Plasma Etching Endpointing by Monitoring Radio‐Frequency Power Systems with an Artificial Neural Network

1996 ◽  
Vol 143 (6) ◽  
pp. 2029-2035 ◽  
Author(s):  
Helen L. Maynard ◽  
Ed A. Rietman ◽  
John T. C. Lee ◽  
Dale E. Ibbotson
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Systems ◽  
Radio Frequency ◽  
Plasma Etching ◽  
Radio Frequency Power ◽  
Artificial Neural ◽  
Frequency Power

Managing Uncertain Inventory in Supply Chain with Neural Network and Radio Frequency Identification (RFID)

2012 ◽  
pp. 155-170
Author(s):  
CKM Lee ◽  
Ng Wenwei Benjamin ◽  
Shaligram Pokharel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Radio Frequency ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Bullwhip Effect ◽  
Distribution Centre ◽  
Frequency Identification ◽  
Artificial Neural

Demand uncertainty leads to fluctuations in inventory position at each echelon of a supply chain causing bullwhip effect, which can lead to significant cost and loss of efficiency and waste of resources. One of the aspects that can reduce potential bullwhip effect is the sharing of real time information for which the recently mass produced Radio Frequency Identification (RFID) can be of great value. The use of RFID technology can also help in increasing the visibility of the flow of goods and material, keeping track of the location and quantity at each distribution centre and warehouses. This will also help in the periodic and near real time optimization of inventory level of goods and material. The data collected with RFID can be analysed in artificial Neural Network (NN) to forecast the future demand. In this chapter, a framework is proposed by combining RFID with artificial neural network so that lean logistics can be realized in the supply chain.

Sign in / Sign up

Export Citation Format

Share Document