How Engineering Tools Influence Decision-Making

💡 We talk about 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆. 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻. 𝗕𝗜𝗠. 𝗔𝗜. But we rarely ask the harder question: 👉 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗵𝗮𝗽𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀? In every firm I’ve observed, the difference between a successful project and a struggling one isn’t what tools were used. At all. It’s how technology influenced 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁, 𝗮𝗻𝗱 𝘁𝗶𝗺𝗶𝗻𝗴. We all know, projects don’t fail in the software. They fail in the ⚡ 𝘀𝗽𝗮𝗰𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗻𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — where workflows, standards, and accountability collide. The firms getting it right understand that: 🧠 A model is not just a deliverable — it’s 𝗮 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁. 🌐 Any platform is 𝗮 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹. 📏 And standards are not a "rule" to "follow" (everyone gets this one wrong) — but 𝗮 𝘀𝗵𝗮𝗿𝗲𝗱 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗳𝗼𝗿 𝗰𝗹𝗮𝗿𝗶𝘁𝘆. That’s when project outcomes shift: 🔥 from teams constantly reacting to clashes, RFIs, and last-minute changes, → to anticipating issues early, aligning decisions, and building predictably. 🚀 from “Who’s fixing this?” → to “We already saw it coming.” At slantis, that’s the work we do daily — helping AEC teams transform technology from a collection of tools into a living system that predicts and prevents friction. But here’s the reflection for leaders: 🔸 Are your teams using tech to 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝘄𝗼𝗿𝗸 — or to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗶𝘁? 🔸 Is your BIM strategy designed to 𝗿𝗲𝗱𝘂𝗰𝗲 𝗻𝗼𝗶𝘀𝗲 — or to 𝗱𝗿𝗶𝘃𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀? 🔸 Do your standards 𝗲𝗻𝗮𝗯𝗹𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 — or 𝗷𝘂𝘀𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗶𝘁? 🔸 Are you measuring 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗯𝘆 𝗼𝘂𝘁𝗽𝘂𝘁 — or 𝗯𝘆 𝗶𝗺𝗽𝗮𝗰𝘁? 🔸 And maybe most importantly — 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗳𝗼𝗿𝗲𝘀𝗶𝗴𝗵𝘁, 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗼𝗻 𝗱𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀? Because in the end, 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗳𝗼𝗰𝘂𝘀. ✨ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗮𝗿𝗲. And the firms that understand this won’t just keep up with the future —  they’ll be the ones designing it. 🧡 /////////////////////////////////////////////////// 👋🏻 I’m Andy!  ♻️ Repost if this resonates.  💬 DM me if you’re building a firm that leads with heart, passion, and vision. Let’s create the future of architecture — together. 🧡✨

🪄 Building Automated Labs from the Bottom Up: Selecting Growth Direction When we think about automated labs, the focus is often on designing workflows for a fully equipped lab, where instruments are orchestrated by a central AI planning system. But what if we shift the perspective? What if we start by exploring how a new tool can augment an existing lab? This approach offers a different way of thinking, whether the lab begins with zero tools (just a microscope) or a single tool (such as a materials synthesis platform). It allows us to focus on the interplay between instruments and their contributions to the broader experimental ecosystem. From this perspective, two critical questions emerge: how does a new instrument coordinate with upstream tools, and how does its data integrate into the decision-making processes already in place? Let's look at this problem from the perspective of decision making, for example of adding microscope to SDL: Level 1: Post-Acquisition Analysis. At this level, the instrument generates data, which is analyzed only after the experiment concludes. The workflow is fixed, providing no feedback mechanism for adapting experiments. While this is the traditional starting point, it limits the potential for iterative learning. Level 2: Real-Time Data Analytics. Here, ML workflows process streaming data during experiments, converting it into forms suitable for human interpretation. Although humans still make all decisions on subsequent actions, this level bridges the gap between raw data and actionable insights. Level 3: Real-Time Decision-Making At this level, ML agents take a more active role, executing commands on the instrument based on predefined reward functions. For example, an AI might optimize imaging parameters or scan regions during the experiment or effectively learn structure-property relations. Level 4: Learning Physics. This level integrates theory into the experimental workflow. Experiments are designed not only to collect data but also to uncover new correlative relationships, entomological, or physical laws. This "theory-in-the-loop" approach elevates experiments from data collection to knowledge generation. Level 5: Upstream Integration with Materials Synthesis: here microscopes seamlessly coordinate with synthesis platforms to inform upstream sample preparation. By autonomously guiding sample preparation and optimization, instruments become active participants in the discovery process. Rather than solely focusing on designing fully operational self-driving labs (SDLs) upfront, what if start to explore the value of growing SDLs incrementally by adding new tools to the ecosystem. Each addition is an opportunity to analyze how the new instrument augments existing capabilities, integrates with workflows, and contributes to the lab's decision-making network. Perhaps the path to SDLs lies not in their design but in understanding how they evolve through stepwise integration of new tools.

