Industrial Production Data Review

Most smart factory teams track dozens of KPIs and still can't tell you if their AI is actually working. I've seen this pattern repeat across industrial deployments - dashboards everywhere, clarity nowhere. The problem isn't too little data. It's the wrong metrics. These 15 metrics across 6 dimensions separate AI theater from real factory intelligence. Production Performance ➞ 1. Throughput Rate Units per time period. The baseline pulse of your operation - if AI isn't moving this number, question everything else. ➞ 2. Overall Equipment Effectiveness (OEE) Availability x performance x quality in one score. The single best measure of whether your floor is actually optimized. ➞ 3. Downtime Frequency Every unplanned stop is lost revenue - and a signal your predictive systems are underperforming. Reliability & Maintenance ➞ 4. MTBF (Mean Time Between Failures) Rising MTBF means AI is catching issues before they cascade. Falling MTBF means you're reacting, not predicting. ➞ 5. MTTR (Mean Time to Repair) AI-driven diagnostics should shrink recovery time relentlessly. If MTTR stays flat, your AI isn't helping operations. ➞ 6. Predictive Maintenance Accuracy The percentage of failures your AI correctly anticipates. Low accuracy here means you're doing reactive maintenance with extra steps. ➞ 7. Anomaly Detection Rate How well AI spots deviations before they become incidents. This is where AI earns its keep on the factory floor. Efficiency ➞ 8. Machine Utilization Idle machines burn capital. AI should optimize schedules to close the gap between capacity and actual output. ➞ 9. Cycle Time Time to complete one production unit. Small AI-driven reductions here compound into massive gains at scale. ➞ 10. Energy Consumption per Unit With energy costs rising, this metric increasingly drives the ROI conversation for industrial AI investments. Quality & Waste ➞ 11. First Pass Yield (FPY) Units produced correctly the first time. Low FPY means rework is eating your margins - regardless of the AI label on the line. ➞ 12. Scrap Rate Material wasted during production. AI-driven process adjustments should push this toward zero over time. Planning & Supply Chain ➞ 13. Inventory Turnover How efficiently stock moves through the system. Slow turnover signals your AI forecasting is disconnected from reality. ➞ 14. Production Forecast Accuracy How closely output matches predictions. This connects factory intelligence directly to supply chain execution. Autonomy ➞ 15. Autonomous Decision Rate The percentage of operational decisions made without human intervention. The ultimate measure of factory intelligence maturity. Smart factory success isn't about deploying AI on the production line. It's about knowing which signals prove AI is driving performance, cutting costs, and building resilience - not just generating more dashboards. ➕ Follow Nick Tudor for practical insights on AI + manufacturing that ship.

Given the concerns I've raised about the industrial production data for all manufacturing increasingly not tracking the performance of trucking freight demand as one would like, I'd like to suggest an alternative that removes the influences of sectors that contribute disproportionately to the headline industrial production for manufacturing index but generate little freight. One chart below. Thoughts: •In this chart, I've removed computers & electronics (NAICS 334); pharmaceuticals (NAICS 3254); aerospace (NACS 3364); and tobacco (NAICS 3122). Computers & electronics are removed because the measurement of 'real' output inflates production from a freight standpoint, coupled with this sector having high value added relative to freight. The remaining sectors are removed because the sectors have very high value added relative to freight. •For example, pharmaceuticals account for ~3% of ton-miles of chemical manufacturer shipments yet account for 36% of the value added. Tobacco products are even more extreme amongst beverage & tobacco products (NAICS 312), accounting for 1% of ton-miles but 40% of the value added. •I've shown this index back to 1993. A few things that stand out: [1]: Manufacturing output in the US today is back to where it was 30 years ago (in 1995). [2]: The Global Financial Crisis is the event from which manufacturing never fully recovered. [3]: Since the GFC, there are two peaks: 2014 and 2018. Those were both strong periods in the truckload sector. Implication: This modified version of industrial production for manufacturing appears to do a far better job of capturing trucking market demand dynamics directionally as an omnibus measure than headline industrial production for manufacturing. The ton-mile index I coauthor still appears to be the best single demand index available, as it incorporates wholesale trade and select retail trade activity. While the ton-mile index is released with a lag, invariably there is a tradeoff between timeliness and representativeness. #supplychain #manufacturing #logistics #transportation #freight #trucking

