Indoor Positioning Systems Can Revolutionise Digital Lean

Tran, Tuan-anh; Ruppert, Tamás; Abonyi, János

doi:10.3390/app11115291

Cited by 16 publications

(11 citation statements)

References 78 publications

(100 reference statements)

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“…[153]- [155]. LM philosophies share the same continuous improvement approach with the technical improvement of I4.0, thus considered as assistance for smart retrofitting [106].…”

Section: Lean 40mentioning

confidence: 99%

See 1 more Smart Citation

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Tran

Ruppert

Eigner³

et al. 2022

IEEE Access

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The ongoing Industry 4.0 is characterized by the connectivity between components in the manufacturing system. For modern machines, the Internet of Things is a built-in function. In contrast, there are legacy machines in deployment functioning without digital communication. The need to connect them became popular to improve overall production efficiency. As building a new smart factory as a greenfield investment is a capital-intensive choice, retrofitting the existing infrastructure with IoT capability is more reasonable than replacing them. However, this so-called brownfield development, or retrofitting, requires specific prerequisites, e.g., digitization status assessment, technical and connectivity development, management requirement, and operational need, representing a significant disadvantage: lack of scalability. In the meantime, Industry 5.0 is under human-centric priority, which poses new challenges to the retrofitted system. Aware of the challenge, this paper provides a systematic overview of brownfield development regarding technical difficulties, supporting technologies, and possible applications for the legacy system. The research scope focuses on available Industry 4.0 advancements but considers preparing for the forthcoming Industry 5.0. The proposed retrofitting project approach can be a guideline for manufacturers to transform their factories into intelligent spaces with minimal cost and effort but still gain the most applicable solution for management needs. The future direction for other research in brownfield development for Industry 5.0 is also discussed.

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“…[153]- [155]. LM philosophies share the same continuous improvement approach with the technical improvement of I4.0, thus considered as assistance for smart retrofitting [106].…”

Section: Lean 40mentioning

confidence: 99%

“…With the help of the DT, a strategic set of KPIs can help the firm self-optimization along with its operation, as demonstrated in the Ref. [155].…”

Section: E Fifth Phase: Implement and Sustain The Changementioning

confidence: 99%

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Tran

Ruppert

Eigner³

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ML-based techniques are mostly used for fault diagnosis [ 43 ] as real-time condition monitoring. Spaghetti diagrams are re-invested based on the IPS data [ 44 ] during Lean 4.0 [ 45 , 46 ]. Furthermore, the placed-based KPIs are the basis for the decision systems [ 47 ].…”

Section: Iot Layerwise Presentation Of Industry 40 and Operator 40 Re...mentioning

confidence: 99%

“…The IPS is one of the key elements of the proposed intelligent space [ 16 ], and it is applied in many cases in manufacturing [ 44 ]. The IPS enables full traceability on the shop floor, the information from which can revolutionize the digital lean concept [ 45 ]. The IPS is applied in the laboratory, facilitating real-time position tracking of objects, e.g., AGVs, workpieces, and operators.…”

Section: Intelligent-space-based Education Of Iot Toolsmentioning

confidence: 99%

Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0

Ruppert

Darányi

Medvegy

et al. 2022

Sensors

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One of the main challenges of Industry 4.0 is how advanced sensors and sensing technologies can be applied through the Internet of Things layers of existing manufacturing. This is the so-called Brownfield Industry 4.0, where the different types and ages of machines and processes need to be digitalized. Smart retrofitting is the umbrella term for solutions to show how we can digitalize manufacturing machines. This problem is critical in the case of solutions to support human workers. The Operator 4.0 concept shows how we can efficiently support workers on the shop floor. The key indicator is the readiness level of a company, and the main bottleneck is the technical knowledge of the employees. This study proposes an education framework and a related Operator 4.0 laboratory that prepares students for the development and application of Industry 5.0 technologies. The concept of intelligent space is proposed as a basis of the educational framework, which can solve the problem of monitoring the stochastic nature of operators in production processes. The components of the intelligent space are detailed through the layers of the IoT in the form of a case study conducted at the laboratory. The applicability of indoor positioning systems is described with the integration of machine-, operator- and environment-based sensor data to obtain real-time information from the shop floor. The digital twin of the laboratory is developed in a discrete event simulator, which integrates the data from the shop floor and can control the production based on the simulation results. The presented framework can be utilized to design education for the generation of Industry 5.0.

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“…Tran et al discuss in [4] the potential of Indoor Positioning Systems (indoor GPS) and its applicability to Lean Management concepts. They identify the high potential of indoor GPS for setup and changeover.…”

Section: Setup and Enhancement Of The Research Techniquementioning

confidence: 99%

Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning

Miller,

Borysenko,

Heusinger

et al. 2021

Sensors

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Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.

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Indoor Positioning Systems Can Revolutionise Digital Lean

Cited by 16 publications

References 78 publications

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0

Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning

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