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Journal : Sinergi

THE FRAMEWORK MODEL OF DIGITAL COOPERATIVE TO EXPLORE ECONOMIC POTENTIAL IN HIGHER EDUCATION Hasbullah Hasbullah; Salleh Ahmad Bareduan
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.011

Abstract

In February 2020, the Indonesian Ministry of Cooperatives and SMEs noted that in the last four years, 81,686 cooperatives were dissolved, leaving 123,048 active cooperatives. This case is a huge challenge for the government to overcome.  Indonesian Internet Service Providers Association (APJII) stated that the number of internet users in Indonesia reached 196.7 million in the middle of 2020. Meanwhile, data from Google & Temasek showed purchasing products via e-Commerce in Indonesia reached US$ 10.9 billion in 2017 and continuously increased in 2020. Most cooperatives in Indonesia run business conventionally with manual transactions, limited time, traditional logistics, and conventional membership administration. Nowadays, the institution with tens of thousands of members no longer effectively runs cooperative conventionally in a disruptive era. A conventional cooperative at a private university in Jakarta was observed in the study. There are tens of thousands of students and staff at the university. Three research questions arise, such as what can not be adequately solved in a traditional cooperative, what tools are used in digital cooperatives, and what shape can be used in the digital cooperative system model to solve issues. This study proposes a framework model in developing a digital cooperative to accommodate a huge amount of membership and enhance business scope. The research identified technology needed to overcome matters cannot be dealt with in a conventional cooperative. It provided a  digital cooperative frameworks model that impacts value creation, value capture, and value delivery, especially in higher education.    
REDUCING THE PRODUCT CHANGEOVER TIME USING SMED & 5S METHODS IN THE INJECTION MOLDING INDUSTRY Daniel Agung; Hasbullah Hasbullah
SINERGI Vol 23, No 3 (2019)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (626.96 KB) | DOI: 10.22441/sinergi.2019.3.004

Abstract

Along with the increasing market of plastic packaging products resulting from the injection process and the rapid development of existing technology, we need a strategy to be able to continue to meet the customer’s needs and to be able to compete in the industry. One of the strategies that may be employed is Lean. Lean is a concept aimed at eliminating existing waste. One of the implementations of Lean’s concept is the SMED (Single Minute Exchange of Dies) concept. SMED is a concept aimed at reducing the changeover time, so the eliminated time can be used in the production process. P.T. BIL is one of the companies engaged in the production of plastic packaging with an injection process. Problems occurring at P.T. BIL was the absence of a measurement process for changeover time. The Operation Analysis Chart concept was used to analyze the carried-out activities. To optimize activities employing the SMED concept, Analytical Cards were used so that the change over time can be reduced. The 5S concept was applied to support the SMED concept, so the waste in the work area could be eliminated. By implementing the SMED concept, we reduced 18% of the change over time.
ANALISIS KEGAGALAN PROSES INSULASI PADA PRODUKSI AUTOMOTIVE WIRES (AW) DENGAN METODE FAILURE MODE AND EFFECT ANALYSIS (FMEA) PADA PT JLC Hasbullah Hasbullah; Muhammad Kholil; Dwi Aji Santoso
SINERGI Vol 21, No 3 (2017)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (535.434 KB) | DOI: 10.22441/sinergi.2017.3.006

Abstract

FMEA (Failure Mode Effect Analysis)  adalah metode yang digunakan dalam mengidentifikasi kemungkinan  kegagalan  pada proses, fungsi  dan design produk  sehingga diketahui penyebab dan  akibatnya  untuk meningkatkan mutu dan reliabilitas produk. Kegagalan proses insulasi pada proses produksi AW (Automotive Wire) mengganggu kinerja produksi PT JLC.  FMEA  digunakan untuk mengidentifikasi dan mencegah potensi kegagalan proses insulasi pada produksi produk AW (Automotive Wire) di PT JLC . FMEA diulas oleh banyak riset sebagai metode efektif dan dijadikan format standar yang digunakan oleh industri otomotif dalam membuat daftar potensi kegagalan sehingga dapat mengetahui penyebab, dampak dan tindakan pencegahan dalam mengatasinya. FMEA menyediakan metode dalam membuat daftar potensi kegagalan produk AW (Automotive Ware) melalui penilaian kuantitatif dengan kriteria  tiga aspek yaitu Tingkat kemungkinan frekwensi terjadi kegagalan (O=Occurence), Tingkat resiko akibat kegagalan (S=Severity) dan Tingkat kemungkinan bisa dideteksi (D=Detection). Dari hasil perhitungan dan analisis FMEA maka dihasilkan daftar urutan prioritas potensi kegagalan  proses insulasi melalui perhitungan pada tiga aspek Occurence (O), Severity (S) dan Detection (D) disertai kemungkinan penyebab, dampak dan solusinya. Dua potensi kegagalan terbesar adalah Ketidaksesuaian warna (terlau tua atau muda), marking tidak tercetak jelas dan permukaan insulasi yang kasar. FMEA  mampu mengidentifikasi penyebab, dampak dan  pencegahan untuk mengantisipasi kegagalan tersebut.
Selection lead logistics provider in consumer goods using AHP – TOPSIS approach Kuwat Suroto; Hasbullah Hasbullah
SINERGI Vol 27, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023.2.006

Abstract

Vendor selection is a strategic activity in order to support the achievement of the company`s success and competitiveness. Significantly, the company has some specific standards in the selection. Therefore, evaluation is needed to see which vendors match the company's criteria. The purpose of this study was to evaluate and select the proposed vendor in a decision support system using the AHP and TOPSIS approaches. The AHP method is used to determine the importance of the criteria, while the TOPSIS method is used to rank alternatives. The results show that Provider 1 has the highest score compared to other alternatives with a value of 0.852. Sensitivity analysis shows that the proposed AHP and TOPSIS methods are robust, suitable for this problem, and have a low rate of change.
Identifying weaknesses and strengths of existing I4.0 Readiness Indices to enhance INDI 4.0 Hasbullah Hasbullah; Salleh Ahmad Bareduan
SINERGI Vol 28, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.1.013

Abstract

Indonesia has low Industry 4.0 (I4.0) readiness in ASEAN and has the INDI 4.0 Instrument (Indonesia Industry 4.0 Readiness Index), which is less comprehensive and accurate. An Initial survey confirmed that only 56.86% of respondents agreed that INDI 4.0 accurately measures readiness in the manufacturing industry. Unlike primary I4.0 indices, INDI 4.0 lacks comprehensive Industry 4.0 dimensions and characteristics, as many literature and other indices cover. This study aims to identify weaknesses and strengths of major I4.0 indices by comparing them to enhance INDI 4.0. This paper identified gaps in existing major I4.0 indices by scoping review method. However, each index contributes to increasing practicality, fulfilling latent needs, and expanding complementary perspectives in measuring readiness and adoption of I4.0 based on studies, viewpoints, uniqueness, and views of each. This study offered a more comprehensive perspective, especially from developing countries like Indonesia, with industries struggling to adopt I4.0 to fill loopholes in existing major indices that are generally from developed countries, so most companies in their study have advanced or implemented I4.0 and are too focused and too oriented on technology. The findings from this paper are expected to contribute to industry, practitioners, and academicians in increasing accuracy when measuring readiness toward adopting I4.0.