Improving an algorithm for classifying error types of front-line workers: Insights from a case study in the construction industry

The objective of this study was to propose improvements in an algorithm for classifying error types of front-line workers. The improvements involved: (a) making recommendations on organizing the data needed to apply the algorithm (e.g. identify actions and decisions that may serve as a reference for analysing the types of errors) and (b) drawing up guidelines for interpreting the questions that are part of the algorithm (e.g. how to define what counts as a procedure). The improvements were identified on the basis of testing the algorithm on construction sites, an environment in which it had not yet been implemented. Thus, 19 occupational accidents which had occurred in a small-sized construction company were investigated and the error types of both workers who had been injured and crew members were classified. The accidents investigated were used as a basis both to illustrate how the improvements proposed should be put in practice and to illustrate how practical insights for safety management might be derived from the algorithm. (C) 2009 Elsevier Ltd. All rights reserved.