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Mirna Lusiani
Department of Industrial Engineering, Universitas Bunda Mulia, Jakarta
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Penentuan Jumlah Operator Untuk Mengurangi Keterlambatan pada Proses Pengemasan Menggunakan Simulasi ProModel Mirna Lusiani; Kenrick Filbert
JIEMS (Journal of Industrial Engineering and Management Systems) Vol 12, No 2 (2019): Journal of Industrial Engineering and Management Systems
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/jiems.v12i2.1669

Abstract

In the current era of modernization, the development of the cosmetic industry in the world is developing very rapidly. One of the cosmetics that is the main requirement for women is lipstick. PT. Cedefindo is one of the cosmetic manufacturers that produce SASC lipstick. During 2017, there were problems in the lipstick packaging process. Based on the results of identification, the delay is caused by the buildup during the weighing process so that the next process becomes slow and sometimes makes the operator idle. The consequence that arises is often not achieving daily production targets. Therefore, this research aims to reduce the delay in the lipstick packaging process, especially the lipstick packaging section. In evaluating and analyzing the lipstick packaging system, research uses system simulation with the help of ProModel software. The result shows that adding would reduce the delay in the lipstick packaging process. Minimizing the production costs related to the packaging process is conducted by reducing operator unused work shifts. Therefore, adding some operators can reduce production costs especially on operator salaries up to 75%. The feasible solution is to assign 3 operators on low demand rate, 4 operators at normal demand rate and high demand rate.
Mengurangi Antrean pada Stasiun Pengisian Bahan Bakar Umum dengan Pendekatan Simulasi Menggunakan ProModel Mirna Lusiani; Anie Belita
JIEMS (Journal of Industrial Engineering and Management Systems) Vol 12, No 1 (2019): Journal of Industrial Engineering and Management Systems
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/jiems.v12i1.1534

Abstract

The gas station has become an important facility for the public, especially for people in large cities such as Jakarta. This condition was caused by increasing demand from year to year. The number of facilities has less number than the customer that came, which could cause queueing in the station. This research was made with the purpose to reduce the number of the queue and increase the number of customers serviced at the gas station. This research located on one of the gas stations in Jakarta at 16.00-18.00 from Monday to Friday. The data used for this research is primary data which was observed directly by the researcher. The data used for this research are the number of customers arrived and service time. Data analysis is using discrete-event simulation with ProModel software. The conclusion of this research is the actual system has a relatively high number of queueing customer which also affect the reduced number of customers that was served. Improvement model was created by decreasing the service time by 10% with the average number of queues by 4 customers for Pertalite and 4customers for Premium. Scenario model is also designed as a proposal by using customer migration scenario from Premium to Pertalite by 30%, 60%, and 100%. The proposed system for the first scenario is by decreasing the service time by 20% with the average number of queues by 8 customers for Pertalite and 1 customer for Premium. For the second scenario is by modifying the queueing system and decreasing the service time by 10% with the average number of queues by 8 customers for Pertalite and 7 customers for Premium. For the third scenario is by opening a new server for Pertalite with the average number of queues by 5 customers.
Co-Authors Anie Belita Kenrick Filbert