Integrating People, Processes, and Technology in Engineering

Transformation thrives when people are empowered to make the most of technology. 🚀 My recent visit to the Bosch production facility for automotive and eBike drives in Miskolc, Hungary, showcased this perfectly. I was deeply impressed to see firsthand how their progress in digitalization and the implementation of the Bosch Manufacturing and Logistics Platform (BMLP) is reshaping their manufacturing operations. BMLP is a globally standardized, open IT platform that connects all stages of production and logistics. During an insightful plant tour, I observed a successful example of how the platform leads to significant improvements in efficiency, quality, and data transparency across the plant. What stood out most was seeing the passionate and enthusiastic team at Miskolc leverage this technology in action and achieving great results towards operational excellence. Here are three key areas where BMLP is contributing to the plant’s digital transformation success, powered by our NEXEED IAS: 1️⃣ Enhanced Efficiency & Reduced Downtime: The module Shopfloor Management enables a closed PDCA cycle in production by consequent integration of all relevant information in one system. This leads to quick reaction in case of deviations to minimize downtimes and safeguard the daily performance targets.   2️⃣ Improved Product Quality: Continuous monitoring throughout production stages helps the team identify issues early, ensuring top-tier quality while driving process improvements.   3️⃣ Change Management: Change management plays a crucial role in digital transformation within a plant. As seen in Miskolc, effectively managing change ensures that the workforce is engaged, and equipped to embrace new technologies, driving sustainable success. In Miskolc we have seen solutions using gamification that help to involve all associates, making the transition both engaging and effective.   I was also excited to see AI in action with a live demo of 8D Analysis using GenAI, cutting failure analysis time by half. By automating the root cause analysis process, engineers are now spending less time on administrative tasks and more on proactive problem-solving – a great example of how technology empowers people. Beyond the production lines, the most rewarding part of the visit was engaging with the team. Their passion for digitalization, commitment to upskilling, and their drive for innovation truly brought home the message: technology is only as strong as the people behind it. A special thank you to the entire Miskolc team for the inspiring discussions and warm welcome – along with Volker Schilling, Klaus Maeder, Joerg Klingler, Volker Schiek, Norbert Jung, Stephan Brand, Aemen Bouafif, and everyone who joined us on this great trip. I’m excited to see what’s next on this incredible digitalization journey!

Engineering transformation is not optional anymore, it’s a race against irrelevance! For years, we’ve all seen the same patterns in product development. - Mechanical, E/E and software teams working in isolation. - Complexity growing faster than our ability to manage it. - Errors discovered too late. - Interfaces that don’t fit. Integration often feels like assembling a puzzle… only to realize that half the pieces were built from entirely different pictures. Weeks, sometimes months, lost not because of bad engineering, but because of fragmented engineering. And yet, despite knowing these problems for years, many organizations are still waiting. Waiting for the “right moment.” Waiting for clearer standards. Waiting for others to move first. That moment is gone. Global competitors have already picked up speed and are exerting pressure. With Model-Based Systems Engineering (MBSE) and AI reaching real maturity, we finally have the tools to fix what we’ve been complaining about for a decade. The question is no longer if transformation will happen. The question is: how fast can you move? Here’s how Vlad and I currently think about it in 9 concrete steps: 1. Adopt and mature MBSE - Build system models that truly reflect your product, not just documentation. 2. Derive domain-specific models from system models - Create consistent, hierarchical product structures across all domains and disciplines. 3. Capture all engineering artifacts From requirements (RFLP) over testing to homologation, make everything explicit and create development templates. 4. Link all artifacts via a knowledge graph - Enable impact chain analysis based on a solid engineering ontology. 5. Standardize and accelerate component development - Align tools, data and processes for each discipline and component 6. Build cross-domain CI/CD pipelines - Enable fast, automated iteration across requirements, architecture, design, simulation and testing. 7. Rationalize the toolchain (APIs over UIs) - Tools must be controllable from the outside enabling agent-based workflows. 8. Make engineering knowledge machine-readable - Document not just the what, but the how and why. Only then can agents effectively navigate engineering-specific challenges. 9. Define the future work split - Clarify what engineers do and what AI agents should handle. Establish strong human-in-the-loop validation. The core message is simple: Engineering excellence in the future will not come from better tools alone. It will come from how well we connect systems, data, people and agents. Companies that start building this foundation now will gain speed. Those who wait will struggle to catch up. What’s missing from your perspective? Which steps would you add to make this transformation truly work? Timmo Sturm | Daniel Spiess | Sebastian Linzmair | Sascha Bach | Rick Bouter

