Type of Document Dissertation Author Wray, Barry A. URN etd-06062008-170827 Title Prediction and control in a just-in-time environment using neural networks Degree PhD Department Accounting and Information Systems Advisory Committee
Advisor Name Title Rakes, Terry R. Committee Chair Clayton, Edward R. Committee Member Rees, Loren P. Committee Member Russell, Roberta S. Committee Member Sumichrast, Robert T. Committee Member Keywords
- Neural networks (Computer science)
Date of Defense 1992-08-13 Availability unrestricted AbstractThe success of the Japanese just-in-time (JIT) with kanban inventory control technique
has caused many manufacturing firms world-wide to implement similar systems
in an attempt to remain competitive. Predicting and controlling the number of
kanbans in an unstable environment is a complex decision involving many stochastic
factors. This research investigates using neural computing (neural networks) to
identify endogenous factors (shop conditions) and exogenous factors (product demand
and supplier schedules) that are correlated with kanban system performance
and to predict the optimal number of kanbans based on the "dynamic" interaction
(changing over time) of these factors inherent in many production environments. The
purpose of the research is to test the interpolative ability of a neural network to synthesize
a multidimensional response surface from sample values and to perform
factor screening on the inputs. First, a JIT shop simulator capable of utilizing different
factor levels is used to generate data on shop performance for different kanban levels
for 560 dynamic shop scenarios. Each combination of shop factor levels, along with
the corresponding optimal number of kanbans, is saved in a data file. The data is
randomly split into 2 files of equal size. The first file is used as training data for a
neural network. The neural network "learns" the relationship between the shop factors
and the correct number of kanbans needed from the training data. After the
training phase, the neural network is tested on its "associative" ability to determine how well it predicts the correct number of kanbans for the shop scenarios in the
second file (data it has never seen). Results are given for different network
paradigms to determine the best paradigm for predicting the number of kanbans in
a dynamic JIT shop. The neural network is also used as a tool for factor screening.
Each factor is analyzed to determine its relative importance in kanban prediction.
Statistical tests are used to gauge the importance of the dynamic information as well
as to examine the relevance of various factor groupings. The results have practical
implications for firms that have adopted, or are considering, the JIT technique.
Filename Size Approximate Download Time (Hours:Minutes:Seconds)
28.8 Modem 56K Modem ISDN (64 Kb) ISDN (128 Kb) Higher-speed Access LD5655.V856_1992.W739.pdf 5.01 Mb 00:23:12 00:11:56 00:10:26 00:05:13 00:00:26next to an author's name indicates that all files or directories associated with their ETD are accessible from the Virginia Tech campus network only.
If you have questions or technical problems, please Contact DLA.