What do we do when it comes to analytics?
Projects and activities are compared on the basis of Key Performance Indicators (KPIs) with other projects or with reference projects. This way routinely benchmarking is established.
Benchmarking can occur on standard indicators such as turnaround time and full utilisation of the budget, but also on indicators such as economic cost & profit, labour costs and resource costs. Anago possesses comprehensive arithmetic to calculate diverse models and extrapolations.
Forecasting the workload, absenteeism, processing times on basis of statistical techniques such as:
- Lineair regression
- Exponential Smoothing
- Trend analysis
- Pipeline forecasting
Optimisation and simulation
Solutions of planning problems can be ‘calculated’ by using various optimisation techniques such as local search algorithms, heuristic search methods and solvers.
Complex planning based on processes can be simulated in time. This approach is meant for situations in which the processes are described by means of rules of conduct.
The statistical determination of ‘polluted’ or missing data in the actual data from the systems. The polluted/missing data can be replaced by statistically correct values, where the data can serve as input for, inter alia, statistical forecasting.
A planning model easily gets complex. How does one determine which values should have parameters, how to determine the accuracy of the forecasts and how to determine the correct values of the processing time?
Anago is able to place a validation model over an existing planning model which validates the model and the parameters based on the actual numbers. With the results of the validation, adjustments can be made on the parameters or even on the planning model itself.
Which project risks and finances are related to the portfolio? Anago enables the organisation to calculate these risks and to present these.
The risk model can be designed in an elementary manner, but Anago also supports econometric risk management calculations, whereby modules are used that are developed with the Matlab Statistics Toolbox.
Analytics of sustainable energy
One of our clients is a consultancy agency for energy conservation and sustainable energy applications. It advises, among others, home construction companies, energy firms and authorities on investments with regard to energy conservation, sustainable energy and climate matters. The basis for this advice forms the Built Environment Analysis Model (BEAM2) model. The essence of the model is to calculate the consequences of energy consumption, CO2 emissions and investment costs of various programmes for demolition, renovation and new developments.