Visual analytics for supply network management: System design and evaluation
作者:
Highlights:
• Supply network scale, scope, and complexity strain managers' cognitive capacity.
• Visual analytics augments and amplifies managers' supply network intelligence.
• We propose a visual analytic framework for supply network management.
• System incorporates multiple interactive visualizations and predictive analytics.
• Multi-phase evaluations reveal significant utility and usefulness of our system.
摘要
We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.
论文关键词:Visual analytics,Supply chain management,Coordinated views,Interactive DSS,Predictive analytics
论文评审过程:Received 29 April 2016, Revised 25 July 2016, Accepted 2 August 2016, Available online 11 August 2016, Version of Record 18 October 2016.
论文官网地址:https://doi.org/10.1016/j.dss.2016.08.003