Impact of Decision-Making in Manufacturing

69% of manufacturing workers say corporate makes decisions without consulting the people who have actual visibility into shop floor issues. That number comes from a 2024 survey of more than 600 manufacturing professionals across all levels. It is not surprising. It is confirming what most of us already know. Decisions made without floor input fail at execution. Not because the logic was bad. Because the people who have to carry them out were never asked what would actually work. I've seen three versions of this play out repeatedly. The first is a scheduling change designed in a conference room. Production planners rework the shift structure to improve utilization. Operators weren't consulted. The new schedule conflicts with maintenance windows, creates bottlenecks at the paint line, and gets quietly overridden within two weeks. The second is a layout redesign. Engineering redraws the floor plan to improve material flow. The operators who run the cells every day could have told them that the proposed staging area floods with WIP every afternoon because the downstream process can't keep pace. Nobody asked. The redesign gets built. The problem moves 40 feet to the left. The third is a technology rollout. A new system goes live with training materials built by the vendor. Operators learn the screens but not the workflow. Within a month, half the floor is running a workaround because the system doesn't match how the work actually flows. Nobody asked them before configuration. The same survey found that 55% of workers facing communication gaps feel undervalued. 94% said communication problems hindered their professional growth. Those numbers connect directly. People who are never consulted stop offering input. People who stop offering input disengage. People who disengage leave. The floor knows what works. The floor knows what doesn't. The only question is whether leadership is structured to hear it before the decision is made. When was the last time a decision about your floor was made without asking the people who run it?

In my early manufacturing days, a senior leader once spent almost one full hour reviewing my mobile bill of ₹395. His goal was clear: Separate personal and official calls. Save costs. Every rupee mattered. At that time, I respected the intent. Years later, with more context, the lense changed. That same leader was responsible for capital expenditure running into crores. One hour spent questioning and saving ₹100 on mobile claim. Very little time spent deeply reviewing large purchase decisions where real money leaked. Fast forward to today, the pattern still exists. Only the target has changed. Instead of mobile bills, we now see cost reduction driven by: ▪️ Junior and middle-level job cuts ▪️ Project funding reduction ▪️ Lower increments While much larger costs stay untouched such as: ▪️ Cost of poor quality which includes rework and rejection ▪️ Regulatory misses, project delays ▪️ Inefficient or broken processes In problem solving, effort alone is not intelligence. Impact is. Solving the easy problem feels productive. Solving the right problem actually saves money. #ProblemSolving #Leadership #Execution #StrategicThinking

Most manufacturing companies don’t have an AI problem. They have a decision making problem. Over the last 2 to 3 years, I have seen a clear pattern across the industry: We have invested in AI. We have built models. We have created dashboards. But in many cases… we haven’t changed how decisions are made. Take demand planning. AI today can predict demand far better than traditional methods. Yet forecasts are still overridden because it doesn’t feel right or we don’t want systems to take decisions. Or supply chain. AI can flag risks early and suggest actions. But decisions still get delayed in reviews and discussions. Or predictive maintenance. AI can anticipate failures. But unless operations, inventory, and planning are aligned, execution doesn’t change. Here’s the uncomfortable truth: AI is not failing in manufacturing. Adoption is. And adoption is not a technology issue. It’s about: • Trust in data over instinct • Embedding AI into workflows (not dashboards) • Leaders willing to be challenged by machines Also, a reality check: AI is no longer a competitive advantage. Everyone has access to similar models and tools. What’s hard to replicate is: • Years of clean, contextual operational data • Process discipline on the shop floor and supply chain • The ability to act on AI-driven insights consistently If you’re a mid sized manufacturing company trying to navigate AI, my simple view: 1. Don’t start with 20 use cases Start with one decision that truly matters (planning, procurement, maintenance) 2. Don’t chase platforms Focus on changing how that decision gets made AI will not transform manufacturing through pilots and presentations. It will transform when: decisions on the shop floor, in planning rooms, and in supply chains start changing consistently. Until then, it’s just… augmentation. Curious to hear from others in manufacturing: Where are you seeing real AI impact vs just activity? #ArtificialIntelligence #Manufacturing #DigitalTransformation #SupplyChain #DecisionMaking #Industry40 #SmartManufacturing #AIinBusiness #Leadership #DataDriven #TechLeadership

