Onward Partners

𝗧𝗵𝗲 𝗳𝗮𝗰𝘁𝗼𝗿𝘆 𝗹𝗼𝗼𝗸𝗲𝗱 𝘀𝘁𝗮𝗯𝗹𝗲 𝗼𝗻 𝗽𝗮𝗽𝗲𝗿. 𝗧𝗵𝗲𝗻 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝘀𝘁𝗮𝗿𝘁𝗲𝗱. Most capacity and factory planning still relies on deterministic logic: average throughput, nominal transport times, static utilization assumptions, and simplified staffing. That works until complexity hits. High product mix, changing batch sizes, sequencing effects, shift patterns, transport constraints, and ramp-up uncertainty all influence how a factory actually behaves. A layout and configuration that looks sufficient in a spreadsheet can still create hidden bottlenecks once real operations begin. ✅ Material flow simulation helps test these assumptions before capital is committed. It answers high-stakes configuration questions before capital is committed or go-live exposes the gaps. 𝗩𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗮𝘃𝗲𝗿𝗮𝗴𝗲𝗱 𝗮𝘄𝗮𝘆. High product mix, variable run lengths, and sequencing effects create patterns that static calculations simply cannot capture. 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝘀𝗵𝗶𝗳𝘁 𝗱𝗲𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗼𝗻 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀. Equipment can be underutilized while labor saturates or vice versa. A layout that flows well in theory can congest under real conditions. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗺𝘂𝘀𝘁 𝗯𝗲 𝗺𝗮𝗱𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝘅𝗶𝘀𝘁𝘀. For Greenfield plants and new line introductions, layout, equipment, and staffing choices must be locked in before real operating behavior can be observed. Simulation is the only way to stress-test decisions that cannot yet be tested in reality. 𝗧𝗵𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗮 𝘄𝗿𝗼𝗻𝗴 𝗰𝗮𝗹𝗹 𝗶𝘀 𝗮𝘀𝘆𝗺𝗺𝗲𝘁𝗿𝗶𝗰. A layout that needs to be redesigned after construction. A line reconfigured after go-live. Staffing corrected during ramp-up. None of that is recoverable without significant cost. Where capital commitments are large and replanning is expensive, simulation provides the certainty that assumptions cannot. Before you commit capital, stress-test the factory. 👉 If you are facing configuration and resource allocation decisions ahead of a factory planning project, a line introduction, or a product portfolio expansion, we are happy to share how we approach it in practice: https://lnkd.in/eAHTD_rX

How do you move from AI pilots to something that works at scale? Right now, many organizations are experimenting with AI and building up first use cases across different areas. At the same time, many struggle to move beyond initial awareness and dispersed exploration toward a more structured approach and consistent value creation. In conversations with clients, three things keep coming up: There is no clear starting point, AI initiatives that run in parallel without traction and the question of how digital maturity fits in. During the webinar, we will walk through a real customer case and show how this transition can be approached in practice. We look at how transparency on the existing AI landscape was created, how use cases were aligned with business value and how guardrails and governance were established as part of the digital operating model.     𝗧𝗵𝗲 𝗳𝗼𝗰𝘂𝘀 𝗶𝘀 𝗼𝗻 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁. 📅 May 5, 2026 | 3:30–4:15 PM CEST    👇 Register directly via the LinkedIn event

𝙊𝙣𝙚 𝙙𝙖𝙮 𝙩𝙤 𝙜𝙤: Embedding AI in Operations – How Manufacturers Move from PoC to Scale: 👉 https://lnkd.in/eCycYQNj AI success is usually less about technology than expected. What makes the difference is how the topic is handled across the organization, especially once initial use cases are in place and the question becomes how to move forward in a more structured way. Tomorrow, Marcel Hagemann and Julian Witowski will walk through a real customer case and share how this was approached in practice. 📅 May 5, 2026 | 3:30–4:15 PM CEST

Last week, we had the pleasure of welcoming Gléverson Lemos, MSc, MBA, Head of Industry 4.0 and Innovation at Embraer, to the RWTH Aachen Campus. Together, we visited RWTH Innovation and its diverse start-up ecosystem, the Fraunhofer IPT with its turbine production cluster, as well as the Smart Automation Lab and several predictive maintenance use cases. “𝘛𝘩𝘪𝘴 𝘥𝘢𝘺 𝘸𝘢𝘴 𝘱𝘦𝘳𝘧𝘦𝘤𝘵. 𝘞𝘦 𝘴𝘢𝘸 𝘮𝘢𝘯𝘺 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘧𝘶𝘭 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦𝘴 𝘵𝘩𝘢𝘵 𝘸𝘪𝘭𝘭 𝘪𝘯𝘴𝘱𝘪𝘳𝘦 𝘧𝘶𝘳𝘵𝘩𝘦𝘳 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘪𝘯 𝘰𝘶𝘳 𝘰𝘸𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘦𝘯𝘷𝘪𝘳𝘰𝘯𝘮𝘦𝘯𝘵.” Thank you for your feedback 👏 . This encourages us to continue driving the digital transformation of manufacturing companies.

