1. bookVolume 27 (2021): Issue 2 (June 2021)
Journal Details
License
Format
Journal
First Published
30 Mar 2018
Publication timeframe
4 times per year
Languages
English
access type Open Access

Analysing the machines working time utilization for improvement purposes

Published Online: 12 Jun 2021
Page range: 137 - 147
Received: 21 Apr 2021
Accepted: 14 Jun 2021
Journal Details
License
Format
Journal
First Published
30 Mar 2018
Publication timeframe
4 times per year
Languages
English
Abstract

The article is a case study of the use of snapshot observation to analyse the factors causing time losses at selected laser burner stations, and to propose changes that will increase the effective utilization of working time. The purpose of this paper is to determine the best and worst utilization of working time at the examined workplaces, analyse the amount of time lost and identify the causes of losses, and propose solutions that will improve the utilization of working time. According to the snapshot observation, procedure 2 main - work and non-work - time fractions and 10 detailed time fractions in the working day were distinguished, and their percentage share for the analysed workstations was calculated. Analyses of the working day time utilization depending on the type of machines, days of observation, single shifts were done and selected results were averaged. The paper indicates that organizational and technical aspects, as well as the employees’ faults, were the main reasons for time losses. Research has shown that the generally examined group of workstations was characterized by a high utilization level of working time. An unfavourable phenomenon was the ratio of the main time to the auxiliary time, the high share of the maintenance time fraction of the workstations, and incorrect organization of the interoperation transport, low workers motivation, rush, and routine. It was found that further improvement of work efficiency and reduce time losses requires paying attention to the optimization of employees’ working conditions, training, motivation systems, and implementation of lean concept tools and MES/CMMS solutions into production.

Keywords

JEL Classification

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