Overall Labor Effectiveness

Overall Labor Effectiveness

Overall Labor Effectiveness (OLE) is a key performance indicator (KPI) that measures the utilization, performance, and quality of the workforce and its impact on productivity.

Similar to Overall Equipment Effectiveness (OEE), OLE measures availability, performance, and quality.

  • Availability – the percentage of time employees spend making effective contributions
  • Performance – the amount of product delivered
  • Quality – the percentage of perfect or saleable product produced

However, OLE helps manufacturers understand the interdependency and trade-offs of shop floor productivity and profitability by measuring the contributions of the workforce. OLE gives manufacturers the ability to analyze the cumulative effect of these three workforce factors on productive output, while considering the impact of both direct and indirect labor.


Measuring availability

There are many factors that influence workforce availability and therefore the potential output of equipment and the manufacturing plant. OLE can help manufacturers be sure that they have the person with the right skills available at the right time by enabling manufacturers to locate areas where providing and scheduling the right mix of employees can increase the number of productive hours. OLE also accounts for labor utilization. Understanding where downtime losses are coming from and the impact they have on production can reveal root causes — which can include machine downtime, material delays, or absenteeism — that delay a line startup.

Measuring performance

When employees cannot perform their work within standard times, performance can suffer. Effective training can increase performance by improving the skills that directly impact the quality of output. A skilled operator knows how to measure work, understands the impacts of variability, and knows to stop production for corrective actions when quality falls below specified limits. Accurately measuring this metric with OLE can pinpoint performance improvement opportunities down to the individual level.

Measuring quality

A number of drivers contribute to quality, but the effort to improve quality can result in a lowering of labor performance. When making the correlation between the workforce and quality it is important to consider factors such as the training and skills of employees, whether they have access to the right tools to follow procedures, and their understanding of how their roles drive and impact quality. OLE can help manufacturers analyze shift productivity down to a single-shift level, and determine which individual workers are most productive, and then identify corrective actions to bring operations up to standards.

OLE calculation

Consider a manufacturer that has full employment, sufficient demand to run the factory at full output, and equipment that is in good operating order. Margins are looking good, but they could be better. OLE can illustrate how the workforce is affecting profit potential.

  • Availability – Utilization is hampered by several items in the plant. First, absenteeism accounts for a capacity shortfall of approximately two percent each period. Also, poor material scheduling and movement causes about one hour of idle time per shift.
  • Performance – Output is down somewhat. An insufficient number of technicians available to set up the equipment often means productive output is stalled at every changeover. The impact: lost productive time of about five percent.
  • Quality – Given the shortfall of productive hours, the supervisor attempts to make up the lost time by running at higher productive rates. The result: Quality begins to slip as the day wears on, and yields drop. The impact is a four percent loss of acceptable product.

The cumulative impact: The OLE value for the plant is 78.2 percent, meaning that this plant only converted 78.2 percent of the factory’s potential for profitable output.

Effective use of OLE uncovers the data that fuels root-cause analysis and points to corrective actions. Likewise, OLE exposes trends that can be used to diagnose more subtle problems. It also helps managers understand whether corrective actions did, in fact, solve problems and improve overall productivity.

See also

External links

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