Business Intelligence can be an enabler for performance improvement across multiple areas of a manufacturing business. In this blog I will focus on Overall Equipment Effectiveness (OEE).
What is Overall Equipment Effectiveness (OEE)?
OEE is a measure of the throughput of acceptable quality units over the scheduled operating time of a piece of equipment, cell, line or plant
Why is improving OEE important?
The motivation is straightforward. If a company can use its total resources better than its competition it will have a lower cost structure, which enables it improve margins and/or better sell commoditized products in competitive markets.
How do you improve OEE?
Measure and monitor how well a piece of equipment performs relative to its designed capacity. Analyze the causes of sub-optimal effectiveness, and implement corrective action.
What can BI enable?
Better visibility and insight into the Key Performance Indicators (KPIs) used to calculate OEE.
(OEE = Availability x Performance x Quality)
Availability – the percentage of actual operating time compared to scheduled operating time
Performance – the speed at which the equipment runs as a percentage of its designed/target speed
Quality – the good units produced as a percentage of the total units started
What can be achieved with better visibility and insight?
Reduced down time – Although equipment breakdowns can not be completely eliminated, minimizing unplanned down time is critical to production performance. BI can be used to display data from Computerized Maintenance Management Systems (CMMS) in ways that help production quickly identify trends and variances in breakdowns and easily drill down into the detail that explains the root causes.
Using BI a Consumer Packaged Goods company found the average Mean Time Between Failure (MTBF) on one of their pieces of equipment ranged from 2 to 6 years. Analysis showed that the replacement parts from a particular vendor consistently resulted in 5-6 year MTBF. By standardizing on that vendor for replacement parts the company was able to significantly reduce down time and postpone capital expenditures for additional equipment.
Increased throughput – Sometimes a piece of equipment does not fail outright but operates at a reduced rate. BI can be used to integrate data from plant automation systems (distributed control systems, programmable logic controllers, and diagnostic monitoring systems) and provide insights to help production get the equipment operating at the ideal run rate.
An Oil and Gas company used BI to continual monitor operating characteristics of equipment such as temperature, pressure, and corrosion thickness. The visualization of real-time information enabled them to identify and repair leaks in gas lines faster. The result was a 3% increase in throughput, which translated into millions of dollars in additional revenue.
Fewer rejects – Even if a piece of equipment has no unplanned down time and is operating at its ideal run rate it still may not be delivering optimal effectiveness due to quality problems. BI can be used to track when rejects occur during a shift and job run, and identify the causes of trends and variances, such as design errors, material problems, and training deficiencies.
An Automotive Component company wanted to improve the quality of its Computerized Numeric Control (CNC) oxy flame cutting machines. Using BI to measure and analyze the causes of rejects they found both operators and maintenance personnel need additional training. After implementation of training the reject rate was reduced from 12% to 2% and non-value adding activities were reduced by 46%.
What is the financial impact?
Although the 3 KPIs of OEE are operational in nature, improving OEE also improves financial performance.
Increased revenue – If a company has $100 million dollars in sales and an overall OEE of 70%, a 1% increase in OEE would create an opportunity for an additional $1.4 million in sales.
Decreased working capital – If two companies are identical expect one has an average OEE of 60% and one has a world class OEE of 85%, the second company will have significantly less capital tied up in equipment, work in process, and labor.
Greater profitability – Reduced down time, increased throughput and fewer rejects means less labor, overhead and material costs associated with non-value adding activity like rework, which directly impacts the bottom line.
While some might feel it’s too difficult to gather all the data needed to better measure and manage OEE, the truth is that the vast majority of data already exists in shop floor systems. It’s just a matter of making it easier to access in a timely fashion and presenting it in a manner that best supports decision making. And that is exactly what BI is designed to do.
Sample OEE Dashboard
This dashboard shows the basics and allows you move the sliders under the dials and see the impact on bar graph in the top section.