A structured and comprehensive flow
Adapt the flow to your manufacturing processes and ingest all relevant enterprise, operational, and external data to improve your production quality.Explore !
The goal of this adapt and apply solution is to show Factory Management and Operations teams how Dataiku can be used to improve the quality of your production and adjust your manufacturing processes in near real-time. More details on the specifics of the solution can be found on the knowledge base.
Providing products with consistent quality is at the forefront of any industrial company’s priorities. And it’s no surprise: consequences when facing a quality drop are multiple, ranging from an increase in production costs, pressure on supply chain management, increase in waste generation, and reduced sales down to fatalities and serious injuries. Overall, as quality drops alone can sustainably damage customer trust and company brand reputation, they demand tight surveillance. With the rise of Industry 4.0, it is now possible to gather more data across factories and supply chains. Fueled by that data, AI and analytics allow industrial companies to make faster, more flexible, and more efficient processes to produce higher-quality goods at reduced costs.
In this context, it becomes a must-do to shift from traditional to predictive quality control across manufacturing processes to build “factories of the future”. Embarking on such a journey demands finding the right starting points: with Production Quality Control, factory managers, production engineers, quality engineers, and maintenance teams can quickly integrate the forecast of AI models in the surveillance of key manufacturing processes. Thanks to full AI explainability, production engineers are in a position to identify the parameters most influencing manufacturing processes. Near real-time insights allow them to communicate efficiently to the right people in the factory and act early if any changes are detected. The first step toward embedding AI in crucial factory processes.