Assaf Weiss

With the increasing push towards implementing AI in production management, most of what gets marketed as AI in manufacturing today is functionally a chatbot: a conversational layer placed on top of an existing dashboard, ERP system, or shift report, able to answer a question about data that already existed before anyone asked it. While these might be useful tools in

Last updated: September 2026 A molding press running a 28-second cycle does not lose an hour at a time. It loses two seconds here on a cooling stage that ran long, four seconds there on a short shot that had to be purged and re-run, and several minutes on a mold change that took longer than scheduled. None of that

Last updated: September 2026 On most food and beverage lines, the machine rarely breaks. The line loses time in the twenty minutes after a changeover, or in the gap between a quality deviation happening and someone downstream noticing the trend on a check sheet. That gap is a systems problem, not a mechanical one, and it changes which OEE software

Last updated: September 2026 A flexible packaging line rarely loses a shift to a breakdown. It loses time in the stretch between one SKU coming off the press and the next one running clean, repeated as many times as the day's job list demands. A single blended OEE score buries that stretch inside "Availability" along with every other stoppage, which

Last updated: September 2026 Two automotive stamping plants can both report 82% OEE and mean two different things by it. One counts a changeover under 10 minutes as planned downtime; the other counts anything under 15. One rounds micro-stops under 60 seconds into performance loss; the other logs them as availability loss. Neither plant manager is lying. They're just running

Every performance management vendor now claims an AI layer. Few can justify what it actually does to improve daily production workflows. That gap matters more in 2026 than it did two years ago. Manufacturers have spent the last cycle connecting machines, digitizing shift reports, and building OEE dashboards. The data is there. What's still missing, on most floors, is the

Last updated: August 2026 Most manufacturers already know their OEE score is too low. What they don't know is which of the dozens of OEE software options on the market will actually close the gap, versus which one will sit half-configured after the trial period ends. This guide compares the best OEE software platforms for 2026 specifically for tracking, calculating,

Every site manager knows the anxiety of a proposed software rollout. The promise of better data usually comes with a caveat: months of floor disruption, heavy IT overhead, and custom coding that forces your operators to change how they work. For most manufacturers, this implementation risk often leads to paralysis. You stick with spreadsheets and whiteboards because the status quo

Most SME manufacturers evaluating production software face the same problem. The options range from entry-level OEE trackers that outgrow quickly, to enterprise platforms that require six-month implementations and a dedicated data team. The middle ground — platforms built specifically for machine-intensive SME operations that want real-time visibility without a lengthy IT project — is smaller than it looks. This guide

Most manufacturers know their production data exists. The problem is that it lives in five different places, arrives hours after the fact, and tells you what happened — not what is happening. A Manufacturing Intelligence Platform was built to close that gap. What Is a Manufacturing Intelligence Platform? A Manufacturing Intelligence Platform (MIP) is a software system that aggregates real-time data

Every continuous improvement manager knows the frustration of the plateau. You run a Kaizen event, map the value stream on a whiteboard, and rally the line teams. For two weeks, waste drops, scrap rates plunge, and Overall Equipment Effectiveness (OEE) ticks upward. Then you walk away to focus on the next bottleneck. Within a month, the process drifts. Old habits

In the vast majority of manufacturing facilities, daily production meetings are intended to be quick, tactical alignment sessions. Operations, Maintenance, Quality, and Plant Management gather to review yesterday’s output, identify performance gaps, and assign corrective actions for the shifts ahead. Instead, these meetings frequently devolve into debates over data validity. When the production log, the maintenance ticket system, and the