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.
Key benefits of smartPM's AI based planning
2 concrete application examples: Artificial intelligence in planning
Control sales performance effectively: forecast closing chances for leads and opportunities
smartPM.solutions uses machine learning methods in order to be able to make the most precise possible forecast of the profit and the time of the conclusion of an opportunity. In contrast to the usual estimates of the closing time by the respective sales managers at the end of the month, which are usually based only on their experience and subjective assessments, the machine learning method uses all relevant factors and experiences of all sales employees to provide the most accurate forecast of the profit time of an opp. Our data science team uses scientifically based and proven mathematical and statistical methods and automatically uses R and Python with their latest libraries for the analysis.
In many cases, sales revenues depend on external factors such as the weather or some macroeconomic values. The multivariate forecasting method allows several external factors to be taken into account. Artificial intelligence automatically selects the most relevant influencing factors and adjusts the prediction to make it even more precise. This also helps with investment planning, for example.
Controlling Fallstudie: KI in der Planung bei der Follmann Chemie Group
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