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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.4 pp.726-734

Productivity Analysis on Batik Production Line Using Objective Matrix (OMAX) Method

Nashtiti Aliafari*, Muhammad Ragil Suryoputro*, Natasya Mazida Rahman
Industrial Engineering Department, Universitas Islam Indonesia, Yogyakarta, Indonesia
Corresponding Author, E-mail:,
May 9, 2019 September 25, 2019 October 18, 2019


The competition of industry was increasing with Batik industry as one of the fastest growing of non-oil and gas industry. CV. XYZ is a company in the field of batik industry. In 2017, there was a fluctuation of sales and there was a gap between production and production targets. Based on data, 10 out of 12 months during 2017 did not meet production targets. Therefore, this study aimed to measure the productivity that could be used as an evaluation and then made plans to increase productivity. In addition, the base of support to increase productivity was ergonomics. In this research, productivity measurement was performed using Objective Matrix method. OMAX is a partial productivity measurement system developed to monitor productivity. The object studied in this research was the productivity level of the cutting work station because it was the first work station of batik production process and considered as the representation of other work station. The subjects were 2 production department leaders and 6 cutting work station workers. The result was the lowest value of productivity occurred in September with a value of performance indicators of 62.60. The lowest productivity index occurred in October to November of 9.8%. The main factor causing low productivity was the empowerment of labor and the use of work time that was not optimal. Increasing the empowerment of labor and the use of work time should have been complemented with a proportional improvement where the needs of labors should be considered.



    Industrial competition was increasing competitively (Suryoputro et al., 2017). One of them was the non-oil and gas processing industry. Based on data from the Central Bureau of Statistics in Indonesia, in the third quarter of 2017 there was an increase in the non-oil and gas processing industry of 5.49 percent which increased compared to the period II / 2017 of 3.89 percent. The batik industry was one of the strongly increasing non-oil and gas industry sectors in Indonesia. Moreover, Indonesia had been known as a pioneer and leader of batik producers on the international stage (Soesanti and Syahputra, 2016).

    Yogyakarta is a city with many batik producers. One of the companies that developed in Yogyakarta batik industry is CV. XYZ. The company produces batik cloth and various kinds of clothing models with batik basic materials. Production is made by request from consumers and some are displays for galleries. In 2017, the company experienced sales fluctuations and declined from the middle to the end of the year. There was an imbalance in which production results were lower than the production target.

    Production division at CV. XYZ has several work stations. Each work station has their own job descriptions. The first work station is pre-cut work station, which duty is preparing raw materials for a cloth. After the raw materials is being prepared, then the cloth will be given to the cutting work station. At this work station, the raw materials will be processed and affects the productivity of the work station thereafter.

    The increasing competition of industrial world makes companies must pay attention to various aspects. Productivity is an interdisciplinary approach to determining effective goals, making plans, applications used to use efficiently, and maintaining quality (Sinungan, 1997). Good productivity is a sign that companies can use resources optimally (Rahmatullah et al., 2017). Then it is necessary to take measurements in order to evaluate and make decisions. Some researchers have conducted research on productivity measurement. There was research on productivity measurement in the textile industry (Tania and Ulkhaq, 2016). Then there was also a study that made mathematical models of productivity measurement for manufacturing systems (Rawat et al., 2016).

    Increasing productivity could be supported by ergonomics because it contributes to all aspects of the working environment (Mossa et al., 2015). The aspects were work place design, environmental factors, man-machine total productivity, equipment & hand tool design, and organizational factors.

    The purpose of this research was to measure the level of productivity in a company. After the measurement was done, it was expected that the company could evaluate it. The results of the evaluation could be used as a reference to increase productivity and further research.

    2. METHOD

    In this study, the object was the level of productivity of one of the divisions in production line, that was cutting work station. The data used was from January to December in 2017. Whilst the subject for this study were using 2 types of subjects. The first subject was experts, namely the head of Production Planning and Inventory Control; and the supervisor. The second subject was the 6 workforce in the production division of the cutting work station.

    The flow of research was shown in Figure 1. The research was divided into several stages, namely problem identification, data collecting, data processing, and analyzing the results of the study (discussion and conclusion).

    This research was using the Objective Matrix (OMAX) method created by James L. Riggs (Sumanth, 1984). OMAX is a partial productivity measurement system that was developed to monitor productivity in a company or in any part, with a productivity ratio that matches the existence of the section. In OMAX it is expected that the activities of company personnel to participate in assessing, improving, and maintaining. This system is a measurement system that is delivered directly to parts of the production process unit. OMAX is divided into 3 parts (section A, B and C) in a matrix as shown in Figure 2.

