Jurnal Agrisistem : Sosek dan Penyuluhan.
Volume 21 Nomor 1.
Juni 2025 Hidayat, et al.
Jurnal Agrisistem Seri Sosek dan Penyuluhan Volume 21 Nomor 1.
Juni 2025 DOI: https://doi.
org/10.
52625/j-agr-sosekpenyuluhan.
p-ISSN 2089-0036
e-ISSN 2722-1938
THE ANALYSIS OF PRODUCTION CAPACITY USING ROUGH CUT
CAPACITY PLANNING METHOD (CASE STUDY AT PT.
UNTUK
INDONESIA HIJAU)
Analisis Kapasitas Produksi Menggunakan Metode Perencanaan Kapasitas Kasar (Studi Kasus di PT.
Untuk Indonesia Hija.
Amri Hidayat *.
Mahmud Achmad .
Munira 1 Master Program of Agro-industrial Engineering.
Hasanuddin University, 90245.
Makassar.
Indonesia 2 Department of Agricultural Technology.
Hasanuddin University, 90245.
Makassar.
Indonesia 3 Indonesian Agricultural Development Polytechnic, 92171.
Gowa.
Indonesia
ABSTRAK
PT.
Untuk Indonesia Hijau is a small-scale company based in Indonesia, specializing in the processing of cocoa beans to produce fermented cocoa beans, cocoa mass, and chocolate bars.
Currently, the company's production process is driven by customer demand, often neglecting the efficient utilization of available facilities and resources, resulting in escalated operational costs.
The existing workstations exhibit underutilized resources while certain machines and workers are overloaded in specific conditions.
The objective of this study is to thoroughly evaluate and analysis the production capacity of each workstation for the production of each product.
This comprehensive evaluation and analysis, employing the RoughCut Capacity Planning method, encompasses production forecasting, calculation of available capacity at each workstation, determination of the required capacity for each workstation, and a rigorous capacity feasibility test by comparing available capacity with required capacity.
The findings of the Rough-Cut Capacity Planning using the CPOF method indicate that nearly all workstations demonstrate positive capacity values over 12 months, indicating that available capacity can satisfy production demands.
However, there exists a considerable excess work capacity, underscoring the need for additional strategies to optimize operation efficiency.
Kata kunci: Analysis Production.
Cut Capacity Planning.
PT.
Untuk Indonesia Hijau @ 202x Unit Penelitian dan Pengabdian Masyarakat Politeknik Pembangunan Pertanian Gowa Halaman Jurnal : https://ejournal.
polbangtan-gowa.
id/index.
php/J-AgrSosekpenyuluhan/article/view/457/version/457 Received : 26 April 2025 Accepted : 27 Juni 2025 Published Online : 30 Juni 2025 * Email Korespondensi: tatamandong@gmail.
INTRODUCTION
Production capacity planning is an important part of effective production system management.
The aim is to avoid wastage of resources and ensure that the company has sufficient capacity to fulfil market demand Poorly managed capacity can lead to imbalances, where certain workstations have a lack of Diterbitkan oleh.
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Volume 21 Nomor 1.
Juni 2025 Hidayat, et al.
capacity while other workstations have excess capacity.
This can lead to higher operational costs and failure to meet market demand (Sugarindra & Nurdiansyah, 2.
Rough Cut Capacity Planning (RCCP) is one of the most common capacity planning methods.
This method allows companies to conduct an initial analysis of capacity based on the main production schedule (MPS) that has been prepared (Najy, 2.
According to (Sugiatna, 2.
RCCP helps determine whether available resources, such as labor and machinery, are sufficient to meet projected production In addition.
RCCP allows companies to conduct an initial feasibility test before embarking on more in-depth capacity planning (Najy, 2.
Capacity planning with RCCP involves several approaches, one of which is Capacity Planning Using Overall Factors (CPOF), which assesses capacity requirements based on historical data and MPS (Master Production Schedul.
(Sugiatna, 2.
(Irawan et al.
, 2.
Through this calculation, the company can identify capacity requirements in each production period and determine whether the available capacity is sufficient or requires further adjustment (Nurhamidah et al.
This study aims to evaluate and analysis the production capacity at PT Untuk Indonesia Hijau (PT.
