An algorithm for data-driven shifting bottleneck detection

Subramaniyan, Mukund; Skoogh, Anders; Gopalakrishnan, Maheshwaran; Salomonsson, Hans; Hanna, Atieh; Lämkull, Dan

doi:10.1080/23311916.2016.1239516

Cited by 37 publications

(17 citation statements)

References 19 publications

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“…Ngamkala and Prombanpong attempted to distribute the load concentrated on a bottleneck by applying an automatic feeder as a buffer [36]. Subramaniyan et al attempted to recognize and distribute the bottleneck based on real-time data collected from the manufacturing management system [37]. In addition, Wang et al analyzed how to classify planned and executed bottlenecks in the job shop [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Jung

Park²,

Lee³

et al. 2021

Journal of Computing and Information Science in Engineering

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Implementing digital transformation in the garment industry is very difficult, owing to its labor-intensive structural characteristics. Further, the productivity of a garment production system is considerably influenced by a combination of processes and operators. This study proposes a simulation-based hybrid optimization method to maximize the productivity of a garment production line. The simulation reflects the actual site characteristics, i.e., process and operator level indices, and the optimization process reflects constraints based on expert knowledge. The optimization process derives an optimal operator sequence through a genetic algorithm, and sequentially removes bottlenecks through workload analysis based on the results. The proposed simulation optimization method improved productivity by ∼67.4%, which is 52.3% higher than that obtained by the existing meta-heuristic algorithm. The correlation between workload and production was verified by analyzing the workload change trends. This study holds significance because it presents a new simulation-based optimization model that further applies the workload distribution method by eliminating bottlenecks and digitizing garment production lines.

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Section: Literature Reviewmentioning

confidence: 99%

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Jung

Park²,

Lee³

et al. 2021

Journal of Computing and Information Science in Engineering

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“…The central aspect of this type of decision support tool is the need for real-time production system data. A study has shown that on an average, 100 data rows are collected per hour per machine by the MES, implying that 500,000 data rows are collected per year per machine (Subramaniyan et al 2016). Therefore, manufacturing companies can collect a large amount of data and use advanced data analytics to make fact-based decisions (O'Donovan et al 2015).…”

Section: Data-driven Maintenance Decisionsmentioning

confidence: 99%

“…Additionally, the existing assessment tools are highly qualitative in nature (Pelaez and Bowles 1994;Singh, Singh, and Kumar 2015) and qualitative approaches have shown to be non-factual (Gopalakrishnan and Skoogh 2018). With the development of computer technology and large amounts of data collected by the Manufacturing Execution System (MES), industries have reached a stage where data-based decisions can be made (Subramaniyan et al 2016). Therefore, in order to provide maintenance prioritization decision support not only on a strategic level but also on an operational level, a data-driven machine criticality assessment is needed.…”

Section: Introductionmentioning

confidence: 99%

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Gopalakrishnan

Subramaniyan

Skoogh

2020

Production Planning & Control

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Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity.

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“…platform to demonstrate it. However, it was identified that the method needs further development to suit the real-world industrial shop floor practice of bottleneck management; towards that, a data-driven approach for the shifting bottleneck identification was proposed in [19], using the online data collected from Manufacturing Execution Systems (MES), to facilitate it for the shop-floor engineers to understand the production system behavior in real-time and thus enable faster actions on the bottlenecks. Although the latter has been an important contribution towards improving industrial practice, the method needs to be further developed to yield results continuously and allow multiple, configurable time granularity, that can be enabled in terms of time-windows in the stream processing terminology.…”

Section: B Challenges and Related Workmentioning

confidence: 99%

“…We aim at an efficient streaming algorithm for the shifting bottleneck detection method of [17], [19] and a configurable methodology to enable deployment considering practicalities in actual systems, including multi-tier computing architectures of IoT-based systems. Our contributions provide:…”

Section: Goals and Contributionsmentioning

confidence: 99%

Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

Najdataei

Subramaniyan

Gulisano

et al. 2019

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Cloud computing is revolutionizing the backbone of data analysis applications, including industrial ones. One of its main pillars is the separation of the logic with which data is accessed (e.g., to study the efficiency of a manufacturing system) from the actual hardware (e.g., server) that maintains and analyses the data. Large distributed cyber-physical systems enabled by, among other technologies, the Internet of Things (IoT), made nonetheless clear that "what to do" with the data and "where to do it" are not disjoint problems; i.e., cloud computing on its own is not enough. Fog and edge computing have emerged as complementary options, to distribute the analysis, helping with challenges by means of close-to-the-source data analysis. We show for a key problem for industrial processes, that of shifting bottleneck detection, how to take advantage of such multi-tier computing architectures, to perform continuous and configurable analysis of data from Manufacturing Execution Systems. We propose a processing framework, STRATUM, and an algorithm, AMBLE, for continuous, data stream processing. STRATUM seamlessly distributes and parallelizes the processing across the tiers and AMBLE guarantees consistent analysis in spite of timing fluctuations, which are commonly introduced due to e.g. the communication system; it also achieves efficiency through appropriate data structures for in-memory processing. The experimental study on a real-world dataset, taken from a production line over two years and including 8.5 million entries, shows the benefits of the proposed solution in enabling configurable and efficient analysis.

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An algorithm for data-driven shifting bottleneck detection

Cited by 37 publications

References 19 publications

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

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