Abstract
Asymmetry of employee productivity is an important issue when planning production work. Estimated time to complete the work depends on many factors, such as employee experience, qualifications, and efficiency of brigades and subcontractors, accidents, unreliable suppliers, material shortages, the need for correction work, fatigue, stress, etc. The paper presents the statistical method—Multivariate Method of Statistical Models—MMSM. It enables the assessment of the impact on the duration of works of its characteristic variables and the prediction of the duration of individual orders. In order to carry out the multi-criteria method (MMSM) analysis, the employees’ working time was measured at the newly opened steel structure production plant in Kielce. The results of the analyses clearly show that thanks to the method used, quick and accurate prediction of employees’ work efficiency is possible. In the analyzed case, the best forecast was obtained using the method of automatic neural networks, where the MAPE error = 0.02%.
Highlights
Planning production works is an extremely important issue, which depends on the material delivery schedule, transport schedule, employment schedule, proper use of available work stations and machines, and many others [1]
Production ventures are exposed to various risk factors that can disturb the pre-planned time for the execution of the element
Beck and Shen even worked on the impact of presidential elections on employee productivity [4]
Summary
Planning production works is an extremely important issue, which depends on the material delivery schedule, transport schedule, employment schedule, proper use of available work stations and machines, and many others [1]. Production ventures are exposed to various risk factors that can disturb the pre-planned time for the execution of the element. The most common risk factors are [2,3]: different employee experience, qualifications and performance of brigades and subcontractors, accidents, unreliable suppliers, material shortages, the need for correction work, fatigue, stress, etc. The variability of the environment in which the project is carried out is a source of uncertainty and risk. This risk relates mainly to the time, cost, and quality
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