UIH) by using the RCCP method and the CPOF approach.
The results of this evaluation are expected to provide recommendations for capacity optimization and improve the company's operational efficiency.
MATERIALS AND METHODS
Production Forecasting using Minitab Software The first step of this research is to perform production forecasting based on historical data (Rachim et al.
The forecasting methods will be evaluated to determine the most accurate one with the smallest error rate.
To choose the most appropriate forecasting method, we must first recognize clear data patterns.
This is important because in forecasting data, we have to make projections based on past data, and patterns that occur in the past will greatly affect the value of the data we are trying to predict.
To determine the forecasting method on time series data, it is necessary to know the pattern of the data so that data forecasting can be done with the appropriate method.
Data patterns can be divided into four types, namely seasonal, cyclical, trend, and irregular patterns (Barbosa et al.
, 2.
Furthermore.
Minitab statistical software is used to process the historical data in a way that matches the previous data pattern (Wiijaya et al.
, 2.
The results of Minitab data processing are then compared with the best.
Average Absolute Percentage Error (MAPE).
Mean Absolute Deviation (MAD), and Mean Square Deviation (MSD) are used to evaluate the forecasting data from these methods.
The method with the lowest error value will be the best model (Lawi & Gunawan, 2.
(Wibisono et al.
, 2.
Create a Master Plan Schedule (MPS) A Master Production Schedule (MPS) is a detailed plan that regulates the amount and time of production for each type of product that must be produced within a specific period.
MPS is prepared by taking into account existing production capacity and time constraints.
This MPS explains the number of product units that need to be produced each period based on demand predictions.
Using historical data and demand patterns (Hanke & Dean, 2.
(Damara & Yusuf, 2.
MPS is used to determine the number of units of products that must be produced each period.
The master production schedule (MPS) provides formal details of production plans and converts them into plans for the needs of raw materials, labor, and work equipment/production machinery.
Therefore, the MPS is an appropriate statement in production including product, time, and quantity for more mature planning in the system (Meirizha & Ardiansyah, 2.
MPS is primarily used to .
convert aggregate planning into specific end products.
assess alternative schedules.
ascertain production capacity.
ascertain production materials needed.
expedite information processing.
utilize capacity Capacity Analysis with RCCP Method Capacity analysis is carried out with the following steps:
Available Capacity Calculation: Calculates the available capacity at each workstation, taking into account the working hours, number of machines, and the number of available labor.
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measurement is carried out using the stopwatch time study method to obtain accurate data (Barbosa et al.
, 2.
Calculation of Required Capacity: Calculate capacity requirements based on the MPS that has been compiled by multiplying the production load by the processing time at each workstation.
Capacity Due Diligence: Conduct a capacity feasibility test by comparing the available capacity with the required capacity.
The analysis uses the Capacity Planning Overall Factor (CPOF) to evaluate whether the available capacity can meet the planned production needs.
If there is a shortage of capacity, an analysis is carried out to find the cause and recommendations for improvement (Meirizha & Ardiansyah, 2.
RESULTS AND DISCUSSION Production Forecasting PT UIH produces three main products: fermented cocoa beans, cocoa mass, and chocolate bars.
All of these products are made according to customer demand, so sales and production data will be the same.
The historical production and sales data of PT UIH is presented below:
Table 1.
Production History Data PT.
UIH for 24 Month (August 2022 Ae July 2.
Month Aug-22 Sep-22 Oct-22 Nov-22 Dec-22 Jan-23 Feb-23 Mar-23 Apr-23 May-23 Jun-23 Jul-23 Aug-23 Sep-23 Oct-23 Nov-23 Dec-23 Jan-24 Feb-24 Mar-24 Apr-24 May-24 Jun-24 Jul-24 Total Fermented Cacao Beans (K.
Cacao Mass (K.
Chocolate bar .
a Source : MS Excel Data Processing Based on the historical data above, a comparative analysis of methods to forecast production or demand is carried out using Minitab software to choose which method produces the least errors (Rachim et al.
For fermented cocoa beans and cocoa mass, our production pattern shows an upward and seasonal trend, so the Holt Winter method and plot decomposition are chosen to be tested.
