Browsing by Author "Berkol, A."

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Comparison of Intelligent Classification Techniques by Practicing a Specific Technology Audit
(2016) Berkol, A.; Kara, G.; Turk, A.
Technology audit activities arc carried out for assessment of firms' technological requirements, capacity or management capability. The aim of these assessments is to define the weaknesses of firms and develop actions in order to improve firms' technological capacity and/or technology management capability. Generally these activities are implemented with survey questionnaires. These questionnaires can be filled by managers of firms or can be implemented as an interview by independent experts. However, evaluating surveys and preparing useful comments related to results can consume lots of time and also contain lots of biases/subjectivity. In accordance to ease the decision making process and provide more verified/accurate results, we develop a methodology based on an Artificial Neural Network (ANN) algorithm which is aimed to behave like a decision maker. And in this study, we use a synthetic data set which is prepared for assessment of technology management capability of selected 70 Turkish firms.
Using Artificial Intelligence Based Expert System for Selection of Design Subcontractors: A Case Study in Aerospace Industry
(2017) Unal, V. O.; Berkol, A.; Tartan, E. O.
As one of the top expectations for type certification of an aircraft, Aviation Authorities (AA) regulate design organization to establish Design Assurance System (DAS). DAS is composed of design, independent monitoring and airworthiness functions in which these functions are specialized for aerospace industry. Besides, Design Organization Approval (DOA) is a milestone to establish a rigid Design Assurance System. By this way, design organization assures aircraft development life cycle by complying with aviation regulations. To meet requirements of Design Organization Approval, Design Organization transfers its authority and technical signatories to its subcontractors to improve effectiveness of the system. So, performance of design subcontractors shall be traceable and measurable to match capability requirements of main contractor. Thus, subcontractor evaluation is a long and complicated process; survey implementation could be misleading in some cases. The purpose of this study is to propose a novel tool to measure performance of a design subcontractor according to necessities of Design Assurance System. Up to now, there is no tool to evaluate aviation design subcontractors. With this tool, contractor firm can evaluate multiple criteria in a single run. AHP is used to prioritize criteria relative to each other one-by-one. Then, for subcontractor selection and subcontractor monitoring, Artificial Neural Network (ANN) is applied to optimize decision making process. Annual Actual Data is applied in AHP model to assess current performance score of subcontractor. To have a long term judgment of this system, the model shall be applied to a design subcontractor for more than once on fixed periods such as quarterly, yearly etc.
Using Artificial Intelligence Based Expert System for Selection of Design Subcontractors: A Case Study in Aerospace Industry
(2017) Unal, V. O.; Berkol, A.; Tartan, E. O.
As one of the top expectations for type certification of an aircraft, Aviation Authorities (AA) regulate design organization to establish Design Assurance System (DAS). DAS is composed of design, independent monitoring and airworthiness functions in which these functions are specialized for aerospace industry. Besides, Design Organization Approval (DOA) is a milestone to establish a rigid Design Assurance System. By this way, design organization assures aircraft development life cycle by complying with aviation regulations. To meet requirements of Design Organization Approval, Design Organization transfers its authority and technical signatories to its subcontractors to improve effectiveness of the system. So, performance of design subcontractors shall be traceable and measurable to match capability requirements of main contractor. Thus, subcontractor evaluation is a long and complicated process; survey implementation could be misleading in some cases. The purpose of this study is to propose a novel tool to measure performance of a design subcontractor according to necessities of Design Assurance System. Up to now, there is no tool to evaluate aviation design subcontractors. With this tool, contractor firm can evaluate multiple criteria in a single run. AHP is used to prioritize criteria relative to each other one-by-one. Then, for subcontractor selection and subcontractor monitoring, Artificial Neural Network (ANN) is applied to optimize decision making process. Annual Actual Data is applied in AHP model to assess current performance score of subcontractor. To have a long term judgment of this system, the model shall be applied to a design subcontractor for more than once on fixed periods such as quarterly, yearly etc.