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The pandemic caused by COVID-19 brought on many challenges. Sustaining manufacturing production is one of them. Some market segments may experience reductions in demands, while other market segments may experience explosions in demand. Companies have to manage these changing needs and supply uncertainties while keeping employees safe and remaining profitable. This paper characterizes the problems encountered in setting up new production lines in two different industries and describes solutions adopted and envisioned.

Predictive maintenance is a maintenance strategy of diagnosing and prognosing a machine based on its condition. Compared with other maintenance strategies, the predictive maintenance strategy has the advantage of lowering the maintenance cost and time. Thus, many studies have been conducted to develop a predictive maintenance model based on a growth of prediction methodology. However, these studies tend to focus on building the predictive model and measuring its performance, rather than selecting the appropriate components for predictive maintenance. Nevertheless, selecting the predictive maintenance policy and target component are as important as model selection and performance measurement. In this paper, a selection method is proposed to improve component selection by referencing current literature and industry expert knowledge. The results of this research can serve as a foundation for further studies in this area.

As cloud computing is increasingly adopted, the trend is to offer software functions as modular services and compose them into larger, more meaningful ones. The trend is attractive to analytical problems in the manufacturing system design and performance improvement domain because (1) finding a global optimization for the system is a complex problem; and (2) sub-problems are typically compartmentalized by the organizational structure. However, solving sub-problems by independent services can result in a sub-optimal solution at the system level. This paper investigates the technique called Analytical Target Cascading (ATC) to coordinate the optimization of loosely-coupled sub-problems, each may be modularly formulated by differing departments and be solved by modular analytical services. The result demonstrates that ATC is a promising method in that it offers system-level optimal solutions that can scale up by exploiting distributed and modular executions while allowing easier management of the problem formulation.

Smart manufacturing, today, is the ability to continuously maintain and improve performance, with intensive use of information, in response to the changing environments. Technologies for creating smart manufacturing systems or factories are becoming increasingly abundant. Consequently, manufacturers, large and small, need to correctly select and prioritize these technologies correctly. In addition, other improvements may be necessary to receive the greatest benefit from the selected technology. This paper proposes a method for assessing a factory for its readiness to implement those technologies. The proposed readiness levels provide users with an indication of their current factory state when compared against a reference model. Knowing this state, users can develop a plan to increase their readiness. Through validation analysis, we show that the assessment has a positive correlation with the operational performance.

Smart Manufacturing Systems have the ability to adapt to rapidly changing requirements. Software components of the manufacturing system—services under Service Oriented Architecture—must be configured dynamically to meet such requirements. Currently, software vendors provide packaged services, so they are not easily reconfigurable. Thus, engineers or production managers face difficulty in composing services with the appropriate functionality and quality. The objective of this paper is to discuss high-level requirements for such a unit service concept and provide an initial use case to illustrate how the unit service concept may apply new technologies to improve service. We propose a decomposition of target service according to standard model, and we claim the limitations of decomposed unit services, and new technologies and opportunities for each decomposed services.

Production planning is carried out at the enterprise, operation, and process levels. Although production plans at higher levels constrain those at the lower levels, the processes for generating those plans are typically not well integrated in practice. Because of that, the schedules at lower levels may not accurately reflect what was planned at the higher levels while the plans at the high level may not be based on prevailing conditions at the lower levels. Simulation models that evaluate the performance of a production plan also need to be integrated with production management systems. This paper provides a background to integration problems associated with simulation-based multi-level production planning by exploring current practices, standards, and tools. We lay a foundation for a standards-based simulation for integrated production planning.