    2.1 Defining Part

    The defining part was divided into 2. First was defining the productivity criteria to be measured. Defining criteria is found in section A line 1. Then, the next step conducted defining the performance value from these criteria. Performance values would be found in section A line 2. The criteria used in this research were raw material productivity (R1), labor productivity (R2), and working hour productivity (R3) as shown in table 1. Selection of these criteria were based on the experts in productivity and based on literature reviews.

    2.2 Quantifying Part

    The quantifying part was a measurement part to show the level of performance of the measurement of each productivity criterion. In Figure 1, it was shown in part B. It consisted of eleven sections from a scale of 0 (lowest) to scale of 10 (highest). Level 10 was the expected productivity, level 3 was the average value, and level 0 was the worst productivity value.

    2.3 Monitoring Part

    Monitoring section was intended to measure weight, performance value and productivity indicators. In Figure 1, it was shown by section C. Weight was the amount of weight from the criteria of productivity to total productivity. The performance value was the multiplication of each score with its weight. Productivity indicators were the representative of increasing or decreasing value in current performance.

    3. RESULTS

    The results of the study were divided into stages according to the research method. The stages were weighting, performance ratio, productivity evaluation, and criteria evaluation.

    3.1 Weighting

    Weighting was carried out for each criterion. The tool used for weighting performed a questionnaire given to experts namely the head of the production department and supervisor. The results of the weighting were shown in the following table (Table 1).s

    3.2 Performance Ratio

    Performance ratio was the level of productivity which was the ratio of each criterion for each measurement period. Performance value was obtained by dividing output (amount of production) with predetermined resources (raw material, labor, working hours). The performance of each ratio could be seen in the table below (Table 2).

    3.3 Productivity Evaluation

    OMAX calculations were carried out for each month, from January to December. Then the performance score and productivity index scores were generated. These values could be used as productivity evaluations in the time span of the data used, namely 2017. The evaluation was done by looking at the performance value (current value). Then an evaluation was carried out through the productivity index value as shown in Table 3 and Figure 3.

    Table 3 was showing the value of performance indicator for each month and the productivity index. The productivity index was measured by comparing the current value with the previous value for each month. To analyze the performance value and productivity index, they were made into graphs as shown in Figure 3 for graphic of performance value and Figure 4 for graphic of productivity index.

    In addition to compare the performance value and productivity index, evaluation could be done through analysis of the value of each criterion. The criteria used for this research were raw material, manpower, and work time. The score for each criterion was shown in Figure 5 (for the graphic of raw material productivity score), Figure 6 (for the graphic of labor productivity score), and Figure 7 (for the graphic of working hour productivity score).


    The criteria used in measuring the objective matrix were obtained from the results of interviews with the head of the production division and the supervisor of the production division. The results obtained that were divided into raw material criteria, labor criteria, and working hour criteria. This criterion were ratios, in which the criteria for raw materials was the ratio between raw materials and production, the criteria for labor was the ratio between labor and production, and the criteria for working hours was the ratio between working hours and production output.

    In this study, measurements were made for the production division of the cutting work station. Thus, the result of the production was a cloth that had been cut with a piece. Then the raw material was obtained from the previous work station, namely the pre-cut work station in meters, the workforce was the workforce at the cutting work station, and the working hours were the work hours specified by the company for cutting work stations.

    Weighting was carried out for each criterion based on the assessment of the expert. In this study, the expert who assesses was the head of the production division and the supervisor of the production division. Weighting with a Likert scale used a tool that was a questionnaire. Weighting results as shown in table 1 placed the workforce in the first place with a weight of 35%. While working hours occupied the second place with 33% and raw materials had a weight of 31%. This was because labor has an important role in productivity for the reason of work in the production division of cutting work stations was still manual or conventional. So the risk of errors was higher and if it was a wrongdoing, the fabric could be wasted so that it could cause productivity to decline. Figure 8

    After weighting each criterion, then calculating the ratio performance of each criterion was performed. The results of the ratio performance calculation were then searched for the average, highest, and lowest values to then been processed into the average, lowest, and target values. The target value was obtained from the highest value added by 10% because the company had a target to increase by 10%. After that, the increase interval for levels 1 and 2 was calculated and levels 4 to 9. Then made an OMAX assessment matrix.

    The OMAX assessment in this study was carried out for the period of January to December 2017. The assessment resulted in the value of productivity which was the value of the performance indicator every month. In addition, assessment produced a productivity index which was a comparison between the periods measured by the previous period. There was an assessment of each criterion in the OMAX matrix.

