TY - GEN
T1 - SCAT Model Based on Bayesian Networks for Lost-Time Accident Prevention and Rate Reduction in Peruvian Mining Operations
AU - Ziegler-Barranco, Ana
AU - Mera-Barco, Luis
AU - Aramburu-Rojas, Vidal
AU - Raymundo, Carlos
AU - Mamani-Macedo, Nestor
AU - Dominguez, Francisco
N1 - Publisher Copyright:
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Several factors affect the activities of the mining industry. For example, accident rates are critical because they affect company ratings in the stock market (Standard & Poors). Considering that the corporate image is directly related to its stakeholders, this study conducts an accident analysis using quantitative and qualitative methods. In this way, the contingency rate is controlled, mitigated, and prevented while serving the needs) of the stakeholders. The Bayesian network method contributes to decision-making through a set of variables and the dependency relationships between them, establishing an earlier probability of unknown variables. Bayesian models have different applications, such as diagnosis, classification, and decision, and establish relationships among variables and cause–effect links. This study uses Bayesian inference to identify the various patterns that influence operator accident rates at a contractor mining company, and therefore, study and assess the possible differences in its future operations.
AB - Several factors affect the activities of the mining industry. For example, accident rates are critical because they affect company ratings in the stock market (Standard & Poors). Considering that the corporate image is directly related to its stakeholders, this study conducts an accident analysis using quantitative and qualitative methods. In this way, the contingency rate is controlled, mitigated, and prevented while serving the needs) of the stakeholders. The Bayesian network method contributes to decision-making through a set of variables and the dependency relationships between them, establishing an earlier probability of unknown variables. Bayesian models have different applications, such as diagnosis, classification, and decision, and establish relationships among variables and cause–effect links. This study uses Bayesian inference to identify the various patterns that influence operator accident rates at a contractor mining company, and therefore, study and assess the possible differences in its future operations.
KW - Accident
KW - Bayesian net-work
KW - Mining
KW - Peruvian
KW - Prevention
UR - https://www.scopus.com/pages/publications/85088231547
U2 - 10.1007/978-3-030-50791-6_45
DO - 10.1007/978-3-030-50791-6_45
M3 - Contribución a la conferencia
AN - SCOPUS:85088231547
SN - 9783030507909
T3 - Advances in Intelligent Systems and Computing
SP - 350
EP - 358
BT - Advances in Human Factors, Business Management and Leadership - Proceedings of the AHFE 2020 Virtual Conferences on Human Factors, Business Management and Society, and Human Factors in Management and Leadership
A2 - Kantola, Jussi Ilari
A2 - Nazir, Salman
A2 - Salminen, Vesa
PB - Springer
T2 - AHFE Virtual Conference on Human Factors, Business Management and Society, and the International Conference on Management and Leadership, 2020
Y2 - 16 July 2020 through 20 July 2020
ER -