IFAC PapersOnLine 52-13 (2019) 1022–1027
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2405-8963 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Peer review under responsibility of International Federation of Automatic Control.
10.1016/j.ifacol.2019.11.329
© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
1. INTRODUCTION
Mathematical models and experimental procedures are
employed to estimate the value of the performance metrics
of manufacturing production systems, which are charac-
terized by their complex, dynamic and stochastic natures.
Even when it is possible through direct observation to
estimate the value of the performance metrics, and to
verify how improving measures impact the system, the
use of mathematical models of the production system,
for predicting its behaviour, constitutes a good analysis
approach. Also, taking into account that decision sup-
port tools based on digital models and simulation are
increasingly used to improve manufacturing competitive-
ness, mathematical models of systems performance facil-
itate a straightforward computer implementation of con-
tinuous monitoring and improvement of the performance
of production systems.
Control (and therefore the measurement of performance)
of manufacturing systems is a daunting task, it needs
information applications as a part of the modern Man-
ufacturing Execution Systems (MES) / Manufacturing
⋆
Universidad Nacional de Colombia sede Bogot´a. Universit´ e de
Technologie de Troyes
Operations Management (MOM) as those belonging to
Industry 4.0. Through MES, the process control systems
is supervised, decisions about the routes that products
follow through the production system and starting point
of operations on products are made, and disruptions of
the manufacturing operations such as machine failures are
handled (Valckenaers and Van Brussel, 2005). MES are
used for acquiring accurate and reliable data from ma-
chines and their components and for inferring meaningful
information from this data, as the system performance.
Overall Equipment Effectiveness (OEE) is an opera-
tional measure tool used for evaluating the manufacturing
systems performance that can be monitored using MES
in both, batch and continuous production. OEE is also an
indicator of process improvement activities for manufac-
turing systems (Dal et al., 2000).
OEE is also applied to identify different types of produc-
tion losses and provides directions about improvement ar-
eas of processes related to manufacturing production sys-
tems performance metrics. OEE is considered to combine
the operation, maintenance and management of manufac-
turing equipment and resources through determining oper-
ational losses which are mainly functions of the availability,
performance rate and quality rate of the machine, produc-
Keywords: Multi-state system, universal generating function, overall equipment effectiveness,
cost-efficient corrective maintenance
Abstract: The monitoring and control of production system performance is a continuing con-
cern within prioritization and optimization decisions regarding manufacturing systems. There
is interest on mathematical models of systems performance that facilitate a straightforward
computer implementation of continuous monitoring and improvement of the performance of
production systems. In this work, it is considered a multi-state system approach for modeling
production manufacturing systems and for measuring its long-term performance. One opera-
tional measure used for evaluating the manufacturing systems performance is overall equipment
effectiveness. Here, the focus on overall equipment effectiveness is the total stoppage time due to
corrective maintenance. This study therefore set out to assess a mathematical modeling approach
for evaluating decisions about cost-efficient corrective maintenance. This approach is used for
computing the manufacturing system states probability distribution, since calculating the total
production cost per unit (total unit cost of production plus total unit cost of maintenance).
In order to evaluate the goodness of the mathematical approach, a continuous time Markov
chain model and a discrete event simulation model are used, by comparing the values obtained
using the three techniques. The results demonstrate the effectiveness of the universal generating
function for assessing system performance metrics. Copyright c 2019 IFAC
*
MindLab Research Group. Systems and Industrial Engineering
Department.Universidad Nacional de Colombia, Bogot´a, Colombia
(e-mail: gabula@ unal.edu.co).
**
Laboratoire de Mod´ elisation et Sˆ uret´ e des Syst` emes (LM2S-STMR).
Universit´ e de Technologie de Troyes, Troyes, 10000 France (e-mail:
nacef.tazi@utt.fr ; eric.chatelet@utt.fr)
BULA, Gustavo
*
TAZI, Nacef
**
CHATELET, Eric
**
Determining Production Systems
Performance Metrics Considering Machine
Downtime