We all know the feeling – wanting to know now what the future holds. With the help of Predictive Analytics, this is now becoming a reality. Many companies are already opting for this method to safeguard their competitive advantage in the future.

Basis for business decision-making

Predictive Analytics have been around since the middle of the 19th century. It concerns statistical methods to make forecasts using historical data. Forecasting is particularly relevant in the financial world: Numerous tools and methods have always been used to create reports as a basis for business decisions. With the evolutionof new technologies such as Artificial Intelligence and Machine Learning, Predictive Analytics has further developed and enabled deeper insights and better predictions. The potential is largely in the use of data and algorithms to analyze relevant control dimensions (e.g. clients, markets and resources) and being able to predict the corresponding decision-making and contract risks, market trends, business developments and customer requirements.

Relevant for the entire business planning

The application of predictive analytics covers all planning in companies. Corporate planning and financial planning lead the way as this is where sales, stock quantities and profit is predicted. Predictive Analytics can also be applied when it comes to procurement, production and marketing: for example in functional planning, where it is used to predict the raw materials price trend so as to improve the purchasing strategy. It can also be used cross-divisionally in the field of personnel and quality management to guarantee quality and capacity utilization. One question should always be asked before application: what added value or potentials will Predictive Analytics bring to the company?

Figure 1: Potentials of Predictive Analytics 

The added value of Predictive Analytics can be divided into three areas: improved decision-making, increase in process efficiency and measurable added value. The inclusion of various data sources and the automation of the prediction process makes the results even more objective and enables more accurate predictions. The automation reduces the administrative costs for larger data sets. All information obtained from the analyses comes with improved support for decision-making, which have a positive impact on the whole company. These decisions are combined with the statistical correlations and the prediction so that strategic decisions can be made within the production, planning and control process.

Process efficiency in particular is the focus of the new methods. It raises the predictive quality using statistical models, which thus increases the efficiency along the value chain. The conception, model management and other matters are only a small part of the additional planning for the forecasting. This enables the measures for reaching targets to be emphasized. Predictive Analytics comes with quantifiable added value in the form of increased sales and reduced costs. This in turn positively influences the ROI. Improved efficiency has positive effects on sales, business risks and costs:

Increasing sales by

  • higher customer satisfaction and competitiveness
  • developing new business models
  • improving the products by way of better understanding of customer requirements
  • optimizing use of resources

Minimizing risk by

  • Data analysis (maintenance intervals, productivity, set-up times)
  • Optimization of the production process

Reducing costs by

  • optimizing production processes
  • reducing personnel and storage costs
  • increasing efficiency along the value chain

The added value provided by Predictive Analytics is produced exactly when new findings have been gained from the available data set. When trends and risks are recognized and analyzed at an early point, it allows targeted measures to be proactively derived.

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