Happy weekend. I’ve been thinking a lot about how innovation travels across industries. And recently, Porsche Cars North America gave me a powerful reminder of how the right tools can become true translational bridges between engineering and biotech. Take Porsche’s active suspension system as one example. The car uses an integrated sensing platform and high-precision analytic tools to “read” the road in real time, then instantly adjusts to keep the vehicle stable. What makes this possible isn’t just hardware—it’s the toolchain that translates raw data into meaningful action. When I look at biotech, the parallel is clear. In cell culture, protein production, or in vivo studies, biology is constantly shifting—just like road conditions. Our outcomes depend on having: 🔧 the right platform — integrated data, monitoring, and control 🔬 the right tools — sensors, analytics, automation, and AI And this is where the translational value becomes obvious: Tools allow us to translate complexity into control. Just as Porsche translates vibration and road noise into stable performance, we translate biological signals into stable, reproducible science. Another strong example is Porsche’s simulation bench (FaSiP). They test components under simulated real-world conditions long before building the full car. This reduces risk and accelerates development. In biotech, the translational tools are different—but the principle is identical: • organ-on-chip → translates human physiology into controlled models • AI prediction → translates molecular patterns into actionable insights • digital twins → translate complex systems into testable simulations These tools don’t just support research—they translate early signals into downstream success, whether in preclinical design, process development, or clinical strategy. Across both industries, I see the same truth: ✨ A good platform supports the journey. ✨ But the right tools enable translation—from data to decision, from concept to reality, from uncertainty to impact. Whether we’re stabilizing a car or developing a therapeutic, tools are what make innovation transferable, measurable, and meaningful.

We keep coming back to the same question in product development: Why is it still so hard to make good decisions at the right time? Despite decades of effort and investment in PLM systems intended to be the “single source of truth”, many engineering and manufacturing teams still work in silos and heavily using Excels. Data is fragmented. Context is lost. And decisions often rely more on tribal knowledge and spreadsheets than on trusted systems. In my new article, I explore a different approach. At OpenBOM, we’ve been thinking about how engineers work, and what kind of support they need - it comes not more data forms to fill out, but intelligent tools that help them in the moment, with access to real-time product and business context to help them with their design. It is a combination of Product Memory and Engineering Co-Pilot. 🔹 Product Memory captures the full lifecycle of a product—design decisions, vendor info, manufacturing challenges, support issues—and makes that knowledge accessible. 🔹 Engineering Co-Pilot brings that information to engineers in real time, right inside their workflows, helping them choose components, estimate costs, avoid mistakes, and stay connected to the broader business. This isn’t a chatbot. It’s not search. It’s a step toward closing the gap between design and operations with intelligent assistant providing information collected from multiple systems available via conversational user interface. The article outlines why the “single system” vision didn’t work—and how a more connected, intelligent, and flexible architecture might. If you’re thinking about the future of engineering, AI assistance, and decision-making across the product lifecycle, I’d love for you to read it and share your thoughts. 👉 Engineering Copilot, Product Memory, and Business Workflows: A New Vision for Product Development [link in the comments] #DigitalThread #ProductDevelopment #PLM #EngineeringTools #AIinEngineering #ManufacturingInnovation #OpenBOM