India's core sector output expanded 0.5% in April, dragging the 𝐈𝐧𝐝𝐞𝐱 𝐨𝐟 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 (𝐈𝐈𝐏) to an estimated 1% growth. But what is IIP?   Picture IIP as a report card for India’s industries, it tracks production in three sectors: 💡 𝐌𝐢𝐧𝐢𝐧𝐠  - Extraction of minerals like coal, crude oil, natural gas, iron ore & other minerals. - 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬: Coal India’s mining operations or ONGC’s oil extraction. - 𝐖𝐞𝐢𝐠𝐡𝐭 𝐢𝐧 𝐈𝐈𝐏: ~14.37% (based on the 2011-12 base year). - 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Mining fuels energy and raw material supply for industries like steel and power.   💡 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠: - Production of goods in industries like textiles, chemicals, food products, machinery, automobiles, pharmaceuticals, and electronics. - 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬: Tata Motors producing cars, Dabur manufacturing consumer goods, or Sun Pharma making medicines. - 𝐖𝐞𝐢𝐠𝐡𝐭 𝐢𝐧 𝐈𝐈𝐏: ~77.63% (the largest share) - 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Manufacturing drives job creation and economic growth, reflecting India’s industrial backbone. 💡 𝐄𝐥𝐞𝐜𝐭𝐫𝐢𝐜𝐢𝐭𝐲: - Generation and distribution of electric power, including thermal, hydro, nuclear, and renewable energy. - 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬:NTPC’s thermal power plants or Adani Green’s solar projects. - 𝐖𝐞𝐢𝐠𝐡𝐭 𝐢𝐧 𝐈𝐈𝐏: ~7.99% - 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Electricity is critical for all industries, and its growth signals infrastructure strength.   Compiled monthly by MoSPI (Ministry of Statistics and Programme Implementation), it measures output against 2011-12 (base = 100) across 407 items like steel or cement   A sluggish IIP reflects an industrial slowdown   𝐓𝐡𝐞 𝐂𝐨𝐫𝐞 𝐒𝐞𝐜𝐭𝐨𝐫𝐬 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧: - The eight core sectors i.e coal, crude oil, natural gas, refinery products, fertilizers, steel, cement, and electricity, make up 40.27% of IIP - Their weak 0.5% growth in April (down from 6.9% in April 2024), driven by slumps in cement, steel, and refineries, pulled IIP down   𝐄𝐱𝐚𝐦𝐩𝐥𝐞: - A steel factory produced 100 tons in 2011-12 (IIP = 100) - In April 2025, it makes 100.5 tons  - This shows a to 0.5% growth

"We’ve got data everywhere, but no answers." A COO of a $100M manufacture told me this last week. They had an $1m ERP, 400 dashboards and 5,000 sensors BUT still couldn't answer why the production line went down on Tuesday or drop in OEE across. Let’s be blunt most manufacturers and asset-heavy companies don’t have a “data strategy.” They have 20 years of data chaos. ERP, MES, SCADA, spreadsheets, PLCs none of them talk. Every team has their own version of the truth. And every “digital initiative” dies in pilot purgatory. Yet when executives hear “data strategy,” they imagine a two-year consulting project full of diagrams and jargon. That’s not what this is. An industrial data strategy is not a report. It’s the operating system of your factory. A mid-market manufacturer we met had spent years investing in tech new MES, shiny dashboards, “AI readiness.” But they couldn’t answer three simple questions: - What’s our real-time OEE across lines? - Why did that line go down last Tuesday? - Which supplier issue is driving our scrap rate? The COO summed it up perfectly: “We’ve got data everywhere, but no answers anywhere.” That’s an architecture problem. The most successful mid-market manufacturers the ones scaling AI and digital aren’t the ones buying more software. They’re the ones that finally said: “Enough chaos. Let’s fix the foundation.” They follow a 4-layer data blueprint that actually works: Ingest → Store → Contextualize → Act 1.Ingest Capture What Matters, Not Everything Stop streaming every sensor tag “just in case.” Decide what data actually drives a business decision. Start by connecting: PLCs (machine states, alarms) MES & ERP (production orders, downtime) Quality systems (defects, batch IDs) Transform and filter at the edge before it ever hits your cloud. Collect less. Use more. 2. Store tructure Your Data or Drown in It You don’t need a “data lake.” You need data discipline. Use the Bronze–Silver–Gold model. With clear constitutional layer. Your "predictive model" is only as good as the structure beneath it. 3.Contextualize Turn Data Into Meaning Most manufacturers already have the data. They just can’t relate machine → operator → supplier → part → defect. That’s where contextualization comes in: Use a Unified Namespace (UNS) to standardize naming and structure. Build a Graph to link data across systems. You can’t run predictive maintenance or AI until your data understands itself. 4. Act Stop Reporting. Dashboards are fine. Closed-loop action is better. Empower domain experts. Delayed or not used data is useless Data → Transformation → Trigger Because in 2026, the manufacturers winning aren’t the ones with more sensors They’re the ones who know how to turn proprietary data into intelligence and action. → Comment “Blueprint” We’ve built a free 90-Day “Manufacturing Data Strategy” the exact 4-layer plan mid-market manufacturers, supply chains & asset heavy organization.