This is because cocoa plants produce seasonally (SyaAoadah et al.
, 2.
, there are two harvest seasons in cocoa plantations in West Sulawesi, namely April to August and November to January.
The production pattern shown tends to be non-seasonal for chocolate bar products.
Therefore, the Single exponential Smoothing, double exponential, moving average, and linear trends methods can be used to forecast if the stationary and non-stationary type data do not contain seasonal patterns (Nurhamidah et , 2.
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Seasonal patterns are fluctuations in data that occur periodically over a period of one year, such as quarterly, quarterly, monthly, weekly, or daily.
Cyclical patterns occur when the data is affected by longterm economic fluctuations, such as those related to the business cycle.
This pattern is difficult to detect and cannot be separated from trend patterns.
Trend patterns are trends in the direction of data in the long term, which can be in the form of increases or decreases.
While irregular patterns are unexpected and random events, their occurrence can affect fluctuations in time series data (Nurhamidah et al.
, 2.
Table 2.
Comparison of Error Values of Forecasting Methods for Fermented Cocoa Bean Products.
Forecasting Method
MAPE
MAD
MSD
Remark Used Not used Not used Not used Table 3.
Comparison of Error Values of Forecasting Methods for Cocoa Mass.
Forecasting Method
MAPE
MAD
Winter's Method additive Winter's method multiplicative Decomposition plot additive Decomposition plot Multiplicative a Source : Minitab software data processing results MSD Remark Not used Used Not used Not used Table 4.
Comparison of Error Values of Forecasting Methods for Chocolate Bar.
Forecasting Method
MAPE
MAD
Linear Trend model Single Exponential Smoothing Double Exponential Smoothing Moving Average a Source : Minitab software data processing results MSD Remark Not used Used Not used Not used Winter's Method additive Winter's method multiplicative Decomposition plot additive Decomposition plot Multiplicative a Source: Minitab software data processing results The comparison of Table 2 shows that the Hold Winter Additive forecasting method was chosen for fermented cocoa bean products with the lowest MAPE value of 36, while the comparison of Table 3 shows that the Holt Winter Multiplicative forecasting method was chosen for the mass cocoa bean products with the lowest MAPE value of 16.
04, which makes it the forecasting method for Cacao Mass products.
For chocolate bar products, the single exponential smoothing method is used because it produces better results with a MAPE value of 2.
43 the following time series graph shows the results of data processing performed by the Minitab application.
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Figure 1.
Time Series Plot of Fermented Cacao Beans.
Cocoa Mass and Chocolate Bar PT.
UIH
Master Planning Schedule Using the appropriate forecasting technique, the Master Planning Schedule (MPS) is created using the chosen forecasting data.
Weekday availability is added to each scheduled month in this schedule.
At the moment, the factory only runs one shift per day, and PT.
UIH works six days a week for seven hours a Before the calculation of the MPS begins, demand and estimates must be aggregated.
In calculating the MPS, the input data required is the result of forecasting based on historical data that has been filled in previously (Syahanifadhel et al.
, 2.
The Master Planning Schedule for PT.
UIH is shown in Table 5 Table 5.
Forecasting Master Production Schedule (MPS) for All PT.
UIH Products Month Production Forecasting Fermented Cacao Beans .
Cocao Mass .
Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Source: MS Excel Data Processing Dark Chocolate bar .
Weekdays Rough Cut Capacity Planning with CPOF Method Available Capacity Calculation There are several stages of the production process for each product made by PT UIH.
For fermented cacao beans products, all production activities are still carried out manually with small tools and machines to help work.
In the production line of cacao mass and chocolate bar products, almost all of them have used machines, manual activities only in molding and packaging activities.
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Table 6.
Production Process Information Each Product PT.
UIH
Production Process Machine and Tools Qty Machine & Tolls Man Power Fermented Cacao Beans Fermentation Fermentation box Drying Drying Floor Grading & sorting dry beans Sortation table Packing dry beans Sewing Machine Cocoa Mass Roasting Roaster Machine Crushing and winnowing Disc Mill & Winnowing Machine Sieving Sieving machine Manual Cleaned Electric turbo Fan Grinding/refining Coanching/refiner machine Molding and Cooling Refrigerator Packing Vacuum machine Chocolate Bar Mixing Coanching/melanger machine Tempering Tempering machine Molding Molds and Refrigerator Packing Manual packing a Source: PT.
