Production Time Calculation: How AI Transforms Manufacturer Accuracy

In manufacturing, every miscalculated minute costs money. A 10% gap between estimated and actual time is enough to seriously eat into the profitability of a project or production batch.

Yet in many industrial SMEs, production time calculation still relies on fixed standards, Excel files or the expertise of a few key people.

Artificial intelligence changes the picture — provided it is integrated intelligently, close to the floor.

Why traditional methods are reaching their limits

For decades, production managers have used two main approaches to estimate operation times:

• Internal standards: routings, allocated times per reference, average historical data.
• Human expertise: shop-floor managers, methods engineers, production leaders.

These approaches remain relevant. But they are poorly equipped to face the growing complexity of industrial processes: material variability, machine configuration diversity, interruptions, micro-stops, changeovers, operator constraints.

The result: under-priced quotes, slipped schedules, tension between methods and the floor, and a competitiveness loss that is hard to quantify.

These problems are part of the common production management challenges found in workshops still run with Excel files or disconnected tools.

How AI applies to production time calculation

AI does not replace methods engineers. It enriches them. Its main strength is processing volumes of historical data that humans cannot analyze alone.

Every production run leaves a trace:

• Actual duration per operation.
• Machine parameters.
• Part or order type.
• Material used.
• Workshop configuration.
• Operator or team.
• Interruptions, scrap and rework.

An AI system trained on this data can, for a new part or new manufacturing order:

• Identify similar past cases.
• Detect the variables that most influenced actual times.
• Produce a dynamic estimate with a confidence interval.
• Refine itself automatically as new production data arrives.

This continuous learning capability is what distinguishes an AI approach from an enhanced spreadsheet.

Benefits for manufacturers

Companies that integrate AI correctly into their time calculation can achieve:

• More accurate quotes, with less risk of under-pricing.
• More reliable production planning.
• Better anticipation of workload per workstation or machine.
• Reduced gaps between estimated and actual times.
• Earlier detection of production drift.

The benefit is not only technical. It is also commercial: estimating better means selling better, planning better and protecting margins.

Integration into ERP, production management or MES

The full power of AI applied to production times is only released when it is connected to the tools teams use daily.

Integrated into an ERP or production management system, an AI module can automatically feed routings, update allocated times based on recent data, or detect anomalies in time records.

Integrated into an MES, it can trigger alerts when drift appears in real time at a workstation.

The goal is not to replace operators or methods. The goal is to amplify their decision-making capacity with reliable, actionable data.

The prerequisites you cannot ignore

An industrial AI solution does not work without usable data. Before talking models, you have to answer very concrete questions:

• Are actual times collected reliably?
• Are operations described in enough detail?
• Is data linked to the right manufacturing orders?
• Are gaps tracked, or only commented verbally?
• Is historical data comparable across time?

If these foundations are not solid, AI will produce fragile estimates. In that case, the priority is not the algorithm. The priority is structuring field data.

That structural work is exactly the scope of a broader industrial production digitalization initiative.

Why an off-the-shelf solution is not always enough

Many manufacturers turn to generic AI modules or standard options inside their ERP. Results can be disappointing when these modules don’t match the realities of the workshop.

The manufacturing processes of a steel structure builder have nothing in common with those of a composite panel maker or a precision machining shop. Available data, its quality and its format vary widely.

An effective solution must therefore be built from the floor up: what data are you able to collect? In what format? With what reliability? These questions determine the architecture of the solution.

The DevTeix approach

At DevTeix, we have been developing custom industrial software for over 30 years. We don’t sell packaged solutions: we start from your reality.

Our approach to production time calculation runs in three phases:

• Field immersion: analysis of your flows, available data and current tools.
• Custom development: building an estimation module fitted to your processes and connected to your systems.
• Continuous improvement: the system learns, refines and evolves with your production.

Our solutions today support more than 150 industrial projects across Europe and beyond.

Conclusion

Accurate production time calculation is no longer just a matter of individual talent or accumulated years of experience. It is data that can be modeled, enriched and automated — provided you rely on the right data and the right tools.

Industrial AI is only useful when it stays connected to the floor. Properly integrated, it improves profitability, lead times and competitiveness.

Want to evaluate the potential of such a tool in your context? Contact our team for a free first call.

Frequently asked questions

How can AI help calculate production times? +

AI can analyze past production runs, identify similar cases, weight the variables that influence actual times and propose a dynamic estimate with a confidence level.

What data is needed to estimate production times with AI? +

You need reliable historical data: actual times per operation, machine parameters, part type, material, operator, workstation, interruptions, scrap and gaps between estimated and actual.

Does AI replace methods engineers or shop-floor managers? +

No. AI enriches their decision with historical data and weak signals. It must remain connected to the floor and be validated by business teams.