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Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

Conversion of monoculture to agroforestry (integrating trees with crops) is promoted as a promising management in reducing N2O emissions from croplands. How agroforestry influences gross N2O emission (N2O + N2 from N2O reduction) and uptake (N2O reduced to N2) compared to monoculture is unknown. We used the 15N2O pool dilution technique to quantify these processes using soil cores (top 5 cm) incubated in the field with monthly measurements over two growing seasons (2018–2019) at two sites (each with paired agroforestry and monoculture) and one site with monoculture only. The unfertilized tree rows showed the lowest gross N2O emissions (P ≤ 0.002). Although tree rows occupied only 20% in agroforestry, gross N2O emissions tended to decrease by 6–36% in agroforestry (0.98–1.02 kg N2O‐N ha−1 yr−1) compared to monoculture (1.04–1.59 kg N2O‐N ha−1 yr−1). Gross N2O emissions were influenced by soil mineral N, soil respiration, and moisture content rather than by denitrification gene abundance. Soil gross N2O uptake was highest in the tree row and decreased with distance into crop rows (P = 0.012). The agroforestry tended to increase gross N2O uptake by 27–42% (0.38–0.44 kg N2O‐N ha−1 yr−1) compared to monoculture (0.30–0.31 kg N2O‐N ha−1 yr−1). In tree rows, soil gross N2O uptake correlated with nirK gene abundance which was indirectly influenced by the low mineral N‐to‐soil CO2‐C ratio. Adjusting the tree and crop areal coverages of agroforestry and optimizing fertilization can further augment the benefits of agroforestry in reducing emission and increasing uptake of N2O in soils.

This paper describes an effort of testing the Core Manufacturing Simulation Data (CMSD) information model as a neutral data interface for a discrete event simulation model developed using Enterprise Dynamics. The implementation is based upon a model of a paint shop at a Volvo Car Corporation plant in Sweden. The model is built for a Swedish research project (FACTS), which focuses on the work procedure of developing new and modified production systems. FACTS has found standardized simulation data structures to be of high interest to achieve efficient data collection in conceptual stages of production development programs. For the CMSD-development team, implementations serve as an approach to validate the structures in CMSD and to gather requirements for future enhancements. CMSD was originally developed to support job shops, but the results of this implementation indicate a good possibility to extend CMSD to also support flow shops.

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.