Resources
Operational guides and references
Practical, operator-grounded guides on downtime, reliability, quality, and the decisions that move production.
37 resources
Food Recall Process: What Happens Hour by Hour in a Plant
An hour-by-hour guide to the food recall process, from containment and traceability through scope decisions, recovery, and corrective action.
Mock Recall Guide: Requirements, Time Limits, and Checklist
A practical guide to food mock recalls, including scheme-specific timing, traceability, mass balance, decision testing, and corrective action.
AI for Manufacturing Without a Data Science Team
Most AI for manufacturing assumes you have data engineers, labeled datasets, and months to build models. Most plants don't. Here is what AI you can actually use without a data science team, starting from the systems you already run.
AI for Reducing Unplanned Downtime: What's Real and What's Hype
AI can cut unplanned downtime by a real amount, but not the way vendor demos suggest. The true cost breakdown, the mechanisms that actually work, why pilots die, how to start, and a calculator for what your downtime costs today.
AI for Predictive Maintenance, Explained for Operators
How AI predictive maintenance actually works, from the P-F curve to the data it needs, how it differs from preventive and reactive, what it realistically saves, where it fails, and where its real value is, without the vendor spin.
How AI Improves OEE: From Tracking the Number to Raising It
Most plants can already calculate OEE. The hard part is raising it. How AI improves OEE across all three pillars, where the losses actually hide, a worked breakdown, a realistic point gain, and why computing the number was never the bottleneck.
Operational AI vs Predictive AI in Manufacturing
Predictive AI tells you what is likely to happen. Operational AI decides what to do about it and drives the action. Here is the difference, why most AI stops at the prediction, and which one a plant actually needs.
Are Your Data Projects Wasting Money and Time in 2026?
Most data and AI projects fail to deliver, and the cause is almost never the technology. Here is what the failure data shows, why it happens, and how operations teams avoid building another dashboard nobody opens.
BI Alternatives for Industrial Operations: Beyond the Dashboard
Looking for a Power BI or Tableau alternative for operations? Swapping one BI tool for another may not fix the problem, because they share a shape. Here are the alternatives, why they share the same limits, and the operations-built option that acts.
Business Intelligence vs Operational Decision Intelligence
BI tells you what happened. Operational intelligence tells you what is happening now. Neither decides what to do or makes sure it gets done. That is operational decision intelligence, and for industrial operations it is a different category.
The Real Cost of Building Your Own Operations Analytics Stack
Building an in-house analytics stack runs $300-500k+ a year, takes 9 to 18 months, and ends at a dashboard. The honest build-vs-buy math for operations: the team, the time, and the hidden costs the license never shows.
Do You Need a Data Warehouse to Run Operations?
Snowflake is powerful infrastructure, but it is a data warehouse, not an answer. Making it useful for operations means building a whole stack on top of it. Here is what it costs, where it falls short for a plant floor, and the shorter path that acts.
Quality and Food Safety in Food and Beverage Manufacturing
In food and beverage, quality and food safety are reliability problems. Here is where quality is lost, what triggers recalls, the systems that govern it (HACCP, FSMA, GFSI), how to control the process, and practical ways to streamline it all.
Institutional Knowledge in Manufacturing: Why It Leaves and What It Costs
Institutional knowledge is the undocumented expertise that keeps plants running. What it is, why it walks out the door this decade, and how to actually keep it.
Maintenance and Reliability in Water and Wastewater Utilities
Water and wastewater utilities run critical, distributed assets on tight budgets. Here are the common failures, the regulatory stakes, and a criticality-based approach to reliability that fits how utilities actually operate.
SMED (Single-Minute Exchange of Dies): The Complete Guide to Quick Changeover
SMED is a method for cutting equipment changeover from hours to single-digit minutes. Here is the internal-versus-external idea at its core, the step-by-step method, the techniques, how to run a SMED event, and what it does for OEE and inventory.
SteelTree vs Tableau for Industrial Operations
Tableau is a premium visualization tool: powerful, expensive to run, and complex enough to need a trained analyst. SteelTree does what operations teams reach for Tableau to do, without the cost and complexity, and it acts on what it finds.
Why Operational Dashboards Don't Change Anything
Most dashboards never change behavior, and there is hard data on how badly. Why operational dashboards fail, the difference between a dashboard and a decision system, and what to build instead of another one nobody opens.
