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.

OLE allows manufacturers to make operational decisions by giving them 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.
OLE supports Lean and Six Sigma methodologies and applies them to workforce processes, allowing manufacturers to make labor-related activities more efficient, repeatable and impactful.[1]

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.

Calculation: Availability = Time operators are working productively / Time scheduled
Example:
Two employees (workforce) are scheduled to work 8 hour (480 minutes) shifts.
The normal shift includes a scheduled 30 minute break.
The employees experiences 60 minutes of unscheduled downtime.
Scheduled Time = 960 min – 60 min break = 900 Min
Available Time = 900 min Scheduled – 120 min Unscheduled Downtime = 780 Min
Availability = 780 Avail Min / 900 Scheduled Min = 86.67%

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.

Calculation: Performance = Actual output of the operators / the expected output (or labor standard)
Example:
Two employees (workforce) are scheduled to work an 8-hour (480 minute) shift with a 30-minute scheduled break.
Available Time = 960 min – 60 min break – 120 min Unscheduled Downtime = 780 Min

The Standard Rate for the part being produced is 60 Units/Hour or 1 Minute/Unit
The Workforce produces 700 Total Units during the shift.
Time to Produce Parts = 700 Units * 1 Minutes/Unit = 700 Minutes
Performance = 700 minutes / 780 minutes = 89.74 %

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.

Calculation: Quality = Saleable parts / Total parts produced
Example:
Two employees (workforce) produce 670 Good Units during a shift.
700 Units were started in order to produce the 670 Good Units.
Quality = 670 Good Units / 700 Units Started = 95.71%

OLE calculation

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.

Example:
Calculation: OLE = Availability x Performance x Quality
Example:
A workforce experiences...
Availability of 87%
The Work Center Performance is 89.74%.
Work Center Quality is 96%.
OLE = 86.67% Availability x 89.74% Performance x 95.71% Quality = 74,44%

Labor Information Tracked by OLE

The following table provides examples of the labor information tracked by Overall Labor Effectiveness organized by its major categories. Using this labor information, manufacturers can make operational decisions to improve the cumulative effect of labor availability, performance, and quality.[2][3]

OLE Category Major Loss Category Example of Loss
Availability

Availability is the ratio of time the operators are working productively divided by the amount of time the operators were scheduled. 		Breakdown





Changeover 		Lack of training and experience
Unplanned absenteeism
Maintenance mechanics delayed
Poorly scheduled breaks and lunches
Material handlers starved the machine

Set-up personnel shortages or delays
Lack of training, skills and experience
Performance

Performance is the ratio of the actual output of the operators divided by the expected output (or labor standard). 		Reduced Speed


Small stops 		Operator inefficiency due to lack of skills, experience or training

Poor operator technique due to lack of skills, experience or training
Quality

Quality has many definitions, but a common one is the ratio of saleable parts divided by the total parts produced. 		Scrap or rework



Yield or start-up losses 		Operator error
Set-up team error
Maintenance mechanic error

Set-up team error
Maintenance mechanic error
Operator error

See also

External links

References

  1. ↑ RP news wires, Noria Corporation. "New KPI measures plants' overall labor effectiveness". Retrieved 2011-11-30.
  2. ↑ Gordon, Gregg (2011). Lean Labor Strategies. page 68: Kronos Publishing.
  3. ↑ Nguyen, Mark. "How Ceradyne Increased Productivity through Effective Labor Management". IndustryWeek. Retrieved 2011-11-30.
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