Analysis of Productivity Measurement in CPO Production Using OMAX Method

ABSTRACT


Introduction
In the present competitive business, manufacturing industries are focusing on improving effective performance to sustain.Productivity is needed to measure production costs precisely and accurately.Productivity is needed to measure production costs precisely and accurately (Anusha & Umasankar, 2020).Productivity is defined as the ratio between input and output as well can be interpreted as a measure of effectiveness and efficiency (Adesunkanmi & Nurain, 2022).Productivity is one of the important factors that affect the performance of a company (Novianti et al., 2019).Productivity is also a major contributor as an indication of the company's ability to survive in the face of business competitiveness (Shebeb, 2018).Productivity can be used as an indicator of business success (Putra & Mursid, 2021).Productivity is not everything, but in the long term, productivity is everything (Parravicini & Graffi, 2019).
TBS is a raw material in processing Crude Palm Oil (CPO) and Palm Kernel Oil (PKO).Crude palm oil (CPO) is a type of vegetable oil which has very diverse derivative products, especially as food ingredients, cosmetics industry, chemical industry, etc. at case study in PT.ABC is an agroindustry that produces Crude Palm Oil (CPO).PT.ABC has set a production target of 3,050 tons/month, with a production process capacity of 30 tons/hour so that the production process runs 24 hours/day with working hours divided into 2 work shifts.Productivity is currently an important factor in company growth (Cruz-Rivero et al., 2020).PT.ABC wants to increase productivity but faces difficulties because it has never measured productivity properly.They use production target achievement as a performance assessment, but the standard production targets do not provide a satisfactory picture of whether the company's productivity is at a good or bad level.In addition, to meet Refined's annual target of 32,738 tons of CPO, the company is estimated to suffer a loss of -640 tons.One method used to measure productivity is the Objective Matrix (OMAX) method, which can measure performance aspects by considering a work unit (Hidayatullah et al., 2022).OMAX sets important criteria for increasing productivity in production line activities (Celina et al., 2022).Factors that influence the decline in productivity will be identified based on measurement results using Failure Modes and Effect Analysis by adopting potential failures that will occur.The priority The goal of corrective action in FMEA is to eliminate the occurrence of failure and enhance current control (Liew et al., 2019).
In this study, the focus extends beyond merely measuring productivity using Objective Matrix (OMAX) in the agricultural industry.It also involves identifying factors that contribute to a decrease in productivity.To identify and take action on the risks that arise using Failure Mode and Effects Analysis (FMEA) by recognizing potential failures (Atin & Lubis, 2020).This proactive approach empowers companies to take preventive measures before any adverse consequences emerge.Furthermore, using FMEA assists companies in prioritizing improvements related to potential failures with significant impact, facilitating more efficient resource allocation by company.
The study by Wahyuni & Alya (2020) applied OMAX to measure the level of productivity in producing plates, also in the manufacturing industry the study (Lesmana et al., 2020) applied productivity measurements in the assembly department.Current productivity calculations do not meet management needs, they need an additional matrix as a measurement matrix, and meanwhile in research (Yahya et al., 2019) Productivity calculations in shipbuilding projects use the Mundel and OMAX models to determine a decrease in the productivity index.Meanwhile, productivity measurements were carried out in small industries, based on a study (Mukti et al., 2021), Aceh Coffee found that there was a discrepancy in the amount of production with the specified targets, so there were deficiencies in determining the input ratio in analyzing the productivity index in substance.The contribution of this study is conducting a comprehensive productivity assessment, focusing on metrics and identifying the causes of productivity decline, especially potential points of failure.The measurement indicators use the Objective Matrix (OMAX) method which includes five ratios, including raw material utilization, energy consumption, labor efficiency, optimization of production targets, and production capacity utilization

Method
Productivity is an illustration of the relationship between the input used and the output produced (Basumerda et al., 2019).The data used to measure CPO production productivity is primary data obtained directly during interviews and filling out questionnaires with managers QC assistant at PT. ABC.In this study, the productivity measurement method employed is the Objective Matrix, which calculates the Crude Palm Oil (CPO) production productivity index.Weighting in the assessment of this productivity index is determined using the Analytical Hierarchy Process (AHP) methodology.Additionally, the research identifies factors contributing to decreased productivity by incorporating potential failures, utilizing the Failure Modes and Effects Analysis (FMEA) method.There are several methods for measuring productivity, the Target Matrix Method is one of the best (Basumerda et al., 2019).

