Variation Reduction in Product Quality and Organisational
Performance: A System Dynamics Approach
Shams-ur Rahman
The Graduate School of Management
The University of Western Australia
Nedlands, Perth, WA 6907, Australia
email: srahman @ecel.uwa.edu.au
Abstract
The paper provides a modelling framework to understand and reduce variation in product quality
through the use of system thinking and statistical methods. It brings together a number of
techniques and methods to address total value delivery system of customer needs identification,
product design and production process control, distribution and services. The model looks at the
causal relationships among the methods and their impact on organisational performance.
Performance is measured in terms of time to market, number of design changes, product quality
as reject rates/ first-pass yield, throughput time and inventory turnover. Currently the model is
being validated using data from the Australian manufacturing companies.
Introduction
The development process of a product follows the sequence of idea generation and design
formulation (product and process), product production and after-production packaging, storing
and distribution and service. All these functions can be represented using a value delivery system
ASSESSMENT PRODUCT SERVICE
(CUSTOMER NEED D DESIGN OF D PRODUCTION ») DISTRIBUTION)
Figure 1: Production development value chain
(Figure 1). These functions are the building blocks by which an organisation creates products
valuable to its customers and enhances its competitive advantage (Porter, 1985). Since: the
process of transformation from one building block to another add value to products and therefore
to the potential customers, any variation around products’ target specifications incurs loss to the
customers, the farther from target, the greater the loss (Taguchi and Clausing, 1990). An emphasis
on gradually minimising errors from the target makes the value delivery system in line with the
principle of continuous improvement. The variations between required quality and actual quality
at every stage of the value chain are referred here as ‘quality variation’ (QV) (Figure 2). The QVs
can be expresses as following:
Quality Variation 1 (QV /) = The gap between customer needs and
management understanding of these needs.
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%. Quality Variation 2 (QV 2) = The gap between management perceptions of
customer needs.and design specifications.
% Quality Variation 3 (QV 3) = The gap between design specifications and actual
products.
© Quality Variation 4 (QV 4) = The gap between actual products and customer
perception about products,
© Total Variation in Quality = f(QV1, QV2, QV3, QV4).
Customer needs/
expectations
Perceived
by customer
Quality variation 4 Quality variation 1
Transformation of
design into physical Understanding customer
products needs by organisation
Quality A Translation of Quality variation 2
needs into
design specifications
Figure 2: Another quality circle
Every function in a value delivery system is supported by two broad categories of support
systems: human resources system and technical system (Porter, 1985). Human resources system
consists of activities involved in creating vision, empowering and motivating human resources.
Technical system provides technical support in terms of know-how, procedures and methods. In
this paper an attempt has been made to identify the causal relationships among various methods
of the technical system. These relationships can be exploited to reduce variations and improve
organisational performance in each stage of the value delivery system
Performance Measures
The APICS Dictionary (1992) defines nine performance measures on which world-class
organisations can compete. In this study we considered some of these indicators to measure
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organisational performance which include time to market, number of design changes, product
quality as rejection rates/first-pass yield, throughput time and inventory turnover.
The Model
The concept of quality variations developed in Figure 2 has been expanded in Figure 3. Figure 3
narrates the activities carried out in an entire value delivery system and provides an overview of
their interactions.
Provide
satisfaction (0
Ensure that
the performance
Is maintained
i aintaining
design characteristics
during production,
Figure 3: An overview of variation reduction process
Integrating the methods and tools appropriate in each stage of the value system a conceptual
model has been developed (Figure 4). This model can be used to understand and reduce variation
Customer Product
orp _™ Seth ‘ASI
Identity Improve Maintain Provide
product design design improvement assurance
characteristics
Figure 4: Variation reduction and performance improvement model
and improve performance. In this model, variation reduction hence, improvement in quality
begins with identifying and understanding customer needs. QFD (Quality Function Deployment)
process allows the design/innovation team to translate customer needs into appropriate product
and process characteristics. TM (Taguchi Methods) enables to make the product robust to input
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|
| oe
and environmental factors, whereas SPC (Statistical Process Control) helps to maintain what has
been achieved in the design stage. The process of ASI (Acceptance Sampling Inspection)
provides an assurance about the quality of the product. The feedback loops provide a mechanism
for continuous improvement. Figure 5 provides the causal loop diagram of the model.
‘Commitment to
reduce quality variation
a ‘Customer
customer satisfaction
needs \ rr cost , Tol
Warranty cost 7
‘Understanding, a Reject rates
and prioritising SS yan
customer needs Quality variation
x it ols
No of design :
changes ‘Translating needs
~~ into design Ee
\ Robust design ————™
Time to
design
io Reponsiveness
Inventory ventory
tumover a
Time to market __"
Figure 5: Causal loop diagram of variation reduction and performance improvement model
Application of the Model
Data has been collected for five large manufacturing companies in Australia. All these companies
claim to have sound quality management procedures in place. Four companies have formally
certified to ISO 9001 quality assurance standard. All companies are involved in design &
development, installation, and servicing functions. At this stage data is being used to validate the
model and test the policy issues regarding market need assessment, investment in technical
system.
References
Porter, M.E, 1985, Competitive Advantage: Creating and sustaining superior performance, The
Free Press, New York.
American Production and Inventory Control Society, 1992, The APICS Dictionary, VA, USA.
Taguchi, G and D. Clausing, 1990, Robust Design, Harvard Business Review, Jan-Feb, pp 65-75
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