A SIMULATION-MODEL FOR DETERMINING MAINTENANCE STAFFING IN AN INDUSTRIAL-ENVIRONMENT

In a plant, the size of a maintenance staff must be related to the level of output.The optimal level of maintenance is essential for maximizing the output of the production process.This paper develops a simulation model to determine the size of a maintenance crew.The heart of the simulation model is the machine servicing model.The model is applied to a local soft drink plant to determine the optimal number of their maintenance crew, and the result of the study is presented

A SIMULATION-MODEL FOR DETERMINING MAINTENANCE STAFFING IN AN INDUSTRIAL-ENVIRONMENT

In a plant, the size of a maintenance staff must be related to the level of output.The optimal level of maintenance is essential for maximizing the output of the production process.This paper develops a simulation model to determine the size of a maintenance crew.The heart of the simulation model is the machine servicing model.The model is applied to a local soft drink plant to determine the optimal number of their maintenance crew, and the result of the study is presented

GTTC Future of Ground Testing Meta-Analysis of 20 Documents

National research, development, test, and evaluation ground testing capabilities in the United States are at risk. There is a lack of vision and consensus on what is and will be needed, contributing to a significant threat that ground test capabilities may not be able to meet the national security and industrial needs of the future. To support future decisions, the AIAA Ground Testing Technical Committees (GTTC) Future of Ground Test (FoGT) Working Group selected and reviewed 20 seminal documents related to the application and direction of ground testing. Each document was reviewed, with the content main points collected and organized into sections in the form of a gap analysis current state, future state, major challenges/gaps, and recommendations. This paper includes key findings and selected commentary by an editing team

Analysis of critical machine reliability in manufacturing cells

Purpose: In an increasingly competitive business environment, machine reliability problem merits special attention in operations of manufacturing cells. This is mainly due to flow line nature of the cellular layout, interdependency of downstream and upstream of machines related to each other. This study investigates the effect of critical machine reliability improvement on production capacity and throughput time in manufacturing cells. Design/methodology/approach: A discrete-event simulation model was developed to investigate the effectiveness of a reliability plan focusing on the most critical production machines in improving the performance level as an alternative to increasing the reliability of all machines. Four machine criticality policies are examined in the simulation experiments. Findings: The results of this experimental study indicated that an improvement of reliability of a limited number of machines leads to an increase in overall production capacity and speed in cellular manufacturing operations. A reliability plan, that focuses on a set of critical machines, potentially offers a more economical alternative to increasing the reliability of all machines in such facility. Research limitations/implications: The results demonstrate that to achieve higher production capacity and shorter throughput times, managers should consider directing more resources to increase the reliability of critical machines, particularly, those with shorter mean time to failure and higher utilization. Originality/value: The designed simulation model is unique in representing the dynamics of a real world manufacturing cell environment by encoding operational functions such as machine failure, maintenance resource allocation, material flow, job sequencing and scheduling. A new machine availability metric is defined as well.Peer Reviewe

The role of the reactor size for an investment in the nuclear sector: an evaluation of not-financial parameters

The literature presents many studies about the economics of new Nuclear Power Plants (NPPs). Such studies are based on Discounted Cash Flow (DCF) methods encompassing the accounts related to Construction, Operation & Maintenance, Fuel and Decommissioning. However the investment evaluation of a nuclear reactor should also include not-financial factors such as siting and grid constraints, impact on the national industrial system, etc. The Integrated model for the Competitiveness Assessment of SMRs (INCAS), developed by Politecnico di Milano cooperating with the IAEA, is designed to analyze the choice of the better Nuclear Power Plant size as a multidimensional problem. In particular the INCAS’s module “External Factors” evaluates the impact of the factors that are not considered in the traditional DCF methods. This paper presents a list of these factors, providing, for each one, the rationale and the quantification procedure; then each factor is quantified for the Italian case. The IRIS reactor has been chosen as SMR representative. The approach and the framework of the model can be applied to worldwide countries while the specific results apply to most of the European countries. The results show that SMRs have better performances than LRs with respect to the external factors, in general and in the Italian scenario in particular

Forecasting workload for Defense Logistics Agency distribution

MBA Professional ReportThe Defense Logistics Agency (DLA) predicts issue and receipt workload for its distribution agency in order to maintain adequate staffing levels and set proper rates for customers. Inaccurate forecasts lead to inaccurate staffing, subsequently leading to inaccurate pricing. DLA’s current regression forecasting model is no longer adequate for predicting future workload for DLA Distribution. We explore multiple forecasting techniques and provide a methodology for selecting a model that is a viable and accurate alternative for DLA. Our methodology encompasses best-fit determination, a comparison of predictability through back-casting, and a sensitivity exercise to see reaction and stability of our selected models’ predictions. Finally, we compare our best performing model with the current regression model to see what would have been reported if our model had been used instead of the current model for recent Program Budget Review (PBR) cycles. Our results suggest that an auto-regressive integrated moving average (ARIMA) model used with critical assessment and managerial judgment offers a viable alternative to the current model for predicting distribution workload.http://archive.org/details/forecastingworkl1094544537Captain, United States ArmyMajor, United States ArmyApproved for public release; distribution is unlimited