When researching the location of a new property, a company can apply the criterion of accessibility to its employees. The company weighs access by public transport, travel by bike or on foot higher than access by car.

Per personnel member, the distance to each potential property is calculated through Google Maps. The resulting data is processed by an algorithm that takes into account the travel weight factor. Per location, this results in a score that can be shown in the score chart, which allows the company to optimize the selection of their new property.

Organizations are concerned with the well-being of their employees. FM Managers would like to clearly see which spaces are performing well or bad in terms of climatic conditions (temperature, humidity, CO2-levels), but only those measured during the time that the space was in use.

The data is provided by using a sensoring system that measures these properties. The environmental conditions are given a specific weight in order to classify them: far too low (value -2), too low (value -1), okay (value 0), too high (value 1), far too high (value 2). The algorithm takes into account this factor when computing the performance score.

Contract managers are keen on assessing the performance of SLA contracts. Typical KPIs that measure Time-to-Complete scores can result in data that remains inconclusive. If you would factor in another qualifier, contract performance can be assessed more clearly: SLA amount.

This allows the contract manager to assess the performance of SLA contracts by weighing the Time-to-Complete score against costs. The algorithm results in a performance score that can be represented in a score chart.