Orderbook models for priority management: A taxonomy of data structures

Abstract

In the 1980's a research team from London Business School designed and installed information systems in a number of UK batch manufacturing companies. The companies had little formal manufacturing planning and control (MPC), and the volatile market and complex production situation of each led to a highly rapid response orientation we characterize as priority management. This style was appropriate for the situation of these companies and the systems designed for them were intended to support their approach, not to replace it with a more formal prescriptive system. After initial projects with no preconceived framework, the focus emerged on the appropriate data structure for order progressing — or order-book model — as the central need of such companies. Orderbook models were then designed for all subsequent companies (17 in total), many of whom still use these models. The article describes the preconditions for priority management by summarizing the sources of complexity — variety, variation, and volume — found in the collaborating companies. These conditions preclude tight formal planning and control, and require data structures which give vision over the relative merits of different priority choices. The orderbook models, designed to give such vision, are not purely company specific but can be classified into types. The classification scheme has three main dimensions: capacity commitment (make-to-stock, etc), product structure/assembly form, and the style of progressing orders through all their different stages. The options within each dimension give a total of 48 possible configurations of orderbook model. These concepts were developed using action research: practical application came first, and theory was developed by generalizing from the experience. This is a non-traditional research methodology in operations management (OM), but will in the future be increasingly important if OM research is to meet the needs of practitioners for relevance as well as the needs of the research community for rigor.