From data to big data in production research: the past and future trends

Kuo, Yong-Hong; Kusiak, Andrew

doi:10.1080/00207543.2018.1443230

Cited by 152 publications

(82 citation statements)

References 214 publications

(160 reference statements)

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“…Apart from this, most of the resources present on the factory floor have an interface from which data can be accessed. However, this interface is usually regulated by the vendor of the product and can have limitations on the data, the platform, and the communication itself (Kuo and Kusiak 2018).…”

Section: Data Collectionmentioning

confidence: 99%

“…The volume of data produced in recent times is significant. This data needs to be easily accessible while also stored securely (Kuo and Kusiak 2018). Data collected can be divided into three categories, structured (digits, symbols, etc.…”

Section: Data Storagementioning

confidence: 99%

“…In a recent study, Kuo and Kusiak (2018) observe how data-driven methodologies are prominent and gaining attention in production research. However, access to data and methods to collect data from the factory floor are typically limited.…”

Section: Introductionmentioning

confidence: 99%

“…The benefits and the difficulty of collecting data, from such a diverse factory floor, is also captured by Kuo and Kusiak (2018). In their discussions they write: Data from different sources will make the processes and systems more coordinated, thereby resulting in higher productivity, efficiency and profitability.…”

Section: Introductionmentioning

confidence: 99%

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Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Farooqui

Bengtsson

Falkman

et al. 2020

International Journal of Production Research

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The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner -a tool for modelling and analysing production systems -and is currently implemented at an automotive company as a pilot project.

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Section: Data Collectionmentioning

confidence: 99%

Section: Data Storagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Farooqui

Bengtsson

Falkman

et al. 2020

International Journal of Production Research

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show abstract

“…In recent years, digital integration and artificial intelligence have accelerated at an explosive rate. This progress inevitably leads to the rapid development of two technologies: big data [1][2][3] and intelligent algorithms [4]. For enterprise production planning or scheduling, although there are many machine learning algorithms [5][6][7][8][9] for this problem, there are still not many data mining methods, especially for multi-objective job shop scheduling problems (MOJSSP) [10].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

Qiu

Zhang

2019

Applied Sciences

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Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. First, through offline machine learning and data mining, attributes of operations are selected to mine the initial population knowledge. Second, an addition–deletion sorting method (ADSM) is proposed to reprioritize operations and then form the rule-based initial population. Finally, the nondominated sorting genetic algorithm II (NSGA-II) hybrid with simulated annealing is used to obtain the Pareto solutions. To evaluate the effectiveness of the proposed method, three other types of initial populations were considered under different iterations and population sizes. The experimental results demonstrate that the new approach has a good performance in solving the multi-variety production planning problems, whether it is the function value or the performance metric of the acquired Pareto solutions.

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Unleashing the power of big data for platform firms: A configuration analysis

Wang,

Yuan,

2023

Manage Decis Econ

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This study investigates the impact of big data on platform performance using a multidimensional perspective and the fuzzy set qualitative comparative analysis (fsQCA) method. We identify two pathways for platforms to leverage big data to achieve performance improvement: “volume‐centric configuration” and “volume‐talent synergistic configuration.” Our results indicate that big data volume and talent of big data are the core conditions impacting platform performance, but an increase in these factors does not always equate to an improvement in performance. The study makes two substantial contributions: (1) identifying the configurations of big data that lead to high performance of the platform and illuminating the complex interplay of causality between the multidimensional nature of big data resources and their supporting conditions and (2) measuring the multidimensionality of big data resources in platform firms through the utilization of operational data of the app in question. This study has significant practical implications, providing guidance to platform firms on identifying appropriate big data resources to gain a competitive advantage and utilizing supporting conditions to address deficiencies in data assets.

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From data to big data in production research: the past and future trends

Cited by 152 publications

References 214 publications

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

Unleashing the power of big data for platform firms: A configuration analysis

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