Reliability-Centered Maintenance (RCM): The Complete Guide
A complete guide to reliability-centered maintenance: the seven RCM questions, the consequence decision logic, the P-F curve, the six failure patterns, how to run a program, the RCM variants, KPIs, and when it is worth the effort.
Common Causes of Downtime in Power and Energy Plants
Forced outages in power plants trace mostly to boiler tubes, turbines and generators, electrical equipment, and controls. Here are the common causes, what they cost, and how to reduce them.
What Condition Monitoring Is and How It Works
Condition monitoring measures an asset's health while it runs, so you can catch a developing problem before it fails. Here are the techniques, continuous versus periodic monitoring, how to set up a program, how it feeds predictive maintenance, and where the value actually leaks out.
Cutting Downtime in Oil and Gas Operations
In oil and gas, downtime means deferred production and high cost, driven mostly by rotating equipment, corrosion, and remote constraints. Here is how to cut it.
Do You Need More Than a CMMS? Where a CMMS Stops Short
A CMMS is the system of record for your maintenance work. It was not built to tell you what matters most, why, or what to do next across your operation. Here is where a CMMS stops, and how SteelTree adds the decision and action layer on top, free to start.
How to Reduce Unplanned Downtime
Unplanned downtime costs manufacturers around $260,000 an hour on average. Here is what causes it, the practices and benchmarks that actually reduce it, the metrics to track, and where to start, grounded in research from Siemens, Aberdeen, and ABB.
Improving OEE in Discrete Manufacturing
In discrete manufacturing, OEE is usually lost to changeovers, minor stops, and reduced speed. Here are the biggest levers to improve each, and where to focus.
Taking Operations Spreadsheets to the Next Level
Spreadsheets run most operations, and Copilot makes them smarter than ever. They still hit a ceiling: a sheet is a manual snapshot, not your live operation, and it cannot act. Here is what the next level looks like, free to start.
Preventive vs Predictive Maintenance: How They Differ and When to Use Each
Preventive maintenance runs on a schedule. Predictive maintenance runs on condition. Here is how the two strategies differ, the three types of preventive maintenance, what predictive costs to stand up, and how to decide which assets get which.
Reducing Unplanned Downtime in Food and Beverage Manufacturing
In food and beverage plants, downtime comes from changeovers, high-speed minor stops, and washdown wear, and it carries food-safety stakes. Here is where it hides and how to cut it.
The Six Big Losses in OEE (and How to Cut Each One)
The Six Big Losses are the six categories that drag down OEE, two for each of its three factors. Here is what each loss is, how it maps to availability, performance, and quality, how to measure and prioritize them, and the right way to cut each one.
SteelTree vs Power BI: Operational Answers, No Data Team Required
Operations teams reach for Power BI to turn their data into answers. SteelTree gives you that without data models, queries, or a data analyst, acts on what it finds, and starts free. Here is how the two compare on capability and cost.
What Asset Criticality Is and How to Rank It
Asset criticality ranks equipment by the consequence of failure so you can focus maintenance where it matters. Here is how to score it, build a criticality matrix, and a scorer.
How to Calculate MTBF (Mean Time Between Failures)
MTBF is total operating time divided by the number of failures. Here is the formula, a worked example, how it differs from MTTF, and a free MTBF calculator.
How to Calculate OEE (Overall Equipment Effectiveness)
OEE measures how much of your planned production time is truly productive. Here is the formula, a worked example, benchmarks, and a free OEE calculator.
How to Measure Your Maintenance Backlog (in Crew-Weeks)
Maintenance backlog is best measured in crew-weeks: estimated labor hours of outstanding work divided by weekly available hours. Formula, healthy range, and a calculator.
Leading vs Lagging Indicators in Operations
Lagging indicators measure outcomes that already happened. Leading indicators measure the activities that predict them. Here is the difference, examples, and how to pair both.
MTTR vs MTBF, Explained: Reliability vs Maintainability
MTBF measures how long equipment runs before it fails. MTTR measures how long it takes to fix. Here is the difference, the formulas, a calculator, and how both drive availability.
OEE vs TEEP: What They Measure and Which One to Track
OEE measures scheduled production time. TEEP measures all calendar time and reveals hidden capacity. Here is the difference, the formulas, and a TEEP calculator.