Untuk Indonesia Hijau The process of fermented beans production is .
fermentation of wet beans for 4 days, .
drying using solar heat on the drying floor, .
manual sorting of dry beans and .
packaging in sacks.
Cacao mass production process starts with .
roasting fermented cocoa beans, .
crushing and winnowing, .
sieving, .
manual cleaning, .
grinding and refining, .
molding and cooling and .
Packaging.
As for the production of chocolate bars, .
mixing all raw materials, .
tempering, .
Molding and cooling and finally .
The table 6 provides detailed information regarding all product production activities carried out by PT UIH.
Table 7.
Calculation of Standard Production Time for All PT.
UIH Products Work
Station
Kg/ WS1
WS2
WS3
WS4
WS5
Total
Work
Station
Kg/ WS1
WS2
WS3
WS4
WS5
WS6
WS7
WS8
Set up .
Run .
Total .
Set up .
Run .
Total .
Total time to kg dry beans .
56% yiel.
Total time to make 200 kg of cocoa mass Hours/ Utility (%) Efficiency .
Hours/ Utility (%) Efficiency .
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Run
Total
WS1
WS2
WS3
WS4
WS5
Total a Source: PT.
Untuk Indonesia Hijau Work Station Kg/ Total time to make 100 pcs or 2 kg chocolate bars Hidayat, et al.
Hours/ Pcs Utility (%) Efficiency .
the first step in calculating capacity is to determine the average time at each Workstation for each The information needed includes the amount of labor time required to produce each unit of The input data in the CPOF technique are standard time, production quantity per month, and proportion (Aisyah et al.
, 2.
Tables 7 are the calculation of standard time to produce each product.
Available capacity is calculated based on working days, working hours, number of shifts, number of machines/workforce, efficiency and utilities available in each work center.
In determining the available capacity with the CPOF method, the following formula is used, the following formula is used: Available capacity = number of machines x number of shifts x working hours x number of working days x utility x efficiency x PH (Sugiatna, 2.
(Subchan & Wiwi, 2.
An example of calculating the available capacity for fermented cocoa beans at workstation 2 in August 2024: Available capacity Aug 24 = 1 x 1 shift x 7 jam x 26 hari x 95% x 95%x 0,36.
Available capacity Aug 24 = 59.
3 hours.
Tables 8, 9, and 10 are the results of calculating the available capacity in each workstation to make each product of PT.
UIH.
Table 8.
Available Capacity for Fermented Cacao Beans (Hour.
Week Work Hours Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 a Source: MS Excel Data Processing Month WS1 .
WS2 .
WS3 .
WS4 .
WS5 .
WS7 .
WS8 .
Table 9.
Available Capacity for Cacao Mass (Hour.
Month Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Work Hours Work Days WS1 .
WS2 .
WS3 .
WS4 .
WS5 .
WS6 .
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WS4
WS5 .
Table 10.
Available Capacity for Chocolate Bars Production (Hour.
Month
Work day
Work Hours Aug-24
Sep-24
Oct-24
Nov-24
Dec-24
Jan-25
Feb-25
Mar-25
Apr-25
May-25
Jun-25
Jul-25
WS1
WS2 .
WS3 .
a Source: MS Excel Data Processing Required Capacity Calculation To determine the required capacity, several data points are required, as shown in table 7.
When calculating the required capacity using the CPOF method, the following formula is used:
Required capacity = demand x standard time (Setiabudi et al.
, 2.
Tables 11, 12 and 13 are the results of calculating the capacity requirement in each workstation to make each product of PT.
UIH.
Table 11.
Required Capacity for Fermented Cacao Beans Production (Hour.
Bulan Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 MPS .
WS1 .
a Source: MS Excel Data Processing WS2 .
WS3 .
WS4 .
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Table 12.
Required Capacity for Cacao Mass Production (Hour.
Month
Aug-24
Sep-24
Oct-24
Nov-24
Dec-24
Jan-25
Feb-25
Mar-25
Apr-25
May-25
Jun-25
Jul-25
MPS
WS1 .
