In the last couple months, I have been working for the Dutch grid operator, Alliander. In this article I would like to tell you my lessons learned about scenario analytics. The future is unpredictable and new views of the future are modelled on a daily basis. The goal of scenario analytics is to streamline these views, such that scenarios are created more effectively and the effects are calculated with the same model. This leads to better decision making based on these analyses and insights.
Alliander is responsible for the distribution of energy to roughly one third of all households in the Netherlands. This includes maintaining a high quality network for the future. Daily questions are rising such as; how many electric cars will be driven, what proportion of the energy will be renewable, which cities will grow in their number of households. Therefore, scenarios are made frequently by various department within the organization. How all these different scenarios are made, is often a big puzzle for organizations. To make it even more complex, external sources like customers and governmental parties have again different views of the world. To streamline these different scenarios we developed a tool with the ability to create new central scenarios and provide insights based on these views created for the entire organization.
There are four important factors which were important, when creating a central platform of scenarios;
Flexibility is important for the users and stakeholders. It has to be at least as good as the tools and methods they are currently using. Multiple usages leads to multiple different requirements. It takes workshops and interviews to determine the required features and the optional ones. To acquire this flexibility you might think of templates and/or user friendly input portals. This way all users are aligned in the same matter and adapt to this new way of working. Make sure that the model still functions with all the optional input.
Transparency is necessary to gain the trust of the users. To gain this trust, we gave all the users access to download the inputs and outputs of the different scenarios. This public availability of scenarios provided the users with insights in the intermediate steps and calculation of the final results. Other ways to create transparency are additional documentation and help modules to guide the user for better experiences.
Comparability is adding value to the designed scenarios. Being able to see the effects of your created scenarios and understand the differences and the impact, you create value. These new insights help users to act on their view.
Uniformity is creating one single view. Instead of creating new scenarios by every department, you could have a look at other existing scenarios. You might find other views in the organization which already design scenarios for your usage. This way you do not have to create a new one.
The results have been of significant impact to the organization. Not only was it used to centralize the analytical underlying model and creating scenarios, people compared their outputs with other departments. The tool is now widely used within the organization and its usage continues to grow.
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