A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems

被引:102
作者
Kang, Ningxuan [1 ]
Zhao, Cong [2 ]
Li, Jingshan [2 ]
Horst, John A. [3 ]
机构
[1] Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China
[2] Univ Wisconsin, Dept Ind & Syst Engn, Madison, WI USA
[3] NIST, Engn Lab, Gaithersburg, MD 20899 USA
关键词
key performance indicator (KPI); manufacturing operation management (MOM); continuous improvement (CI); production systems; dependency; relationship; ISO; 22400; SERIAL PRODUCTION LINES; FLEXIBLE MANUFACTURING SYSTEMS; THROUGHPUT ANALYSIS; BOTTLENECKS; QUALITY; DESIGN; IMPROVABILITY; RESPECT; MODELS;
D O I
10.1080/00207543.2015.1136082
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.
引用
收藏
页码:6333 / 6350
页数:18
相关论文
共 54 条
[1]   Establishing and improving manufacturing performance measures [J].
Ahmad, MM ;
Dhafr, N .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2002, 18 (3-4) :171-176
[2]  
[Anonymous], 2014, 224001 ISO
[3]  
[Anonymous], 2013, SIMULATION
[4]   Quality/Quantity Improvement in an Automotive Paint Shop: A Case Study [J].
Arinez, Jorge ;
Biller, Stephan ;
Meerkov, Semyon M. ;
Zhang, Liang .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2010, 7 (04) :755-761
[5]   Bottlenecks in Bernoulli Serial Lines With Rework [J].
Biller, Stephan ;
Li, Jingshan ;
Marin, Samuel P. ;
Meerkov, Semyon M. ;
Zhang, Liang .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2010, 7 (02) :208-217
[6]  
Buzacott J., 1993, Stochastic Models of Manufacturing Systems
[7]   c-bottlenecks in serial production lines: Identification and application [J].
Chiang, SY ;
Kuo, CT ;
Meerkov, SM .
MATHEMATICAL PROBLEMS IN ENGINEERING, 2001, 7 (06) :543-578
[8]   Bottlenecks in Markovian production lines: A systems approach [J].
Chiang, SY ;
Kuo, CT ;
Meerkov, SM .
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 1998, 14 (02) :352-359
[9]   DT-bottlenecks in serial production lines: Theory and application [J].
Chiang, SY ;
Kuo, CT ;
Meerkov, SM .
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 2000, 16 (05) :567-580
[10]  
Cooper R., 1989, Journal of Cost Management for the Manufacturing Industry, P34