Overall Equipment Effectiveness as a Maintenance Management Analysis Tool for a Crumb Rubber Production Line

--Overall Equipment Effectiveness (OEE) calculations were carried out on a field crumb production line in order to determine its amelioration potentials and evaluate the maintenance policy of the factory. The OEE of individual machines making up the field crumb line as well as that of the entire line has been calculated using data collected over a one month period. The OEE values obtained ranged from as low as 24.1% for the pre-breaker to just over 60% for crepper 4 and increases with increase in any of the three production parameters – machine availability, performance efficiency, or rate of quality product. These results show that the line possesses enormous amelioration potentials. The factory productivity is found to be greatly enhanced by even slight improvements of the OEE value. Skeletal improvement strategies implemented, turned out very encouraging results a 14% rise in the OEE value of the pre-breaker was obtained. Other machines on the line showed a similar improvement. The production line OEE has witnessed an increase from 43.7% to 49.9% corresponding to an increase in productivity of 0.42 tons per hour. Plant productivity can therefore be boosted by use of OEE as a maintenance management analysis tool. Keywords-Overall Equipment Effectiveness, machine availability, performance efficiency, rate of quality product, productivity.

The factory is in a dire need of a well defined maintenance policy to carter for these problems. This research work therefore focused on developing a maintenance management analysis tool that will act as a mirror to the maintenance department and consequently ignite improvement strategies on the overall performance of the factory. The following specific objectives were defined: to calculate the OEE for each machine and subsequently that for the entire production line in order to be able to propose methods of ameliorating their performance; encourage the institutionalization of the use of OEE for maintenance management analysis and benchmarking. A

. The Choice of Overall Equipment Effectiveness (OEE) for this Factory
The choice of a particular analysis tool is strictly a function of the factory in question. In this research OEE was chosen for the following reasons: its simplicity and ability to promote the most needed synergy among workers. OEE is comparatively cheap to implement since the data required was to be manually collected. It has the advantage of being the single analysis tool that takes into consideration all three major production improvement parameters -quality rate, performance efficiency, and machine availability (i.e. time, speed and quality of equipment operation). OEE is thus preferred for this factory since it will provide an integral solution to the problems of quality, performance efficiency and availability encountered by the factory. Wherever OEE has been implemented, dramatic successes have been the outcome, like in Japan where it catapulted Japanese products in the world market at the expense of the Americans. Japan's productivity improved a dramatic 68% between 1977 and1986, far outpacing the 26% U.S increase over the same period. [3]. This is the period when TPM was most popular in Japanese factories. So why not give CDC the chance to experience such marvelous results.

B. OEE Calculations
The OEE index could be calculated for single machines or for an entire plant. For a series of machines with different capacities, if the series is "balanced" (i.e. the nameplate capacity of all contributing machines is designed to produce at reasonably close to the same rate), the speed of the fastest machine is taken as the ideal cycle time for all the machines in the series or production line [4]. This forces the maintenance department to recognize all other bottlenecks and strive to improve them. In cases where a series is not balanced, if any of the machines has a nameplate capacity that far exceeds the others, i.e. if there exist an "accumulator" or a "buffer", or a piece of equipment that was introduced to the line as an after-thought, such should be neglected [5], [6] For entire plants, a straight average or weighted average method can be used. In the straight average method, a straight average is calculated for each OEE factor by summing the percentages for that factor across all processes, and dividing the results by the number of processes. Plant OEE is then obtained as always by multiplying the three OEE factors; as illustrated in equation (1). A weighted average is a more sophisticated approach that independently weighs each process by the planned production time for the same process and sums these results. This number is then divided by the sum of all the planned production times. [4]. OEE = OTR x OPR x QR (1) Where; OEE is the Overall Equipment Effectiveness OTR is defined as Operation Time Ratio OPR is the Operation Performance Ratio and QR is the Quality Rate While reference [7] fixes the OEE index of any sound production unit at 85%+, reference [8] prefers to leave 85%+ to world class manufacturing firms and recommends any value above 45% as acceptable for average industries worldwide.

