Title page for ETD etd-06062008-170827

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
  • Neural networks (Computer science)
Date of Defense 1992-08-13
Availability unrestricted
The 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.

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