In complex engineering projects, clarity is everything. What you see here is more than just a set of MEP shop drawings — it’s a fully coordinated visual narrative of how design intent becomes build-ready reality. From primary/secondary circuits to pump layouts, thermal systems, and integrated routing, each layer of this model shows: - Precision in engineering — every pipe, valve, and component placed with constructability in mind. - Cross-discipline coordination — mechanical, electrical, and plumbing systems aligned to remove clashes before they reach the site. - A data-driven workflow — ensuring every element is traceable, measurable, and compliant with project standards. - Better decision-making — allowing teams to visualize performance, maintainability, and sequencing earlier in the cycle. This is the level of detail that transforms project delivery—from reactive problem-solving to proactive, intelligent execution. If your teams, too, are exploring how improved BIM coordination and LOD-aligned modeling can reduce redesign loops, accelerate approvals, and bring predictability to site execution, I’d be happy to exchange insights. CARTOTECH #precision #MEP #BIM #Coordination

Engineers have more power than they realize. Every product, feature, or system that shapes our world starts with a technical decision — made by an engineer. What you choose to build (or not build)… how you design it… the trade-offs you accept… these are decisions that shape company strategy, user behavior, and even society. Leaders may set direction, but engineers define reality. Every line of code, every architecture choice, every "let’s ship it" moment carries influence. So if you’re an engineer: Don’t wait for permission to think strategically. Speak up. Ask "why." Understand the impact of your work beyond the sprint board. Because real influence isn’t about titles — it’s about how deeply your work shapes what’s possible.

The Fab Whisperer: The Hidden Cost Engine Inside Our Fab In semiconductor manufacturing, fabs typically track cost per wafer, cost per layer, and variance against budget and its 'financial drivers'. These metrics reflect the outcomes of timed operational decisions. However, few fabs monitor the cost of poor operational decisions as they occur. An often-overlooked aspect is flow efficiency, which is directly influenced by daily decisions made on the floor. Operational decisions, therefore, appear to be free. Decisions made by systems and people every minute—such as expediting a lot, running a suboptimal batch, breaking a recipe sequence, or holding a tool for engineering—may seem minor individually. Yet, collectively, they can significantly impact throughput, cycle time, personnel productivity, and ultimately, cost. A live decision-driven economic mode of operations starts by shifting the mindset from viewing cost as a financial outcome to treating it as a real-time operational signal. A decision-driven cost model reinforces that. It connects operational behavior directly to economic consequences by translating flow disruptions into throughput loss, and throughput loss into dollars. Suddenly, a priority override is no longer just an operational choice — it’s a measurable cost. A batch break is not a convenience — it’s lost capacity. When this visibility is embedded into daily operations — dispatching, scheduling, control rooms — decision-making changes. Not because people are told to behave differently, but because they can finally see the cost of their decisions. Creating visibility around the costs associated with decisions-driven inefficiencies will hold leaders accountable not just for results, but for the decisions that lead to those results. Building a translation layer that converts everyday fab decisions into flow impact and then into economic value, while instrumenting key decision points so they are visible and measurable is essencial. Creating a feedback loop that links each decision to its throughput and cost impact live, and embedding this visibility directly into dispatching, scheduling, and daily operations, will fundamentally change behaviors and decision mechanisms. This must be reinforced with clear governance on who can make which decisions and under what conditions, and by introducing new KPIs that track decision-induced losses. Starting simple but consistently exposing the economic consequences of actions enables fabs to move from reactive execution to disciplined, economically driven operations. We do not just lose millions due to a single significant mistake; rather, we lose it through thousands of seemingly reasonable decisions made daily, whose cost implications are often misunderstood. #TheFabWhisperer #Semiconductor #FabOperations #ManufacturingExcellence #CostReduction #Throughput #CycleTime #OperationalExcellence #LeanManufacturing