Outdated standard times negatively affect production planning, cost calculations, and delivery performance. In contrast, modern AI-based approaches create transparency, reduce the workload for work preparation teams, and improve key KPIs such as OEE, delivery reliability, and unit costs.
Yet in many manufacturing companies, standard times are defined once and then left untouched for years. Over time, this leads to increasing deviations between planned and actual processing times. The result: outdated standard times undermine both production planning and product cost calculation. Unnecessary waiting times, longer lead times, and limited delivery reliability are direct consequences.
The root causes often lie in day-to-day operations: time studies are time-consuming, tie up valuable resources, and are rarely economically viable when dealing with high product variety. The issue remains unresolved. Standard times are reused, estimated, or copied – even if they no longer reflect reality. Today, however, there are far more efficient ways to optimize standard times – without traditional stopwatch-based time studies.
Why Traditional Methods Fall Short
This challenge is well known to work preparation teams: recording and maintaining standard times is a considerable effort. Especially in discrete manufacturing, where different product variants and production types run in parallel, systematic time tracking is hardly feasible.
As a result, standard times gradually become outdated. Deviations between planned and actual values remain undetected, leading to planning errors. Production bottlenecks, excess inventories, and inaccurate cost calculations occur because the underlying data is no longer valid. Still, the issue is often postponed – not due to lack of awareness, but because traditional approaches are too resource-intensive.
Many in work preparation know their standard times are no longer realistic. Intuition also points to the shopfloor areas with the largest discrepancies. Yet a practical, continuous solution is missing. The phrase “We really ought to address this, but…” epitomizes the problem – and so, valuable potential remains untapped. Time to optimize standard times – with a new approach.
The New Way: Optimizing Standard Times with Artificial Intelligence
This is where a modern, AI-powered approach comes in. Instead of relying on manual time studies, the solution uses existing data from ERP, MES, MDE, and BDE systems. These production data sources are already available in good quality in many companies – particularly when modern cloud MES solutions like SAP Digital Manufacturing are in use. They provide a reliable data foundation on which a continuous optimization system can be built.
The comparison of planned and actual times is fully automated. Deviations are identified systematically without requiring manual analysis by work preparation. The solution accounts for real-world shopfloor conditions. Even if feedback data isn’t always complete or perfect, the system delivers valuable insights and pinpoints the most critical areas for optimization.
A central component is the use of artificial intelligence. The AI detects patterns in deviations and generates concrete proposals for realistic, optimized standard times. Work preparation teams retain full control: suggestions can be reviewed, modified, and approved. In addition, the results can be seamlessly integrated into the continuous improvement process (CIP), enabling structured and sustainable process optimization.
The key benefit: comparing planned and actual times becomes an automated part of daily operations. Standard times remain up-to-date and reliable – without manual time studies.
Value Creation Through Reliable Standard Times: Why Optimization Pays Off
Accurate, up-to-date standard times are a key lever for efficiency in manufacturing. They form the basis for robust capacity and scheduling planning, precise cost calculations, and economic shopfloor control. Those who optimize and automate systematically create room for productive work in work preparation and establish a reliable data foundation for resource, customer delivery, and cost planning.
Ultimately, standard time optimization directly contributes to improved KPIs: OEE, delivery reliability, and unit costs can be measurably improved – without ongoing time studies or disruptive process changes – simply through continuous, automated comparison of master and actual production data.
The cbs Fast Value Approach
Starting this new journey is easier than expected. The cbs Fast Value approach enables a structured, step-by-step implementation that delivers measurable benefits in multiple stages – or “plateaus”:
- Visualizing deviations
In the first step, planned and actual times are systematically compared. This generates a reliable picture of where significant deviations occur and provides a solid foundation for the next step. - Analyzing root causes
Next, the causes of deviations are examined – outdated work plans, changed workflows, or new variants may all affect standard time validity. - Using optimization proposals
AI suggests realistic, data-driven standard times. Work preparation decides which proposals to adopt or where targeted time studies are still needed – for example, with new, complex, or rare processes. Time studies become more focused and efficient. - Ensuring continuous updates
In the final phase, the continuous comparison is fully automated. Standard times remain up to date, and manual maintenance efforts are minimized.
Best of all: measurable results are visible after just a few weeks. The solution is flexible and scalable – additional production areas can be integrated step by step.
Benefits of AI-Based Standard Time Optimization
The advantages are clear:
- Reliable planning and costing data
- Elimination of manual time studies – focus on value-adding activities
- Greater transparency in high-mix production
- Reduced waiting times, buffers, and lead times
- More stable processes and better resource utilization
- Direct impact on OEE, delivery reliability, and unit costs
- Scalable process improvement without system changes
Conclusion: Rethinking and Targeting Standard Time Optimization
Standard time optimization doesn’t have to remain an unresolved, ongoing issue. With an AI-driven approach and a structured Fast Value methodology, standard times can be improved efficiently, reliably, and sustainably. The stopwatch is obsolete – optimization is now part of digital shopfloor management.
This approach is practical, effective, and delivers results quickly – even when feedback data is not perfect. The solution creates transparency, simplifies work preparation, and supports the continuous improvement process. Standard time optimization becomes a key success factor for stable, efficient manufacturing operations.
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