An Overview of Next-generation Manufacturing Execution Systems: How important is MES for Industry 4.0?

With Industry 4.0 related initiatives, a brand new array of opportunities has emerged for organizations to face the ordeals that come with managing ever-growing manufacturing needs. The exponential increase in the complexity of supply chain management has put a real strain on manufacturing operations. In order to succeed, organizations must turn to solutions such as manufacturing execution systems (MES) in order to stay competitive. In this research chapter, we discuss the impact of MES in organizations, whilst describing the process for going from a theoretical concept to a hands-on system which runs the shop floor operations.

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Implementing the Cognition Level for Industry 4.0 by Integrating Augmented Reality and Manufacturing Execution Systems

Advanced Information Networking and Applications - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-44041-1_83 ◽

2020 ◽

pp. 957-967

Author(s):

Alfonso Di Pace ◽

Giuseppe Fenza ◽

Mariacristina Gallo ◽

Vincenzo Loia ◽

Aldo Meglio ◽

...

Keyword(s):

Augmented Reality ◽

Industry 4.0 ◽

Manufacturing Execution Systems ◽

Manufacturing Execution

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The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES)

Journal of Innovation Management ◽

10.24840/2183-0606_003.004_0003 ◽

2016 ◽

Vol 3 (4) ◽

pp. 16-21 ◽

Cited By ~ 169

Author(s):

Francisco Almada-Lobo

Keyword(s):

Cloud Computing ◽

Paradigm Shift ◽

Industry 4.0 ◽

Manufacturing Systems ◽

Production Control ◽

Shop Floor ◽

Manufacturing Execution Systems ◽

The Future ◽

Smart Factories ◽

Manufacturing Execution

Industry 4.0 dictates the end of traditional centralized applications for production control. Its vision of ecosystems of smart factories with intelligent and autonomous shop-floor entities is inherently decentralized. Responding to customer demands for tailored products, these plants fueled by technology enablers such as 3D printing, Internet of Things, Cloud computing, Mobile Devices and Big Data, among others create a totally new environment. The manufacturing systems of the future, including manufacturing execution systems (MES) will have to be built to support this paradigm shift.

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Next Generation Industrial IoT Digitalization for Traceability in Metal Manufacturing Industry: A Case Study of Industry 4.0

Electronics ◽

10.3390/electronics10050628 ◽

2021 ◽

Vol 10 (5) ◽

pp. 628

Author(s):

Michail J. Beliatis ◽

Kasper Jensen ◽

Lars Ellegaard ◽

Annabeth Aagaard ◽

Mirko Presser

Keyword(s):

Industry 4.0 ◽

Manufacturing Industry ◽

Careful Consideration ◽

Added Value ◽

Shop Floor ◽

Circuit Boards ◽

Next Generation ◽

Road Map ◽

Industrial Iot ◽

Production Cycles

This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

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Manufacturing Execution Systems: Examples of Performance Indicator and Operational Robustness Tools

CHIMIA International Journal for Chemistry ◽

10.2533/chimia.2016.616 ◽

2016 ◽

Vol 70 (9) ◽

pp. 616-620

Author(s):

Yannick Gendre ◽

Gérard Waridel ◽

Myrtille Guyon ◽

Jean-François Demuth ◽

Hervé Guelpa ◽

...

Keyword(s):

Performance Indicator ◽

Manufacturing Execution Systems ◽

Manufacturing Execution

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Fault Diagnosis and Reliability Growth for Information System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.590.763 ◽

2014 ◽

Vol 590 ◽

pp. 763-767

Author(s):

Zhi Hui Huang

Keyword(s):

Information System ◽

Bayesian Network ◽

System Reliability ◽

Functional Module ◽

Decision Problems ◽

Reliability Growth ◽

Failure Data ◽

Manufacturing Execution Systems ◽

Growth Planning ◽

Manufacturing Execution

This paper aiming at the zero-failure data and uncertain-decision problems exist in the information system reliability growth process, it proposes to build the Bayesian network topology of FMEA. It adopts Leaky Noisy-OR model, and it analyses the probability that the subsystem functional module will go wrong in quantity. It solves the problem of identifying the vague and incomplete information exists in the complex system rapidly and accurately, laying the foundation for further study of the reliability growth comprehensive ability assessment of system based on the Bayesian network. In this paper, on the background of Manufacturing Execution Systems (MES) engineering, aimed at research on models and evaluation methods of reliability growth for MES, enclosing reliability of MES task and design target, reliability growth test and analysis methods, it proposes the goal of MES reliability growth planning.

