PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

Dong, Sun; Huang, Renfei; Chen, Yuanzhe; Wang, Yong; Zeng, Jia; Yuan, Mingxuan; Pong, Ting-Chuen; Qu, Huamin

doi:10.1109/tvcg.2019.2934275

Cited by 24 publications

(16 citation statements)

References 47 publications

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“…The paper raises several points that can be useful in the design of smart PPC systems (such as the integration of digital twins and IoT technologies like edge gateways and edge computing) but does not address this explicitly. In a related study, Sun et al (2020) propose a visual analytics approach to production planning, to address the need for solutions that will enable a quick response to sudden changes in the operations and market environment, and with the ability to handle the deluge of data in emerging industry 4.0 manufacturing systems.…”

Section: Emerging Uses Of Smart Technologies For Smarter Ppcmentioning

confidence: 99%

“…Furthermore, the distinction in the application of emerging technologies at the tactical and operational levels is not always clear, and use cases often overlap. Examples of use cases include real-time visualization and scenarios' simulations (Sun et al, 2020), product quality control, and integrated production-maintenance scheduling (Biondi et al, 2017).…”

Section: Design and Development Considerationsmentioning

confidence: 99%

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Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

et al. 2021

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In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

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Section: Emerging Uses Of Smart Technologies For Smarter Ppcmentioning

confidence: 99%

Section: Design and Development Considerationsmentioning

confidence: 99%

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

et al. 2021

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“…++++ [40,41] Product personalization It is a process of delivering customized goods and services to the customers as per their needs and desires. ++ [42] Virtualization It is the creation of a virtual-rather than actual-version of something, such as an operating system or production process.…”

Section: Name Description Effect On Optimization Examplesmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.

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“…Monitoring and analyzing these event sequence data can help managers understand factory conditions, quickly respond to various sorts of events, and optimize the productivity of factories. A variety of visual techniques have been introduced to help users exploring anomalous events [8], [40], [115], [121], [132] and optimizing manufacturing plans [45], [97].…”

Section: Industry 40mentioning

confidence: 99%

“…Users are allowed to interactively explore the inefficiencies and reschedule manufacturing plans accordingly. PlanningVis [97] is a multi-level visualization system to support interactive exploration and comparison of production plans. This technique juxtaposes heat maps, line charts, and bar charts to visualize the differences between two plans, and thus, optimizing production plans.…”

Section: Industry 40mentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

Cited by 24 publications

References 47 publications

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

Visual Progression Analysis of Event Sequence Data

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