Forecasting Spare Parts Demand Using Statistical Analysis

Spare parts are very essential in most industrial companies. They are characterized by their large number and their high impact on the companies’ operations whenever needed. Therefore companies tend to analyze their spare parts demand and try to estimate their future consumption. Nevertheless, they face difficulties in figuring out an optimal forecasting method that deals with the lumpy and intermittent demand of spare parts. In this paper, we performed a comparison between five forecasting methods based on three statistical tools;Mean squared error (MSE), mean absolute deviation (MAD) and mean error (ME), where the results showed close performance for all the methods associated with their optimal parameters and the frequency of the spare part demand. Therefore, we proposed to compare all the methods based on the tracking signal with the objective of minimizing the average number of out of controls. This approach was tested in a comparative study at a local paper mill company. Our findings showed that the application of the tracking signal approach helps companies to better select the optimal forecasting method and reduce forecast errors.

Combining Lean Concepts &Tools with the DMAIC Framework to Improve Processes and Reduce Waste

Six Sigma DMAIC methodology has been applied to systematically apply lean manufacturing concepts and tools in order to improve productivity in a local company specialized in the manufacturing of safety and fire resistance metal doors, windows, and frames. In-depth analysis of the plant processes unfolded different critical processes, specifically foam injection process and sheet metal cutting process. Throughout the different project phases, various improvements had been implemented to reduce production cycle time from 216 min to 161 min;non-value added activities in the different processes were identified and eliminated. Plant layout and machine reconfiguration reduced backtracking and unutilized space. Percentage of defective doors (needing rework) dropped from 100% to only 15%. The successful implementation of this project is largely due to top management active involvement and participation of workers and operators in all stages of the project. Finally, new policies and mentoring programs are introduced to maintain improvements.

Leveraging Six Sigma Tools and Methodology to Improve Student English Language Performance at Elementary School

Purpose: The purpose of this paper is to discuss how the Six Sigma (SS) methodology can be used to improve the performance of students in English as a second language and enhance the teaching process by utilizing quality tools in an educational environment. Design/Methodology/Approach: The paper uses the different quality improvement tools within the Define, Measure, Analyze, Improve and Control (DMAIC) phases to improve student performance in an English language class at a private school. Findings: Quality tools, such as cause and effect diagrams and quality function deployment, have been successfully applied within the Six Sigma DMAIC framework in the educational sector to improve the performance of students and enhance the teaching process. Practical Implications: This paper helps school administrations to analyze student performance and implement improvement actions to improve their performance. Originality/Value: The principal objective of this paper is to demonstrate how quality improvement tools can be introduced and successfully applied in an elementary school to improve student performance and communication between the school administration, teachers and parents.

Modeling Inclement Weather Impacts on Traffic Stream Behavior

The research identifies the steady-state car-following model parameters within state-of-thepractice traffic simulation software that require calibration to reflect inclement weather and roadway conditions.The research then develops procedures for calibrating non-steady state carfollowing models to capture inclement weather impacts and applies the procedures to the INTEGRATION software on a sample network.The results demonstrate that the introduction of rain precipitation results in a 5%reduction in light-duty vehicle speeds and a 3%reduction in heavy-duty vehicle speeds.An increase in the rain intensity further reduces light-duty vehicle and heavy-duty truck speeds resulting in a maximum reduction of 9.5%and 5.5%at the maximum rain intensity of 1.5 cm/h,respectively.The results also demonstrate that the impact of rain on traffic stream speed increases with the level of congestion and is more significant than speed differences attributed to various traffic operational improvements and thus should be accounted for in the analysis of alternatives.In the case of snow precipitation,the speed reductions are much more significant(in the range of 55%).Furthermore,the speed reductions are minimally impacted by the snow precipitation intensity.The study further demonstrates that precipitation intensity has no impact on the relative merit of various scenarios(i.e.the ranking of the scenario results are consistent across the various rain intensity levels).This finding is important given that it demonstrates that a recommendation on the optimal scenario is not impacted by the weather conditions that are considered in the analysis.