Mask cycle time and serviceability improvement through capacity planning and scheduling software

2002 ◽  
Author(s):  
M. Caron ◽  
D. Fronckowiak ◽  
P. Hayes ◽  
P. Miller
Keyword(s):  
Cycle Time ◽  
Capacity Planning ◽  
Planning And Scheduling

scholarly journals Development of smart insole for cycle time measurement in sewing process

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eung Tae Kim ◽  
Sungmin Kim
Keyword(s):  
Cycle Time ◽  
Plantar Pressure ◽  
Capacity Planning ◽  
Pressure Sensors ◽  
Good Alternative ◽  
Labor Cost ◽  
Cost Calculation ◽  
Plantar Pressure Distribution ◽  
Operation Cycle ◽  
Smart Insole

AbstractA smart insole system consisting of pressure sensors, wireless communication modules, and pressure monitoring software has been developed to measure plantar pressure distribution that appears in sewing process. This system calculates the cycle time of each operation by analyzing the real-time plantar pressure data. The operation cycle time was divided into the time done by machine and by manual and calculated by adding the two types of time. By analyzing the cycle time, it is possible to estimate the type of operation a worker is performing. The ability to calculate accurate cycle time and to manage a large volume of data is the advantage of this system. Establishing an accurate cycle time of all operations would be of great help in improving the production process, capacity planning, line efficiency, and labor cost calculation. The system is expected to be a good alternative to the conventional manual measurement process. It will also be able to meet the high demand from garment manufacturers for automated monitoring systems.

Capacity planning and scheduling in Grid computing environments

2008 ◽  
Vol 24 (5) ◽  
pp. 404-414 ◽  
Author(s):  
Ali Afzal ◽  
A. Stephen McGough ◽  
John Darlington
Keyword(s):  
Grid Computing ◽  
Capacity Planning ◽  
Planning And Scheduling ◽  
Computing Environments

Predicting Patient Length of Stay Using Artificial Intelligence to Assist Health Care Professionals in Resource Planning and Scheduling Decisions

2022 ◽  
Vol 30 (8) ◽  
pp. 0-0
Keyword(s):  
Artificial Intelligence ◽  
Length Of Stay ◽  
Health Care Professionals ◽  
Capacity Planning ◽  
Resource Planning ◽  
Absolute Error ◽  
Decision Makers ◽  
Planning And Scheduling ◽  
Healthcare Facilities ◽  
Resource Capacity Planning

Artificial Intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today’s healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research’s findings have practical and theoretical implications in AI and HSC management.

Capacity Planning and Scheduling in Project Oriented Production

2004 ◽  
Vol 37 (4) ◽  
pp. 317-322
Author(s):  
Bozena Skolud ◽  
Marc Zolghadri
Keyword(s):  
Capacity Planning ◽  
Planning And Scheduling

scholarly journals Simulation scheduling in food industry application

2011 ◽  
Vol 20 (No. 1) ◽  
pp. 31-37 ◽  
Author(s):  
S. Simeonov ◽  
J. Simeonovová
Keyword(s):  
Production System ◽  
Capacity Planning ◽  
Food Industry ◽  
Production Capacity ◽  
Coffee Production ◽  
Planning And Scheduling ◽  
Delivery Date ◽  
Basic Features ◽  
Planning Problems ◽  
Simulation Systems

Nowadays manufacturers are facing rapid and fundamental changes in the ways business is done. Producers are looking for simulation systems increasing throughput and profit, reducing cycle time, improving due-date performance, reducing WIP, providing plant-wide synchronization, etc. Planning and scheduling of coffee production is important for the manufacturer to synchronize production capacity and material inputs to meet the delivery date promised to the customer. A simulation model of coffee production was compiled. It includes roasting, grinding and packaging processes. Using this model the basic features of the coffee production system are obtained. An optimization module of the simulation SW is used for improving the current structure of the production system. Gantt charts and reports are applied for scheduling. Capacity planning problems related to coffee production are discussed.  

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