# Line Balancing Techniques for Assembly: From Static Timing to >85% OEE Efficiency with MTM In the current industrial scenario, marked by the transition towards Industry 4.0, line balancing based on simple arithmetic averages is the root cause of an **OEE (Overall Equipment Effectiveness)** stagnating below 70%. Achieving a high-performance assembly line requires moving beyond traditional "stopwatch" observation towards scientific predermined time systems. ## 1. The Anatomy of Imbalance: Why the "Average" Fails The theoretical **Takt Time** often fails in practice because it does not account for human variability (the "average error"). We must differentiate between: - **Theoretical Takt Time:** The pace required to meet demand. - **Operating Takt Time:** The pace adjusted for the target OEE. $$ \text{Operating Takt Time} = \frac{\text{Theoretical Takt Time}}{\text{OEE Target}} $$ ## 2. Measurement Methodologies: Stopwatch vs. MTM/MOST Traditional timekeeping is susceptible to the **Hawthorne Effect** and subjective bias. In contrast, predetermined time systems like **MTM (Methods-Time Measurement)** and **MOST (Maynard Operation Sequence Technique)** work with "surgical" units called **TMU (Time Measurement Units)**. - **1 TMU = 0.036 seconds** - **1 hour = 100,000 TMU** Using TMU allows for a granularity that traditional stopwatches simply cannot match, especially in high-speed assembly. ## 3. Ergonomics and Fatigue Constraints (ISO 11228) Safety and productivity are two sides of the same coin. According to **ISO 11228** standards, allowances (fatigue coefficients) must be mathematically integrated into the work cycle to prevent absenteeism and quality drops. $$ \text{Standard Time} = \text{Basic Time} \times (1 + \sum \text{Allowances}) $$ ## 4. Smoothing Algorithms and the Smoothness Index (SI) The real challenge is identifying the true bottleneck—not just the slowest station, but the one with the highest variance. Using **Ranked Positional Weight (RPW)** algorithms, we aim to minimize the **Smoothness Index (SI)** to ensure an even flow. $$ SI = \sqrt{\sum_{i=1}^{n} (T_{max} - T_i)^2} $$ A lower SI indicates a more balanced line where idle time is minimized across all workstations. ## 5. The Cronometras Solution: Digital Twins Fed by Clean Data The bridge between theory and a >85% OEE lies in "Clean Data." Validating movements in TMU allows for the creation of **Digital Twins** that accurately predict line performance before a single station is moved. ## FAQ: Frequently Asked Questions **How do allowances impact my OEE?** Allowances account for necessary rest. If not integrated into the Takt Time, the line will naturally slow down to accommodate fatigue, causing a drop in unplanned OEE. **Is MOST profitable for short production runs?** Yes. While the initial analysis takes longer than a simple stopwatch study, the resulting standards are more robust and reusable, leading to a 15-20% reduction in "hidden" inefficiencies. **How can I reduce line variance?** Through method standardization and the use of tools like CronometrasApp, which identifies outliers in real-time using the Chauvenet Criterion. --- [Request a free demo](/en/contact) to discover how the scientific method can optimize your assembly lines.