Development of the effective information and analytical support of the OSH management system

Abstract

Purpose: Studying urgent problems in the OSH management field in the in European countries to create effective information and analytical support for the OSH management system. Design/methodology/approach: An analytical review of open sources, a comparative analysis of the legislative framework of different countries and logical conclusions based on existing opportunities at the current stage of development of the country were used to study current problems in the field of labour protection management and find ways to create effective information and analytical support. Improved IS "Vizit" was tested for 2018-2019: to identify undeclared labour, the dynamics of various types of labour violations has been studied; to predict the load of inspectors, the quarterly dynamics of inspection actions was studied; the accumulated statistics were processed using multiple regressions; for 22 enterprises, employees of all levels were remotely trained in labour protection issues. Findings: Information and analytical support for the OSH management system has been developed. On the basis of indirect signs it allows to identify undeclared work cases, to predict the labour inspectors’ inspection activities by quarters, to provide effective distance learning of enterprise employees and labour inspectors. The distance learning system for labour protection was tested at 22 enterprises: the head of the enterprise, the heads of departments and employees of the enterprise passed the training. Since 2018 (start to use of this information and analytical support), the dynamics of inspection actions and various types of labour violations have been monitored. Research limitations/implications: Information and analytical support was tested on the example of Ukrainian labour legislation. However, it can be adapted to the legislation of another country. Practical implications: The proposed information and analytical support using indirect evidences provides an opportunity to identify undeclared work and that significantly reduces the inspection visits number in order to monitor and detect violations of the law; makes it possible to predict the inspection activities and the workload of labour inspectors; contributes to the organizations managers and employees’ effective training, and the inspectors training remotely (and therefore is less costly). Originality/value: A non-standard approach to the identification of undeclared work on indirect grounds using information and analytical support for the OSH management system is proposed.