"It looked good on paper. But on site, it cost us £5,000 an hour." On a recent OLE rail project, the team planned an 8-hour overnight shift to install cantilevers. Each hour was priced at £5,000, expensive but carefully budgeted. Then reality hit. The possession began at 10 pm, but essential equipment didn't arrive on site until midnight. Two critical hours were lost immediately, costing £10,000 before anyone even started. At 3 am, a key machine broke down, forcing the team to wait another hour for a fitter to arrive. Another £5,000 quietly slipped away. By shift end, only half the planned work was complete. £15,000 of productivity vanished, unnoticed until days later.  AND they would still have to go back and complete the remaining cantilevers on another shift, doubling exposure to cost and risk. But here’s the real issue: Everyone knows they've lost productivity, but few see the immediate financial impact. If you could see costs escalating in real time, not days or weeks later, you could act decisively, minimise loss, and avoid repeating costly mistakes. Clear visibility of the true cost as it happens transforms your decision-making from reactive to proactive. That doesn’t just protect your margins, it protects your entire project's success. Here's a thought: Should we be writing the actual cost of each shift directly onto every shift record?

Most “decisions” in manufacturing   aren’t decisions at all. They’re opinions dressed up as strategy. I see it all the time. “We should invest in automation.”   → Based on what data? “Operators need more training.”   → Which operators? On what? Says who? “We need to reduce inventory.”   → By how much? Where? What’s the impact? No data.   Just opinions. And when everyone has an opinion   but nobody has numbers,   the loudest voice wins. Not the right one. Here’s what data actually does: • It takes ego out of the room   • It shows what’s broken, not who to blame   • It proves what works, not what sounds good   • It separates real problems from noise  Without data, you’re guessing.   And hoping you guessed right. With data, you know. You know which machine slows everything down.   You know which process wastes time or material.   You know where training will actually make a difference. Data turns opinions into decisions.   And decisions into results. But here’s the truth: Most plants collect data.   They just don’t use it. Dashboards full of numbers nobody checks.   Reports nobody reads.   Metrics that don’t change a single thing. That’s not data.   That’s noise in a spreadsheet. Real data answers a question.   Real data drives a decision.   Real data changes behavior. If your data isn’t doing that, stop collecting it. Next time you’re in a meeting and someone says,   “I think we should…” Ask one question: “What does the data say?” If there’s no answer,   there’s no decision. Just another opinion. What’s one decision at your plant   that needs data, not opinions? Drop it below 👇

𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝘃𝘀. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗶𝗻 𝗦𝗺𝗮𝗿𝘁 𝗙𝗮𝗰𝘁𝗼𝗿𝗶𝗲𝘀: 𝗔𝗿𝗲 𝗪𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗙𝘂𝗹𝗹 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹? In manufacturing, both “decision-making” and “smart factories” are well-understood concepts. However, when these come together, the critical question emerges: Who should be making the key decisions that shape smart factory transformation—central leadership or local plant teams? A industry analysis titled IoT Signals Manufacturing Spotlight by Microsoft & Intel (2022) sheds light on this very issue. 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗦𝘁𝘂𝗱𝘆 𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝗜𝗧 𝗛𝗼𝗹𝗱𝘀 𝘁𝗵𝗲 𝗥𝗲𝗶𝗻𝘀: For IT infrastructure, IoT platforms, and factory software, over 50% of decisions are made by the corporate IT function. 𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝗢𝗧 𝗧𝗲𝗮𝗺𝘀 𝗦𝘁𝗶𝗰𝗸 𝘁𝗼 𝗧𝗵𝗲𝗶𝗿 𝗡𝗶𝗰𝗵𝗲: Operational Technology departments are highly involved only in areas like OT security (45%), OT data tools (48%), and OT infrastructure (43%), with limited influence elsewhere. 𝗧𝗵𝗲 𝗜𝗧-𝗢𝗧 𝗗𝗶𝘃𝗶𝗱𝗲: Decision-making remains siloed, with IT controlling digital platforms and data while OT oversees core operational tools—creating fragmented ownership and hindering seamless integration. 𝗖𝘅𝗢𝘀 & 𝗛𝗥 𝗗𝗿𝗶𝘃𝗲 𝗖𝗵𝗮𝗻𝗴𝗲 𝗮𝗻𝗱 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴: Decisions on workforce upskilling (HR, 47%) and change management (CxOs, 31%) are managed centrally. 𝗟𝗼𝗰𝗮𝗹 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 𝗛𝗲𝗮𝗱𝘀 & 𝗜𝗧,𝗢𝗧 𝘁𝗲𝗮𝗺 𝗛𝗮𝘃𝗲 𝗠𝗶𝗻𝗶𝗺𝗮𝗹 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆: Despite their proximity to day-to-day operations, plant leaders often have less than 10% decision authority, even over operational and data tools. 𝗪𝗵𝗮𝘁 𝗗𝗼𝗲𝘀 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻? 𝗣𝗿𝗼𝘀: Centralization ensures consistency, alignment, and cybersecurity—essential for scaling digital transformation. 𝗖𝗼𝗻𝘀: Low factory-level authority risks missing out on site-specific innovation, rapid troubleshooting, and custom solutions. 𝗙𝗿𝗼𝗺 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝘀𝘁𝗮𝗻𝗱𝗽𝗼𝗶𝗻𝘁, 𝘁𝗵𝗶𝘀 𝗿𝗮𝗶𝘀𝗲𝘀 𝗮 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Is a one-size-fits-all, top-down approach truly capable of unlocking the unique value each plant can offer? Best practice suggests otherwise. Mature organizations combine maturity assessments with targeted autonomy—adjusting the level of centralization based on each site’s specific needs and readiness. Consultation with factory heads is not optional—it’s essential. The people with first-hand experience are best equipped to identify pain points and propose targeted, high-ROI solutions. For example, deploying RFID where material handling waste is highest, not simply where it fits a global strategy, can yield dramatic improvements. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐢𝐧? Balanced decision-making. Central teams create standards and ensure alignment, while local teams adapt and innovate. It’s not central or autonomous—it’s both, calibrated through ongoing maturity assessment.

𝐈𝐧 𝐭𝐡𝐞 𝐔𝐒, 𝐮𝐩 𝐭𝐨 𝟐.𝟏 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐣𝐨𝐛𝐬 𝐦𝐚𝐲 𝐫𝐞𝐦𝐚𝐢𝐧 𝐮𝐧𝐟𝐢𝐥𝐥𝐞𝐝 𝐛𝐲 𝟐𝟎𝟑𝟎 (𝐃𝐞𝐥𝐨𝐢𝐭𝐭𝐞 & 𝐓𝐡𝐞 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐞). And it’s already visible on the floor. Not in reports.   In decisions. Who goes to which machine.   Who is actually qualified.   Who can step in right now. This is where production starts losing time… quietly. Because when there’s no clear answer: Supervisors call around.   Teams improvise.   Someone “good enough” steps in. And the line slows down. Across US plants, this is becoming structural. Skills shortages are driving: → more overtime   → more errors   → more unplanned downtime (National Association of Manufacturers) Not because machines fail. Because the right person is not there when it matters. On the floor it looks like this: 5–10 minutes to decide who should take a position.   Another delay because the operator hesitates.  And just like that… 30–40 minutes gone.   No escalation.   No visibility. Just silent loss. What I see in most plants: Skills exist.   People are capable. But… There is no real-time visibility of who should be where. So decisions are made fast…   but not always right. And this is where things start to shift. When skills are actually connected to daily operations: You don’t call three people.   You don’t guess.   You don’t “try and see”. You already know who goes there. Based on: → verified competencies   → actual permissions   → current situation on the floor  This is what changes behavior. In one of the projects we supported: → downtime reduced by 25%   → safety improved   → resource usage became predictable  Not because of more training. Because assignments stopped being guesswork. And looking at the direction of the US market: The gap is not getting smaller. It’s getting more expensive. So the question is no longer: “Do we have skilled people?” But: “Do we know who should be where… in real time?” That’s usually where the real improvement starts. #USManufacturing #ManufacturingExcellence #SkillsMatrix #OperationalExcellence #SmartFactory #DigitalTransformation

