Obtaining real-time and real-effective OEE measurements (Part 3)
Finding a way to compare one company’s financial performance against another is rooted in the ability to find a common ground for evaluation. This common denominator measurement, at least from a financial perspective, has been around for over 5 centuries. Luca Pacioli in 1494 first described the system of double-entry bookkeeping used by Venetian merchants. He was the first to use a method of debits and credits in journals and ledgers that is still the basis of today’s accounting systems.
His common denominator approach to analyzing a business has of course been well used over the century’s and is still used today for comparing the financial health of a company. Finding a common measurement for measuring and ultimately comparing manufacturing company’s production performance has not been the focus of many modern manufacturing companies until just recently.
How does today’s management identify areas of best practices? What method is used to determine success in lean initiatives? How is senior management evaluating their production facilities regarding better asset availability and improved product flow? Is it realistic to use a common set of production measurements to compare one production facility to another? Of course, without a “common denominator” to measure performance how does management compare their production processes across their operating units? The answer unfortunately for many corporations is…they simply do not compare the factory floor performance from one company to the next. Why not?
To answer the question of plant-to-plant performance comparison it is imperative to reflect back to Pacioli and his approach long ago. First of all, each operating unit needs to have the “granularity” of production measurements which will allow useful metric(s) for comparison. This granularity is equivalent to Pacioli’s debit and credit journal entries. And once this granular activity is captured then these “factory floor transactions” can be rolled into a “scoreboard.” Just like financial transactions can be rolled-up into a balance sheet and a Profit/Loss Statement (scoreboard); so can the factory floor activities be summarized into a common set of metrics to help understand the “health” of the production side of the business? I believe it can.
The metric most discussed today is Overall Operating Effectiveness or OEE and it is one of several measurements that can provide this common denominator “scoreboard” to help realistically compare one production facility effectiveness to another. Much as one can measure the difference between the total assets and the total liabilities to find stockholder’s value or equity, so can the measurements of Resource Availability along with Production Quality and Performance (rate of production) provide a single measure of OEE for comparison purposes.
It is common knowledge that a company could not produce a balance sheet without all the detailed accounting transactions. Yet even today some manufacturing companies try to produce OEE without all the necessary detail or granularity that is required.
For example, too often an OEE system is introduced to the factory that tries to capture resource up-and-running time against the time which, for one reason or another, it is not available. That is, how often is that resource really available for work when there is work to be produced on that resource? If the resource, let’s say a machine, is always available and never expectantly goes down when work is available to run on it, then the resource has 100% availability. If your factory floor assets never go down then there is no need to measure availability. However, downtime occurrences do happen to most production facilities. Therefore, it is a good idea to measure the availability and compare to other assets in the same facility and potentially to other production facilities within your corporation.
To display this availability data, the floor assets (machines) can be electronically wired into a standard PC network. Usually this network will have an Open Process Control (OPC) Server on it to capture and translate signals being sent from each machine wired into the network. When the machine starts running a signal or “tag” is sent to the OPC server along with the IP address of the machine. With electronic signals (tags) indicating the status of the machine, up/down time can be captured automatically and electronically without operator intervention. This data can then be displayed in real-time so management can visually see the amount of time an asset is up and running, and of course when it is not working. These systems are relatively inexpensive to install and sometimes can provide real-time graphs and charts. So let’s see what this is doing to provide good granular data for comparison purposes.
As mentioned above, the availability is less than 100% if the machine is not available to run when parts are available at the machine. The crucial questions are: How can management determine the underlying reason(s) that caused the downtime to occur and is that information available through data at the machine?
- Can the OEE system identify the person(s) attending to the machine at down times?
- Who set the machine up and is there a link between set-up and unexpected downtime?
- What was the material in the machine when it went down?
- What was the part (item number) being produced when the machine was stopped?
If your new OEE system cannot identify this level of granularity then you might consider another approach. Why? Because the measure has no meaning if you cannot determine the root cause of the problem.
And once you are able to do that, then you are half way there. Now you need to find a way to eliminate or minimize the downtime but without the granularity (the detail transactions) you are out of luck.
There are several metrics that can be used to compare factory productivity, performance, work flow, resource effectiveness, both hard assets (machines) and labor. As mentioned above OEE is the most common way so let’s make sure we understand the underlying measurements of Overall Equipment Effectiveness.
As outlined in part of this series, let’s start with asset AVAILABLITY. This is the measure of the amount of time the hard assets (machines) are being used as a percentage of the time that there is work for them to product. For example, if the machine has a full shift’s worth of work (8 hours) but for one reason or another the machine only was “available” and producing product for only 7 of the 8 hours then you have 87.5% Availability. But Availability is only one of the necessary measurements if your goals is to produce Overall Equipment Effectiveness and not just a downtime analysis.
QUALITY will require the capture of good pieces produced out of all the pieces produced. In other words, total pieces less any scrap. So if 90 out of 100 pieces/parts produced and therefore 10 scrapped then 90% is the Quality measurement. Any rework could affect this calculation but for now let’s concentrate on “first pass” measurements of OEE.
PERFORMANCE is the measure of the rated speed or rate of production compared to the actual rate of production. The level of detail data capture is an important consideration when determining the “best” fit for your organization and, of course, the most cost effective system to implement. The Availability can be produced for the entire factory but usually will have to be broken down to individual pieces of equipment. Quality also can be produced at the total factory level but again is most useful captured at the asset level which means at each machine included in the OEE analysis. But Performance will not only require the specific equipment but also a part association. This part association is required to get the correct rate of production which can vary with each part unless you have highly focused lines that runs only one part all the time.
It is this triumvirate of factors/data that will give management a true comparison of factory floor metrics that could at last be a comparable system for factories rivaling Pacioli’s system for accounting. Over six centuries later we still have valuable lessons to learn.