Product planning is the first phase of new product development or current product
enrichment under Quality Function Deployment (QFD) regime. With the advent of
sophisticated technology in the manufacturing sector, the demand for
a defect-free product at a cheaper price becomes a commonplace in the
market, and for the manufacturers it becomes a
competitive race. One can win the race if and only if the customers' requirements are fulfilled. If
their demands can be fulfilled in advance, you will trademark a `wow' in the market, a symbol
of contentment to the fullest extent. QFD as a methodology of quality consideration in
productproduction planning, is effectively functioning
since its inception and has been applied successfully in different
sectors. As the process of QFD starts with `Voice of the
Customers (VoC)', the customers' requirements are stated
in their own language. It addresses the customer satisfaction
regarding the technical design of any product or service. The
`House of Quality' (HOQ) shows the way in which each
of the customers' requirement is technically defined
and how experts plan to design the product/service
that fulfils the customer requirements in a prioritized way.
Since its development and use in product planning, HOQ has got significant
research interest due to its success in prioritizing customer requirements with its
corresponding technical definition. As most of the firms face trouble in managing constraints, it may
be difficult to adjust the products/service which includes all the requirements. Prioritization
of customer requirements demands immediate consideration of the most important
requirement to keep the customer happy or reduce the level of dissatisfaction. HOQ becomes very
much operative in a situation that ranks customer requirements in the order of priority
with respective technical definitions. This paper proposes an algorithm that may be used to
train an intelligent agent who can construct the HOQ in a controlled environment. Once
the customer requirement changes, HOQ will adjust all the changed requirements and
prescribe accordingly. Customer requirements change more rapidly now than before due to
the environmental changes and positive learning effect active in the market. A
manufacturer needs an intelligent agent who will feed the changed requirements into the model for it
to provide revised prescriptions. |
