Visualisation and forecasting in the forestry supply chain with the help of Shiny
The quality of the forest products is inherently linked to the geographical location and environmental conditions. A Shiny app for a leading forestry company in the UK has been developed to visualise the spatial dimension of historical harvesting data, with the intention of giving the users the information they need to confirm, or make their inferences, about the potential product yields in future harvesting locations. In this talk we will discuss how the app design has been adapted along the development process according to the user needs and the data challenges found, both framed in the organisational context of a large company. The use of Shiny as a decision-making tool in the forestry supply chain can increase its efficiency and can have a positive impact on both the company’s profits, and the management of the natural resources.
Teresa is a researcher with a background in modelling environmental and chemical processes using Python. She currently uses R as her main programming language in her role as a data scientist in a Knowledge Transfer Partnership project between Tilhill Forestry and the University of the Highlands and the Islands, facilitated by Innovate UK. Teresa's work is related to understanding how to make the most out of data in an organisational context, and it ranges from assessing the data maturity of a company to developing a practical decision-making tool using Shiny.