Applied Artificial Intelligence in business planning
Do you have time consuming data-collecting processes and inaccurate forecasts? AI solutions in Integrated Business Planning help save time, improve data quality and accuracy. Forecasts can be generated using classical statistical methods, like Linear Trends, Holt-Winters Exponential Smoothing or Auto-Regressive Integrated Moving Average (ARIMA). Theses statistical methods can be ‘good enough’ for linear patterns, but more complex cases and big data require AI – Machine Learning methods (Neural Networks).
We use both classical statistical methods and AI (more than 40 networks are tested).With the smartPM Sales Performance Forecasting Module the forecast with the best fit is suggested. Especially in times of increased volatility and uncertainty, precise forecasts and high data quality can be crucial to make appropriate decisions rapidly.
A lot of people talk about AI in planning - we apply AI.
Key benefits of smartPM's AI based planning
Some examples how it works: Artificial Intelligence in Planning
Sales Performance: Forecast closing chances for leads and opportunities
smartPM.Solutions use Machine Learning and Artificial Intelligence methods to deliver the most accurate probabilities of winning an opportunity and for estimating the actual closing month. Contrary to common closing month estimation made by the opportunity owners, based only on their experience and subjective feelings, ML methods use all the relevant factors and previous experience of all opportunity owners to deliver the most accurate winning probability estimation. We leverage our expert mathematical and statistical knowledge and tools like R and Python with their most up-to-date libraries.
Flexibility and robustness
Depending on the data and its dimensionality, some methods may be more, some less accurate. For the winning probability estimation, up to three different ML-methods are compared. FP&A Experts who think that artificial intelligence should be supported by human intelligence, may fine tune the methods by choosing the most appropriate kernel and activation functions or number of layers and nodes for Neural Networks. Users with less mathematical background may use the default functions to let the system automatically choose the most accurate method.
While choosing the most appropriate method for revenue forecasting, there is no ultimate recipe – it is all depends on the data. That is why we use different statistical and ML-methods to be able to compare them and choose the most accurate one for the future forecasting. Also here we give the freedom of fine-tuning and adjustment of parameters of the number of trained networks, or letting the system to make the entire work for you.
In many cases, sales revenue depends on external factors like weather or some macroeconomic values. The multivariate forecasting method allows for taking several external factors into account. AI will automatically pick the most relevant of them and adjust the forecast to make it even more accurate.