Visualizing time-dependent key performance indicator in a graph-based analysis

Hesse, Stefan; Spehr, Marcel; Gumhold, Stefan; Groh, Rainer

doi:10.1109/etfa.2014.7005110

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…TISCSOFT [38] is a decision-support tool for optimizing transportation infrastructure in supply-chain networks. Other researchers have created dashboard-based tools using traditional key performance indicators for supply-chain decision-making [39, 40]. Techniques based on value stream mapping (VSM) have also gained prominence for visualizing supply chain performance [40].…”

Section: Related Workmentioning

confidence: 99%

Generating Contextual Design for Environment Principles in Sustainable Manufacturing Using Visual Analytics

Ramanujan

Bernstein

Totorikaguena³

et al. 2018

Journal of Manufacturing Science and Engineering

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Design for Environment (DfE) principles are helpful for integrating manufacturing-specific environmental sustainability considerations into product and process design. However, such principles are often overly general, static, and disconnected from production contexts. This paper proposes a visual analytics-based framework for generating DfE principles that are contextualized to specific production setups. These principles are generated through interactive visual exploration of design and process parameters as well as manufacturing process performance metrics corresponding to the production setup. We also develop a formal schema for aiding storage, updating, and reuse of the generated DfE principles. In this schema, each DfE principle is associated with corresponding product lifecycle data and the evidence that led to the generation of that principle. We demonstrate the proposed visual analytics framework using data from an industry-led experiment that compared dry ice based and oil based milling for a specific production setup.

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Section: Related Workmentioning

confidence: 99%

Generating Contextual Design for Environment Principles in Sustainable Manufacturing Using Visual Analytics

Ramanujan

Bernstein

Totorikaguena³

et al. 2018

Journal of Manufacturing Science and Engineering

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“…Hesse et al [106] study effects of various performance-based metrics on inventories. Visually tracking metrics using value stream mapping (VSM) has gained wide adoption for summarizing and monitoring supply chain performance [107].…”

Section: Va Tools For Sust Lifecycle Designmentioning

confidence: 99%

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities

Ramanujan

Bernstein

Chandrasegaran

et al. 2017

Journal of Mechanical Design

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The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages—design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

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Graph-based prediction of missing KPIs through optimization and random forests for KPI systems

May

Fang

Eitel

et al. 2022

Prod. Eng. Res. Devel.

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Key performance indicators (KPIs) are widely used to monitor and control the production in industry. On an aggregated level, often represented as graphs or interrelated KPI systems, a comprehensive overview is given. However, missing or inaccurate sensor data and KPIs, as well inconsistencies in KPI based management are a major hurdle disturbing operations. To counter the impact of such missing KPIs, we propose a value optimization based approach to reconstruct the values of missing KPIs within a KPI system. While the approach shows successful reconstruction in the case study, the value optimization can be sped up through a random forest prediction of the initial optimization set. Thus, the inclusion of previous knowledge about the system behavior proves beneficial and superior to the pure optimization based approach, as validated by both randomized and simulation-based measurement data.

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Visualizing time-dependent key performance indicator in a graph-based analysis

Cited by 3 publications

References 25 publications

Generating Contextual Design for Environment Principles in Sustainable Manufacturing Using Visual Analytics

Generating Contextual Design for Environment Principles in Sustainable Manufacturing Using Visual Analytics

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities

Graph-based prediction of missing KPIs through optimization and random forests for KPI systems

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