A. Materials
The materials used included essentially measuring instruments such as a stop watch, vernier caliper, meter rule or tape; and a beam balance.

B. Method
An OEE diagnostic study was carried out to assess both machine performance and work methods in each work station, and data regarding machine performance was collected along the FC production line. Each machine time was broken down into the "six big machine losses"; equipment failure, setup and adjustment, idling and minor stoppages, reduced speed, process defects and reduced yields (tables I and II); and their respective OEE values calculated "as it is". Goals and targets [7] were then set up for the amelioration of the OEE index for the pre-breaker in particular, (the worst performing machine), and the FC line in general, within a well defined time frame and a factory performance improvement strategy was then put in place. After the implementation of these strategies, data was again collected and a new OEE index calculated for the FC line. This process is iterative until the desired OEE index for the factory is attained. Tables I and II present the breakdown of each machine time into the six big losses. The calculations for OEE per machine and for the entire FC line for the month of June; "as it is"; is presented in table III while table IV shows the same calculations for the month of August after OEE amelioration strategies had been effected on the production line. Even though the machine availabilities are relatively high (ranging from 68.8% to 100%), the OEE per machine is astonishingly low in each case -and subsequently for the entire production line. This is a reflection of the potentials the factory possesses. From table III we can see the bottlenecks of each machine: the low OEE value for the Pre-breaker is due mainly to its low operation performance of barely 30.6%; whereas machine availability improvement is a more pressing need for Crepper 2. Since the machines on the entire FC line are in series, a bottleneck machine is a real sore to the entire line.

RESULTS AND DISCUSSION
To improve the effectiveness of each piece of equipment experiencing a loss, a project team made improvements to eliminate losses. A chronic machine (that with lowest OEE value) is focused upon and this machine is expected to show marked improvement under an intensive overhaul. This kind of team work has a dual benefit: it proves the effectiveness of OEE as a maintenance management analysis tool and gives the maintenance staff hands-on experience. It provides group leaders with the experience needed to improve other equipment in their individual work centres.
The pre-breaker was focused upon and its OEE value was further analyzed, in order to devise improvement strategies.
The line productivity in June was 0.66 tons of crumb rubber per hour and this rose to 1.08 tons per hour corresponding to the new OEE value of 49.93%. Thus productivity is directly dependent on the OEE value of the production line.

IV. CONCLUSION
It is expected that the adoption of OEE calculations as a maintenance management analysis tool will save the company from making inappropriate purchases, and help management focus on improving the performance of machinery and plant equipment already owned. OEE index can be used to identify the greatest areas of improvement so that management can start with that which will provide the greatest returns on assets.
The OEE formula shows how improvements in quality, machine availability, working through breaks and more can affect the bottom line in a factory. The derived OEE percentage is easy to understand and displaying this single value where all factory personnel can view, makes for a great motivational technique. It could soon become common to glance at it, as the speedometer in a car. By giving the employees an easy way to see how they are faring in overall equipment utilization, production speed, and quality, they will strive for a higher value.
The data used for this work was manually collected. This is quite a tedious thing to do and the human factor introduces errors in the process, uses manpower hours and is cumbersome. This means OEE calculations cannot be done at sufficiently close intervals thereby erroneously portraying the tool as inefficient. The data collection could be automated for better efficiency, more reliable results, and economy.
As far as OEE per machine is concerned, it would be more informative to consider the rate of quality at the level of individual machines rather than the laboratory results that are carried out only at the end of the production process. In this way particular quality defects could be attributed to particular machines with precision. This will improve upon the precision of the OEE value obtained and the quality of the product enormously.
OEE index is an easy to use tool which can improve factory performance enormously and contribute to the industrialization drive in sub-SaharanAfrica.