Capacity Expansion Through Bottleneck Analysis for an OEM Company

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Date

2024

Authors

Arda Ali Bayraktar
Gülşah Coşkunseda
Ege Salman
Ayhan Özgür Toy
Nazlı Karatas Aygün

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Springer Science and Business Media Deutschland GmbH

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Green Open Access

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Abstract

In collaboration with Totomak A.Ş this study addresses a critical challenge in heavy machining: identifying and resolving bottlenecks in a recently established crankshaft production line. Our approach to the problem includes a series of steps and advanced computer simulations that meticulously uncover problems and formulate solutions to ensure that production seamlessly matches original plans. In addition the research examines various scenarios subjecting each scenario to a comprehensive cost analysis. These efforts contribute significantly to the overall goal of increasing operational efficiency in the heavy machining sector. Beyond the immediate benefits the study provides a practical and evidence-based method that can address the complex details of manufacturing systems. This has the potential to improve efficiency and strengthen competitiveness in this important industry providing a clear path to greater productivity and success. © 2024 Elsevier B.V. All rights reserved.

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Keywords

Bottleneck Analysis, Capacity Analysis, Capacity Expansion, Decision Support System, Simulation, Artificial Intelligence, Competition, Crankshafts, Efficiency, Bottleneck Analysis, Capacity Analysis, Capacity Expansion, Comprehensive Costs, Cost Analysis, Critical Challenges, Oem Companies, Operational Efficiencies, Production Line, Simulation, Decision Support Systems, Artificial intelligence, Competition, Crankshafts, Efficiency, Bottleneck analysis, Capacity analysis, Capacity expansion, Comprehensive costs, Cost analysis, Critical challenges, OEM companies, Operational efficiencies, Production line, Simulation, Decision support systems, Capacity Analysis, Capacity Expansion, Bottleneck Analysis, Simulation, Decision Support System

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International Symposium for Production Research ISPR 2023

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718

End Page

732
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