It is precisely this ability to predict the future that makes it so valuable since it opens enormous opportunities for growth. To do this, it is essential that we ask the right questions in order to obtain answers that help us to carry out actions on specific business objectives. Based on these initial questions, predictive analysis aims to build an analytical model that looks for trends, repetitive patterns or predictable behaviors to ultimately represent and predict, through a mathematical formula, a future behavior or result, usually accompanied by a probability index.
To achieve this, we must use descriptive analytics that facilitate the structuring of past and present data to make projections about the future. For example, price variations of the different materials and formats in the last few months, sales performance for of each type of customer or the average delivery time for the last year.
Prescriptive analytics, for its part, allows us to act in the face of possible scenarios such as knowing when is the best time to stockpile each type of material based on the sales forecast or assessing whether reducing delivery times by a day will result in an increase in sales volume. In short, it is about optimizing resources and production.
However, for all this to be possible, high quality information must be collated and introduced into the system, which will then determine the probability index of the predictions obtained. Therefore, not just any data is valid. This data can be obtained from different internal sources (MES, ERPs, etc.) and external ones (web or OPC-UA systems of our machines). The data must be interconnected so that it is no longer "hidden" and can be cross referenced to enable suitable solutions to be found. And this is where smart programs (MES + and Analytics) can get the most use out of the data, turning the Smart Factory into a reality.
This advanced manufacturing execution system can offer a complete overview of each and every one of the production processes, and all this in real time. Precise information on the state of the machines (availability, maintenance, loading), the warehouse’s capacity to satisfy the material needs for the orders, their progress, their manufacturing phase (cutting, folding, painting, assembly, etc.) and delivery times.
This complete overview of the manufacturing of the plant provides answers and solutions for any eventuality. For example, to warn operators if dates are not going to be met, if they are going to run out of material or if production must be redistributed due to overload or maintenance.
At the same time, this software enables intelligent, automatic, planning using feedback based on real-world incidents (unscheduled stops, urgent orders, lack of personnel, etc.) and data analytics. It is even capable of generating a virtual simulation of any possible contingency. For example, imagine that suddenly an order comes in for a high volume of material. The system visualizes whether the machines will be able to assume the workload within the deadlines, evaluating possible delays to other orders. If the green light is given, the operator validates and launches the new planning.
In short, predictive analytics tries to use information we already have to predict information we don’t have. Going one step further, it is able to recommend the best option within a range of possible actions by predicting the result of each of them. A failsafe oracle that gets the best performance out of the plant, with maximum efficiency and productivity and significant savings in time and costs.
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