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Purchases, conversations, access to information, music and movies: more and more of our online life is mediated by complex algorithms that are designed to make the experience of the Web customized and more “personal”. These algorithms can process an amount of heterogeneous data that would take enormous resources for the human mind to cope with, and find valuable patterns in it. Their use is not limited to our online experience as similar algorithms have been also implemented, for example, to inform policymakers: suggesting where to deploy police forces around the urban context, assessing criminality risk scores of offenders, or allocating high school students to the most suited school. While the consequences of the decisions made by algorithms have a great impact on people’s lives, the way they are built and designed makes them de facto “black boxes”: a series of legal and technical barriers prevents from accessing and understanding how a certain input influences a given output. Overseeing their decision processes becomes then of the utmost importance. This paper argues that visualizations can become a powerful tool to monitor algorithms and make their complexity accessible and usable by visually showing the relation between the inputs and the outputs in a manner that mimics an observational study approach. The paper analyzes a case study developed as an experiment to test opportunities and criticalities in using visualization to represent the presence and the activity of algorithms. This represents a shift from the main purpose of visualizations and Data Visualization in general: since its strong suit is to support human decision- making processes by transforming data into knowledge, the substitution of people by machines in this activity seems to make visualizations obsolete. A computer doesn’t need to “see” the data to make a decision – or at least not in the same way as people do – no matter how multidimensional and hetero- geneous the data is. With the diffusion of algorithms, the need to inspect their accountability and performance will simply move visualizations at a later stage. From a decision-making tool visualization becomes a monitoring and awareness tool.

Demand planning has become a key issue for the performance of supply chains. However, the right quantity is dependent on many factors. Besides market influences, like changing demands, there are also inner-company variables such as the availability of resources. Decision makers often lack a clear picture of what influences their decisions and perceive a state of complexity. There are several theoretical models for managing complexity, but they are not designed to identify the complexity in demand planning. The aim of this paper is to establish a methodology for visualizing and reducing the complexity in the demand planning process. The first result is a model for visualizing the complexity in the planning process. The model shows the factors which influence planned quantities in a chronological order, and, makes cause-effect relations regarding time, responsibility and system support visible. The second result is a structured compilation of methods and tools to actively influence the complexity in the demand planning process. The identified approaches are either assigned to complexity design - reducing complexity by simplifying the supply chain - or complexity control - reducing complexity by decreasing the uncertainty in planning.