Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

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. Therefore, this paper predicts completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late jobs due date from its completion time. A well-known 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

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

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 plac

An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production

The determination of completion time to produce a new product is one of the most important indicators for manufacturers in delivering goods to customers. Failure to fulfil delivery on-time or known as tardiness contributes to a high cost of air shipment and production line down at other entities within the supply chain. The uncertainty of completion time has created a big problem for manufacturers of audio speakers which involved semiautomatic production lines. Therefore, the main objective of this research is to develop an integrated model that enhances the artificial neural networks (ANN) and system dynamics (SD) methods in estimating completion time focusing on the cycle time. Three ANN models based on multilayer perceptron (MLP) were developed with different network architectures to estimate cycle time. Furthermore, a proposed momentum rate equation was formulated for each model to improve learning process, where the 3-2-1 network emerged as the best network with the smallest mean square error. Subsequently, the estimated cycle time of the 3-2-1 network was simulated through the development of an SD model to evaluate the performance of completion time in terms of product quantity, manpower fatigue and production workload scores. The success of the proposed integrated ANNSD model also relied on a proposed coefficient correlation of causal loop diagram (CLD) to identify the most influential factor of completion time. As a result, the proposed integrated ANNSD model provided a beneficial guide to the company in determining the most influential factor on completion time so that the time to complete a new audio product can be estimated accurately. Consequently, product delivery was smooth for on-time shipment while successfully fulfilling customers’ demand

Similarity Assessment and Retrieval of CAD Models

Ph.DDOCTOR OF PHILOSOPH

Application of Soft Computing Techniques for Cell Formation Considering Operational Time and Sequence

In response to demand in market place, discrete manufacturing firms need to adopt batch type manufacturing for incorporating continuous and rapid changes in manufacturing to gain edge over competitors. In addition, there is an increasing trend toward achieving higher level of integration between design and manufacturing functions in industries to make batch manufacturing more efficient and productive. In batch shop production environment, the cost of manufacturing is inversely proportional to batch size and the batch size determines the productivity. In real time environment, the batch size of the components is often small leading to frequent changeovers, larger machine idleness and so lesser productivity. To alleviate these problems, “Cellular Manufacturing Systems” (CMS) can be implemented to accommodate small batches without loosing much of production run time. Cellular manufacturing is an application of group technology (GT) in which similar parts are identified and grouped togeth..