Leading producer of long steel in Latin America Brazilian Gerdau is using data and machine learning predictions to save money, while maintaining the same level of production quality. Gerdau had a lot of data coming in from its numerous manufacturing machines but struggled to use that data. While providing some cost saving predictions, the traditional tools it used couldn't make real time adjustments and had trouble evaluating multiple nonlinear relationships between the data. The predictions the traditional tools made were low-accuracy and Gerdau wanted to improve accuracy by adding AI in manufacturing to the process. The steel producer turned to Fero Labs. The 2016 startup sells a platform that uses machine learning to optimize factory predictions.The initial challenges are around working out what to measure, working out how to measure it, working out what it means and then working out what to do about it. Gerdau worked on its machine learning models in Fero Labs' platform for weeks, throwing all kinds of data into it and seeing what results the models produced. That data included the chemical and physical properties of the production materials and the final steel product. With steel, for example, data included what the thickness of the final product will be and what the temperatures are during various parts of the manufacturing process.Before using Fero Labs, Gerdau, which already had sensors on its machines, faced problems with the clarity of the machine data it was collecting and sending to numerous other software programs it uses. The Fero platform identified some of the bad or missing data coming in, alerting Gerdau quality departments to data they had to drill down into and clean. After cleaning up its data, Gerdau focused on getting accurate predictions about the mechanical properties of the steel it made based on the data coming in. The company then focused on creating live-prediction dashboards that could show operators real-time results as they work the machines.Gerdau then focused on using Fero Labs to optimize its chemical formulas to use the least amount of raw materials possible to make steel with the mechanical properties it needed. The company experimented with those predictions, creating models that could detail how they could move leftover materials to different products, to further cut down on waste.Gerdau is now trying to make its predictions more accurate. Gerdau has set up the Fero platform to send a monthly report detailing the predictions the platform has made and if or how results differed from those predictions. Gerdau is working on optimizing its predictions and results based on those reports and creating new reports, which include one that details how much money the company could save if the predictions worked correctly. Gerdau has reduced production costs, so far, by about 9% since using Fero Labs
Leading producer of long steel in Latin America Brazilian Gerdau is using data and machine learning predictions to save money, while maintaining the same level of production quality. Gerdau had a lot of data coming in from its numerous manufacturing machines but struggled to use that data. While providing some cost saving predictions, the traditional tools it used couldn't make real time adjustments and had trouble evaluating multiple nonlinear relationships between the data. The predictions the traditional tools made were low-accuracy and Gerdau wanted to improve accuracy by adding AI in manufacturing to the process. The steel producer turned to Fero Labs. The 2016 startup sells a platform that uses machine learning to optimize factory predictions.The initial challenges are around working out what to measure, working out how to measure it, working out what it means and then working out what to do about it. Gerdau worked on its machine learning models in Fero Labs' platform for weeks, throwing all kinds of data into it and seeing what results the models produced. That data included the chemical and physical properties of the production materials and the final steel product. With steel, for example, data included what the thickness of the final product will be and what the temperatures are during various parts of the manufacturing process.Before using Fero Labs, Gerdau, which already had sensors on its machines, faced problems with the clarity of the machine data it was collecting and sending to numerous other software programs it uses. The Fero platform identified some of the bad or missing data coming in, alerting Gerdau quality departments to data they had to drill down into and clean. After cleaning up its data, Gerdau focused on getting accurate predictions about the mechanical properties of the steel it made based on the data coming in. The company then focused on creating live-prediction dashboards that could show operators real-time results as they work the machines.Gerdau then focused on using Fero Labs to optimize its chemical formulas to use the least amount of raw materials possible to make steel with the mechanical properties it needed. The company experimented with those predictions, creating models that could detail how they could move leftover materials to different products, to further cut down on waste.Gerdau is now trying to make its predictions more accurate. Gerdau has set up the Fero platform to send a monthly report detailing the predictions the platform has made and if or how results differed from those predictions. Gerdau is working on optimizing its predictions and results based on those reports and creating new reports, which include one that details how much money the company could save if the predictions worked correctly. Gerdau has reduced production costs, so far, by about 9% since using Fero Labs
