Pain Point: Overall Employee Effectiveness, The Other OEE

OEE has been a well-respected acronym meaning Overall Equipment Effectiveness.  If the maximum output capacity for a piece of machinery was optimized to produce 100 units per hour, and only 20 unites were produced, that represented a 20% delta in lost production, lost productivity, lost profits.

OEE is a hierarchy of metrics evaluating how effectively a manufacturing operation is utilized. The results are stated in a generic form which allows comparisons between manufacturing units in differing industries. It is not however an absolute measure and is best used to identify scope for process performance improvement, and how to get the improvement.

When cycle time is reduced, the OEE will increase; more products are produced for less resources. Increased changeovers (set-ups) will lower the OEE in comparison. OEE measurement is also commonly used as a key performance indicator (KPI) in conjunction with lean manufacturing efforts to provide an indicator of success.

OEE breaks the performance of a manufacturing unit into three separate but measurable components: Availability, Performance, and Quality. Each component points to an aspect of the process that can be targeted for improvement. OEE may be applied to any individual work center, or rolled up to department or plant levels. This tool also allows for drilling down for very specific analysis, such as a particular part number, shift, or any of several other parameters. It is unlikely that any manufacturing process can run at 100% OEE. Many manufacturers benchmark their industry to set a challenging target; 85% is not uncommon.

The flaw in the data for OEE data:  Overall Employee Effectiveness

Just as one measures the effectiveness of a machines optimal production output versus actual output, the same is true with workers on the plant floor.  The Internet of Things, Big Data, and other metric collection processes neglect the single greatest impact on productivity:  People.

Machine to Machine (M2M) Communications are utilized more frequently than People to People (P2P) Communications on the plant floor. Louise Dickmeyer, President of People Driven Performance, suggested, “Automation controllers typically offer communication modules to enable them to support a variety of industrial protocols, to facilitate machine to machine communications. Those who use protocols by MTConnect may even relate machine data and direct connectivity to business systems such as MES (manufacturing execution systems) and ERP (enterprise resource planning) systems.  None of these data keep employees engaged, safe, accurate, or productive.”

Dickmeyer insisted all this Big Data ignore the plant floor personnel with 20% absenteeism rates, hours gossiping spreading company rumors, instead of working to maximum capacity.  Know what a machine can produce optimally does not keep a seasoned experienced welder engaged for another five years, for whom no replacement can be hired.  Boosting productivity to 90% versus 85% does not prove nearly so efficacious when workers’ compensation claims are increasing two-fold because of poor employee training and heightened employee distractions.  When quality finished goods are compromised, when employee safety is jeopardized, when end-user customers complain about missed order dates or inaccurately delivery of product ordered, these are PEOPLE issues, not machinery issues.  The OEE data collected is fine and even perhaps useful information; too often it is used in lieu of employee plant floor communication.  Operations managers on a plant floor deal with personnel issues every day; that pain point and devising better employee communication is vastly more salient than whether the CNC output was 84.2% or 86.1%.  The hours spent each day by plant floor supervisors managing people regarding quality, training, safety, conflict resolution, prove to be costly, direct, and time consuming.  These are the real pain points of management and serve as a deadly virus attacking productivity.  Dickmeyer insisted that looking at Overall Employee Effectiveness must come first.

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