Objective Matrix (OMAX)
The Objective Matrix (OMAX) serves as the chosen measurement method for monitoring productivity within the company.Employing OMAX for measurement purposes results in an abundance of data.It provides an objective set of criteria that aligns with the collective interests of the entire company and offers flexibility in the measurement process.(Sayuti et al., 2021).The Objective Matrix (OMAX) method can identify the causes of decreased productivity (Putra & Mursid, 2021).The OMAX productivity measurement model is a measurement tool that offers distinct advantages.It empowers management to assign weights to criteria based on their relative importance within the company, enhancing objectivity and flexibility in the measurement process.(Nurwantara, 2018).OMAX, as a performance measurement method, assesses various criteria by assigning weights to calculate the overall enterprise productivity index.(Lesmana et al., 2020).The Objective Matrix has a unique feature, namely by combining workgroup performance criteria into a single matrix (Mukti et al., 2021).To use the OMAX method.the stages of the process using the Objective Matrix method (Handayani & Susilowati, 2021): The calculations of the ratio per criterion are divided into efficiency and effectiveness criteria.1) Efficiency Criteria, shows the level of use of company resources such as the number of workers, use of working hours, energy, raw materials and capital that is as efficient as possible.The ratios used in this criterion are showed in Equation ( 1) -( 3).
Ratio 1 (working hours productivity) = Total product Working Hours Used (1) Ratio 2 (energy used productivity) = Total product Energy Used (2) Ratio 3 (material used productivity) = Total Product Material Used

x100%
(3) 2) Effectiveness Criteria, shows how the company achieves results when viewed from the point of view of time, accuracy and quality, which are included in these criteria, among others.The ratios used in this criterion are showed in Equation ( 4)-( 5) Ratio 4 (optimization of production target) = Actual Production Production Plan x 100% (4) Ratio 5 (optimization of production capacity) = Working Hours Used Material Used (5) c.Determination of final targets (level 10), short-term targets (score 3) and lower score (score 0).d.Determination of productivity intervals (scores 1 -2 and 4 -9) For an increase in productivity value adjusted by way of interpolation as shown in Equation ( 6)-( 7).
For Increases level 1 and 2 = In making the OMAX table, a weighting technique with a reliable method is needed.For weighting The Analytical Hierarchy Process (AHP) is a tool used to discover the prioritization of various decision options through pairwise comparisons of the decision elements to general criteria (Varshney et al., 2021).The score is obtained from the performance of each ratio that approaches the productivity level.To calculate value, use the following formula: x 100% (10)

Analytical Hierarchy Process (AHP)
Many methods need to be used to determine the weight of the rating index in the assessment process.In the 20th century, Professor Saaty of the University of Pittsburgh, USA, put forward a comprehensive method of qualitative and quantitative systematic analysis.It is an analytical hierarchical process (AHP) (Qin & Kang, 2019).The analytical hierarchical process method (AHP) is introduced, which can deal with complex and immeasurable multi-objective decision-making problems (Ren et al., 2019).The Analytical Hierarchy Process (AHP) has been a favorite tool of research experts from various fields such as engineering, technology, manufacturing, production, social sciences, etc.It has proved to be a reliable and efficient technique.(Khan, 2020).The step for implementing of AHP as follows (Prasetyo et al., 2023): a. Creating a Hierarchical Structure b.Determine assessment criteria and alternatives Determining criteria and alternatives using pairwise comparisons is done with a scale of 1-9 very well in order to be able to express an idea.The pairwise comparisons can be seen in Table 1.If the results of the Consistency Ratio CR < 10% or 0.1 then the questionnaire must be repeated, and if the Consistency Ratio (CR) > 0.1, then the calculation results can be decided correctly.

Failure Modes and Effect Analysis (FMEA)
Failure mode and effect analysis (FMEA) aims to improve operational performance of a productor process (Liew et al., 2019).Its outcome is the RPN (Risk Priority Number), which guides recommendations for prioritizing the maintenance and improvement of the most critical risk factors.The RPN score is computed using Severity (S), Occurrence (O), and Detection (D) (Suryoputro et al., 2019).In this research, the approach was employed to detect the occurrences of failures, encompassing (Soewardi & Wulandari, 2019): a.The extent of damage (severity), indicating the degree of harm inflicted on the process.b.The frequency (occurrence), signifying the potential for a failure to transpire.c.The level of detection (detection), highlighting the capacity to identify failures before they materialize.
The processes for different types of FMEA share fundamental similarities.They revolve around cause-and-effect connections, identifying the initial source of an error.This source can be found within the system or even outside it, in the vicinity where the error manifests.This approach enables the potential to influence the nature of the errors encountered.A comprehensive analysis can pinpoint the root cause of the error (Dumitrescu et al., 2016).

