Interpretive Structural Model of Key Performance Indicators for Sustainable Manufacturing Evaluation in Cement Industry

This paper aims to analyze the relationships among the Key Performance Indicators (KPIs) for sustainable manufacturing evaluation in the cement industry. The initial KPIs have been identified and derived from literature, and then validated by industry survey. As a result, three factors dividing into a total of thirteen indicators have been proposed as the KPIs for sustainable manufacturing evaluation in cement industry. Interpretive structural modeling (ISM) methodology is applied to develop a network structure model of the KPIs. The results show the indicators of economic factor are regarded as the basic indicator, while the indicators of environmental factor are indicated to be the leading indicator. Of those indicators, raw material substitution is regarded as the most influencing indicator. The ISM model can aid the cement companies by providing a better insight in evaluating sustainable manufacturing performance

Interpretive Structural Model of Key Performance Indicators for Sustainable Manufacturing Evaluation in Cement Industry

This paper aims to analyze the relationships among the Key Performance Indicators (KPIs) for sustainable manufacturing evaluation in the cement industry. The initial KPIs have been identified and derived from literature, and then validated by industry survey. As a result, three factors dividing into a total of thirteen indicators have been proposed as the KPIs for sustainable manufacturing evaluation in cement industry. Interpretive structural modeling (ISM) methodology is applied to develop a network structure model of the KPIs. The results show the indicators of economic factor are regarded as the basic indicator, while the indicators of environmental factor are indicated to be the leading indicator. Of those indicators, raw material substitution is regarded as the most influencing indicator. The ISM model can aid the cement companies by providing a better insight in evaluating sustainable manufacturing performance

Analysis of Inpatient Hospital Staff Mental Workload by Means of Discrete-event Simulation

Many process improvement tools have been applied to the healthcare industry to improve safety and efficiency. However, nearly all of these tools have neglected to explicitly quantify mental workload of healthcare providers despite the consensus that it is related to human performance. This research uses the Improved Performance Research Integration Tool (IMPRINT), a discrete-event simulation (DES), to quantify mental workload. Specifically, this research examines staff members in an inpatient unit at the Wright-Patterson Medical Center to detect workload differences between staff, identify trends which lead to high workload demands, evaluate the influence of patient load on mental workload, and test a workload-leveling process improvement. Results from this study indicate workload differences between staff types and finds that task urgency and complexity play a role in the overloading of tasks. The relationship between predicted mental workload and increased patient load is mostly linear; however, the slopes are different between staff types, indicating that staff types are predicted to be affected unequally by increases in patient demand. Lastly, the task sharing process improvement provides mixed results; idle time and average workload become more balanced, but overload time becomes more unbalanced. Overall, this study demonstrates the usefulness of IMPRINT at evaluating medical systems