Discrete-event simulation of a production process for increasing the efficiency of a newspaper production

Riskadayanti, O; Yuniaristanto,; Sutopo, Wahyudi; Hisjam, Muhammad

doi:10.1088/1757-899x/495/1/012026

Cited by 9 publications

(21 citation statements)

References 7 publications

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“…Dengan permasalahn tersebut maka Model ARIMA dipilih karena memberikan nilai terkecil dari kesalahan persentase absolut rata-rata (MAPE) [2]. Selain itu, produksi surat kabar dituntut memiliki tingkat efisiensi yang tinggi karena siklus waktu produksinya sangat terbatas [3]. Metode Discrete Event Simulation (DES) adalah dipilih sebagai solusi dalam pemodelan sistem sebenarnya yang dapat menggambarkan berbagai komponen dalam proses produksi koran, hal ini digunakan agar bisa mendapatkan skenario terbaik dalam proses produksi koran [3].…”

Section: Pendahuluanunclassified

“…Selain itu, produksi surat kabar dituntut memiliki tingkat efisiensi yang tinggi karena siklus waktu produksinya sangat terbatas [3]. Metode Discrete Event Simulation (DES) adalah dipilih sebagai solusi dalam pemodelan sistem sebenarnya yang dapat menggambarkan berbagai komponen dalam proses produksi koran, hal ini digunakan agar bisa mendapatkan skenario terbaik dalam proses produksi koran [3]. Industri koran juga melakukan penelitian mengenai pengiriman bersama untuk memamngkas biaya yang dikeluarkan oleh perusahaan, hal ini dilakukan dengan perhitungan biaya distribusi menggunakan pendekatan Vehicle Routing Problem with Time Windows (VRPTW) hingga dapat menghemat hingga 72.22% [4].…”

Section: Pendahuluanunclassified

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Pemilihan Metode Peramalan Jumlah Permintaan Koran dengan Tingkat Kesalahan Terendah

Sutopo

A'yun²,

Ardian³

et al. 2021

MATRIK

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Pada era digital saat ini, banyak sekali industri yang mengalami dampak dari digitalisasi, salah satunya adalah industri surat kabar (koran). Adanya digitalisasi menyebabkan permintaan koran semakin fluktuatif dan sulit diprediksi. Hal ini juga menyebabkan tingkat retur atau pengembalian koran dari agen-agen yang cukup tinggi dan tentu saja akan memberikan kerugian yang cukup besar bagi perusahaan. Untuk itu, perlu dilakukan penentuan métode peramalan jumlah permintaan koran yang memiliki tingkat kesalahan terkecil sehingga dapat membantu perusahaan mengurangi kerugian akibat retur koran. Penelitian ini menghitung peramalan permintaan menggunakan beberapa metode antara lain trend line analysis, double exponential smoothing, dan two months moving average. Selain itu, penelitian ini juga membandingkan hasil peramalannya dengan penelitian terdahulu yang menggunakan metode ARIMA. Pemilihan metode peramalan yang terbaik dilakukan dengan membandingkan tingkat kesalahan (MAPE) dari tiap-tiap metode kemudian dipilih metode dengan tingkat kesalahan terkecil. Berdasarkan perbandingan yang dilakukan, dapat diketahui bahwa metode peramalan yang memiliki tingkat kesalahan terkecil adalah metode trend line analysis dengan nilai MAPE sebesar 2,94%. Oleh karena itu, metode peramalan yang terbaik untuk melakukan peramalan permintaan jumlah koran di Kota Surakarta adalah metode trend line analysis.

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Section: Pendahuluanunclassified

Pemilihan Metode Peramalan Jumlah Permintaan Koran dengan Tingkat Kesalahan Terendah

Sutopo

A'yun²,

Ardian³

et al. 2021

MATRIK

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show abstract

“…Since DES is very popular in assessing and optimizing stochastic processes, a wide variety of applications exist in the literature. Some of them are as follows: optimization of raw material allocation process (Windisch et al, 2015), increasing operational and manufacturing efficiency (Troncoso-Palacio et al, 2018;Riskadayanti et al, 2019;Knapčíková et al, 2020), optimization of transportation problems (Afrapoli et al, 2019;Behiri et al, 2018), line balancing (Bongomin et al, 2020;Yemane et al, 2020), and stock management (Gittins et al, 2020;Amorim-Lopes et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Serper¹,

Şen²,

Uslu³

2022

ibr

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Optimization models enable organizations to find the best solution and respond to the demand from an uncertain environment and stochastic process promptly and with less engineering effort. This study aims to optimize the number of seasonal agents and customer prioritization needed for a call center system using big data analytics and discrete event simulations to improve customer satisfaction. The study was carried out based on data from a leading heating and ventilation company's call center. The K-means clustering technique was used to determine customer segmentation on 6-million-customer data. For prioritization, the making of a Recency-Frequency-Monetary (RFM) analysis was applied. The system was modeled using ARENA simulation software, and performance parameters were measured depending on the segments obtained. The results show that the simulation model performed with data analytics gives better results for a beneficial financial impact with numerical values in customer prioritization, reducing the average waiting time of the most prioritized customers by more than 90%, and for the least prioritized customers, it increased the average waiting time by approximately just 40%. However, with the company segments, the increase in the average waiting time of the least prioritized customers was approximately 300%.

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“…In article of Riskadayanti and Yuniaristanto, Flexsim software was used to model the actual production system. Analysis of the results of the simulation conducted to determine the utility level of each machine [20]. Zhang and Duan et al combined Flexsim simulation software to simulate and optimize the sofa production process which would make the production more smooth, balanced, orderly and efficient [21].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Manufacturing Production Line Simulation of Super Capacitor

2021

jrc

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The production line of a super capacitor is a complex production line, and so many factors need to be considered during the designing process. In this paper, the plant simulation software of Siemens company is used to simulate the super capacitor production line, to verify the rationality of the application of digital twin technology in the complex production line designing process. Using the approximate parameters to study the influence of Automated Guided Vehicle (AGV) characteristics on the production line. Simulation in software environment can well demonstrate the actual production process. It is convenient to model the production line in the software environment. The malfunction and other factors in the actual production process can be replaced in software as the failure rate in the simulation environment. Data statistics and other functions can directly see the changes in various parameters in the production line, to get a better optimization scheme. The changes in actual can be "copied" to the simulating process, which will make benefit in establishing a production line.

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Discrete-event simulation of a production process for increasing the efficiency of a newspaper production

Cited by 9 publications

References 7 publications

Pemilihan Metode Peramalan Jumlah Permintaan Koran dengan Tingkat Kesalahan Terendah

Pemilihan Metode Peramalan Jumlah Permintaan Koran dengan Tingkat Kesalahan Terendah

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Intelligent Manufacturing Production Line Simulation of Super Capacitor

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