The origin of Decision Support Systems – from the 60s to today
As early as the mid-1960s, computerised quantitative models were being used to help in decision-making and planning, according to A Brief History of Decision Support Systems. Decision Support Systems aided in recurring business planning facilitated production planning, and were instrumental in developing Management Information Systems.
As technology continued to evolve, so did the context in which Decision Support Systems could be used. Traditionally, these systems were developed in academic contexts where access to some of the first time-sharing computer systems was possible.
Technological innovation also brought with it guiding principles that provided an ‘execution framework’ within which DSS could operate, for example, from more standard analytical and financial analysis and modelling to what MIT doctoral student Steven Alter called “optimization models that provide guidelines for action by generating an optimal solution consistent with a series of constraints”. These operations were being performed in the 80s (when Alter was doing his PhD); the sophistication level of Decision Support Systems in a modern context has amplified exponentially.
The 1980s also happened to be when DSS started being used in forestry operations, management and planning. These first systems were “typically hard-coded, and designed to address relatively narrow, well-defined problems…such as pest management for specific pests on specific species,” according to research, but the evolution of technology has broadened the scope of DSS significantly.
One such example is the Huerka Forest DSS in Sweden which showcases the power of DSS through four different software programmes that work seamlessly together to provide rich forest values, data and multi-criteria analytics to support forest planning. Instead of a narrow scope of focus with no room for overlaying of data or overly sophisticated interpretation, Huerka provides insight into “timber and biofuel production, carbon sequestration, dead wood dynamics, habitat for species, recreation and susceptibility to forest damages”, says research from the Swedish University of Agricultural Sciences.
As pressures on forests continue, from climate change to deforestation and encroaching human habitat, Decision Support Systems become more important in predicting risk mitigation and preservation measures, what trends are significant in successful forest planning, what extreme weather and climate events are most likely to have the most significant impact and much more.
Forest Research UK also has a range of free Decision Support Systems to support forest owners and managers in understanding forest impacts and what actions can be taken to increase forest resilience. Some of these systems include ClimateMatch, which compares a site’s future climate to current locations in Europe, and forestGALES, which estimates the probability of wind damage to conifer stands.
Even in the 1980s there was talk of Artificial Intelligence’s role in aiding DSS processes, but as forestry’s problems become more complex and more variables are added to the equation, AI will be at the centre of decision-making. Iris Technology is a company specialising in Intelligent Decision Support Systems, using AI techniques based on Machine Learning and Deep Learning to support businesses’ decision-making activities. Through AI, they can predict anomalies requiring corrective action, predict the biodegradation process of certain biocomposites and coatings in the green chemicals industry, and even integrate patient-specific data into therapies. This is just some of what DSS and AI can do when they come together.
For forestry, AI integrates seamlessly with DSS, harnessing predictive power to make more informed, future-proof decisions. A great example of AI’s role in these Decision Support Systems is a joint forestry project run by the Karlsruhe Institute of Technology and EDI GmbH in Germany. This project uses “a cloud-based decision support system…that uses AI to support local foresters when deciding where to log or when to plant new trees”. All of this is housed on a mobile app.
Putting this kind of decision-making power in the hands of forest managers is fundamental to ensuring sustainable forestry practices and planning.