Developing a grey decision support system for new product development risk assessment
Mansour Mehralizade 1,* - Seyed Meysam Mousavi 2
1 M.Sc. Student, Department of Industrial Engineering, Shahed University, Tehran, Iran Email: [email protected]
2 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Email: [email protected]
ABSTRACT
Satisfying customer needs and requirements is an important issue in today’s business environment. One way of reaching this goal is developing new products. New product development (NPD) is an important task for organizations and manufacturers. Due to the importance of this task and the high level of risk associated with this issue, in this paper a new decision support system for new product development is presented. This system applies tools such as grey theory, House of Quality (HOQ), Functional Analysis System Technique Diagram (FAST), Cost and Risk Analysis Method (CRAM) and Integrated Evaluation Matrix (IEM). The presented approach of new product development evaluation and assessment proves to be applicable in real world problems since it is using grey sets to address uncertain business environment.
Keywords: New product development (NPD), Grey Theory, Decision support system (DSS), House of Quality (HOQ), Functional Analysis System Technique Diagram (FAST), Cost and Risk Analysis Method (CRAM)
1. INTRODUCTION
Increasing competitiveness in addition to fast changing situations in existing markets makes companies seek methods to make higher quality products with the lowest cost and in the shortest possible time. The ability to make changes to existing designs quickly, in other words agility in product development and production, has become a crucial strength in order to be at the leading edge or to just survive in today’s market. One of the most prominent ways for companies to remain competitive is to avoid time and cost overruns when making changes to existing designs while maintaining quality (Zhao et al., 2014).
In order to deal with the risk caused by uncertainty in projects, organizations have created and adopted
‘incremental innovation’, which step by step makes novel functions, performance characteristics and technologies that rely on existing product information. As a result, adaptive design, where existing products are modified to produce new solutions that satisfy a change in needs, new requirements or the desire for improvement, constitutes 75– 85% of new product development projects (Duhovnik & Tavcar, 2002). In order to efficiently and effectively deal with the introduction of a new product solution, it is paramount that the impact of engineering change, i.e., effort, span time, technical difficulty, quality, fulfilment of customer requirements, and cost, be identified and assessed as early as possible during the product life cycle.
In the context of a project, and especially in a competitive market, the manager has to continually change his response to risks in order to increase the success rate. He has to take into consideration the set of potential risks before the launching of the project, as well as when running the project. The manager has to evaluate different developments of the project when choosing between exclusive technological novelties for a product.
The risk treatment strategy must take into account the repercussion of the novelty on the set of potential risks, to keep the project on budget and on time. Therefore, risks have to be correctly evaluated and the strategies correctly chosen to obtain a realistic estimate (cost/duration) of the project (Marmier et al., 2013).
Recently, manufacturing industries in a global competition continue to experience considerable changes.
Many traditional manufacturing firms are increasingly challenged by companies of developing countries with low-cost labor bases and the survival of many existing operations is continually in doubt. Basically,
manufacturing sector directly underpins the export of a country, strengthens the service-based economy, and complements the scientific and engineering research base. Thus, the advice to manufacturers is that, to sustain such competitiveness, they should ‘move up the value chain’ and focus on delivering knowledge- intensive products and services (Besch, 2005).
An NPD process consists of several steps which are designed to move new products through the product development pipeline from idea to commercialization. Urban and Hauser’s generic NPD process includes five phases: opportunity identification, design, testing, introduction, and life cycle management (Figure 1) (Urban & Hauser, 1993).
In this paper a new decision support system for new product development is presented. This system applies tools such as grey theory, House of Quality (HOQ), Functional Analysis System Technique Diagram (FAST), Cost and Risk Analysis Method (CRAM) and Integrated Evaluation Matrix (IEM). The presented approach of new product development evaluation and assessment proves to be applicable in real world problems since it is using grey sets to address uncertain business environment.
2. Methodology
The presented decision support system (DSS) is depicted in figure 2. The following steps form the presented NPD DSS:
First grey data on costs and risks of projects are gathered. Then, house of quality (HOQ) with grey input is used to link customer needs and product features. In the next step, Functional Analysis System Technique Diagram (FAST) with grey input is used to convert product features to product design activities. After that Cost and Risk Analysis Method (CRAM) with grey input is used to quantify risk measures and assess various risk associated factors that affect product development. Finally, Integrated Evaluation Matrix (IEM) is used to evaluate and compare different NPD projects. This DSS suggests the best alternative to managers under grey uncertainty. Table 1 presents the steps of this method.
Table 1. Steps of the presented approach
Fifth step Fourth step
Third step Second step
First step
Grey
IEM
Grey
CRAM
Using grey FAST Using Grey
HOQ Data gathering
Figure 2. The presented DSS
Step 1: Data gathering
Grey data associated with risk, costs and return of NPD projects are gathered by using the following paths:
Using experts judgments
Using historical data and past experience
Using similar data of similar past project
Step 2. Using Grey House of Quality (HOQ)
In this step. HOQ is used to link customer needs and product features. The following is carried out:
Gathering customer needs and addressing that as an evaluation criterion
Obtaining grey weights of criteria from customers and normalizing them
Defining features of design to cover customer needs
Compute grey score of each product feature versus customer needs
Creating HOQ using the grey values denoting customer needs and products features.
This step is depicted in figure 3.
Figure 3. Grey HOQ
Step 3. Using Grey FAST
In this step in order to convert design features to designing activities FAST is used. In this method activities are broken into smaller tasks by asking HOW. Therefore, by asking and answering questions this method enables converting design features to designing activities. Figure 4 presents FAST diagram.
