Nowadays the majority of organizations operating in manufacturing field recognize the importance ...of including the Human Factor contribution in the industrial process optimization (Hong et al. 2007).
Food and Drink Industry (FDI) has a strong contribution to EU-28’s economy as reported by Eurosta...t (2014). FDI involves more than 4,25 millions of workers divided in 289000 companies with a relevant degree of diversification. Far away from the common way of thinking the FDI sector has an accident frequency comparable to those of other industrial sectors.
The Human Factors contribution in the scope of the industrial process optimization presented in t...his case study had to deal with considerations regarding the physical and mental workload requirements of different workstations and the capabilities of the operators assigned to them. The scope was to provide the industrial management with a better way to allocate human resources to tasks having different workload requirements.
Nowadays, several pieces of equipment are running over their expected life-time. An equipment rev...amping could solve the situation, but, it is often not possible for economical reasons, regulatory constraints, etc..
This paper discusses some examples where human performance and or human error prediction was achi...eved by using a modified version of the Rasch model(1980), where the probability of a specified outcome is modelled as a logistic function of the difference between the person capacity and item difficulty.
Risk assessment in manufacturing work environments gives a relevant contribution to health and sa...fety management for the operators: hazards are identified, and the associated risks are quantified in order to promote the risk mitigation and to improve the safety level for all the workers involved.
Occupational accidents are commonly collected in large databases by National Workers Compensation Authorities and companies’ safety and prevention teams. The analysis of the data can be difficult because the database elements are characterized by many parameters, which are not of a numerical nature. Data mining techniques could represent an efficient tool for the identification of useful information in large databases. In 2011, a two-level clustering method, made of SOM and numerical clustering, obtained positive results in identifying critical accident dynamics.
Data were collected in an automotive production plant during a campaign of observations performed by safety experts. A period of one week of observations was done during which safety experts monitored the working activity of an assembly line. All accident-precursors identified were reported in a format and immediately analysed and classified according to HFACS.