An icon-based data image construction method for production data visualization

Visualization takes advantage of human's rapid and flexible pattern recognition capacity to provide a powerful information-processing environment. It is especially suitable for identifying patterns that are difficult, or impossible, to be defined by a mathematical or computer model. This paper presents an icon-based method for constructing images to visualize the abstract, multidimensional production data stored in tables. The method employs a simple, topology-preserving mapping to convert numbers in a table to icons. This mapping strategy not only makes the meaning of the resulted data image easy to understand, but also makes the maximum number of dimensions and data items that could be displayed be virtually unlimited. Visual patterns in the data image are formed by adjacent icons that have similar grey scales. The area sampling techniques could be applied to reduce the size of the data image while still retaining its visual patterns. The proposed visualization method has been applied to several production planning and control activities, including analysing WIP patterns, production indices, and the machine idle cost performance of production plans. These industrial case studies demonstrate that the ability to quickly and easily examine large amount of manufacturing data through visualization is critical for manufacturing companies to understand their performance, discover possible problems and take necessary actions so that they could remain competitive in the market.