Step 4. CRAM
In this step to assess impacts of risks and cost CRAM by using grey input is used. To better present this step, CRAM is broken in four parts and each part is presented in a separate table. Tables 2 and 3 and 4 and 5 present the steps of this method.
Step 5. IEM
Alternative NPD projects are evaluated using the IEM. This step results in showing the best alternative.
The DSS in this step provides a suggested alternative to the main decision makers. Table 6 presents the IEM.
Figure 4. FAST diagram
Table 2. CRAM part 1
5 4
3 2
1
Activity Analysis Design features
Activity breakdown Activity weight
Design Activity Relative weight
Design feature 1
Upper bound Lower
bound Upper
bound Lower bound
Upper bound Lower
bound
Design Activity --- ----
Design Activity Design
feature n+1
Design Activity
Total n+2
Table 3. CRAM part 2
9 8
7 6
Cost Analysis
Product enhancement index Goal cost
Real product cost Design Cost
1
Upper bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound 2
3 --- n+1 n+2
Table 4. CRAM part 3
12 11
10
Risk Analysis Risk criteria
1
Risk response time risk Time risk
Risk of responding to
risk Technology
risks Risk or risk
response costs Excessive cost
risk
Upper bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound
2
3 --- n+1
n+2
Table 5. CRAM part 4
17 16
15 14
13
Quantified risk analysis
Risk level Risk level
Monetarized risk
1 Cost overrun risk Technology risk Lateness risk
Upper bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound
2
3 --- n+1
Total
n+2 Upper
bound Lower
bound
Table 6. IEM
Customer evalatuion (1-9) Customer needs score
Customer needs
Alternative 3 Alternative 2
Alternative 1
Upper bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Upper
bound Lower
bound Customer needs 1
1
Customer needs 2 2
--- ---
n n
Customer evaluation mean n+1
Annual sale mean n+2
Goal unit price n+3
Total product cost n+4
Sale price n+5
Annual benefit n+6
Cost to benefit n+7
risk n+8
Total reliability n+9
Integrated evaluation index n+10
3. Application example
In order to show the application of the presented NPD evaluation DSS, in this section an application example is presented. Table 7 presents the HOQ.
Table 8. HOQ
The rest of the steps are carried out using the process described in section 2. Finally the alternatives are ranked based on the process. Table 9 depicts the IEM of application example.
Table 9. IEM
4. Conclusion
New product development is an important task for firms. This important task is a risky one which could lead to either success or failure. One way to improve the reliability of such method is using methods to assess risk of NPD projects. In this paper to facilitate this process a new decision support system based on grey uncertainty was introduced. Tools used in this process are House of Quality (HOQ), Functional Analysis System Technique Diagram (FAST), Cost and Risk Analysis Method (CRAM) and Integrated Evaluation Matrix (IEM). This process makes NPD evaluation easier for managers and main decision makers. For future research, extending this method to address fuzzy uncertainty and addressing dynamic changes in NPD process could provide interesting research directions.
REFERENCES
[1] Ayağ, Z. (2016). An integrated approach to concept evaluation in a new product development. Journal of Intelligent Manufacturing, 27(5), 991-1005.
[2] Cooper, R. G., & Kleinschmidt, E. J. (1987). New products: what separates winners from losers? Journal of Product Innovation Management, 4(3), 169-184.
[3] De Toni, A., & Nassimbeni, G. (2003). Small and medium district enterprises and the new product development challenge: evidence from Italian eyewear district. International Journal of Operations &
Production Management, 23(6), 678-697.
[4] Dewi, D. S., Syairudin, B., & Nikmah, E. N. (2015). Risk Management in New Product Development Process for Fashion Industry: Case Study in Hijab Industry. Procedia Manufacturing, 4, 383-391.
[5] Ievtushenko, O., & Hodge, G. L. (2012). Review of cost estimation techniques and their strategic importance in the new product development process of textile products. Research Journal of Textile and Apparel, 16(1), 103-124.
[6] Kwak, Y. H., & LaPlace, K. S. (2005). Examining risk tolerance in project-driven organization. Technovation, 25(6), 691-695.
[7] Salari, M., & Bhuiyan, N. (2016). A new model of sustainable product development process for making trade-offs. The International Journal of Advanced Manufacturing Technology, 1-11.
[8] Thamhain, H. (2013). Managing risks in complex projects. Project Management Journal, 44(2), 20-35.
[9] Tyagi, R. K. (2006). New product introductions and failures under uncertainty. International Journal of Research in Marketing, 23(2), 199-213.
[10] Urban, G. L., Hauser, J. R., & Urban, G. L. (1993). Design and marketing of new products (Vol. 2, pp.
0-2). Englewood Cliffs, NJ: Prentice hall.
[11] Duhovnik, J., & Tavcar, J. (2002). Reengineering with rapid prototyping. TMCE 2002: Proceedings of the fourth international symposium on tools and methods of competitive engineering, Wuhan, PR China.
[12] Marmier, F., Gourc, D., & Laarz, F. (2013). A risk oriented model to assess strategic decisions in new product development projects. Decision Support Systems, 56, 74-82.
[13] Besch, K. (2005). Product service-systems for office furniture: Barriers and opportunities on the European market. Journal of Cleaner Production, 13(10), 1083–1094.
[14] Urban, G.L. & Hauser, J.R. 1993, Design and marketing of new products, 2nd ed., New Jersey: Prentice Hall
[15] Zhao, S., Oduncuoglu, A., Hisarciklilar, O., & Thomson, V. (2014). Quantification of cost and risk during product development. Computers & Industrial Engineering, 76, 183-192.
