6. Conclusion and Discussion
6.2 Implication and Limitation
Forecasting models are useful for product lifecycle management (PLC), if the lifecycle stage of the product or technology can be defined, a development project can be planed in time. By using the decision diagram proposed in this research, researchers can select a proper forecasting model based on the pattern of the database to predict the lifecycle stages of a product and technology. Then the strategies of different stages can be designed in advance to face the challenge and minimize the failure of the products.
Therefore, the PLC concept is not only a forecasting tool; it can also be used for planning and controlling (Kotler, 2003).
When forecasting the future growth and market for products, forecasters need to study the shape and the characteristics of the growth curve before selecting a suitable model. Although the time-varying extended logistic model has better performance for forecasting short lifecycle products, care must be taken when using this model. Since
the extended logistic model is developed under the assumption of S-curve model, the extended logistic model may only be suitable for data that grows as an S-curve and may not be suitable for linear data or for curves with many anomalous data points.
One contribution of this study is to verify that the time-varying extended logistic model can better predict the technology product lifecycle than the simple logistic model and the Gompertz model. Therefore, the problem of setting the correct upper limit for the two traditional growth curve models can be avoided by using the extended logistic model. Another contribution is to propose a decision procedure to help predictors select suitable model among the three models. So when the time-varying extended logistic model cannot produce the convergent results and the inflection point of the growth curve has occurred, predictors can utilize the characteristics of the simple logistic and the Gompertz models to set the upper limit and to forecast.
The assumption of growth curve models may be the limitation for this study. This research evaluates the forecasting performance of the three growth curve models. When using the growth curve models, predictors assume the historical data will contain all information that will influence the future development of the given technology or products. However, future is very uncertain and may not be predicted solely with historical data. Therefore, in addition to the growth curve models, some qualitative forecasting methods which do not use historical data, such as Delphi, scenario, or environmental monitoring can also be applied at the same time to gain more accurate prediction results.
The result of this study can be applied to forecast the technology product lifecycles and to define what the lifecycle stage the product is current in to help managers plan strategies in advance. However, the lifecycle of technology products becomes shorter and shorter, when the existing product will be substituted be new products is a
challenging issue. There are some forecasting models to discuss and forecast the substitution process. Therefore, the future research can focus on comparing these substitution models to see which one is more suitable for technology products, especially for short PLC technology products.
Table 16 Strategies for different product life cycle stages
Introduction Growth Maturity Saturation
Characteristics z Low growth z Rapidly rising growth z Peak growth z Declining growth Adopters z Innovators (2.5%) z Early adopters (13.5%) z Majority (68%) z Laggards (16%) Marketing objectives z Create product awareness
and trail
z Maximize market share z Maximize profit while defending market share
z Reduce expenditure and milk the brand
Product strategy z Offer a basic product z Offer products
extensions, service, and warranty
z Diversify products z Phase out weak items and models
Promotion strategy z Build product awareness among early adopters and dealers
z Build awareness and interest in the mass market
z Stress brand differences and benefits
z Reduce to level needed to loyals
Price strategy z Charge cost-plus z Price to penetrate market
z Price to match or beat competitors’
z Cut price Place strategy z Build selective distribution z Build intensive
distribution
z Build more intensive distribution
z Go selective: phase out unprofitable outlets R&D strategy z Innovation orientation z Function orientation z Customer orientation z Price orientation Innovation strategy z Personal inspiration
z Brainstorming
z Combination with current product concepts
z Former R&D experiences
z Imitation
z Quality function deployment (QFD)
z Manufacturing process improvement
z Robust design
z Functions combinations
z Alternative materials or technologies
Patent strategy z Intensive patent applications
z Patent allocation z Patent license z Design patent
z Patent development on accessories
Sources: Adapted from Chen, Liu, & Tzeng (2000) and Kotler, Keller, Ang, Leong, and Tan (2006).
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