Management strategies for industrial laboratories with knowledge memory

We present a simplified abstraction of an industrial laboratory consisting of a two-stage network, Research (R) and Development (D). Ideas and prototypes are incubated in the R stage, the projects departing this stage are assessed, and, if favorable, the project proceeds to the D stage. Revenue is generated from the sale of products/solutions that are outputs of the D stage, and the sale and licensing of patents that are generated at both stages. In our discrete time model, in each time period the managers of the industrial laboratory are given a constant amount of money to invest in the two stages. The investments determine the capacities of the stages based on linear unit costs. A novel feature of the model is “knowledge stocks” for the stages, which represent the accumulated know-how from practicing research and development activities; higher knowledge stock implies lower cost. The memory in knowledge stocks makes current investment decisions have long term impact on costs and profits. Three strategies for profit maximization are investigated. In myopic profit maximization we show the existence of multiple equilibria and the phenomenon of state entrapment in suboptimal regimes, which are absent in the other strategies. Numerical results illustrate the main features of the model.