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Developing A Mechanism For Learning In Engineering Environments

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2002 Annual Conference


Montreal, Canada

Publication Date

June 16, 2002

Start Date

June 16, 2002

End Date

June 19, 2002



Conference Session

Global Engineering Education

Page Count


Page Numbers

7.390.1 - 7.390.9



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Paper Authors

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Reza Ziarati

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Osman Nuri Ucan

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Martin Ziarati

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Martin Ziarati, David Stockton, MIEE Osman Nuri Ucan

Dogus University/De Montfort University/Istanbul University

Abstract: The concept of learning has invariably been related to a classroom environment and/or industrial seminars, workshops, etc. The recent development in Artificial Intelligence, particularly in Neural Network applications offer interesting opportunities in developing continuous learning mechanisms for industrial applications in specific sectors. This paper gives information about neural models and an application example elucidating how a learning system can be developed for determining and forecasting parts quantities in a supply chain. If a continuous system can reliably predict numbers of parts required at the right time and at the right place, then the entire production schedule throughout the entire supply chain and within each organisation within it can be planned. All information flow routes and material flow paths can be optimised. The possibilities are very promising. The challenge, however, is as to how these learning systems can be validated and used with Computerised Enterprise Resource Planning (CERP) packages already used in industry

Keywords: Learning Systems, Artificial Neural Network (ANN), Material and Information flows in an organisation


The existing Enterprise Resource Planning (ERP) packages such as SAP and Oracle do not have a learning mechanism. The example given in this paper offers an interesting opportunity to develop a learning system for these ERP packages hence leading to more reliable predictions and forecasts.

The problem of determining the required number of parts in a supplier chain system is well known. The Economic Order Quantity (EOQ) approach invariably is employed for prediction of required quantities by many businesses particularly supplier chains1-3. To find the ‘exact’ quantities, the EOQ approach is often complemented by a series of “rule of thumb” expressions. These rules are applied on a basis of the historical learning and hence to reduce the effect of the deficiency of the EOQ method.

The problem of deciding on a required number of parts are further complicated by seasonal variations. This paper offers an alternative approach to the EOQ approach by adopting a neural network model. The neural networks are primarily suited to identifying trends and patterns, particularly when there is a large amount of data. The predictive and forecasting ability of the neural network are of particular interest in parts supply and sales.

The down-side of these networks are the initial stage of application. Adequate data needs to be initially available for training of the network. Then the training phase needs to be

Proceedings of the 2002 American Society for Engineering Educational Annual Conference & Exposition Copyright © 2002, American Society for Engineering Education Main Menu

Ziarati, R., & Ucan, O. N., & Ziarati, M. (2002, June), Developing A Mechanism For Learning In Engineering Environments Paper presented at 2002 Annual Conference, Montreal, Canada. 10.18260/1-2--10438

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