If you are currently working on AI in operations, you have probably seen how quickly things become unclear once the first use cases are in place. What to focus on, how to structure things and what comes next is often not obvious.  We will walk through this in an upcoming webinar, using a real customer case to show how a more structured and scalable approach can be built in practice. The focus is on creating transparency, aligning usecases with business value and putting the right guardrails and governance in place. 📅 May 5, 2026 | 3:30–4:15 PM CEST 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 𝗮𝗻𝗱 𝗿𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://lnkd.in/eWfXF4jT 

When clients ask us about 𝙡𝙤𝙬-𝙝𝙖𝙣𝙜𝙞𝙣𝙜 𝙛𝙧𝙪𝙞𝙩 𝙞𝙣 𝘼𝙄, they usually mean: find the simple use case. We've come to think about it differently. The conventional interpretation is about simplicity: find the easy use case, the well-defined problem, the clean dataset. Start there. But in manufacturing environments, simplicity is often generated by digital maturity. What looks easy in a plant with decades of structured sensor data looks nearly impossible in one still running on paper logs. Use cases like predictive maintenance or planning optimization are powerful, but they require clean historical data, reliable sensor coverage, and system integration that many plants are still building toward. We think the more useful question is not "how simple is this?" but "how much does this depend on a foundation that isn't there yet?" Because some of the most accessible AI use cases have almost nothing to do with digital maturity. They work with the information that already exists that is just in a form that traditional analytics can't touch. Most industrial operations are sitting on top of a lot of unstructured knowledge that has never been accessible at scale: 1️⃣ 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗰𝗮𝗽𝘁𝘂𝗿𝗲: Technicians carry decades of fault patterns and workarounds that are never written down. AI can document insights hands-free by voice. 2️⃣ 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: Plants are drowning in PDFs: OEM manuals, safety sheets, change orders. AI makes that searchable from one common interface. 3️⃣ 𝗡𝗼𝗻-𝗰𝗼𝗻𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀: QC teams write narrative reports that sit unanalysed at scale. AI reads across thousands to surface defect patterns and supplier correlations. 4️⃣ 𝗦𝗵𝗶𝗳𝘁 𝗵𝗮𝗻𝗱𝗼𝘃𝗲𝗿: Shift logs are information-dense and almost entirely unused. AI can flag unresolved issues, track recurring equipment mentions, and brief incoming teams. 5️⃣ 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻: Staying current with standards and permits is a constant challenge. AI helps teams query their own compliance documentation, identify gaps, and prepare for audits None of this requires perfect data or full integration. It requires access to information that already exists, in the form it already exists. That is what makes these use cases genuinely easy to start and why they create early, visible value. What they also do, is surface what is missing. The procedures that were never written down consistently. The fault descriptions that are too varied to be useful. AI does not create those gaps. It just makes them harder to ignore. These use cases are not simple in the traditional sense. What makes them viable is that they are relatively independent from the maturity requirements many plants have not met yet. Scaling them, however, is a different question entirely.

When connectivity does not scale, digital transformation stalls ⚠️ Many manufacturing companies have already invested in #IIoT: machines are connected, sensors installed, and initial use cases implemented. Yet the real impact often fails to materialize. The reason is simple: Systems are connected, but not structured. Data remains fragmented, interfaces grow over time, and every new use case requires significant integration effort. In our upcoming webinar, we focus on what actually makes IIoT scalable. Together with our long-standing client GAPLAST GmbH, we will share practical insights into how structured architectures enable interoperability, consistent data flows, and scalable digital operations. In this webinar, we will focus on: • Moving from isolated connectivity to integrated operations • Why structure matters more than tools • Key characteristics of scalable IIoT architectures • How IIoT enables analytics, automation, and AI 📅 April 22, 2026 🕒 3:00–3:45 PM CEST 💻 Online 👇 𝙎𝙚𝙘𝙪𝙧𝙚 𝙮𝙤𝙪𝙧 𝙨𝙚𝙖𝙩

🚀 Tomorrow: Last chance to move beyond IIoT pilots Many manufacturing companies have already invested in connectivity, sensors, and initial use cases, but real impact at scale often fails to materialize. Data remains distributed across multiple systems, interfaces grow organically, and every new use case requires significant integration effort. Together with GAPLAST GmbH, we will discuss what is often missing: a structured and scalable IIoT architecture. In this webinar, we will show: 💠 How to move from isolated connectivity to integrated IIoT environments 💠Why interoperability must be treated as a design principle 💠How to structure data flows and interfaces for scalability 💠How IIoT becomes the foundation for analytics, automation, and AI The webinar is based on real-world experience rather than theory. 📅 Tomorrow | 3:00–3:45 PM CEST 🔗 Secure your spot: https://lnkd.in/eGA_BTn6