In this article you will read:

  • Definition of Predictive Analytics
  • Advantages of predictive analytics
  • Examples: Predictive analytics in practice

Definition: What "predictive analytics" means

Predictive analytics is by definition a mathematical principle that uses algorithms and artificial intelligence (AI) to derive probabilities from historical and current data. In this way, patterns, correlations and trends can generally be uncovered. Originally coming from the field of statistics, companies now use the method for forecasts of various kinds, including price forecasts.

How predictive analytics works

The method uses structured and unstructured data, for example from internal and external IT systems (big data/data mining). Predictive analytics collects this information using text mining, among other methods, and combines it with elements of game theory and/or simulation methods. Thanks to machine learning, the algorithms independently draw insights from their own data processing and automatically develop predictions on this basis.

The underlying software has become more accessible and user-friendly over time thanks to user interfaces suitable for the subject area. This, together with its increasing accuracy, makes the method interesting for procurement.

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Advantages of predictive analytics

Demand and price forecasts are among the most important elements of strategic procurement processes because they strongly influence decisions. That is why predictive analytics is used, for example, for supplier management, controlling, commodity group management, sales and expenditure management. These and other areas can use the process for the following purposes, for example:

  • Payment and payment target analyses
  • Invoice analyses
  • Order placement
  • Risk assessment
  • Service control
  • Compliance rule monitoring

Predictive analytics is particularly interesting for price forecasts and - closely related to this - sales volume calculation. Different suppliers, production processes, transport routes and political circumstances and legal requirements that vary from country to country make manually calculated forecasts difficult. Predictive analytics software has advantages here because it is significantly faster and more precise. It offers, for example:

  • Control of prices in real time
  • Development of scenarios
  • Long-term price forecasts for new products

In practice, this means that companies can use predictive analytics to keep an eye on the market and the competition and, thanks to the software, estimate future demand and price developments. This makes it possible to order their own requirements at the most favourable time.

Examples: Predictive analytics in practice

Its capabilities recommend predictive analytics for a whole range of application areas. That is why numerous industries use the method for different purposes. A selection of examples:

  • Financial services: Financial institutions use machine learning techniques and quantitative tools to predict credit risks.
  • Automotive industry: Companies developing autonomous vehicles analyse sensor data from connected cars to improve driver assistance algorithms.

Medical technology: An asthma management device records and analyses patients' breathing sounds and provides instant feedback via a smartphone app to help people manage asthma and the lung disease COPD.

Aerospace: To improve aircraft uptime and reduce maintenance costs, an engine manufacturer created a real-time analytics application that predicts the performance of oil, fuel, aircraft start, mechanical and control subsystems.

  • Automation and engineering: A plastics and film manufacturer saves €50,000 a month with a condition monitoring and predictive maintenance application that reduces downtime and minimises waste.
  • Energy supply: Advanced forecasting apps use models that monitor available power plant capacity, weather and seasonal consumption.