➠ 𝐃𝐮𝐫𝐚𝐛𝐥𝐞 𝐠𝐨𝐨𝐝𝐬 𝐨𝐫𝐝𝐞𝐫𝐬 𝐣𝐮𝐦𝐩𝐞𝐝 𝟓.𝟑% 𝐥𝐚𝐬𝐭 𝐦𝐨𝐧𝐭𝐡. PMI contracted for the 10th straight month. Both are true. And that’s the problem. December manufacturing data tells a split story [sources: ISM, Federal Reserve, U.S. Census Bureau]: → Manufacturing PMI: 47.9 — still contracting   → Capacity utilization: 75.6% — below historical average   → Manufacturing output: +0.2% MoM — growth, but uneven   → Durable goods orders: +5.3% to $323.8B — volatility, not stability   → Input prices: ISM Index 58.5 — costs remain elevated  On paper: demand exists.   On the floor: margins are disappearing. The gap between these two realities? Hidden operational losses. In most plants I work with, the real enemy isn’t: ❌ lack of machines   ❌ lack of people   ❌ lack of orders  It’s fragmented signals: • Downtime marked “temporary” but never investigated   • Maintenance reacting instead of predicting   • Production waiting on decisions that come too late  When PMI stays below 50, these invisible leaks quietly decide who protects margins — and who doesn’t. The plants pulling ahead right now aren’t adding systems. They’re making existing operations visible, measurable, and actionable: • Real-time escalation that doesn’t wait for shift end   • Predictive maintenance replacing reactive firefighting   • Machine-level data driving root-cause improvement  At 75.6% capacity utilization, recovering just 2–3 internal percentage points often delivers faster and safer returns than chasing external growth. If you lead production in this environment, now is the time to protect margins — not just volume. What hidden capacity have you recovered this year? I regularly share what actually works on factory floors. Follow along if this perspective resonates. #ManufacturingLeadership #PlantManagement #OperationalExcellence #ManufacturingData #FactoryOperations

Improvement Community: Manufacturing Trends (March Roundup) The Institute for Supply Management March 2026 Manufacturing PMI report signals continued expansion in the U.S. manufacturing sector, with the PMI rising to approximately 52.7—its strongest level since 2022. This marks multiple consecutive months above the 50 threshold, indicating sustained, though modest, growth. However, the composition of that growth reveals underlying instability. A key driver of the PMI increase was a sharp rise in supplier delivery times, which paradoxically boosts the index but reflects worsening supply chain disruptions rather than true demand strength. These delays are tied to geopolitical tensions and logistics constraints, signaling fragility in global supply networks. At the same time, inflationary pressures intensified significantly. The Prices Paid Index surged to its highest level since mid-2022, driven by rising energy costs, tariffs, and material shortages. This creates a challenging environment where manufacturers must balance cost increases with pricing strategies and margin protection. Demand signals were mixed. While production and some new orders remained in expansion territory, forward-looking indicators such as order backlogs and employment weakened. Manufacturing employment continued to contract, suggesting companies remain cautious about long-term demand stability. Overall, the report highlights a manufacturing sector caught in a tension between growth and disruption—expanding on paper, but constrained by cost pressures, supply chain inefficiencies, and macroeconomic uncertainty. The data reinforces that current expansion is not yet indicative of a fully healthy or resilient industrial base, but rather a system still recalibrating in a volatile global environment. Five Key Insights 💠 Growth ≠ Strength PMI expansion is being driven partly by supply delays—not purely demand—masking underlying weakness. 💠 Inflation Is Re-Accelerating Input costs are rising sharply, signaling renewed pressure on margins and pricing strategies. 💠 Supply Chains Remain Fragile Delivery delays highlight ongoing geopolitical and logistics disruptions impacting operations. 💠 Labor Hesitation Signals Uncertainty Continued contraction in manufacturing employment reflects cautious outlooks from leadership. 💠 Forward Demand Is Softening Weakening backlog and new order trends suggest potential slowing in future production cycles. Question Is our current definition of “efficiency” unintentionally creating waste elsewhere in the system? Full Article Link https://lnkd.in/ewKwHZ5M

