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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal Industrial Servicess OPERATION EXCELLENCE: Journal of Applied Industrial Engineering Jurnal INTECH Teknik Industri Universitas Serang Raya
Sumarsono Sudarto
Universitas Mercu Buana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Integrasi forecasting pada rantai pasok manufaktur komponen otomotif Jepang di Indonesia dengan penerapan metode classic dan regresi Rio Patria; Sumarsono Sudarto
Operations Excellence: Journal of Applied Industrial Engineering VOL 12, NO 3, (2020): OE NOVEMBER 2020
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/oe.2020.v12.i3.011

Abstract

The need of spark plugs as a replacement component has a potential demand, especially for a Company from Japan which establish in the last 40 year ago as manufacture of spark plugs in Indonesia. Sales forecast of spark plugs that develop by Company for aftermarket class has big error. It makes a question ‘how to improve the accuracy of forecasting spark plugs in the aftermarket class to reduce losses in the supply chain process’, such as loss due to inventory, production and transportation. In this study, it was found that using ARIMA and MLR methods could increase level of accuracy than current forecasting method by Company. It was found that the increment in forecasting accuracy by using ARIMA and MLR was able to reduce operational costs up to 25.05% per year in overtime costs and 40.21% per year in finished goods inventory costs. In addition, reducing the cost of shipping materials by 24.90% per year, and reducing inventory costs on suppliers by 25.74% per year.
Optimalisasi Persediaan Produk Sodium Hypochlorite Menggunakan Pendekatan Linier Programming Rusman Zaenal Abidin; Sumarsono Sudarto; Sawarni Hasibuan
Jurnal INTECH Teknik Industri Universitas Serang Raya Vol. 6 No. 2 (2020)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/intech.v6i2.2706

Abstract

Pembatasan angkutan produk pada masa hari raya Idul Fitri merupakan kendala yang sering dialami oleh perusahaan yang produknya mempunyai masa kadaluarsa yang pendek. Perencanaan produksi yang kurang tepat dapat berdampak pada proses dekomposisi yang menambah biaya produksi. Penelitian ini bertujuan untuk mengoptimalkan perencanaan persediaan pada masa setelah hari raya Idul Fitri dengan menggunakan metode linier programming. Penelitian ini berfokus pada perencanaan persediaan produk sodium hypochlorite yang mempunyai lifetime sebentar. Metode linier programming yang digunakan adalah metode simpleks yang dapat mengakomodasi kendala-kendala yang terjadi. Hasil penelitian menunjukkan untuk meminimalkan biaya produksi produk sodium hypochlorite maka diperlukan 180 ton yang melalui proses dekomposisi dan 720 ton disimpan dalam tangka dan 180 ton yang diproses kembali. Hasil tersebut menunjukkan tidak memerlukan fasilitas pembuatan tangki baru yang memerlukan biaya yang tidak sedikit. Implementasi linier programming dapat membantu mengambil keputusan yang tepat berdasarkan kendala-kendala yang terjadi pada suatu proses persediaan produk.
Analysis of vulnerability and capability for development of supply chain resilience framework M. Syahri Nur Afif; Sumarsono Sudarto; M. Ibrahim Ats Tsauri; Hery Sumardiyanto
Journal Industrial Servicess Vol 8, No 2 (2022): October 2022
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/jiss.v8i2.15936

Abstract

The current COVID-19 pandemic has significantly impacted all sectors, including the automotive industry. The automotive industry is one of the industries that contribute significantly to economic growth in Indonesia. With supply chain disruptions and vulnerabilities amid the COVID-19 pandemic, the demand for supply chain resilience is echoing in the business world. It is essential to develop resilience capabilities promptly because supply chain vulnerabilities can cause severe financial loss for organizations. This research aims to group the variables of vulnerability and capability to perform processing more easily in prioritizing the appropriate vulnerabilities and capabilities. The factor analysis method is designed to group variables in many factors with almost the exact nature and characteristics, making it easier to simplify and analyze further. The results of the study found that the vulnerability variable has three factors, namely (i) financial vulnerability, (ii) operational vulnerability, and (iii) external vulnerability, then these three factors are divided into thirteen indicators. While the factors that are formed from the ability variable are five factors, namely (i) cooperation, (ii) anticipation, (iii) financial strength, (iv) capacity, and (v) flexibility, then the five factors are divided into thirteen indicators. Following the research objectives, grouping and simplifying these priority factors can become a reference for researchers or companies to manage supply chains that are more resilient amid disruptions due to the impact of the COVID-19 pandemic effectively and efficiently.
Efek Mediasi Implementasi Social Sustainability Terhadap Kinerja Perusahaan di Masa Pandemi Covid-19 Bowie Prasetyo; Sumarsono Sudarto
Operations Excellence: Journal of Applied Industrial Engineering Vol. 14, No. 2, (2022): OE JULY 2022
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/oe.2022.v14.i2.054

Abstract

This study aims to examine the mediating effect of the implementation of Social Sustainability on the manufacturing supply chain to find the right model for resilience to future economic disasters.  This study took data as a sample of 150 respondents from all 160 companies in supply chain that were able to survive during the pandemic. Using the variables Management Commitment and Financial Support as well as SEM-PLS tools  to test the hipothesis of a direct or indirect relationship to company performance.  The research findings indicate that the effect of the Financial Support variable on Company Performance was initially positive and significant in a direct relationship, turned insignificant if there was a Social Sustainability implementation variable as a mediating variable.  Same things are shown in the influence of the Management Commitment variable on Company Performance. This finding can be the first step of research on the implementation of Social Sustainability in Indonesia which is still very rarely done and illustrates that during the pandemic when this research was conducted, the focus of the company was to survive the impact and respondent considered the implementation of Social Sustainability to have a negative impact and burden the company’s finances and Performance.
Analysis of machine repair time prediction using machine learning at one of leading footwear manufacturers in Indonesia Yulio Agefa Purmala; Sumarsono Sudarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1727-1734

Abstract

Machine breakdowns in the production line mostly finish in more than 18minutes, since the machine that needs repair more time is done on theproduction line, not in the machine warehouse. Historical machinebreakdown data is digitally recorded through the Andon system, but it is stillnot being used adequately to aid decision-making. This research introducesan analysis of historical machine breakdown data to provide predictions ofrepair time intervals with a focus on finding the best algorithm accuracy.The research method uses machine learning techniques with a classificationmodel. There are five algorithms used: logistic regression (LR), naive bayes(NB), k-nearest neighbor (KNN), support vector machine (SVM), andrandom forest (RF). The results of this study prove that historical machinebreakdown data can be optimized to predict machine repair time intervals inthe production line. The accuracy of LR algorithm is slightly better than theother algorithms. Based on the receiver operating characteristic–area undercurve (ROC-AUC) performance evaluation metric, the quality value of theaccuracy of LR model is satisfied with a percentage of 69% with adifference of 0.5% between the train and test data.
Co-Authors Bowie Prasetyo Hery Sumardiyanto M. Ibrahim Ats Tsauri M. Syahri Nur Afif Rio Patria Rusman Zaenal Abidin Sawarni Hasibuan Yulio Agefa Purmala