gender differential paper IJCRB

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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS
JUNE 2011
VOL 3, NO 2
The model manager uses the results generated by the digital simulation manager and statistical
analysis manager to run the dynamic programming model. The primary functions of the model
manager are to:
• Prompt the statistical analysis manager to provide significant performance measure results;
• Prompt the digital simulation manager to restructure the simulation model to fit the
implemented pull system stages;
• Choose the buffer size for evolving pull system stages by evaluation of the significant
metrics results of the simulation and regression:
• Prompt the digital simulation manager to run the simulation model using chosen buffer sizes
and report results to the statistical analysis manager;
• Run the dynamic programming model to determine production costs of alternatives;
• Respond to interactive queries and generate reports.
The decision logic subsystem may be designed to be a combination of simulation model and
expert system. With the decision logic subsystem, the problem processing subsystem analyzes
the data from the DSS data base and from interaction with the decision maker to generate
candidate solutions, optimize solutions and validate solutions. The solution validation model
component interacts with the decision maker to present reports, what-if alternatives and answer
simple queries interactively.
Using this framework as a guideline for the structure, a computer scientist can work with the
production manager to develop a fully functional DSS. After the DSS is created, thorough
validation and testing of the DSS should take place before implementation. Validation and
testing of the DSS framework can be accomplished in a similar manner to that of simulation
model validation.
9. Conclusions
This research provides three types of decision support for the transition from a traditional push
production system to a pull system design: (1) a method to determine the significant metrics of
an evolving assembly system, (2) a method to estimate the transition functions of a system
design evolving from a traditional push system to a pull system, and (3) a decision support
system framework which gives guidelines for development of software employing these
methods. The push simulation results were typical of those expected from a traditional push
system. The push system showed relatively poor quality and high flexibility. Moderate to high
congestion with variability was also noted. Process utilization was low, matching the high
system flexibility. Queue times were low for the three critical processes. The regression analysis
indicated that the significant performance metrics for this system were: wave solder quality,
placement material flow, 1R material flow, placement quality, IR flexibility, IR quality and wave
solder flexibility. Buffer size did not seem to significantly influence quality as long as the
product was allowed to flow freely from station to station. When the cell down rule was added to
the installed protocol of pull scheduling, quality became sensitive to buffer size. A significant
increase in simulation run time was noted with the implementation of the cell down rule. For this
particular system, it seemed that the improvements made in manufacturing system and process
redesign were not sufficient to allow the enforcement of the cell down rule without jeopardizing
significant metrics.
When the last major pull system implementation stage was completed and further buffer size
changes in that stage did not result in performance measure improvement, the transition endpoint
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