    Raw material criteria had the lowest score in February with a ratio of 0.57121 as shown in figure 5. However, in January, March, June and November also included at the lowest level, namely level 0. It was while achieving the highest score in December with a ratio of 0.81971. In January, February, March and June raw materials were not used properly.

    On the criteria of labor, as shown in figure 6, the highest score occurred in May with a ratio of 710.67. While the lowest score occurred in September with a ratio of 211.67. Then the criteria for working hours, as shown in figure 7, also experienced the highest score in May with a ratio of 25,381 and the lowest score in September with a ratio of 6,978. The factor that led to the low score in September was the lack of supervision from the relevant parties on the workforce when carrying out the production process which also affected the lack of supervision of the use of working hours.

    In Figure 1, it was shown a graph of the value of performance indicators or productivity each month. Based on the graph, the highest productivity value occurred in May and the lowest occurred in January and November. The highest productivity in May was 837.40 because of increased sales but the use of raw materials, empowerment of labor, and the use of work time. In May, there was the period leading to Eid al-Fitr so that increased demand causes production to also increase. While the lowest productivity occurred in September, which amounted to 62.60 due to the empowerment of labor and the use of less optimal work time. One factor as mentioned earlier was the lack of supervision. Empowerment of workers was directly proportional to the performance of workers. And the performance of workers influenced by task and mental demand, equipment design, work organization, work posture, and work environment (Arunfred and Pearl, 2017).

    Productivity index was changed in productivity of the previous period. The productivity index graph was shown in Figure 1. The highest increase was shown in November to December, which was 413%, a fourfold increase from the previous month. This was due to the empowerment of the workforce and the used of work time began to be increased again despite not as many requests as in previous months. While the decline occurred in October to November, which amounted to 9.8%. Thus, in order to increase productivity, it required to increase the empowerment of labor and the use of work time.

    Increasing the empowerment of labor and the use of work time should be complemented with a proportional improvement where the needs of labors should be considered (Arezes et al., 2015). There were many ways to make improvements while the needs of labors are considered. One way was to use a lean implementation. The implementation would have the benefits expected if there was careful attention to working conditions analysis (Maia et al., 2012).

    Analysis and evaluation of the cause of decreasing performance value were discussed with experts. Fishbone diagram was used to make in depth analysis. The causal factors in making fishbone diagrams were man, environment, machine, method, and material. In human factors, there were several causes, one of it was the fluctuating workload. When there were many work orders, the workload became high, and vice versa. Thus, workload management was needed so that the workload could be shared equally. It was needed a better understanding of how and when workload and associated fatigue could negatively impact performance of the labor (Gore, 2018). The workload management could be adapted from the schedule of incoming order, as was done in patient based scheduling research (Huggins and Claudio, 2019). Fatigue management was also important to avoid excessive workload (Caldwell et al., 2019).

    The other causes were training and experience of the labor. Training was needed to give the labor skill and experience, whilst the experienced labor could share their skill and knowledge to the new worker. To make training more effective, it was needed a better understanding of requirements for effective learning and options for optimizing learning and preventing skill and knowledge decay which focus on improving the quality of training (Santos et al., 2016). Thus, it was needed to make learning organization culture. It had been proven that learning organization culture could influence growth and development of an organization (Hussein et al., 2016).

    In environment factor, convenience was the cause of the decreasing of performance value. Based on interview with the labor, they often felt uncomfortable with existing facilities. Based on research, physical and psycho-social environment was one of the factors effecting work satisfaction (Kaya, 2015). Thus, it is needed to re-design the workstation based on ergonomics perspective. In method factor, repetitive work was done by the labor. When the work environment was uncomfortable and work must be done repetitively, it could have an impact on the level of labor productivity. The increasing pace of work could reveal a higher muscular load and leads to fatigue or musculoskeletal disorders (Cascio, 2019). There was a method to eliminate or redesigning manual task to reduce the risk of musculoskeletal disorders, namely participatory ergonomics method (Burgess-Limerick, 2018).

    Material and machine factors were related to each other. If the quality of material used as below standard, then it could not be used for production and could make a longer waiting time. And if the machine used was damaged because of the lack of maintenance, or made the raw material damaged, there would also be a longer waiting time. If the waiting time was longer, it could affect the working hour of the labor and cycle time of the production. Thus, the quality management of machine was the key to reducing downtime and defect (Fujishima, 2017).


    Productivity of CV. XYZ experienced the highest point in May which was 837.40 and the lowest point in September which was 62.60. Whereas based on the productivity index, there was the sharpest increase from November to December and the most drastic decline from October to November.

