Friday, July 28, 2017

Proposing a Conceptual Model to Apply OEE to a Continuous Process

Antoine Snyman

Even though the demand for the platinum group metals (PGM) grew roughly 10 times over the last 40 years, the price remained the stable.  Due to this, organisations in the PGM sector are faced with the challenge to produce more with their existing resources and engineering assets.  Naturally these organisations respond with cost reduction; a focus which may have a severely negative impact on the organisation’s performance (Pretorius, 2004) and focus should rather be placed on asset management; however, very few organisations operating in the primary processing stages of the PGM sector pay attention to the measurement of utilisation and performance of assets. 

The objective of this article is to present a model based on the principles Overall Equipment Effectiveness (OEE) to suggest how asset performance can be measured in a continuous process (specifically concentrators) in the PGM sector of a major mining house in South Africa.  The model can be applied to any continuous process.

OEE, a well-known metric express the effectiveness of equipment, is calculated as:
The availability component of OEE is represented by equation 2 and illustrated in Figure 1.

Figure 1:  OEE Time Allocation Model
Performance[1] is normally calculated by relating the actual performance of equipment to nameplate performance thereof.  This metric cannot be applied to a continuous process due to the varying nameplate performances of multiple equipment items forming the process chain. Hence, a new metric for performance is expressed in equation 3.   
Quality is the third and perhaps the most difficult component to measure since continuous processes have no distinct product which can be inspected in real time.  A plausible basis for determining the quality of the output is to analyse random samples of the product, but the results thereof are only available three to five days post production, rendering OEE reactive. The solution offered focusses on the different components which have an influence on the final product quality.  This approach starts off by selecting the days with the best quality products based on previous analysis and then determining a “best range” for each manageable component, i.e. the best range for Crusher Grind is between 75 – 80%.  If the actual differs by 10%, the metric can only score 90% for this component.  Each actual value is then compared to its “best range” and used in the quality calculation, as expressed in equation 4.
Similarly, the reagent dosages are a combination of reagents which should all be in specific range and it is of extreme importance that this metric should be tailored for every plant.  The reagent dosages calculation is seen in equation 5.

The articles Amadi-Echendu (2004) and Amadi-Echendu et al. (2010) make that point that performance of an engineering asset should be approached from a value doctrine to address the difficulties in the PGM market. This article briefly described a conceptual model on how OEE can be applied to a continuous process. The challenges surrounding the proper definition of the parameters and collection of pertinent data is addressed in a different article.

Amadi-Echendu, J. Managing physical assets is a paradigm shift from maintenance.  Engineering Management Conference, 2004. Proceedings. 2004 IEEE International, 2004. IEEE, 1156-1160.

Amadi-Echendu, J. E., Willett, R., Brown, K., Hope, T., Lee, J., Mathew, J., Vyas, N. & Yang, B.-S. 2010. What is engineering asset management? Definitions, concepts and scope of engineering asset management. Springer, Ch, pp 3-16.

Pretorius, P. Getting back to basics: Productivity revisited.  Engineering Management Conference, 2004. Proceedings. 2004 IEEE International, 2004. IEEE, 1284-1288.

[1] Performance is also referred to as “Efficiency” in literature. Performance of the asset should not be confused with the nameplate performance of a specific piece of equipment.


Antoine obtained his bachelor’s degree in industrial engineering from the University of Pretoria in 2006.  Post studies, he started his career in consulting where he quickly got awarded the responsibility and accountability for the management and delivery of large projects for key clients.  Currently, he is a specialist in his field with responsibilities ranging across the entire value chain of the mining and processing industry.  He furthermore completed his master’s degree in engineering, with focus on Engineering Management in 2015 and is currently working at Lonmin Plc in Marikana, South Africa.
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