Manufacturing is at an inflection point. Over the next 36 months, U.S. manufacturing and CPG companies will either embed AI into how they decide — or they will be outpaced by those who do. This isn’t about robots or automation theater. It’s about decision speed, data structure, and execution velocity. AI collapses decision latency. It exposes inefficiencies we’ve normalized for decades. It turns operational data into real-time action. That changes everything. Companies using AI-driven planning and execution are already seeing: • 20–40% productivity gains • 15–30% inventory improvement • Meaningful reductions in scrap, downtime, and working capital • Faster onshoring with competitive unit economics That’s not incremental advantage. That’s market share. The next generation of manufacturers won’t win because they’re bigger. They’ll win because they’re faster — mentally, operationally, and structurally. If AI isn’t already embedded in how your company plans production, allocates capital, and runs day to day, you’re not “behind.” You’re exposed. This isn’t about replacing people. It’s about replacing slow thinking. The window is open right now. It won’t stay that way. Get moving — or someone else will take your customers with a smile.

The daily production meeting is one of manufacturing's most valuable rituals. It's also frequently one of our most expensive misallocations of management time...it's also about to drastically change. The gold standard: A short, focused conversation that allocates resources and sets priorities to win today In most cases, it's a debrief on yesterday. What did we make? How much did we miss the number by? What were our top losses? What actions are open? Let's review the action log... Let me be provocative: I don't care what you made yesterday. That product has been made. It's in the warehouse or on a truck. The work is done What I care about is what you're going to do today to hit your business objectives (+how the events of yesterday shape decisions today) Leaders are using real-time dashboards to make this conversation more targeted. But it takes real discipline to let go of the past and focus on the future How does — and how could — AI change this picture? Near term: AI can do something most teams struggle with manually. It will rank today's priorities by business impact. Not by who's loudest in the room. Not by what broke most recently. By the actions that move your numbers. The "next best action" across your full metric set, with the trade-offs made visible IDC predicts over 40% of manufacturers will have AI-driven autonomous scheduling in place in 2026 - THIS YEAR! The technology to run this meeting differently already exists. That alone changes the meeting Longer term: As our processes become increasingly automated, this meeting starts to look fundamentally different In a world where AI is calling real-time shots on scheduling, maintenance, quality interventions — the 8am meeting doesn't review outcomes. It governs decisions McKinsey frames it this way: Organizations will need to manage AI agents the way they manage people — with performance reviews, accountability, and the ability to retrain or retire underperformers The focuses shifts: - You're not discussing what broke...the maintenance order is raised, the part is ordered, the time is scheduled - You're not discussing what you'll make....the schedule is updated in minutes, materials ordered, resources assigned What the meeting will become is a review of the decisions your AI routines made in the last day. A challenge of the logic behind anything sub-optimal, and an action plan to improve the data and reasoning that drives action today In other-words: Your Daily Production Meeting will be the frontline of your AI Governance strategy Very different skills. Different questions. Different leaders at the front of the room The daily cadence isn't going away. Its center of gravity will shift — from forensics to orchestration BCG research suggests only 14% of frontline workers have received any AI upskilling. So when I ask — are your teams ready for that meeting? — I think we both know the honest answer #Manufacturing #AI #OperationalExcellence #DigitalTransformation

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