Results and Discussion
The objective potential that has been formed is arranged in the form of a questionnaire adjusting the availability of features from each objective potential with the assistant quality controller and production assistant.The results of determining the criteria as a source of performance calculations can be seen in Table 2.The ratio calculation exploits CPO production data for 2022.Data from each of these criteria include working hours, energy use, raw material requirements, production targets, and optimal production capacity.Ratio data for each criterion can be seen in Table 3.
Level 0 as the lowest target indicated from the results of calculating the lowest ratio, level 3 as the standard target cause the average ratio calculation based on the ratio calculation results, and level 10 as the highest target is known from the results of estimating the highest ratio of each ratio.The results of calculating the realistic productivity value and OMAX level of CPO production from January to December can be seen in Table 4. Levels 0, 3, and 10 serve as benchmarks for determining the OMAX level.The OMAX level is determined through an interpolation calculation, resulting in a range between levels 0 and 10.The OMAX level obtained in this process serves as the foundation for evaluating performance scores in each period.Therefore, performance indicator calculations can be carried out.You can find the OMAX levels in the Table 5.The process of ascertaining the performance score involves aligning the outcomes of ratio calculations in each time frame with the OMAX level found in Table 6, which serves as a performance benchmark.This approach enables the performance scores for each measured period to reflect the productivity level associated with each ratio.The recapitulation of performance score in the Table 6.
To establish the weight, we evaluate each of these criteria through pairwise comparisons, assessing their relative importance using the AHP (Analytical Hierarchy Process) method, based on the questionnaire responses from both the manager and production assistant.The results of weighting using AHP after calculation using software Microsoft excel 2021 like in Table 7.
The results of the AHP calculation to determine the consistency index (CI) has a result of 0,10283, then testing the consistency or consistency ratio (CR), from the results of the consistency calculations carried out, it is obtained CR = 0,09.The comparison value can be determined to be consistent, if <0,10 or below 10%, therefore the determination of the weight comparison value on the productivity ratio is consistent.Vol. 21, No. 2, 2023, pp. 109-119 Ade Dwi Sakti (Analysis of Productivity Measurements in CPO Production Using OMAX Method) Through the evaluation of productivity performance for the 2022 period, it is evident that the peak performance occurred in March with a value of 959,79.Furthermore, when analyzing the productivity index growth by comparing the most recent index with the preceding one (Previous IP), the highest increase was observed in September, amounting to 161,09%.The lowest accretion from the latest productivity to the previous period's productivity occurred in the May period with a decrease in productivity of -88.05%.CPO productivity index like in the Table 8.In the 2022 period, the peak of the productivity index was reached in March at 219,93%.This increase was due to the calculation of the ratio performance score of 3, namely the productivity of raw material utilization which was very high until it reached level 10.This underlined that the efficiency of material utilization plays an important role in increasing productivity, based on the relative importance of each ratio.In contrast, the lowest CPO (Crude Palm Oil) production productivity occurred in July with a decrease of -67,33% because, based on the calculation of performance scores, all ratios in this period were below the set performance standards.Meanwhile, based on the score calculation, the factor that influenced the decline in CPO production productivity was the performance of ratio 4, namely optimizing production targets, evidenced by a ratio of 4 having the lowest score calculation result with a performance score of 45.The growth of the CPO productivity index is shown in Figure 1.Identify factors that cause a decrease in productivity while striving to achieve production targets set through risk evaluation using the FMEA (Failure Modes and Effects Analysis) method.During the application of this method, potential failures and their causal factors are detected through the development of questionnaires, in collaboration with managers and quality control assistants as exemplified in Table 9.The factors that influence the failure to meet production targets have adjusted to the risks that are the main priority for improvement, including failure in planning to set standard targets, which is the company's priority to increase optimization of production targets, then decreasing CPO quality and excessive workload.These causal factors are assessed by the plant manager based on their severity, frequency, and detectability, resulting in the calculation of Risk Priority Numbers (RPN).The suggested enhancements include integrating Internet of Things (IoT) technology through sensors and automation for the production process, upgrading workplace facilities with a focus on Occupational Health and Safety (K3) equipment at every workstation, providing ongoing training to boost operator creativity, and investing in agricultural technology as a monitoring system.These measures aim to attain higher production targets and enhance overall efficiency.

Conclusion
The highest CPO production productivity index in 2022 was in the March period with an index of 219.93%.The lowest productivity level occurred in the July period with an index of -67.33%.Based on the score calculation, the performance ratio that has the lowest performance score is ratio 4, namely optimizing production targets, which is the cause of the decline in CPO production productivity at PT.A B C. The factors that influence the failure to meet production targets have adjusted to the risks that are the main priority for improvement, including failure in planning to set standard targets, which is the company's priority to increase optimization of production targets, then decreasing CPO quality and excessive workload.Recommended improvements include investing in agricultural technology as a monitoring system, integrating Internet of Things (IoT) technology through sensors and automation in production processes and improving workplace facilities with a focus on Occupational Health and Safety (K3) equipment at each workstation, providing continuous training to increase operator creativity.These steps aim to achieve higher production targets and improve overall efficiency.
of scores, weights and value

Value
of Performance Indicator To obtain the most current measure of productivity, you sum up all the existing productivity values from each criterion.To calculate the productivity index, use the following formula:

Fig
Fig 1.Current Indicator Productivity against Indicator Productivity Previous

Table 1 .
Pairwise Comparison Criteria Calculation of the Consistency Index (CI) and Consistency Ratio checks

Table 2 .
Identification of Performance Criteria

Table 3 .
Recapitulation Input of Productivity

Table 4 .
Result of Calculation The Performance Ratio and OMAX Level

Table 5 .
Level of Objective Matrix

Table 6 .
The Recapitulation of Performance Score

Table 7 .
Result of AHP

Table 8 .
Productivity of CPO Production Current Indicator Productivity against Indicator Productivity Previous Ade Dwi Sakti (Analysis of Productivity Measurements in CPO Production Using OMAX Method)

Table 9 .
Failure Mode and Effect Analysis