scholarly journals Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 39 ◽  
Author(s):  
Borja Ramis Ferrer ◽  
Usman Muhammad ◽  
Wael Mohammed ◽  
José Martínez Lastra
Keyword(s):  
Information And Communication Technologies ◽  
Operations Management ◽  
Performance Indicators ◽  
Manufacturing Systems ◽  
Research Work ◽  
Key Performance Indicators ◽  
Manufacturing Operations ◽  
Automation Systems ◽  
Discrete Manufacturing ◽  
Critical Issues

The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, such as industrial machines, which are involved in production processes. The early detection of malfunctions and/or improvable system values permits the anticipation to critical issues that may delay or even disallow productivity. Advances on Information and Communication Technologies (ICT)-based technologies allows the collection of data on system runtime. In fact, the data is not only collected but formatted and integrated in computer nodes. Then, the formatted data can be further processed and analyzed. This article focuses on the utilization of standard Key Performance Indicators (KPIs), which are a set of parameters that permit the evaluation of the performance of systems. More precisely, the presented research work demonstrates the implementation and visualization of a set of KPIs defined in the ISO 22400 standard-Automation systems and integration, for manufacturing operations management. The approach is validated within a discrete manufacturing web-based interface that is currently used for monitoring and controlling an assembly line at runtime. The selected ISO 22400 KPIs are described within an ontology, which the description is done according to the data models included in the KPI Markup Language (KPIML), which is an XML implementation developed by the Manufacturing Enterprise Solutions Association (MESA) international organization.

Model Driven Key Performance Indicators Concepts for Manufacturing Execution Systems

2012 ◽  
Vol 245 ◽  
pp. 173-178 ◽  
Author(s):  
Thomas Schulz ◽  
Andrei Chelaru
Keyword(s):  
Production Process ◽  
Operations Management ◽  
Performance Indicators ◽  
Production Control ◽  
Key Performance Indicators ◽  
Model Driven ◽  
Manufacturing Operations ◽  
Manufacturing Execution Systems ◽  
Definition Of ◽  
Manufacturing Execution

The intention of this paper is to give some methodical approach in Key Performance Indicators for Manufacturing. Within this it covers the defining of Key Performance Indicators (KPI) and Manufacturing Execution Systems (MES). The purpose is to analyze the influences of equipment, personnel and inventory to the production process and how to apply them in making decisions. This paper gives an overview about definition of Manufacturing Execution Systems (MES) and the area of standardization of object models. The KPI for the manufacturing operations management include the areas personnel, equipment, inventory and production process. It contains also a collection of key performance indicators (KPI) to be used in the area of production control and monitoring to assess and define the targets of production processes.

scholarly journals The flexibility of industrial additive manufacturing systems

2018 ◽  
Vol 38 (12) ◽  
pp. 2313-2343 ◽  
Author(s):  
Daniel R. Eyers ◽  
Andrew T. Potter ◽  
Jonathan Gosling ◽  
Mohamed M. Naim
Keyword(s):  
Additive Manufacturing ◽  
Operations Management ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Detailed Examination ◽  
Multiple Sources ◽  
Content Type ◽  
Commercial Practice ◽  
Systems Perspective ◽  
Manufacturing Operations

Purpose Flexibility is a fundamental performance objective for manufacturing operations, allowing them to respond to changing requirements in uncertain and competitive global markets. Additive manufacturing machines are often described as “flexible,” but there is no detailed understanding of such flexibility in an operations management context. The purpose of this paper is to examine flexibility from a manufacturing systems perspective, demonstrating the different competencies that can be achieved and the factors that can inhibit these in commercial practice. Design/methodology/approach This study extends existing flexibility theory in the context of an industrial additive manufacturing system through an investigation of 12 case studies, covering a range of sectors, product volumes, and technologies. Drawing upon multiple sources, this research takes a manufacturing systems perspective that recognizes the multitude of different resources that, together with individual industrial additive manufacturing machines, contribute to the satisfaction of demand. Findings The results show that the manufacturing system can achieve seven distinct internal flexibility competencies. This ability was shown to enable six out of seven external flexibility capabilities identified in the literature. Through a categorical assessment the extent to which each competency can be achieved is identified, supported by a detailed explanation of the enablers and inhibitors of flexibility for industrial additive manufacturing systems. Originality/value Additive manufacturing is widely expected to make an important contribution to future manufacturing, yet relevant management research is scant and the flexibility term is often ambiguously used. This research contributes the first detailed examination of flexibility for industrial additive manufacturing systems.

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