Predictive technology management for the identification of future development trends and the maximum achievable potential based on a quantitative analysis

Abstract

A company’s ability to find the most profitable technology is based on a precise forecast of achievement potential. Technology Management (TM) uses forecasting models to analyse future potentials, e.g. the Gartner Hype Cycle, Arthur D. Little’s technology lifecycle or McKinsey’s S-curve model. All these methods are useful for qualitative analysis in the planning of strategic research and development (R&D) expenses. In a new approach, exponential and logistic growth functions are used to identify and quantify characteristic stages of technology development. Innovations from electrical, mechanical and computer engineering are observed and projected until the year 2025. Datasets from different industry sectors are analysed, as the number of active Facebook users worldwide, the tensile yield point of flat bar steel, the number of transistors per unit area on integrated circuits, the fuel efficiency per dimension of passenger cars, and the energy density of Lithium-Ion cells. Results show the period of performance doubling and the forecast for the end of the technological achievement potential. The methodology can help to answer key entrepreneurial questions such as the search for alternatives to applied technologies, as well as identifying the risk of substitution technology.