Ahmarofi, Ahmad Afif and Ramli, Razamin and Zainal Abidin, Norhaslinda (2017) Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks. International Journal of Supply Chain Management, 6 (3). pp. 82-90. ISSN 2050-7399

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Abstract

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Besides, it is found that little attention has been given in analysing completion time at production line from previous literatures. Therefore, this paper fill the knowledge gap by predicting completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late job’s due date from its completion time. A wellknown company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market place

Item Type:	Article
Uncontrolled Keywords:	Completion time, production line, data mining, ANN
Subjects:	Q Science > QA Mathematics
Divisions:	School of Quantitative Sciences
Depositing User:	Mdm. Sarkina Mat Saad @ Shaari
Date Deposited:	10 Jul 2024 07:32
Last Modified:	10 Jul 2024 07:32
URI:	https://repo.uum.edu.my/id/eprint/31028

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