Insights from manufacturing scheduling for work allocation in knowledge-intensive firms

Work allocation is a critical function in knowledge management, with ramifications for a firm´s financial performance, output quality, system throughput and capacity, customer service and worker satisfaction. This is especially true for knowledge-intensive firms (KIFs) which specialize in the production of knowledge by adding value to information in their transformation processes. This research examines the utility of manufacturing models for work allocation in KIFs. Specifically, the dynamic complexity of KIFs´ workload allocation can be represented by machines (knowledge workers) with different tooling (experience and expertise) and products (projects) with different routing (staffing) requirements over a common set of tasks. By building on established practices used for assigning work within the paradigm of flexible manufacturing systems, this research suggests ways in which to manage knowledge-intensive firms. In particular, the study demonstrates the use of an algorithmic approach to work allocation using a heuristic in combination with two different priority rules. Modeling techniques are used to assess the long-run impact of this approach on a sample KIF´s revenues, throughput and capacity. The results of the models demonstrate how, for a case study organization, different systematic approaches to workload allocation would affect firm performance and exploit its inherent flexibility