IT/OT integration is how you de-risk growth.     If the top floor can’t see the shop floor in real time, quality slips, downtime grows, and batch release slows. In our world of compliance and complex supplier networks, blind spots turn into audit findings and missed delivery windows.     Here’s the core move I see working. Combine the real and digital worlds across product and production so horizontal data flows become routine. Think engineering models, test results, materials, building processes, automation code, and performance data moving between teams. Then connect the vertical path. Executives, planners, and operators sharing the same context so decisions line up with actual conditions. That’s where you get predictive maintenance instead of unplanned stops, data‑centric supply chain adjustments instead of last‑minute expedites, energy transparency that feeds credible sustainability metrics, and stronger cybersecurity plans that account for both IT and OT exposure.     Pharma adds constraints, but the pattern still holds. IoT devices can read modern and legacy equipment, extending the digital thread into your supplier ecosystem so logistics, production timing, and potential disruptions show up early. A closed loop between development, production, and optimization tightens traceability and speeds corrective action. Digital twins let engineering teams iterate quickly on both process and line design without risking validated operations.     Pick one high‑stakes decision and wire it end to end. For many, that’s batch release. Map the horizontal data you need across quality tests, materials, and line performance. Then build the vertical connection so insights reach the teams that plan, schedule, and approve. Keep the scope small, include cybersecurity from day one, and define the single source of truth for that decision. When it works, scale to the next decision. 

𝗦𝗼𝗺𝗲 𝗱𝗮𝘆𝘀 𝗜 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗻𝗴 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗲𝗮𝗺𝘀 𝗶𝘀 𝗵𝗮𝗿𝗱𝗲𝗿 𝘁𝗵𝗮𝗻 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗻𝗴 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗮𝗰𝘁𝘂𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀... 𝗮𝗻𝗱 𝗚𝗼𝗼𝗴𝗹𝗲 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗲 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗮 𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 ‘𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀. For many organizations, building a seamless digital thread between PLM systems like Windchill, a PTC Technology and ERP platforms such as SAP or Oracle isn’t just a technical integration challenge—it’s a people, process, and mindset challenge. 𝟭. 𝗣𝗟𝗠 𝗮𝗻𝗱 𝗘𝗥𝗣 𝘀𝗽𝗲𝗮𝗸 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 Engineering lives in revisions and configurations. ERP lives in effectivity, costing, routings, and compliance. Bridging these worlds requires more than an interface—it requires alignment of definitions, ownership, and timing. 𝟮. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗿𝗲𝗻’𝘁 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱—𝗼𝗿 𝗮𝗿𝗲𝗻’𝘁 𝗳𝗼𝗹𝗹𝗼𝘄𝗲𝗱 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆 If engineering change, BOM management, or release processes vary team to team, the digital thread becomes fragile. Integration only exposes process gaps that used to be hidden. 𝟯. 𝗧𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗵𝗶𝗳𝘁 𝗶𝘀 𝗹𝗮𝗿𝗴𝗲𝗿 𝘁𝗵𝗮𝗻 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 A digital thread changes roles, responsibilities, downstream expectations, and even the definition of “done” for engineering deliverables. Without strong change management, resistance is natural. 𝟰. 𝗗𝗮𝘁𝗮 𝗺𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗶𝘀𝗻’𝘁 𝘄𝗵𝗲𝗿𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘁𝗵𝗶𝗻𝗸 𝗶𝘁 𝗶𝘀 A digital thread is only as strong as the data flowing through it. Duplicates, incomplete attributes, inconsistent naming, and tribal knowledge make automation tough. 𝟱. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗲𝗮𝘀𝘆 𝗽𝗮𝗿𝘁—𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗮𝗿𝘁 APIs, middleware, and connectors can be put in place relatively quickly. Sustaining a single source of truth long‑term requires governance, data stewardship, and cross‑functional accountability. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Digital thread success isn’t a technology project—it’s a transformation initiative. Companies who treat it as an enterprise change journey (not just a systems integration) see the highest adoption and the fastest value realization. If your organization is exploring a PLM‑to‑ERP digital thread—or struggling to scale one—you're not alone. It’s one of the most impactful and challenging steps in digital transformation. Happy to connect with anyone navigating similar challenges. 🚀 #ConsultingWithCharacter #EnterpriseArchitecture #SmartManufacturing #PLMStrategy #PLMTransformation

𝗣𝗲𝗼𝗽𝗹𝗲. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗜𝗻 𝘁𝗵𝗮𝘁 𝗼𝗿𝗱𝗲𝗿. One of the world’s leading economists, Daron Acemoglu, makes a deliberately provocative point: AI is not meaningfully improving productivity at the macro level. You can debate the timing. You can debate the metrics. But the signal deserves attention. When I speak with boards and executive teams, I repeat the same framework: Technology transformation - including AI - is people, process, technology. In that order. And “people” is not a soft concept. It is the hardest and most expensive part. Real productivity gains require: • Role redesign - not just AI copilots layered on top • Process simplification before automation • Incentive alignment with new ways of working • 𝗥𝗲𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 the workforce for the AI era You cannot drop AI into a legacy operating model and expect transformation. AI is a force multiplier. It amplifies whatever system it enters. If the organization is fragmented or under-skilled - AI scales that. If the organization is aligned, trained, and outcome-driven - AI accelerates that. The real board-level question isn’t “Are we investing enough in AI?” It’s: • Are we redesigning how work gets done? • Are we preparing our workforce for new decision models? • Are we investing in skills at the same level we invest in technology? People. Process. Technology. In that order. #AIrevolution #ArtificialIntelligence #LargeLanguageModels #GenerativeAI