Assessing whether the manufacturing contaminated the product🤔 A failure Investigation in production is required to assess the possibility of actual contamination of the product or material under examination even if the laboratory investigation indicates that there are good grounds to believe that the test had serious deficiencies. There is still a finite possibility that the detected contamination came from the product. The Failure Investigation in production should predominantly be focused on observations or circumstances, which might have affected the sterility of the product during the manufacturing process or related operations. The investigation should at least consider the following: ✔Comprehensive review of the BMR including deviations contained therein, of sterilisation parameter records of components and of the product lot (if applicable), accountability of number filled vs number sterilised (for terminal sterilisation), results of the bioburden monitoring of the compounding solution, filter integrity test data, preparation records of disinfectants etc. ✔Review of Media Fill results at least of the last 2 years, if applicable ✔Review of all available results of environmental control, including personnel monitoring and water, in the production environment, covering data of at least 3 months before the event and all data generated subsequent to the event until present, taking into account the contaminants’ trend. Test data of disinfectant solutions, if appropriate. ✔Results of in-process controls of the production/manufacturing area (e.g. particle counts, differential pressures) ✔Results of container integrity tests or similar controls, where applicable ✔Review of training records ✔Review of validation documentation ✔Review of any recent changes that may have been made to the process. The fishbone diagram below provides more areas for consideration. Any relevant observation should be carefully assessed with respect to the process and the properties of the product. Some examples of observations that can be made are listed below: 👉Faulty or incomplete sterilisation cycles 👉Faulty filtration processes 👉Faulty lyophilisation cycles 👉Increased bioburden results of the compounding solution before sterilisation 👉Critical identification results of bioburden of the compounding solution before sterilisation 👉Transgression of holding times of solutions and equipment 👉Occurrence of risky, non-validated manipulations in the filling line Inadequate training 👉Faulty and/or unacceptable behaviour of personnel involved in manufacturing 👉Faulty air handling systems 👉Environmental monitoring action conditions affecting critical areas 👉Failed Media Fill trials, with Failure Investigation pending or inconclusive 👉Failed (re -) validation studies, with Failure Investigation pending or inconclusive 👉Failed cleaning operations 👉Inadequate sampling procedure and transfer to the laboratory. www.inglasia.com

A Welcome Strong Month for Industrial Production Industrial production rose a healthy 0.6% in June, following an upwardly revised 0.9% gain in May. The manufacturing IP index rose 0.4% in June, after a 1% rise in May. The two-month gain brings manufacturing industrial production to its highest level since October 2022. While two months do not a trend make, the back-to-back gains are good news for a manufacturing sector that has mostly been moving sideways. The recent strength has been driven by consumer goods, where production was up 1% on the month and 2.8% from a year ago. This is a bit stronger than expected, given other measures that suggest gradually slowing consumer spending. A cautionary note: The strongest element of consumer IP in June was utilities production, buoyed by the heat wave that affected much of the country last month. But auto production was also very strong, and non-energy nondurables consumer goods production showed a solid gain. With the exception of construction, nonindustrial supplies output growth was also relatively healthy in June, rising 0.5%. Production of business equipment has been weaker, falling 0.4% in June and down 1.2% over the past year. The overall picture for manufacturing remains somewhat mixed. Employment in the sector has been flat, and the Institute for Supply Management's Purchasing Managers Index (PMI®) for manufacturing has been slightly in contraction territory in recent months. And a slowing economy may weigh on the sector going forward. Still, the June production numbers are welcome good news. #manufacturing #Industrialproduction #IP #consumergoods #motorvehicles

