Analysis of asset location data to support decisions in production management and control

Gyulai, Dávid; Pfeiffer, András; Bergmann, Júlia

doi:10.1016/j.procir.2020.05.035

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…Workflow evaluation refers to characterization of dynamic production systems by computing process-related metrics or Key Performace Indicators (KPIs) for implementing situation-aware production control [107]. In the era of cyber-physical environments, state-of-the-art tracking systems monitor, evaluate and control production in smarter ways than ever before.…”

Section: Workflow Evaluationmentioning

confidence: 99%

“…To develop novel analytics solutions for improving production control and management process, the correct use of RTLS data is of utmost importance. Consequently, Gyulai et al [107] present a spatial processing method to clean trajectory data for the purpose of computing different KPIs (similar to [12]) to evaluate the performance of production systems. The method applies a discrete event simulator model using Siemens Tecnomatix Plant simulation to create a test bed reflecting the operation of an assembly system consisting of four lines each with fifteen workstations.…”

Section: Workflow Evaluationmentioning

confidence: 99%

“…The above review suggests that several approaches have been investigated in the literature for efficiency monitoring, production control, safety and collaboration. Most of the reviewed studies consider using standard ML or data mining methods with only a few exceptions: for example, [12,107] compute KPIs for evaluating workflow efficiency from operational data without ML involvement, and [156] presents an analytical method for processing raw trajectory data. However, the literature supporting the utilization of RTLS data based on ML is too shallow -there are few studies available and the applied ML techniques are simplistic.…”

Section: Summary: Analytics On Position Based Movement Datamentioning

confidence: 99%

See 2 more Smart Citations

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner

Smith

Rana

et al. 2022

Preprint

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Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for futuredevelopment and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications.

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Section: Workflow Evaluationmentioning

confidence: 99%

Section: Workflow Evaluationmentioning

confidence: 99%

Section: Summary: Analytics On Position Based Movement Datamentioning

confidence: 99%

See 1 more Smart Citation

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner

Smith

Rana

et al. 2022

Preprint

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“…They describe how data can be collected by tracking products, components, employee and equipment. Within the scope of this paper, no more detailed consideration of the technical functioning is given and reference is made to [5,6,14].…”

Section: Factory Planningmentioning

confidence: 99%

Use Cases of Real-Time Locating Systems for Factory Planning and Production Monitoring

et al. 2021

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Production companies operate in an increasingly dynamic and unpredictable market environment. The ever shorter innovation cycles and the associated decreasing product life cycles require production systems and processes to change continuously. Furthermore, rising complexity in market enfactories through product variety leads to an increased coordination effort in factory operations. To stay competitive, decision making in factory planning as well as production planning and control (PPC) must be done target-oriented and in the shortest time possible. A fundamental basis in this respect is a reliable and consistent database, needed for the overall and holistic improvement of the logistics performance of a company's internal supply chain. However, the collection and preparation of data is associated with a high expenditure of time and thus represents a great challenge. Additionally, required data in practice is often inhomogeneous or inconsistent, which makes it difficult to track ongoing processes precisely. Due to the continuous availability of exact positions at any time, Real-Time Locating Systems (RTLS) offer the potential to provide movement data of essential elements of the production system such as material supply, resources and workforce with comparatively little effort. To demonstrate the resulting potentials for factory planning and PPC, use cases for RTLS have been identified and implemented in the Learning Factory of the Institute for Production Systems and Logistics (IFA). After a short introduction of the IFA Learning Factory and the principle of Real-Time Locating Systems, the use cases in factory planning, factory operation and production monitoring are described. Having summarized the achieved benefits provided for the training participants in the IFA Learning Factory a summarizing conclusion is given, underlining the great possibilities of real-time localization in production environments and providing an outlook on further research activities.

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“…Additionally, some solutions can be unreliable and inaccurate when used indoors. For instance, Global Positioning System (GPS) tags are unreliable indoors, particularly in steel and reinforced concrete buildings [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Bluetooth 5.1: An Analysis of Direction Finding Capability for High-Precision Location Services

Pau

Arena

Gebremariam

et al. 2021

Sensors

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This paper presents an in-depth overview of the Bluetooth 5.1 Direction Finding standard’s potentials, thanks to enhancing the Bluetooth Low Energy (BLE) firmware. This improvement allows producers to create location applications based on the Angle of Departure (AoD) and the Angle of Arrival (AoA). Accordingly, it is conceivable to design proper Indoor Positioning Systems (IPS), for instance, for the traceability of resources, assets, and people. First of all, Radio Frequency (RF) radiogoniometry techniques, helpful in calculating AoA and AoD angles, are introduced in this paper. Subsequently, the topic relating to signal direction estimation is deepened. The Bluetooth Core Specification updates concerning version 5.1, both at the packet architecture and prototyping levels, are also reported. Some suitable platforms and development kits for running the new features are then presented, and some basic applications are illustrated. This paper’s final part allows ascertaining the improvement made by this new definition of BLE and possible future developments, especially concerning applications related to devices, assets, or people’s indoor localization. Some preliminary results gathered in a real evaluation scenario are also presented.

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Analysis of asset location data to support decisions in production management and control

Cited by 12 publications

References 15 publications

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Use Cases of Real-Time Locating Systems for Factory Planning and Production Monitoring

Bluetooth 5.1: An Analysis of Direction Finding Capability for High-Precision Location Services

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