A Scorecard‐Markov model for new product screening decisions
| Published date | 24 August 2010 |
| Pages | 971-992 |
| DOI | https://doi.org/10.1108/02635571011069068 |
| Date | 24 August 2010 |
| Author | S.L. Chan,W.H. Ip |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
A Scorecard-Markov model for
new product screening decisions
S.L. Chan and W.H. Ip
Department of Industrial and Systems Engineering,
The Hong Kong Polytechnic University, Hung Hom, Hong Kong
Abstract
Purpose – The paper aims to propose a novel strategic approach, named a Scorecard-Markov model,
combining an evaluation scorecard and a hidden Markov model (HMM) for new product idea screening
(NPIS) decisions.
Design/methodology/approach – A scorecard is constructed to evaluate new product ideas on
several criteria, including c ustomer needs, marketing str ength, competency, manufactu ring
compatibility, and distribution channels, involving a consideration of risk buy. A HMM is then
developed accordingly to predict the overall performance of new ideas in terms of success probability.
To implement the model, it is trained and tested by the historical dataset of a world-class, leading
company in the power tools industry through a case study.
Findings – The approach is proven to be encouraging and meaningful. The scorecard can serve as a
guide for new product idea evaluation to convert experts’ linguistic judgments to quantifiable and
comparable data, whereas the HMM can determine the success probability of new product ideas to
support NPIS decision making based on their computed evaluation performance. The optimal cut-off
value for making either a go or kill decision on each idea can thus be determined. Concerning the case
company, a go decision should be made when the probability lies in the interval [0.53, 1].
Practical implications – Themodel can prevent companies from undertaking risky and failed new
product development projects. Further, it is believed that this study can assist decision makers in
choosing winning new product ideas towards commercialization in an effective and certain manner,
thus enhancing the new product success rate in the innovation industry.
Originality/value – The approach incorporating the scorecard method and HMM is novel.
Illustrated by the case study, the application of this approach to NPIS decisions is confirmed to be
effective.
Keywords New products, Markovprocesses, Product management; Decisionmaking
Paper type Research paper
1. Introduction
Recently, ever-changing customer demands, technological innovations, and intense
global competition challenge companies to succeed in satisfying dynamic market
requirements through new product launch. Companies have realized that successful
new product launch to deliver true customer value is a competitive weapon to defeat
rivals and to dominate the market. A successful new product facilitates firms to achi eve
commercial success, such as through yielding long-term return on investment and
satisfying the needs of both potential and current customers. To survive an d succeed in
this business era, companies have to dedicate much effort to new product development
(NPD) through, for example, identifying customer needs (CN) for continuous NPD
(Melissa, 2005; Liu et al., 2008) improving product quality (Kwong and Bai, 2005;
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
The authors would like to express their sincere thanks to the Hong Kong Polytechnic University,
for its financial support of this research work project number RPV2.
New product
screening
decisions
971
Received 10 December 2009
Revised 25 January 2010,
9 March 2010
Accepted 19 April 2010
Industrial Management & Data
Systems
Vol. 110 No. 7, 2010
pp. 971-992
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635571011069068
Swink et al., 2006), and accelerating the NPD process towards commercialization
(Melissa, 2005; Swink et al., 2006; Xu et al., 2007).
Despite growing research efforts and investment made to the issue, the success rate
of NPD is not improved. The practice of NPD still involves a high risk of failure and is
costly. Stevens and Burley (1997) cited that only one new product can be launched
successfully to achieve commercial success from 3,000 raw new product ideas.
Berggren and Nacher (2001) estimated that the new product failure rate is around
95 percent. Bianchi (2004) further indicated that over 50 percent of new products
launched have failed within two years and caused a total loss of $100 billion per year.
This is mainly attributed to ineffective decision making for new product idea screening
(NPIS). Costs often rise dramatically as NPD projects move towards commercialization.
Once mangers decide to further develop new product ideas, they are unlikely to
terminate ongoing projects (Balachandra, 1984; Cooper, 1994; Schmidt and Calantone,
1998). Instead, they prefer to take risks and invest more in trying to complete the NPD
projects. This then results in new product failure in terms of economic loss. However,
research on the NPIS decision support model to truly improve new product success rate
remains to be limited.
NPIS at the front end of the NPD process plays a crucial role in NPD practice.
The initial NPIS has the highest correlation with new product performance as compared
to other NPD activities (Cooper and Kleinschmidi, 1986). Performing an initial NPIS to
eliminate potentially inferior and failing NPD projects before development, production,
and commercialization can rescue companies from investing unrecoverable money,
time, and resources. Therefore, there is a great necessity to abolish inferior NPD projects
as early as possible at the front end of the NPD process before considerable investment
is to be made. This not only reduces the failure rate of NPD projects, subsequently
preventing firms from suffering further economic losses, but also helps focus efforts and
resources on developing other NPD projects being worthy of additional attention to
achieve success. Making a right NPIS decision to select potentially profitable NPD
projects towards commercialization is the first step to succeed in NPD.
Research efforts on identifying various criteria to evaluate NPD projects, employing
the fuzzy set theory to deal with linguistics evaluation criteria for the screening
evaluation, and using scoring and weighting methods to prioritize new product ideas
are obvious. Although these approaches to NPIS are found to be helpful, they can only
assist NPD teams in:
.evaluating ideas based on multiple criteria;
.gathering a suggested value, such as the success level, overall performance score
and level of preference, as a guide for the screening decision; and
.ranking ideas, followed by distinguishing the best from the worst.
These approaches themselves are incapable of certainly making either a go or kill
decision for all ideas. They are most likely for new product evaluation rather than new
product screening decision making. NPIS is often inadequately performed using
existing approaches. Without determining the optimal cut-off value of the success
probability in distinguishing successful ideas from failure, NPD teams still face the
dilemma of making exact NPIS decisions on all new product ideas. Given this, there is
plenty of room for investigation that creates a great opportunity of conducting this
study. Further, due to the reliable mathematics framework of hidden Markov model
IMDS
110,7
972
To continue reading
Request your trial