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We used Sandis simulation tool to study a historical battle of Loimola in summer 1944. The first goal was to study war history and address a novel method in Finnish environment. The second goal is to publish the methods used in military analysis. Instead of the classified questions and scenarios in modern Finnish defense, the battle of Loimola 27th July 1944 was used to get a public example of the analysis method. As a third goal, this simulation is an example of the tens of historical simulation studies needed to validate simulation software. Sandis simulation tool has been used for cost-effect and tactical analysis in Finnish defense forces. With Sandis the human operator is responsible for tactical decisions during the scenario building phase. During the calculation phase the computer calculates the combat losses, platoon/squad states (operational, defeated or destroyed), ammunition consumption and also other values not used in this study. During the analysis phase, charts, tables and killer-victim scoreboards are created. Our case study focuses on the day of 27th July 1944, when the Soviets assaulted the Finnish defense lines near Lake Suovajärvi. The unit hierarchies, weapons, weapon parameters, positions and movements of the units were set as input. The results match the historical events: the Finnish losses were 30 according to the literature; simulated loss distribution (95%) was between 25 and 44, mean 35. In the simulations the attacking units are defeated and stopped as in literature. The ratio of losses caused by artillery and light infantry weapons did not conflict the values from larger area available from the literature. Thus in this case study, Sandis performed well. In future work, different historical battles could be simulated in order to get better insight of our history and key issues of war, helping to improve our future defense.

SEED Center Paper Final Report of Task Group MSG-088. Final Report of Task Group MSG-088 Data Farming is a process that has been developed to support decision-makers by answering questions that are not currently addressed. Data farming uses an inter-disciplinary approach that includes modelling and simulation, high performance computing, and statistical analysis to examine questions of interest with large number of alternatives. Data farming allows for the examination of uncertain events with numerous possible outcomes and provides the capability of executing enough experiments so that both overall and unexpected results may be captured and examined for insights. In 2010, the NATO Research and Technology Organization started the three-year Modeling and Simulation Task Group “Data Farming in Support of NATO” to assess and document the data farming methodology to be used for decision support. This final report documents the completed work of this Task Group, designated MSG-088. It includes a description of the six realms of data farming as well as the two case studies performed during the course of MSG-088. This report leverages the knowledge of data farming experts to codify data farming, describing how best to perform data farming to gain insight into questions in support of decision makers.