This module Data Acuity President Mr. Jim Desrosiers explains the importance of looking at Loss Deployment and Overall Equipment Efficiency when assessing the efficiency of a manufacturing process and presents the use case of Catania Oils as an example.

Video Transcript

[0:00] Video Commentator

In this module of manufacturing data analytics, we will focus on Overall Equipment Effectiveness, or OEE, and introduce a metric called Loss Deployment. Data Acuity President Jim Desrosiers explains.

[0:26] Jim Desrosiers President and Founder of Data Acuity, Inc.

OEE as a metric provides us with a clearer understanding of the difference between the quantity of sellable product an asset could make versus the actual product that asset made. The key insight that we're looking to gain from this metric is a full understanding of which resources we should assign to which priority problems. OEE is a top level measurement of our efficiency, and it breaks down into three separate buckets.

[0:53] Video Commentator

In this example, Catania Oils is looking to identify potential efficiency gains, leveraging the tool sets from ICONICS and Data Acuity. Catania Oils, Dan Brackett explains.

[1:03] Dan Brackett Catania Oils Vice President of Operations

The first thing we were looking to do was to determine how many cases per minute we were producing so that we could determine how fast or the efficiency of our lines and that led us to ICONICS and to Data Acuity to better zero in on that information.

[1:19] Jim Desrosiers

OEE is when we look at the potential for machines versus what it's really producing. The key is to focus the right resources on the right problems. To do that, OEE breaks your performance into three buckets. These three buckets are availability, performance, and quality.


Availability gives us a measurement of the amount of time an asset was operating compared to the amount of time that asset was scheduled to be operating. This does not account for the time that the asset was scheduled to not be operating. Performance gives us a measurement of the amount of product that the asset actually produced during the operating time, compared to the ideal amount of product that asset could have produced. Quality gives us a measurement of the good product versus the bad product, but only for the product actually produced. Drilling into these three buckets allows us to quickly analyze the true nature of the loss of efficiency. We're going to start by taking a look at a Pareto chart, which will list our loss of efficiency as either a quantity a time or a quantity of events.

[2:23] Video Commentator

The next level is to be able to correlate the scores to another set of data.

[2:27] Dan Brackett

We collect data regularly through ICONICS. And so, we get data from every line from every product and from every shift and that data is accumulated in the database, and a report comes out every day for us to be able to determine how each one of those lines is running.

[2:45 ] Video Commentator 

Another metric often overlooked, and which is critical to making gains in efficiency is Loss Deployment, which unlike OEE, considers factors beyond simply the machines operation. It simply asks if we made a calculation that we can produce 500 bottles of oil per day, but we only produce 300. Where did those 200 bottles go?

[3:03] Jim Desrosiers

Loss Deployment as a metric includes OEE, but it actually gives us much greater insight into the true loss of efficiency. For Loss Deployment, we're going to start by figuring out what's the total amount of product we could make in an ideal situation 24/7 365 no loss of efficiency. From there, we're going to back down to five buckets. The first bucket is going to be how much product did we actually make in reality sellable product. The second bucket is going to be what was our loss of efficiency that can be attributed as the fault of the machine itself or the asset itself. The third bucket is going to be what was our loss of efficiency that can be attributed to the process around the asset, for example, waiting for: waiting for material waiting for operation and waiting for instructions. The next bucket is going to be loss of efficiencies that are attributed to required actions. For example, preventive maintenance is required, but it does cost us efficiency. A clean out or a changeover is required, but it does cost us efficiency. And the final bucket is going to be those losses in efficiency that were actually intention. For example, we've scheduled not to run the line on a Sunday, or we've scheduled not to run the line during a break time. As we indicated earlier, the metric Loss Deployment fully includes OEE. But the additional buckets give us even greater insight into the potential loss of efficiency. With 30 years of experience deploying automation systems, we want to not focus just on a single machine fault or quality defect, but the fact of the entire process. This insight allows us to capitalize on even greater efficiency gains.