The development of a multiple supplier tool management system for optimum tool inventory sizing based on reliable delivery forecasting of CBN grinding wheels for nickel base alloy turbine blade fabrication is confronted in this work. The basis for the development of the system is given by the historical data on tool management consisting of the chronological series of CBN grinding wheel shipment and delivery dates between one manufacturing company (customer) and four external tool manufacturers (suppliers) in a supply network. If historical data are highly variable, stochastic management methods, such as time series analysis, are either inapplicable or responsible for excessive inventory sizing. Alternative approaches are given by special analysis and modelling methodologies, such as those based on fuzzy logic, which deal with deterministic events but are also capable to take into account unpredictable factors for better results in prediction and forecast. In this paper, supplier-dependent dressing cycle time predictions for each external tool manufacturer in the supply network are obtained through a set of multiple-input-single-output adaptive neuro-fuzzy inference systems (ANFIS). The ANFIS predictions can be utilized by the customer to evaluate the supplier reliability in the delivery of CBN grinding wheels, which represents a critical decision parameter in the dressing order allocation procedure and a key reference factor for the implementation of flexible tool management strategies.

Multiple Supplier Neuro-Fuzzy Reliable Delivery Forecasting for Tool Management in a Supply Network

TETI, ROBERTO;D'ADDONA, DORIANA MARILENA
2003

Abstract

The development of a multiple supplier tool management system for optimum tool inventory sizing based on reliable delivery forecasting of CBN grinding wheels for nickel base alloy turbine blade fabrication is confronted in this work. The basis for the development of the system is given by the historical data on tool management consisting of the chronological series of CBN grinding wheel shipment and delivery dates between one manufacturing company (customer) and four external tool manufacturers (suppliers) in a supply network. If historical data are highly variable, stochastic management methods, such as time series analysis, are either inapplicable or responsible for excessive inventory sizing. Alternative approaches are given by special analysis and modelling methodologies, such as those based on fuzzy logic, which deal with deterministic events but are also capable to take into account unpredictable factors for better results in prediction and forecast. In this paper, supplier-dependent dressing cycle time predictions for each external tool manufacturer in the supply network are obtained through a set of multiple-input-single-output adaptive neuro-fuzzy inference systems (ANFIS). The ANFIS predictions can be utilized by the customer to evaluate the supplier reliability in the delivery of CBN grinding wheels, which represents a critical decision parameter in the dressing order allocation procedure and a key reference factor for the implementation of flexible tool management strategies.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/194372
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