    The evaluation that could be given was that in September it reached the lowest point due to decreasing demand but the used of work time and empowerment of labor was not optimal. Factors affecting the productivity were empowerment of labor and used of work time. Thus, a lean implementation was needed to increase productivity while considering the needs of labors.



    Flow of research.


    Objective matrix


    Graphic of performance value.


    Graphic of productivity index criteria evaluation.


    Graphic of raw material productivity score.


    Graphic of labor productivity score.


    Graphic of working hour productivity score.


    Fishbone diagram for the cause of decreasing performance value.


    Weight of the criteria

    Performance ratio

    Performance indicator value and monthly productivity index


    1. Arezes, P. M. , Dinis-Carvalho, J. , and Alves, A. C. (2015), Workplace ergonomics in lean production environments: A literature review, Work, 52(1), 57-70.
    2. Arunfred, N. and Pearl, B. (2017), A study on occupational ergonomics and its impact on employees performance in optical industry, Indian Journal of Applied Research, 7(5).
    3. Burgess-Limerick, R. (2018), Participatory ergonomics: Evidence and implementation lessons, Applied Ergonomics, 68, 289-293.
    4. Caldwell, J. A. , Caldwell, J. L. , Thompson, L. A. , and Lieberman, H. R. (2019), Fatigue and its management in the workplace, Neuroscience & Biobehavioral Reviews, 96, 272-289.
    5. Cascio, W. F. (2019), Training trends: Macro, micro, and policy issues, Human Resource Management Review, 29(2), 284-297.
    6. Fujishima, M. , Mori, M. , Nishimura, K. , and Ohno, K. (2017), Study on quality improvement of machine tools, Procedia CIRP, 59, 156-159.
    7. Gore, B. F. (2018), Space Safety and Human Performance, Butterworth-Heinemann, Oxford.
    8. Huggins, A. and Claudio, D. (2019), A mental workload based patient scheduling model for a cancer clinic, Operations Research for Health Care, 20, 56-65.
    9. Hussein, N. , Omar, S. , Noordin, F. , and Ishak, N. A. (2016), Learning organization culture, organizational performance and organizational innovativeness in a public institution of higher education in malaysia: A Preliminary study, Procedia Economis and Finance, 37, 512-519.
    10. Kaya, Ö. (2015), Design of work place and ergonomics in garment enterprises, Procedia Manufacturing, 3, 6437-6443.
    11. Maia, L. C. , Alves, A. C. , and C. P. Leão (,2012), Design of a Lean methodology for an ergonomic and sustainable work environment in textile and garment industry, Proceedings of the ASME 2012 International Mechanical Engineering Congress & Exposition (IMECE2010), 1843-1852.
    12. Mossa, G. , Boenzi, F. , Digiesi, S. , Mummolo, G. , and Romano, V. A. (2015), Productivity and ergoonomic risk in human based production systems: A jobrotation scheduling model, International Journal of Production Economics, 171(4), 471-477.
    13. Rahmatullah, S. , Katili, P. B. , and Wahyuni, N. (2017), Analisa produktivitas pada divisi produksi PT.XYZ menggunakan metode objective matrix (OMAX), Jurnal Teknik Industri, 5 (1), 99-104, Available from:
    14. Rawat, G. S. , Gupta, A. , and Juneja, C. (2016), Productivity measurement of manufacturing system, Materialstoday: Proceedings, 5(1), 1483-1489.
    15. Santos, J. , Baptisda, J. S. , Monteiro, P. R. R. , Miguel, A. S. , Santos, R. , and Vaz, M. A. P. (2016), The influence of task design on upperlimb muscle fatigue during low-load repetitive work: A systematic review, International Journal of Industrial Ergonomics, 52, 78-91.
    16. Sinungan, M. (1997), Produktivitas Apa dan Bagaimana, Aksara Jakarta Press, Bumi.
    17. Soesanti, I. and Syahputra, R. (2016), Batik production process optimization using particle swarm optimization method, Journal of Theoritical and Applied Information Technology, 86(2), 272/s.
    18. Sumanth, D. (1984), Productivity Engineering and Management, McGraw Hill Book Co., New York.
    19. Suryoputro, M. R. , Sugarindra, M. , and Erfaisalsyah, H. (2017), Quality control system using simple implementation of seven tools for batik textile manufacturing, IOP Conference Series: Materials Science and Engineering, 215(1),
    20. Tania, F. and Ulkhaq, M. (2016), Measurement and analysis of productivity at PT. Tiga Manunggal synthetic industries with objective matrix method, Industrial Engineering Online Journal, 5(4), cited 2018 Jul 1, Available from: