Predictive Analytics – Artificial Intelligence | SSI SCHAEFER



warehouse operators are often faced with the question how can I promptly recognize and actively respond to changes in my business as well as changes that occur over time in the warehouse predictive analytics enables these changes to be analyzed faster and in a much more targeted manner using AI assisted technologies predictive analytics is a process whereby predictions for the future are made based on historical data and using various analysis methods predictive analytics is also commonly known as big data this is because the more extensive and the further back in the past the data go the greater the potential for predictions using these data interrelationships in data records are determined using statistical algorithms and mathematical methods the probability of a specific event occurring is then calculated using these data records for a conventional reporting is concerned the existing situation is analyzed however when using predictive analytics a prediction is made and you look into the future with predictive analytics we gather all these data in big data databases and build on this with relevant KPIs and analyses we can therefore learn about interactions in the warehouse at the same time enabling us to derive measures faster and in a more targeted manner that's because this analysis gives us much more information regarding interrelationships in the warehouse in terms of transport predictive analytics can be used to optimize routes and minimize the number of empty trips for example we can also use predictive analytics in the intra logistic sector for example to plan our stock levels better when we have order forecasts to hand predictive analytics and data mining have a lot in common mathematical methods are used for both processes but predictive analytics also makes use of machine learning techniques previously we always found that when we turned one screw it changed the position of seven others with predictive analytics we know how we can turn all these screws at the same time in the future the integrity of the data always determines the quality of predictions of this kind a prediction can only ever be as good as the data made available to the system you

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