International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
A Customer Driven Decision Support System
based on Quality Function Deployment
Venu P1 and Dr. Joeju M Issac2
1
Research Scholar, Karpagam University, Coimbatore, India
2
Professor, Dept. of Mechanical Engineering, Kothamangalm, Kerala, India
ABSTRACT
The product development phase is a big challenge to all levels of decision makers of a company. The fluctuating customer
requirements, the increasing number of competitors, the reach of customers to international market via latest global
communication technologies etc. have increased the complexity of a decision making system. Quality Function Deployment is a
systematic technique employed by many of the companies and managers across the world to identify the customer voice so that it
can be effectively converted to product features. This paper intends to generate a Quality Function Deployment based framework
for upgrading traditional decision support system to a more customer oriented environment which will ultimately result in a
design giving priority to customer voices and values. The framework uses fuzzy logic, intelligence algorithms and Java
Programming language to bridge the gap between customer to manager, manager to customer and manager to product
development.
Keywords: Customer driven, decisive QFD, Intelligent QFD, Global Object
1. INTRODUCTION
Decision making is a situation where managers do something when they do not know what to be done [1]. This
“something” in a product development refers to a systematic action taken to disassemble the complexity of a product
under design or under conceptualization. Decision Support Systems has been approached in different way by different
managers but the similarity is that this system support the decisions made and to provide insight about decision situations.
The product development phase is a challenge to any company as it requires input from existing customers, potential
customers, technicians, engineers and managers. The decision support systems should be designed in such a way that all
level of decision makers should be aware of the customer voices which have to be converted to technical features. The
traditional systems capture the customer responses in the lower level but fail to propagate it into the higher levels due to
various reasons.
Quality Function Deployment (QFD) is a systematic process having the capability of capturing customer voices that can
be converted to product features. This paper illustrates the incorporation of various phases of QFD into the decision
support system to propagate the customer requirement in its proper form to all levels of product development. The
decisions that come up at all the phases will be validated and verified against the customer requirements before being
implemented into the design.
2. QUALITY FUNCTION DEPLOYMENT
Quality function deployment (QFD) is a planning tool used to fulfill customer expectations. It is a disciplined approach to
product design, engineering, and production and provides in-depth evaluation of a product.
Quality function deployment focuses on customer expectations or requirements, often referred to as the voice of the
customer. It is employed to translate customer expectations, in terms of specific requirements, into directions and actions,
in terms of engineering characteristics, that can be deployed through:
Product planning
Part development
Process planning
Production planning
Service
The QFD matrix called House of Quality (HOQ) is considered as the basic matrix used for deployment of customer voices
to product features. The HOQ contains a large amount of data which is recorded from different levels of input. These data
are again processed to obtain results which when interpreted or refined until and actionable level is reached. The
actionable level of data should be error free so that it can be directly adopted for design development. These data also
should be clear enough so that any decision support system can validate it. Often, more than one matrix will be needed
depending on the complexity of the project. The process is accomplished by creating a new chart in which the HOWs
(technical descriptors) of the previous chart become the WHATs (customer requirements) of the new chart, as shown in
Figure 1. This process continues until each objective is refined to an actionable level. The HOW MUCH (prioritized
technical descriptors) values are usually carried along to the next chart to facilitate communication. This action ensures
Volume 3, Issue 1, January 2014
Page 465
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
that the target values are not lost during the QFD process. If the target values are changed, then the product is not
meeting the customer requirements and not listening to the voice of the customer which defeats the purpose of QFD.
An example of the complete QFD process from the beginning to the end is shown in the flow diagram in Figure 1. The
first chart in the flow diagram is for the product-planning phase. For each of the customer requirements, a set of design
requirements is determined, which, if satisfied, will result in achieving customer requirements. The next chart in the flow
diagram is for part development. Design requirements from the first chart are carried to the next chart to establish partquality characteristics. The term part-quality characteristics are applied to any elements that can aid in measuring the
evolution of quality. This chart breaks down the design requirements into specific part details. Once the part-quality
characteristics have been defined, key process operations can be defined in the process-planning phase. The next step is
process planning where key process operations are determined from part-quality characteristics. Finally, production
requirements are determined from the key process operation.
Figure 1 Different phases of QFD
3. DECISION SUPPORT SYSTEM
Decision Support Systems (DSS) are a specific class of computerized information system that supports business and
organizational decision-making activities. A properly designed Decision Support System is an interactive software-based
system intended to help decision makers compile useful information from raw data, documents, personal knowledge,
and/or business models to identify and solve problems and make decisions. DSS can be integrated any kind of business,
industry and system with separate modules for handling the customized operations. In production industry DSS comprises
the capability of supporting both the process operator and manager in the decision-making process, allowing them to take
advantage of the degrees of freedom of the physical system to make it work in a way where the optimized process control
variables are under statistical control, resulting in optimized output factors that, in turn, secure higher productivity and
improved quality [].
The decision support system as viewed by many as a tool to support the top level management in making strategic
decisions. To a large extend this is true, but recent works related to this field shows that the DSS system when coupled
with other key systems can be much more efficient and effective to make decisions at all levels of production.
4. DSS WITH MULTI-TIER QFD SUPPORT
The proposed Decision Support System with multi-tier architecture is shown in figure 2.
Volume 3, Issue 1, January 2014
Page 466
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Figure 2 Multi-Tier DSS Framework
The main purpose of DSS system is to support in all the decisions that happens in all levels of product development and
manufacturing. All the phases of QFD process when coupled with the stages of product life cycle can help the decision
makers of each level to have a concrete idea of the problems under their span and accordingly make decisions. The key
element in this framework is the communication between four different tiers of systems which are:
1. Data base – QFD phases
2. QFD phases – Product phases
3. Product phases – User
4. User – QFD phases
The connectivity in each of these tiers determines the efficiency of the entire framework. The framework uses Artificial
Intelligence, Fuzzy logic and equations, data mining techniques etc. to spread the data across the framework effectively.
The QFD matrices employed at each stage of deployment is a critical positive factor, as these matrices organizes the data
so perfectively that it is very east to insert, modify, retrieve or delete the data.
References
[1] Howard R. A. (1980),” An assessment of decision analysis; Operations Research”, 28(1); 4-27.-635, 1992.
[2] Karel Terblanche Swanepoel, (2004) "Decision support system: real-time control of manufacturing processes",
Journal of Manufacturing Technology Management, Vol. 15 Iss: 1, pp.68 - 75
[3] Power, D. J. (2002). Decision support systems: Concepts and resources for managers. Westport, Conn., Quorum
Books
[4] A.Kengpol (2006) “Using information quality techniques to improve production planning and Control”, International
Journal of Management, 23(1), pp. 53-60
[5] K E. Turban, and J. E. Aronson (2001) Decision support systems and intelligent system,. NJ, USA: Prentice-Hall,
Inc.
[6] J. Hussein, S.K., Mohammed Abdulraoof., 2011, "Enhancing Product Planning via Utilizing Quality Function
Deployment with Fuzzy Logic," International Journal of Digital Content Technology and its Applications. Volume 5,
Number 3, March 2011
[7] H Sullivan, L.P., 1986, “Quality Function Deployment,” Quality Progress, 19(6):39–50.
Venu P, pursuing Ph.D from Karpagam University, Coimbatore, India, has received Master’s Degree in
Production and Industrial Engineering from Mahatma Gandhi University, Kerala, India. He is having an
experience of more than twelve years in the field of engineering education. He is currently working as
Lecturer in Nizwa College of Technlogy, Nizwa, Oman.
Volume 3, Issue 1, January 2014
Page 467