Building Value Chains for the Next 30 Years 12

From Shock to Strategy 2025