Managing without measuring: A study of an electricity distribution company

Purpose: The purpose of this paper is to present an exception to the common belief "If you can't measure it, you can't manage it". It aims to show how in certain situations particular practices, attitudes and cultures can remove the need for individual performance measurement. Design/methodology/approach: First, the paper identifies the usual roles of performance measurement in managing individual employees as described by control and motivation theorists. Second, it identifies a market-leading organisation where managers deliberately refuse to use their top-level performance measurement system to manage the performance of individual employees. A case study is carried out to test what non-measurement mechanisms fulfil the roles of individual performance measurement in this organisation. Findings: Building on situations observed at this company, a set of possible characteristics of companies that do not require formalised individual performance measurement systems in order to achieve high performance standards is put forward. Practical implications: Managers should not always assume that individual performance measurement is the only way to achieve excellent performance. This study shows that, by granting responsibilities and providing appropriate support, managers can channel workers' enhanced motivation towards meeting wider organisational goals. Originality/value: This work broadens the understanding of how excellent performance can be achieved. It shows that excellence can be achieved through practices based on shared values linked to motivation, trust, and a common sense of mission, without the need to install individual performance measurement systems based on cybernetic principles. © Emerald Group Publishing Limited.

Automated 3D Structure Inference of Civil Infrastructure Using a Stereo Camera Set

The commercial far-range (>10m) infrastructure spatial data collection methods are not completely automated. They need significant amount of manual post-processing work and in some cases, the equipment costs are significant. This paper presents a method that is the first step of a stereo videogrammetric framework and holds the promise to address these issues. Under this method, video streams are initially collected from a calibrated set of two video cameras. For each pair of simultaneous video frames, visual feature points are detected and their spatial coordinates are then computed. The result, in the form of a sparse 3D point cloud, is the basis for the next steps in the framework (i.e., camera motion estimation and dense 3D reconstruction). A set of data, collected from an ongoing infrastructure project, is used to show the merits of the method. Comparison with existing tools is also shown, to indicate the performance differences of the proposed method in the level of automation and the accuracy of results.