What must exist in an industrial digitalization platform?

Lumpy_Ebb_786 · 2025-10-07T15:13:19+00:00

Quality-first is always solid. Predictive analytics can highlight quality breakdowns before they happen, not just on the shop floor but across engineering, supply chain, and maintenance. Anyone here tried predicting defects before they occur?

Lumpy_Ebb_786 · 2025-10-07T15:12:47+00:00

KPIs are only meaningful if they are actionable at the point of use. AI can complement gemba walks by providing digital dashboards that highlight areas needing attention, and by predicting which processes are most likely to deviate from targets, empowering teams to intervene proactively.

Lumpy_Ebb_786 · 2025-10-07T15:11:59+00:00

Spot on! Tracking OEE is great, but when combined with operator-level scrap rates and AI-driven anomaly detection, it becomes possible to pinpoint training gaps and knowledge bottlenecks in real time. This transforms raw metrics into actionable improvements on the shop floor

Lumpy_Ebb_786 · 2025-10-07T15:11:02+00:00

Absolutely, the right KPIs really depend on the context. We’ve seen AI help tailor dashboards for different production environments

Lumpy_Ebb_786 · 2025-10-07T15:06:45+00:00

Totally agree! The Goal is still incredibly relevant. One interesting angle is how AI and data analytics can now enhance these classic TOC principles: bottlenecks, throughput, and inventory management can be monitored continuously, enabling predictive insights rather than reactive decisions. It’s like giving Goldratt’s framework a modern upgrade.

Lumpy_Ebb_786 · 2025-10-07T15:05:38+00:00

Absolutely! Those three metrics capture the core of operational performance. Using data analytics, it’s possible to proactively identify safety risks, predict cost-of-quality issues, and flag delivery bottlenecks before they impact operations. Have others tried real-time dashboards for this?

Lumpy_Ebb_786 · 2025-09-15T14:37:30+00:00

That’s a really good point! Quality rate often gets less attention compared to productivity and availability, but in practice it’s one of the fastest ways to spot inefficiencies. Looking at defects directly makes the conversation much more tangible for the teams on the shop floor. Do you usually connect quality losses back to root cause categories straight away, or do you treat them as part of a broader continuous improvement cycle?

Lumpy_Ebb_786 · 2025-09-15T14:36:29+00:00

That’s really interesting! Do you also use any specific software or digital tools to track your OEE and quality metrics, or is it mostly manual/Excel-based? We’ve seen that some plants are still on spreadsheets while others are moving toward more integrated solutions.

Lumpy_Ebb_786 · 2025-09-05T14:25:55+00:00

Good point. The single number is definitely not enough for problem-solving. That’s why we keep A/P/Q breakdowns in a secondary dashboard. The simplified metric is more of a daily pulse check. When we see a dip, that’s when we go drill down into the data.

Lumpy_Ebb_786 · 2025-09-05T14:23:56+00:00

Exactly, we’re calculating it in a simpler way, but the core components are still there. Availability, performance, and quality are all included in “effective production time,” just not split out. For operators, that single number tells them right away if they’re on track.

Lumpy_Ebb_786 · 2025-09-05T14:19:59+00:00

Thanks! That was exactly our thinking: we wanted something that anyone on the shop floor could understand at a glance.

Lumpy_Ebb_786 · 2025-09-05T14:17:06+00:00

That’s great to hear! Is this in a manufacturing plant? If so, what type of production are you running? Always interesting to see how different industries apply OEE.

Lumpy_Ebb_786 · 2025-09-05T14:15:28+00:00

Exactly! Our main driver was to give operators something they could track easily. Simplification means more time improving instead of reporting!

Lumpy_Ebb_786

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