An architecture based on RAMI 4.0 to discover equipment to process operations required by products

Pisching, Marcos A.; Pessoa, Marcosiris A. O.; Junqueira, Fabrício; Filho, Diolino José dos Santos; Miyagi, Paulo Eigi

doi:10.1016/j.cie.2017.12.029

Cited by 100 publications

(78 citation statements)

References 22 publications

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“…Pisching et al [30] had proposed an architecture based on Reference Architecture Model 4.0 that is used to find and select the most suitable equipment to process operations according to the requirements of each product during manufacturing process. This architecture is supported by CPS and IoT technologies and contributes for smart production and the development of digital manufacturing systems, where each product is processed individually, meeting one of the main I4.0 features: Mass customization and on-demand production abased on customer requirements.…”

Section: Lean Production Enhanced By Cyber-physical Systemsmentioning

confidence: 99%

How Industry 4.0 can enhance Lean practices

Pereira¹,

Dinis‐Carvalho²,

Alves³

et al. 2019

FME Transactions

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The global industrial landscape has deeply changed over the last few years and the Industry 4.0 concept has emerged, being enabled by successive disruptive innovations and technological development that have transformed manufacturing processes. This concept is being pointed out as the fourth industrial revolution that embraces a set of new technologies that are shaping the future manufacturing vision. However, Lean Production is a widely used manufacturing approach that brings several benefits to organizations. Despite the integration between Industry 4.0 and Lean Production is being researched in the recent years, the impacts that result from the implementation of new technologies in established lean practices is not clear. The purpose of this study that consists in a systematic literature review is assessing how these emerging disruptive technologies can enhance lean practices and analyse their impacts and benefits for organizations that are moving towards this new industrial paradigm.

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Section: Lean Production Enhanced By Cyber-physical Systemsmentioning

confidence: 99%

How Industry 4.0 can enhance Lean practices

Pereira¹,

Dinis‐Carvalho²,

Alves³

et al. 2019

FME Transactions

View full text Add to dashboard Cite

show abstract

“…Reference [30] develops an architecture proposal based on RAMI 4.0 to provide the functionalities that allow the guidance of the products in productive systems using a mechanism similar to the Domain Name System (DNS) used on the Internet. In this sense, the architecture allows for choosing the equipment that will execute a given operation through the communication between equipment and products during the manufacturing processes.…”

Section: State Of the Artmentioning

confidence: 99%

Reference Models for Digital Manufacturing Platforms

et al. 2019

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This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g. design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.

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“…The idea is supported by other requirements for Industry 4.0, such as self-organisation, self-adaptation, reconfigurability, self-awareness etc. (Pisching et al, 2018;Dinardo et al, 2018;Wan et al, 2017). They will be part of the process management of an intelligent network of a value chain.…”

Section: Introductionmentioning

confidence: 99%

Performance of an automated process model discovery – the logistics process of a manufacturing company

Halaška

Šperka

2019

Engineering Management in Production and Services

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The simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.

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An architecture based on RAMI 4.0 to discover equipment to process operations required by products

Cited by 100 publications

References 22 publications

How Industry 4.0 can enhance Lean practices

How Industry 4.0 can enhance Lean practices

Reference Models for Digital Manufacturing Platforms

Performance of an automated process model discovery – the logistics process of a manufacturing company

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