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Die synchrone Produktion*/Synchronous manufacturing - An approach for semi-autonomous production planning and control and human-based decision support

wt Werkstattstechnik online ◽

10.37544/1436-4980-2015-04-46 ◽

2015 ◽

Vol 105 (04) ◽

pp. 204-208

Author(s):

D. Kreimeier ◽

E. Müller ◽

F. Morlock ◽

D. Jentsch ◽

H. Unger ◽

...

Keyword(s):

Decision Support ◽

Real Time ◽

Production Planning ◽

Data Exchange ◽

Production Control ◽

Production Planning And Control ◽

Planning And Control ◽

Manufacturing Execution Systems ◽

And Control ◽

Manufacturing Execution

Kurzfristige sowie ungeplante Änderungen – wie Auftragsschwankungen, Maschinenausfälle oder Krankheitstage der Mitarbeiter – beeinflussen die Produktionsplanung und -steuerung (PPS) von Industriefirmen. Trends wie Globalisierung und erhöhter Marktdruck verstärken diese Probleme. Zur Komplexitätsbewältigung bei der Entscheidungsfindung zur Fertigungssteuerung kommen in der Produktion Werkzeuge der „Digitalen Fabrik“, beispielsweise Simulationsprogramme, oder IT (Informationstechnologie)-Lösungen, wie Manufacturing Execution Systems (MES), zum Einsatz. Eine Verknüpfung dieser Bereiche würde einen echtzeitfähigen Datenaustausch erlauben, der wiederum eine echtzeitfähige Entscheidungsunterstützung bietet. Der Fachbeitrag stellt hierfür einen Lösungsansatz vor.   Sudden and unsystematic changes, such as fluctuations in order flow, machine failures, or employee sick days affect the Production Planning and Control (PPC) activities of industrial companies. Trends like globalization and increased market pressure intensify these problems. To master the complexity of decision-making in production control, tools of the digital factory (e.g. simulation systems) or IT systems (e.g. Manufacturing Execution Systems (MES)) are applied in manufacturing. Combining these areas would enable real-time capable data exchange which, in turn, provides real-time capable decision support. This article presents an approach for solving this problem.

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Modeling a holonic agent based solution by Petri nets

Computer Science and Information Systems ◽

10.2298/csis111223031p ◽

2012 ◽

Vol 9 (3) ◽

pp. 1287-1305 ◽

Cited By ~ 8

Author(s):

Carlos Pascal ◽

Doru Panescu

Keyword(s):

Resource Allocation ◽

Petri Nets ◽

Petri Net ◽

Coordination Mechanisms ◽

Allocation Mechanism ◽

Simulation Experiments ◽

Agent Based ◽

Manufacturing Execution Systems ◽

Resource Allocation Mechanism ◽

Manufacturing Execution

One of the key design issues for distributed systems is to find proper planning and coordination mechanisms when knowledge and decision capabilities are spread along the system. This contribution refers holonic manufacturing execution systems and highlights the way a proper modeling method - Petri nets - makes evident certain problems that can appear when agents have to simultaneously treat more goals. According to holonic organization the planning phase is mainly dependent on finding an appropriate resource allocation mechanism. The type of weakness is established by means of the proposed Petri net models and further proved by simulation experiments. A solution to make the holonic scheme avoid a failure in resource allocation is mentioned, too.

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Middleware Application, Suitable to Build an Automated and Connected Smart Manufacturing Environment

10.5772/intechopen.97397 ◽

2021 ◽

Author(s):

Muzaffar Rao ◽

Thomas Newe

Keyword(s):

Mobile Robots ◽

Industry 4.0 ◽

Manufacturing Systems ◽

Smart Manufacturing ◽

Manufacturing Execution System ◽

Manufacturing Environment ◽

Industrial Internet ◽

A Cell ◽

Smart Industry ◽

Manufacturing Execution

The current manufacturing transformation is represented by using different terms like; Industry 4.0, smart manufacturing, Industrial Internet of Things (IIoTs), and the Model-Based enterprise. This transformation involves integrated and collaborative manufacturing systems. These manufacturing systems should meet the demands changing in real-time in the smart factory environment. Here, this manufacturing transformation is represented by the term ‘Smart Manufacturing’. Smart manufacturing can optimize the manufacturing process using different technologies like IoT, Analytics, Manufacturing Intelligence, Cloud, Supplier Platforms, and Manufacturing Execution System (MES). In the cell-based manufacturing environment of the smart industry, the best way to transfer the goods between cells is through automation (mobile robots). That is why automation is the core of the smart industry i.e. industry 4.0. In a smart industrial environment, mobile-robots can safely operate with repeatability; also can take decisions based on detailed production sequences defined by Manufacturing Execution System (MES). This work focuses on the development of a middleware application using LabVIEW for mobile-robots, in a cell-based manufacturing environment. This application works as middleware to connect mobile robots with the MES system.

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