Models of technology growth are often conceived in terms of long-run trends in performance and price because, in general, short-run parameter stability and even the form of the growth function have proved elusive. Yet short-run growth models are arguably more useful for managers and research scientists, because the majority of their decisions are concerned with discretionary spending and operations rather than longer run strategic plans and investments. Our research explores short-run growth in microcomputer technologies by specifying growth models and parameter estimates for six commercially important computer technologies over short time periods with weekly data. Observations were acquired in a homogeneous market, limited to a collection time frame of less than two years. Data was collected at granular, weekly intervals, with concurrent tests to determine whether parameters were stable over successively longer intervals; conversely candidate growth models from longer 'strategic' planning horizons were tested to determine whether they scaled down to operational planning horizons. We found that an exponential model of performance-to-price growth is supported over short time horizons in all but one microcomputer technology (nonvolatile RAM). The exponential model and technology specific parameter estimates that are valid over short horizons were found to accurately scale up to longer planning horizons. We expect our results to contribute to more accurate price-performance forecasting at the corporate and product level; better decision making concerning inventory management, capacity utilization, lead and lag times in supply-chain operations, and efficiency of logistics.
|Number of pages||14|
|Publication status||Published - 1 Jun 2007|
- Exponential performance growth
- Moore's Law
- Supply-chain operations
- Technology economics