Industry 4.0 becomes more and more important in recent years because manufacturing industries are facing huge challenges in improving productivity for global competition. Industry 4.0 provides new opportunities to improve the resource and process efficiencies by combining information and communication technologies such as autonomous robots, internet of things, cloud computing, big data, augmented reality, additive manufacturing, etc. This integrated cyber-physical production system raise complexity within production system that implies new competencies required for industrial engineers which has been proven for years that the role of industrial engineer greatly influences a manufacturing industry’s success by designing, implementing, improving and optimizing a complex processes of an integrated systems that consist of people, money, knowledge, information, equipment, energy and materials. The competencies required in Industry 4.0 could be categorized under technical competencies and social competencies. A learning factory with real world system is often used to train students a new set of competencies by hands-on and direct experiences. Therefore, a learning factory can make a substantial contribution to competencies enhancement for industrial engineering students. This paper presents a conceptual of Industry 4.0 Lab as a learning factory for industrial engineering education as an enabler of students’ competencies in industry 4.0 era. A fully automated small production of filling bottle system integrating and demonstrating various Industry 4.0 concepts technologies is chosen. The learning modules and didactic approach are developed by integrating industrial engineering body of knowledge, Industry 4.0 value drives and Industry 4.0 levers in the creation of technical and social competencies required.

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The purpose of this research is to identify risks and causes of risk that occur in the flow of the company’s supply chain, identify the linkages between risks and design treatment strategies that can be used to reduce the emergence of risk agents. The research methods used are Interpretive Structural Modelling, MICMAC Analysis, and House of Risk. The identification results show that there are 12 risks and 26 causes. Based on the analysis, the relationship between one risk and other results in 8 levels and 2 quadrants. The results of the analysis also show that 15 causes of risk will be carried out by designing a handling strategy. There are 11 treatment strategies proposed to reduce the probability of risk causes. From this analysis, it can be concluded that the procurement process needs to carry out mitigation based on the highest priority, such as changing the business process to e-procurement, differentiating the format between the revised PR and the original PR, updating the cost evaluation data regularly.

Saat ini, penggunaan sekelompok robot otonom semakin meningkat, terutama untuk aplikasi yang berurusan dengan bahan dan atau situasi berbahaya. Dalam hal ini, pergerakan robot otonom di mana tidak ada campur tangan manusia pada proses eksekusi sangat penting. Masalahnya adalah bagaimana kelompok robot otonom ini dapat tiba secepat mungkin ke lokasi target untuk melakukan tugas yang diberikan. Jika itu termasuk pergerakan kelompok robot otonom maka partikel swarm optimization (PSO) adalah salah satu metode tersedia yang sederhana namun kuat. Logika fuzzy sebagai sistem logika telah terbukti dapat dikombinasikan dengan berbagai aplikasi atau metode untuk mendapatkan hasil yang lebih optimal. Salah satunya adalah kombinasi logika fuzzy dengan metode PSO. Makalah ini menerapkan metode optimasi fuzzy-PSO untuk mensimulasikan sekelompok pergerakan robot ke lokasi target menggunakan pemrograman awal. Hasil optimasi fuzzy-PSO, kemudian dibandingkan dengan hasil optimasi PSO klasik. Ditemukan bahwa robot dengan gerakan optimasi fuzzy-PSO tiba di target lokasi rata-rata 40% lebih cepat dibandingkan robot dengan gerakan optimasi PSO klasik. Kata kunci : particle swarm optimization, logika fuzzy, pergerakan robot Abstract Now days, the use of a group of autonomous robots are grown increasingly, especially for an application dealing with hazardous material and or dangerous situation. In this case, autonomous robot movement where there is no interference from a human on the execution process is very important. The concern is how this group of autonomous robots could arrive as fast as possible to the target location to perform the tasks given. If it includes the movement of groups of autonomous robots then particle swarm optimization (PSO) is one of a simple yet powerful method available. Fuzzy logic as a logic system has been proven can be combined with various numbers of applications or methods to get a more optimal result. One of them is the combination of fuzzy logic with PSO method. This paper implemented the fuzzy-PSO optimization method to simulate a group of robots movement to the target location using scratch programming. The fuzzy-PSO optimization results, then compared to the results of classic PSO optimization. It is found that the robots with fuzzy-PSO optimization movement arrived at the location target in average more than 40% faster compared to the robots with classic PSO optimization movement.

The increasing number of high buildings resulted in rising demand for building materials, especially wire mesh. The research was conducted at a steel company to increase production capacity by reducing setup time on drawing machine and improving work posture on the welding machine. Setup time decreased 5.86 minutes and effective capacity increased 21% as a result of setup process re-arrangement using Single Minutes Exchange of Die (SMED) technique and Maynard Operation Sequence Technique (MOST) is used to determine process time. Work posture analysis using Ovako Working Posture Analysis System (OWAS) in the welding machine and development wire mesh’s table decrease number of operating procedures from 7 activities to 6 activities and change working postures from bad work postureto good work posture.