The emerging concept of smart manufacturing systems is defined in part by the introduction of new technologies that are promoting rapid and widespread information flow within the manufacturing system and surrounding its control. These systems can deliver unprecedented awareness, agility, productivity, and resilience within the production process by exploiting the ever-increasing availability of real-time manufacturing data. Optimized collection and analysis of this voluminous data and subsequent distribution of extracted information to guide decisionmaking throughout the enterprise necessitate, however, a complex and dynamic process. To establish and maintain confidence that smart manufacturing systems function as intended, performance assurance measures will be vital.The activities for performance assurance span manufacturing system design, operation, performance assessment, evaluation, analysis, decision making, and control. Changes may be needed for traditional approaches in these activities to address smart manufacturing systems. This paper reviews the current methods and tools used for establishing and maintaining required system performance. This paper then identifies trends in data and information systems, integration, performance measurement, analysis, and performance improvement that will be vital for assured performance of smart manufacturing systems. Finally, we analyze how those trends apply to the methods studied and propose future research for assessing and improving manufacturing performance in the uncertain, multi-objective operating environment.

The emerging concept of smart manufacturing systems is defined in part by the introduction of new technologies that are promoting rapid and widespread information flow within the manufacturing system and surrounding its control. These systems can deliver unprecedented awareness, agility, productivity, and resilience within the production process by exploiting the ever-increasing availability of real-time manufacturing data. Optimized collection and analysis of this voluminous data to guide decision-making is, however, a complex and dynamic process. To establish and maintain confidence that smart manufacturing systems function as intended, performance assurance measures will be vital. The activities for performance assurance span manufacturing system design, operation, performance assessment, evaluation, analysis, decision making, and control. Changes may be needed for traditional approaches in these activities to address smart manufacturing systems. This paper reviews the current methods and tools used for establishing and maintaining required system performance. It then identifies trends in data and information systems, integration, performance measurement, analysis, and performance improvement that will be vital for assured performance of smart manufacturing systems. Finally, we analyze how those trends apply to the methods studied and propose future research for assessing and improving manufacturing performance in the uncertain, multi-objective operating environment.

Sustainability has become a very significant research topic since it impacts many different manufacturing industries. The adoption of sustainable manufacturing practices and technologies offers industry a cost effective route to improve economic, environmental, and social performance. As a major manufacturing process, the machining system plays an important role for sustainable manufacturing on the factory floor. Therefore, technologies for monitoring, analyzing, evaluating, and optimizing the sustainability impact of machining systems are critical for decision makers. Modeling and Simulation (M&S) can be an effective tool for success of sustainable manufacturing through its ability to predict the effect of implementing a new facility, process without interrupting real production. This paper introduces a methodology that provides a traditional virtual Numerical Control (NC) machining model with a new capability — to quantitatively analyze the environmental impact of machining system based on Life Cycle Assessment (LCA). The objective of the methodology is to analyze the sustainability impacts of machining process and determine a better plan for improving the sustainable performance of machining system in a virtual environment before work orders are released to the shop floor. Testing different scenarios with simulation models ensures the best setting option available can be chosen. The virtual NC model provides the necessary data for this assessment. In this paper, a list of environmental impact indicators and their metrics has been identified, and modeling elements for sustainable machining have been discussed. Inputs and outputs of the virtual machining model for sustainable machining are described. A case study to experiment the proposed methodology is discussed.

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The need for an open, inclusive, and neutral procedure in selecting key performance indicators (KPIs) for sustainable manufacturing has been increasing. The reason is that manufacturers seek to determine what to measure to improve environmental sustainability of their products and manufacturing processes. A difficulty arises in understanding and selecting specific indicators from many stand-alone indicator sets available. This paper presents a procedure for individual manufacturers to select KPIs for measuring, monitoring, and improving environmental aspects of manufacturing processes. The procedure is the basis for a guideline, being proposed for standardization within ASTM International. That guide can be used for (1) identifying candidate KPIs from existing sources, (2) defining new candidate KPIs, (3) selecting appropriate KPIs based on KPI criteria, and (4) composing the selected KPIs with assigned weights into a set. The paper explains how the developed procedure complements existing indicator sets and sustainability-measurement approaches at the manufacturing process level.