Everything has its price. The only question is when and how high it will be. Because ordering at the right time saves costs. With predictive analytics, the most favorable moment can be predicted. Here you can find out how the method works and what advantages it offers shoppers.
Predictive analytics is a mathematical principle that uses algorithms and artificial intelligence (AI) to derive probabilities from historical and current data. In this way, patterns, relationships and trends can generally be discovered.
Originally coming from the field of statistics, companies now use the process for various types of forecasts, including price predictions.
This is how predictive analytics works
The method uses structured and unstructured data from internal and external IT systems (big data / data mining). Predictive analytics collects this information using text mining, among other things, and combines it with elements of game theory and simulation processes.
Thanks to machine learning, the algorithms independently draw findings from their own data processing and use this as the basis for automatic predictions.
The underlying software has become more accessible and user-friendly over time thanks to user interfaces that are suitable for specific departments. This and its increasing accuracy make the method interesting for shopping.
Benefits of predictive analytics
Demand and price forecasts are among the most important elements of strategically designed procurement processes because they have a strong influence on decisions.
That is why predictive analytics are used for supplier management, controlling, product group management, sales and expenditure management. These and other areas can use the procedure for the following purposes:
• Payment analysis
• Billing analysis
• Procurement
• Risk assessment
• Service control
• Compliance rule monitoring
Predictive analytics is particularly interesting for price predictions and - closely related to this - sales volume calculation. Different suppliers, production processes, transport routes as well as political circumstances and legal requirements that differ from country to country make manually calculated forecasts difficult.
Software for predictive analytics has advantages here because it is significantly faster and more precise. A typical predictive analytics software offers:
• Control of prices in real time
• Development of scenarios
• Long-term price predictions for new products
In practice, this means that companies with predictive analytics keep an eye on the market and the competition and, thanks to the software, can assess future demand and price developments.
This makes it possible to order your own needs at the most favorable time. You can contact us at HData system for excellent predictive analytics solution.
Predictive Analytics in Practice
• The capabilities of a predictive analytics software make it essential for a whole range of application areas. Therefore, numerous industries use the method for different purposes. Some of which include:
• Financial services: Financial institutions use machine learning techniques and quantitative tools to predict credit risk.
• Automotive industry: Companies that develop autonomous vehicles analyze sensor data from networked automobiles and thus improve driver assistance algorithms.
• Medical technology: An asthma management device records the breathing sounds of patients, analyzes them and offers immediate feedback via a smartphone app to make it easier for those affected to cope with asthma and the lung disease.
• Aerospace: To improve aircraft uptime and reduce maintenance costs, an engine manufacturer created a real-time analytics application that predicts the performance of the oil, fuel, aircraft takeoff, mechanical condition, and control subsystems.
• Automation and mechanical engineering: A plastics and film manufacturer saves thousands of dollars a month with an application for condition monitoring and predictive maintenance that reduces downtimes and minimizes waste.
• Energy supply: Advanced forecasting apps use models that monitor the available capacity of power plants, the weather and seasonal consumption.
Applications of Predictive Analytics
1) Predictive maintenance
Predictive analytics is also used in mechanical engineering and automation processes - this is called predictive maintenance. Predictive maintenance is based on maintenance-relevant data, i.e. sensor data that is generated during the production of goods.
These collected sensor data help to correctly determine the maintenance status of a machine or system. For this purpose, predictive maintenance is used to calculate a forecast for the future and thus to make maintenance plannable. Repair measures can thus be arranged in good time.
The methods are basically the same, but the application is different. Machine breakdowns and downtimes lead to high costs, as they cause entire productions to fail. Thanks to predictive maintenance, however, it is possible to analyze and predict machine failures.
2) Fraud detection
The investigation of fraud cases often requires the analysis of very large amounts of data. But it can also be automated! With the help of predictive analytics methods, criminal behavior can be detected in data. Consequently, these appear as an anomaly in the data patterns.
In this way, the company optimizes its own security and increases the trust of its customers in the company. As a rule, this is used in the insurance and financial sectors to uncover misuse of assets, corruption or bribery and also falsification of financial data.
3) Medicine
What is the best therapy? Statistical methods and machine learning can also be used in medicine to derive additional knowledge and identify patterns. Similar to predictive maintenance, sensor data, images or structured data are also used in medicine to understand relationships or to make predictions with predictive analytics.
Another area of application in medicine is personalized medicine. With the help of studies and a large amount of data from other patients, decisions can also be made regarding therapies and treatments for individual patients.
The treatment of diseases is often challenging and each patient shows an individual behavior pattern to certain therapies. In this respect, an algorithm can also be a decision-making aid in medicine and ensure that the optimal therapy is found.
4) Climatology and meteorology
Know what the weather will be like tomorrow. That would be nice, but as it is with the weather, it usually turns out differently than you think. But in the meantime, the predictions have improved because the models can be simulated better and more precisely.
Statistical processes and machine learning techniques help to make forecasts about the weather even more precise.
Similar to the weather, certain currents and dynamics can be calculated in the ocean.
In order to know where a particle will be in the ocean tomorrow, the influences must first be understood and analyzed in order to then create forecasts using predictive analytics.
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