Industrial activity entered October on an uneven footing. The latest Index of Industrial Production (IIP) data for October 2025 shows industrial growth slowing to 0.4% from 4.0% in September owing to manufacturing momentum softening and both mining (–1.8%) and electricity (–6.9%) pulling the index down. Even so, a few sectors continue to show strength: basic metals grew 6.6%, petroleum products 6.2%, and auto production 5.8%, which indicates that core industrial demand hasn’t collapsed. What stands out is the contrast: strong performance in a few heavy-weight sectors but broad weakness in others. This suggests industrial activity is going through a patchy phase, influenced by weaker export demand and short-term domestic factors such as fewer working days during the festival month. The next couple of readings will be important in understanding whether this is just a one-off dip or the start of a slower industrial cycle. #CentricityWealthTech #Centricity

𝗧𝗵𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗜𝗻𝗱𝗶𝗮’𝘀 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗻𝗱𝗲𝘅 𝗼𝗳 𝗘𝗶𝗴𝗵𝘁 𝗖𝗼𝗿𝗲 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 (𝗜𝗖𝗜) 🏭 Let us understand how India’s industrial performance is measured? The Index of Eight Core Industries (ICI) is a key economic indicator that tracks the eight pillars of India’s industrial infrastructure: ⚡Coal | Natural Gas | Crude Oil | Refinery Products | Fertilizers | Steel | Cement | Electricity | The "Index of Eight Core Industries" (ICI) is a subset of the "Index of Industrial Production" (IIP). These industries account for 40.27% of India's industrial output (IIP), making ICI a crucial tool for policy decisions, economic forecasting, and investment planning. 🔍 𝗪𝗵𝘆 𝗗𝗼𝗲𝘀 𝗜𝗖𝗜 𝗠𝗮𝘁𝘁𝗲𝗿? 📊 Early Economic Signal: Released a month before the IIP, it helps gauge industrial momentum. 🏗 Industrial Growth Benchmark: Reflects GDP trends, employment patterns, and infrastructure expansion. 💰 Investment & Policy Planning: Helps shape interest rates, sectoral policies, and business strategies. ⚙️ Energy & Infrastructure Development: Serves as a guide for the long-term planning in construction, power, and transportation. 🌍 Global Competitiveness: Impacts trade balance, cost of production, and India's global standing. 📊 𝗛𝗼𝘄 𝗶𝘀 𝘁𝗵𝗲 𝗜𝗖𝗜 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗱? ✅ Base Year: 2011-12 (aligned with national income accounts). ✅ Formula: Laspeyres’ fixed-base formula, with each industry weighted by its economic contribution. ✅ Data Sources: Six government agencies ensure transparency and reliability. 📈 𝗧𝗿𝗲𝗻𝗱𝘀 & 𝗣𝗼𝗹𝗶𝗰𝘆 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: 🔹 Growth in Core Sectors: Electricity, steel, and refinery output are rising, indicating industrial expansion and higher energy demand. 📌 National Electricity Plan (2022-32): Aims for >900 GW installed capacity by 2031-32. 📌 National Steel Policy (2017): Targets 160 kg per capita steel consumption by 2030-31. 🔸 Challenges in Crude Oil & Natural Gas: Weak performance here raises energy security concerns and increases import dependency. 🔹 Proactive Interventions: ✅ PLI schemes and viability gap funding (VGF) are pushing clean/green energy and infrastructure growth. 🚀 𝗥𝗲𝗰𝗲𝗻𝘁 (𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿 𝟮𝟬𝟮𝟰) 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: ✅ ICI grew 4.0% YoY, signalling strong industrial activity. 📈 Growth in Coal, Electricity, Steel, Cement, Refinery Products, Fertilizers, and Crude Oil. 📉 Natural Gas was the only sector to decline. 𝗘. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: The ICI serves as a barometer for India's industrial strength. A 4% growth in December 2024 points to a positive economic outlook. However, sustaining this momentum will require strategic investments in infrastructure, clean energy, and core industries. 📢 Your thoughts on India’s industrial growth? Let’s discuss! AI Assisted.