Productivity Analysis Of Split Stone Production Using Objective Matrix (Omax) Method (A Case Study)

One of the building materials that has a high business prospect is split stone. Some building material management companies in one of their departments produce split stone, which in its production there are several types of products based on size to meet various consumer demands. In production, especially in the 2021 period, there will be fluctuating or uneven production levels, where in one month there will be high production, and in another period there will be a decrease in production. Based on this, this study aims to analyze the calculation of productivity to identify what factors cause the productivity of split stone production to fluctuate. This research was assisted by using the objective matrix (OMAX) and fault tree analysis (FTA) methods. the results of the productivity recapitulation found that the attendance data component obtained an average score of 2.3 which is the lowest average when compared to other components. therefore with these results, attendance is the strongest factor affecting fluctuating productivity.


INTRODUCTION
One building materials business with good prospects is the split stone business. Split stone is a stone building material obtained by splitting or breaking large stones into small sizes. Split stone is also often referred to as split stone (Indriani et al, 2017).
Several natural stone management companies in one of their departments produce split stones. In their production, several types of products are based on size to meet various consumer demands. In production, especially in the 2021 period, there will be fluctuating or uneven production levels, where in one month there is a high production, and in another period there is a decrease in production.
A decrease in production can be caused by several factors, such as raw materials, operators, production machines, and others. This decline in productivity impacts the company's income which decreases and is also not balanced between periods due to fluctuating productivity. The occurrence of these fluctuations indicates the need for a productivity analysis with the help of the data contained in the company to identify what factors cause fluctuating productivity. Furthermore, after the cause is found, it is continued with suggestions regarding improvements that can be made by the company so that further improvements can be made. It is vital to carry out productivity measurements to see productivity measures that can determine whether the company meets productivity targets for production or not. (Irwansyah defy et al, 2022) In processing and calculating this data using the Objective Matrix (OMAX), this method will help determine how the production level in a company is assisted by ratios or criteria that contain the main factors or components in the company's output. For example, in other studies that perform productivity analysis using the Objective Matrix method and can identify criteria that cause productivity declines (Andry et al, 2017). Another method, fault tree analysis (FTA), is used to see and analyze the causes of fluctuating productivity in production. Many things are factors that cause decreased productivity and failure, one of the things that most often is the human factor (Boryczko et al, 2022).

METHODS
This research was conducted for one month in one of the split stone industrial companies, namely PT. Lisna Jaya Utama. This research was carried out with the object of split stone production data for one year, namely split stone production in 2021. Data processing was assisted using the Objective Matrix method. The level of productivity with the help of the OMAX method uses eight criteria or ratios, in which ratio data is obtained using company data from the field directly. The data set will be made into a matrix by going through several stages of data processing, namely the ranking of the weight values, the calculation of the upper and lower intervals, and the classification of types per value (bad, good enough, good) showed in

RESULT AND DISCUSSION Rank values
The weight here is defined as the level of importance of a criterion set in percent, where the result of the overall weight of the standards is 100%. Determination of the weight show in table 2, here was obtained from interviews with the production department. The provision of each weight is given to each criterion or ratio previously determined, where in this study, eight ratios were determined with each weight being different from one another (Andry et al, 2017).

Upper and lower interval calculation
The upper interval and the lower gap are searched by calculating the average standard value, target value, and worst ratio value. The results of these calculations will later be used to fill in the body of the matrix in the Objective Matrix (OMAX) method. The worst ratio value will be used as the 0-level value for the related ratio component. Then the average standard value will be used as a level 3 value for the related ratio components. Finally, the target value is used as the value at level 10 in the corresponding ratio component.  Then an analysis of the calculation of each ratio for the entire period is carried out, where the value of the ratio in each period is substituted for the component value of the overall ratio to determine the classification of the value of each component in each period. For example, it can be seen in the analysis of the January period in table 5 below.: In the table 5, it can be seen that the K1 ratio is included in the performance of 8, which indicates the K1 ratio is good, then the K2 ratio is considered quite good with performance 3, and for the ratio K6, it goes into bad with performance 1. Then the results of the analysis in other periods it is shown in table 6 below: These results have been classified based on the performance of each ratio in each period. For the description of the red color itself is a ratio with poor performance, then for the green color is a ratio with quite good performance, and for the yellow color is a ratio with good performance. Then after the overall results are obtained, the average is carried out for each ratio. The table shows that the K6 ratio (attendance) gets an average value of 2.5 or the lowest when compared to the average value in other ratios, and the K7 ratio (engine performance) it gets an average value of 4 or an average value. Highest compared to the different average ratios.
The results of data analysis using the OMAX method obtained performance indicator values. The following shows the performance indicator values with the graph in Figure 1 per month period.

Figure 1. Whole Period Performance Indicator
From the table of performance indicators, it can be seen that the company's productivity level of split stone production is still not stable. The graph shows the periods of February, March, April, and May, which experienced a significant decline from January. Then from June to August, there was a substantial increase in productivity after a decline until May. Then in September there was a decline again followed by the rise in October.
In response to this, a corrective step was taken using fault tree analysis (FTA). In the fault tree analysis, improvements were made to the leading cause of productivity instability in the production. Located in the recapitulation of scores in each ratio, ratio six, or the ratio regarding absenteeism, appears to have the lowest average score (2.5) compared to the score obtained in other ratios. Then the criteria or ratios that are less productive are then analyzed using the fault tree analysis method, can seed in figure 2.

CONCLUSION
The following conclusions are obtained from the results of processing company data using the help of Objective Matrix (OMAX) and the Fault Tree Analysis method.: 1. In the company's productivity, there was a significant decline in productivity in February, March, April, May, and also September. Where this can be seen in the productivity index, it can be seen that in February, there was a decline of up to -3.3%, then March -20%, April -15%, May -13.7%, and September -20%. The calculation of performance indicators in January produced the highest performance indicator with a score of 450, for the lowest performance indicator result was in May with a score of 205 points. 2. Referring to the results of the scores of each ratio in each month, in the recapitulation of productivity, all ratios are above the standard (3) except for the ratio 6 or absenteeism criteria. For this ratio, 6 has average productivity for each period of 2.5 or still below the standard, namely 3, so the attendance component is the main factor in the decline in productivity. 3. To overcome the fluctuating productivity, problem-solving can be done with the help of Fault Tree Analysis on the main factor in the form of absenteeism components. Namely, it can be in the procurement of a definite schedule regarding the time of material arrival and production time.