This chapter presents the research background of forecasting lifecycle products and the importance of selecting the suitable forecasting models. The motivation, purpose and process of this research are also discussed in this chapter.
1.1 Motivation
With the rapid introduction of new technologies and fast design to satisfy consumer demand, electronic products and services are often replaced within a few years. The product life cycle for electronic goods, which used to be about ten years in the 1960’s, fell to about 5 years in the 1980’s and is now less than two years for consumer electronic products such as cell phones and computers. As product life cycles become shorter, less data are available for market analysis and technology forecasting.
Given the current market situation, smaller datasets must be used to forecast future trends of new electronic products and services. Hasted and Ehlers (1989) define a small dataset as the dataset which covers only short time intervals with fewer than 30 data points.
A product life cycle is typically divided into four stages that include introduction, growth, maturity and decline (Kotler, 2003). The product lifecycle is often modeled using growth curves or sigmoid curves which have an inflection point and approaches a fixed limit (Bass, 1969; Mahaian, Muller, & Bass, 1990; Morrison, 1995; Morrison, 1996; Kurawarwala & Matsuo, 1996; Kurawarwala & Matsuo, 1998; Bengisu &
Nekhili, 2006). Growth curves are widely used in technology forecasting (Frank, 2004;
Levary & Han, 1995; Meade & Islem, 1995; Meade & Islem, 1998; Meyer & Ausubel, 1999; Meyer, Yung, & Ausubel, 1999; Rai & Kumar, 2003) since technology product growth is often very slow during the introduction stage (e.g., a new product replacing a
mature product) which is then followed by rapid exponential growth when barriers to product adoption fall. The growth then approaches a market share limit. The limit reflects the saturation of the marketplace with the product or the replacement of the product with another. The curve also models an inflection or break point where growth ends and decline begins.
Many growth curve models have been developed to forecast the penetration rate of technology based products with the simple logistic curve and the Gompertz curve the most frequently referenced (Morrison, 1995; Morrison, 1996; Bengisu & Nekhili, 2006;
Meade & Islem, 1995). However, when using these two models to forecast market share, care must be taken to set the upper limit of the curve correctly or the prediction will become inaccurate (Bengisu & Nekhili, 2006). The upper limit is the maximum possible value and represents the maximum penetration rate or sales volume. Setting the upper limit to growth can be difficult and ambiguous. If the product will likely be popular and used for decades, then the upper limit is set to 100% of the penetration rate. This means that the product will be completely replaced only after everyone in the market has purchased the product. However, when marketers consider new technology products such as computer games or new model cell phones, the value for the upper limit to market share growth can be difficult to estimate. That is, a computer game can be quickly replaced by another game after only reaching 10% market share.
In order to avoid the problem of estimating the market share capacity for the simple logistic and the Gompertz models, Meyer and Ausubel (1999) proposed the extended logistic model. Under this model, the capacity (or upper limit) of the curve is not constant but is dynamic over time. Meyer and Ausubel (1999) also proposed that technology innovations do not occur evenly through time but instead appear in clusters or “innovation waves.” Thus, they formulated an extended logistics model which is a
simple logistics model with a carrying capacity k( )t that is itself a logistics function of time. Therefore, the researchers extend the constant capacity ( ) of the simple logistic model by embedding the carrying capacity in the constant. Chen (2005) applies the embedded carrying capacity concept to develop a time-varying extended logistic model and the study uses the durable electronics products to confirm the model has better performance than the Fisher-Pry model and the Gompertz model. However, it will need more data to verify whether the time-varying extended logistic model can also better forecast the short lifecycle technology products.
k
1.2 Research Purpose
The emergence of short product lifecycles has been addressed in the supply chain and inventory management literature (Kurawarwala & Matsuo, 1996; Kurawarwala &
Matsuo, 1998; Zhu & Thonemann, 2004) and there is general agreement that improved prediction of these lifecycles will benefit the management of supply chains, inventories, and product design. However, these new technology lifecycles are a modern phenomenon and the data sets (which characteristically have fewer data points and shorter time periods) challenge the assumptions and applications of traditional forecasting methods.
Traditional forecasting models, like the simple logistic and Gompertz models, require that the upper limit of the curve be estimated prior to the forecast. Since it is difficult to estimate the demand of a new product or the arrival of a substitute product with limited data, traditional approaches are considered unreliable and inaccurate.
Therefore, a time-varying extended logistic model with flexible capacity is proposed where the capacity (or upper limit) of the curve is not constant but is dynamic over time.
The purpose of this research is to evaluate the performance of the time-varying
extended logistic model, the simple logistic model and the Gompertz models when forecasting both long and short technology product lifecycles. Six time-series datasets describing market penetration rates and sixteen datasets describing cumulative sales volumes were used to evaluate model performance. The electronic consumer goods datasets consist of six sets representing long product lifecycles and 16 sets representing short product lifecycles. Not only to compare the fitting and forecasting performances, and the pros and cons of the three models, but a decision diagram of selecting a suitable forecasting model is also proposed, and the China RFID patent applications is used as a case study to demonstrate the model selection process. The case is also an example to present how to apply the technology forecasting model to realize the current and future development of an industry.
1.3 Research Process
Chapter 1 of this paper provides an introduction and Chapter 2 discusses literatures about technology forecasting methods, the challenges of forecasting short product lifecycles and traditional and newly developed technology forecasting models including the simple logistic model, the Gompertz model, and the time-varying extended logistic model. Chapter 3 presents the methodology and the analytical process of this study.
Chapter 4 describes comparison results of the models’ prediction performances and provides the suggestions for using the models. Chapter 5 provides an empirical case of China RFID patent analysis. The last chapter provides a summary and conclusion as well as the limitations of the study. Figure 1 presents the research framework and process of this dissertation.
Motivation and Problem Determinations
Case Study
China RFID Patent Application Analysis Selection of Models
1.
3. 5.
7.
2.
The simple logistic model
The Gompertz model The time-varying extended
logistic model
Model Comparison
Fitting Performance
Predicting Performance
6.
Comparison Results
Pros and Cons of Models Suggestions for Applying
Models
Literature Review
4.
Methodology
Data Collection and Setting Decision Diagram for
Model Selections
Figure 1 Research framework and process