WS2 .
WS3 .
WS4 .
WS5 .
WS6 .
WS7 .
WS8 .
a Source: MS Excel Data Processing Table 13.
Required Capacity for Cacao Mass Production (Hour.
Month Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 MPS .
WS1 .
a Source: MS Excel Data Processing WS2 .
WS3 .
WS4 .
WS5 .
Feasibility Test for Production Capacity After calculating the required capacity and the available capacity, the two figures can be compared to see if the main production schedule is feasible.
The comparison is expressed as the percentage of load capacity (%LC).
If the %LC is negative, there is a temporary shortage of capacity, and vice versa.
The capacity feasibility test compares the available and required capacity.
Here is the formula and calculation example for %LC (Wijaya et al.
, 2.
(Abdullah et al.
, 2.
% LC (WS1 Aug Ao.
= ((Available capacity Aug Ao24 Ae Capacity required Aug Ao.
/Available capacity Aug Ao.
x 100 % % LC (WS1 Aug Ao.
= (.
1 hours Ae 59.
0 hour.
/71.
1 hour.
) x 100 % % LC (WS1 Aug Ao.
= 17 % Based on the calculation example above, it can be concluded that the production capacity for work station 1 for the production of fermented cocoa beans is not experiencing obstacles because the available capacity exceeds the required capacity by 17%.
Below are the results of the calculation using the formula above for all production lines of PT.
UIH:
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Table 14.
Recapitulation of Load Capacity Percentage for Cocoa Fermented Beans Month MPS .
WS1 (%) Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 a Source: MS Excel Data Processing WS2 (%) WS3 (%) WS4 (%) WS5 (%) Table 15.
Recapitulation of Load Capacity Percentage for Cocoa Mass Month MPS .
Aug-24
Sep-24
Oct-24
Nov-24
Dec-24
Jan-25
Feb-25
Mar-25
Apr-25
May-25
Jun-25
Jul-25
WS1
(%)
WS2
(%)
WS3
(%)
WS4
(%)
WS5
(%)
WS6
(%)
WS7
(%)
WS8
(%)
a Source: MS Excel Data Processing Table 16.
Recapitulation of Load Capacity Percentage for Cocoa Mass Month Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 MPS .
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The results of the study show that in August 2024, the % Loading Capacity calculation results at all workstations showed negative figures at all workstations.
This shows that there is excess work capacity at these workstations if they are to produce 5,636 kg of dry fermented beans.
This is also evident in the % LC calculations from April 2025 to July 2025, with a production plan exceeding 7,000 kg, showing that the % LC calculations for all workstations are negative.
Therefore, it is necessary to increase the capacity of all workstations to meet customer demand.
In the LC% calculation simulation, a production volume of 5,200 kg is required to achieve a positive LC% value across all workstations.
Therefore, it can be concluded that a production volume of 5,200 kg represents the current maximum monthly production capacity for fermented cocoa beans.
Table 15 and Table 16 show the calculated %LC values for the production of cacao mass and chocolate bar products all show a positive %LC, the average %LC number of all product production lines is above 50% so that the production unit utility is low.
UIH is currently implementing a Just in Time (JIT) production system to reduce production costs due to the low utilization of production capacity for both products.
CONCLUSIONS AND IMPLICATIONS
In this study it can be concluded that almost the entire production line of the three products of PT.
UIH
occurred excess available capacity, only in the production of fermented beans for 4 months experienced a lack of available capacity.
With the results of capacity analysis with the RCCP method.
MPS needs to be revised so that the production capacity of PT UIH can be optimized.
For fermented cocoa bean products, in months where production capacity is still very low.
PT UIH needs to create a strategy to obtain raw material supply so that production capacity can be optimized.
Anticipating potential production bottlenecks in the previous two work stations, production facilities need to be upgraded, such as the drying floor needs to be upgraded to a drying machine, as well as the addition and repair of fermentation boxes.
Cacao mass and chocolate bar products have a very high %LC, therefore PT UIH needs to make a good marketing strategy in the future.
These two products need to be encouraged so that demand from customers and the market can meet the installed production capacity.
REFERENCES