Publication Details

This paper was originally published as Watts, T., McNair, C. J., Baard, V. & Polutnik, L. (2009). Structural limits of capacity and implications for visibility. Journal of Accounting & Organizational Change, 5 (2), 294-312.


Purpose - This paper fills the gap between defining and measuring the productive limits of a machine or system, and the impact of various assumptions about the productive potential of the nature and informativeness of capacity cost management systems. The authors focused on the various ways in which multi-dimensional limits (for example, time, space, volume and/or value-creating ability) can be used to define productive capacity. Specifically, our research suggests that the limits used in establishing the capacity cost management system restrict the amount and nature of the information the system is capable of providing to management.

Justification – Two reasons are identified for studying the impact of capacity measurements on organizations. First, firms which make the best use of their resources can be expected to outperform their competitors. The second arises from the potential structuration effect of capacity metrics. Such an investigation makes capacity a visible, and hence an actionable, construct.

Design/Methodology – To explore these issues, a combination of analytics and qualitative field research methodology was used. The measurement dimensions were developed by analyzing the different reports, baseline measures, and metrics included in the various capacity models as suggested by the literature. These analytics were enriched with observations obtained from field research.

Findings – Maximizing the value created within an organization starts with understanding the nature and capability of all the company’s resources. The outcome is the identification of capacity systems specifically suited for particular types of operations, both manufacturing and service.

Practical implications - Such frameworks would allow organisations in developing economies, to make visible, the drivers of waste and productivity and to identify the primary assumptions and implications of various capacity limits.



Link to publisher version (DOI)