Adopting Industry 4.0 (I4.0) is an effort to gain competitiveness through technological innovation for enhancing productivity and efficiency. Indonesia left behind in launching the policy timeline of the I4.0 initiative, compared to Singapore, Thailand, Malaysia, and Vietnam. In an official government report, Indonesia's I4.0 index showed a low score at an average of 1.992 (scale 0 to 4). Indonesia designed INDI 4.0 (Industry 4.0 readiness index Indonesia) in 2018 to prepare industry readiness. It lacks accuracy and is less comprehensive in capturing I4.0 readiness, especially in the factory operation aspect. INDI 4.0 just provides very few questions to capture extensive information in measuring I4.0. This study aimed to develop a comprehensive I4.0 index by enhancing INDI 4.0 in the factory operation aspect. By exploring issues in the I4.0 readiness index, the research extensively searched the journal articles and some other I4.0 indexes used in some countries. Finally, the paper designed a comprehensive I4.0 index with determinant indicators comprising data life cycle (sources, collection, storage, analysis, and transmission) and smart product life cycles (designing, planning, monitoring, quality, and maintenance). This model is expected to be an essential contribution to improve INDI 4.0 in Indonesia. The I4.0 phenomena will undoubtedly influence all countries, and more research into this topic and other critical variables affecting I4.0 preparation are required to complete this study. To improve this research, additional research from other academics is needed to fill in the gaps, incompleteness, and loopholes.

PurposeThis paper aims to present an approach of utilizing Petri net (PN) to model and schedule collaborative design and manufacturing activities at a cyber manufacturing centre (CMC).Design/methodology/approachA conceptual PN model consisting of all activities at the CMC is drafted. This model is then simplified to generate a conflict free PN for scheduling analysis purposes. Based on this simplified PN, generalized scheduling algorithms were developed. The algorithms were used in spreadsheets to process and analyze the operation database before finally transforming it into a dynamic finite scheduling sequence in the form of Gantt chart.FindingsThe PN is found to be very useful in modelling and analysing the scheduling sequence for a CMC with collaborative activities resembling flow shop with shared resources.Originality/valueThe models and methods described in this paper are practical means of utilizing PN in managing the scheduling and manufacturing activities.

-This paper presents a scheduling heuristic to minimize the makespan of a re-entrant flow shop using bottleneck analysis. The heuristic is specifically intended for the cyber manufacturing centre (CMC) which is an Internetbased collaborative design and manufacturing between the Universiti Tun Hussein Onn Malaysia and the small and medium enterprises. The CMC processes scheduling resembles a four machine permutation re-entrant flow shop with the process routing of M1,M2,M3,M4,M3,M4 in which the first process at M1 has high tendency of exhibiting dominant characteristic. It was shown that using bottleneckbased analysis, an effective constructive heuristic can be developed to solve for near-optimal scheduling sequence. At strong machine dominance level and medium to high job numbers, this heuristic shows slightly better makespan performance compared to the NEH. However, for smaller job numbers, NEH is superior.

Particle Swarm Optimization (PSO) algorithm is often used for solving RFID Network Planning (RNP) problems. However, the direct correlation between RNP parameters (coordinates and power settings of RFID readers) and PSO solutions is rarely shown. This is due to the fact that most researches done in this field focus more on the development of new variants of PSO and the optimization result. For that reason, this paper tends to investigate the correlation between RNP parameters and PSO solutions. One of RNP objectives (Optimal Tag Coverage) is taken as an example. The formula of optimal tag coverage is elaborated in order to expose the allocation of RNP parameters in the formula. In addition, a representation system for embedding RNP parameters in PSO solution is explained. This paper can also serves as an early guideline for solving RNP problems using PSO algorithm.

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Flow shop scheduling is a common operational problem in a production system. Effective flow shop scheduling can help the company to improve the management system, hence increase income. Artificial Bee Colony (ABC) is a system that is widely used for scheduling optimization in a production system since 2005. However, the fundamental ABC system uses a heuristic approach to obtain an optimum solution which may not be the optimum solution at all. The ABC system is tested on the speed to obtain the optimum solution for a flowshop scheduling problem and measures the applicability of the schedule in terms of makespan. A simple model of ABC algorithm was developed to identify the effectiveness of the ABC for solving flow shop scheduling problem compared to other established methods. Result shows the ABC model is capable of producing best makespan in flow shop problem tested.

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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.