Abstract
Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.
Highlights
The massive and heterogenous amount of data produced by Supply Chain (SC) actors raises the need for developing data analytics solutions to support decision making activities [1]
The collected data from the results of the study are categorized based on the following dimensions to answer our research questions: the use cases of Visual Analytics (VA) in the SC, the decisions a VA system aims at supporting, the data that is used for VA, the types of visualizations employed, and the type of analytics implemented
One of the aims of this review is to identify the decisions that are supported by a VA system
Summary
The massive and heterogenous amount of data produced by Supply Chain (SC) actors raises the need for developing data analytics solutions to support decision making activities [1]. Visual Analytics (VA) plays a key role in analyzing the vast amount of data collected by different SC actors as the result of their day-to-day supply network operations, taking from suppliers and manufacturers to warehouses, logistics and retailers. Many analytical approaches have been proposed to support decision making for SC processes and activities [2], [3]. These studies are mainly focused on the big data capabilities of companies. The study presented in [2] included data visualization as part of their proposed architecture for SC analytics; in this
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