Artificial Intelligence (AI) in manufacturing is no longer the future – it’s the present. Companies are investing in AI solutions, software add-ons, automation, planning systems, and forecasting algorithms, expecting quick results. But one essential condition is often forgotten:AI is not almighty.
First – if the processes themselves are not clear and structured, no system will fix them automatically.
Second – no AI tool can function without data. If the data is inaccurate, delayed, or missing altogether – AI becomes an expensive but ineffective tool.
And what about the people? Managers and decision-makers are expected to control situations and solve problems – that’s their role. So this issue is not only about AI. Supervisors, shift leaders, technicians – all face situations daily where quick and accurate decisions are needed. But if they don’t have reliable data, decisions are based on intuition, experience, or manually collected information. That’s not necessarily bad – but it’s slow, limited, and often not precise.
And here’s a paradox: many tools aim to eliminate the human factor (“if there’s a worker – there’s a problem”), yet when it comes to understanding the situation on the shop floor, we still hear: “We don’t need tools, we already see and know everything.” The problem with “knowing” is that when the knower leaves, the knowledge disappears too.
Data – The Foundation of Decisions
As various studies show, data quality is a critical factor in AI effectiveness. Most manufacturing projects fail to deliver expected results due to lack of data or poor data quality. AI algorithms learn from data – if the data is wrong, AI learns incorrectly. If there’s no data – it can’t learn at all.
But wait… the same logic applies to humans. A manager without accurate data about the production process cannot objectively assess the situation. They rely on what they see, what employees tell them, or what they manually record. Manual data collection has limits – not everything can be observed, not everything can be written down, and most importantly – it takes time.
AI vs Human in Manufacturing
We often hear the question: will AI replace humans in manufacturing? The answer is no. But AI can help humans make better decisions. And for that, a reliable data source is needed.
Imagine a shift supervisor who sees in real time all stoppages, breakdowns, work speed, product changes – what’s happening with every machine at this moment. Could they make decisions as effectively as AI? Yes – if the data is accurate, fast, and reliable. Even more – if employees themselves see this data, decisions can be made faster, even without managerial intervention (this leads to task delegation and responsibility empowerment).
Historical process data also helps make more accurate predictions for equipment maintenance and performance planning.
This is exactly what the Lean2S production monitoring system ensures. It operates on the “2S – Simple & Smart” principle: easy to use, but intelligent in functionality. Lean2S MS captures all key production parameters in real time and presents them clearly, visually, and understandably. The employee simply follows the data and makes decisions – no guessing.
Lean2S value proposition is not just data collection, but the ability to see the process itself, not just the result. This means managers and supervisors can observe how stoppages form, when breakdowns occur, how work speed changes, and what factors cause product switches. This is not retrospective analysis – it’s real-time process monitoring. Such information allows not only reaction but also prediction and prevention.
What ddata collection methods work?
Traditional methods – manual logs, Excel sheets, live observation, employee interviews – are still widely used. But they have drawbacks: delayed, incomplete, or subjective information, and require time to process. It’s a different story when you’re not trying to find out what happened, but analyzing the situation based on directly received data.
Lean2S helps manufacturing companies identify bottlenecks, reduce downtime, and increase efficiency in real time. Implementation takes as little as 1.5 hours, results are visible immediately, and process specifics become clear within a week. That means data value reveals itself very quickly – and that’s not theory, it’s practice.
Why it matters?
Production managers, technicians, and supervisors will recognize themselves in this scenario: decisions made “by gut feeling,” current status checked by “going to see,” problems solved “when it’s already too late,” and breakdowns “appear” exactly when intensive work is needed – and it turns out “we knew about the issue long ago.”
This creates room for guessing or even wrong assumptions, leading to decisions that don’t eliminate root causes. Lean2S offers a different path – decisions based on facts.
So before investing in AI or similar solutions, or questioning managerial competence or inactivity, it’s worth asking a simple question:Do we currently have accurate data about what’s happening in our production?
If not – the first step should be Lean2S.