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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

Automatic detection of the best forecasting method

Different forecast outcomes are clearly visualized, suggesting the forecast with the best fit

Scenario comparisons and 'What-if' analyzes

Scenario based 'What-if' analyzes enable better, flexible decision-making and proactive action-taking

Cost and time savings through faster processes and automated analyzes

Big data sets can easily be handled and the forecast precision is notable

Selection and consideration of many internal and external influencing factors

Many parameters like region, company size, products etc. are considered and weighted

Self-learning systems (neural networks) give precise estimates

The most relevant parameters are automatically selected to maximize accuracy and avoid mistakes

Planning and forecasts at the push of a button

Precise, quick and reliable answers to the most common management quesitons allowing for agile decision-making

Customer opinion

"As an innovative company, we are always actively looking for new processes and solutions. has convinced us of the advantages of their new AI-based models and their forecast quality.”
Ingmar Gon_Director Supply Chain Planning_Follmann Chemie Group
Ingmar Gon
Director Supply Chain Planning
Follmann Chemie Group

2 concrete application examples: Artificial intelligence in planning

Control sales performance effectively: forecast closing chances for leads and opportunities 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.

Multivariate forecasting

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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