What is high OEE costing you?

Posted in Blog on October 19th, 2020

This is part 2 of a 4-part blog series exploring the common business challenges facing the manufacturing industry.

Overall Equipment Effectiveness is a popular KPI (Key Performance Indicator) used to help improve the productivity of manufacturing processes and accurately measure true plant productivity.  Its key measurable components are Availability (downtime), Performance (speed) and Quality (defects).

The real-time data captured from an automated OEE system such as production downtime, shortstops and speed losses can provide real visibility into the effectiveness of plant machinery and processes needed to make measurable productivity improvements.

So, what constitutes ‘world-class’ OEE?  What % OEE should you really be aiming for?  Is it a valid measure of plant productivity?

Let’s take an example: a plant manager is tasked with increasing OEE from 60% to 65%, to improve profitability by ££.  He meets his objective (or even overachieves) – but the resulting profits are not delivered – what’s going on?!  In short, the OEE objective has been met, but potentially only at the cost of increased labour, inefficient scheduling and/or increased maintenance costs.

How to improve overall manufacturing performance?

Manufacturers are typically tasked with maximising production output, whilst maintaining or increasing profits.  Labour uncertainty, not least due to Brexit and more recently COVID-19, leaves manufacturers needing to ensure they optimise the performance of the workforce into order to achieve production targets.

Labour and OEE should be measured in tandem.  Machines generally have predictable costs, including depreciation rates and maintenance charges, but it is the workforce who are critical to the manufacturing process, and a highly effective workforce is the key to maximising performance.

Labour – don’t measure OEE without it!

Just as availability, performance and quality are key OEE metrics, these should also be measured in the context of labour effectiveness.  Measuring OEE in isolation can otherwise yield the following inconsistencies:

  • If staffing issues exist, and operators are not available to start a machine, it will adversely affect OEE – but this is not a fault of the machine itself.
  • A high OEE suggests good machine availability, but does not reflect the labour cost of this: does the machine require frequent maintenance? Are operators being redirected from other areas in order to achieve high OEE?
  • OEE needs to be measured in context; what purpose do individually excellent OEE ratings have when processes further down the production line cannot cope with increased production (thus moving the bottleneck with no overall productivity gains?)

So, how can you accurately measure the labour cost of achieving target OEE scores?  Where staffing is required to keep a machine running, there are inevitably variable costs involved.  Operators have different salary scales; overtime rates are typically higher.  What is maintenance costing you?  How often are supervisors required to intervene?

When measuring labour costs, companies should consider the following variables:

  • Availability
    • What to do in case of staff shortage?
    • What is the impact of social distancing on the production line?
    • Do we always have enough skilled staff?
    • Scheduling: not just staff numbers, but ensuring the right skills are in the right place at the right time.
    • Downtime costs directly caused by labour (un)availability such as shift changes, shortage of operators or even unplanned maintenance.
  • Performance
    • What training and instructions are in place? Do staff have the necessary skills?
    • What is the maintenance schedule? What about unplanned maintenance?
    • Processes and procedures (guidelines, tools, materials) – all have an impact on performance.
  • Quality
    • Can we be sure that the right staff are in the right place at the right time to maximise productivity?
    • Do staff have knowledge of the relevant processes and procedures to perform their role?

Labour effectiveness and OEE together make a powerful combination of KPIs to ensure maximum productivity.  Accurate measurement of the parameters above helps identify causes of bottlenecks and inefficiencies and establishes a more productive workforce by helping identify where additional support (training, processes etc) may be needed.

In summary, yes, manufacturers should continue to measure OEE and this is a valid KPI.  However, it needs to be seen in context with other KPIs such as labour efficiency variance, maintenance costs and overall productivity.  A clear understanding of both labour and OEE metrics are key to identifying productivity bottlenecks and maximising production output.

Cimlogic help manufacturers discover how efficiently they are utilising KPIs such as OEE and labour variance in the production process, to highlight cost savings and opportunities for improvement.

To find out more please email us [email protected]

Would you like to understand what impact your workforce has on production costs and profitability? Register here to our upcoming webinar on Fri 6th Nov 2020 at 11am – Labour Variance in Manufacturing.