The ability to predict the future is met with justified skepticism. From fortune tellers and sportscasters, to financial investors, many have tried to profit from predictions for the future. Application of prediction models in the manufacturing industry is met with this same skepticism: factories have been hesitant to invest in innovative solutions, such as using data science to foretell manufacturing outcomes. But the recent buzz around Industry 4.0 and a constant push for cost optimization is driving many companies to revisit the possibility of leveraging factory data to become more efficient.
This talk will provide a framework to help manufacturers identify what problems they’re trying to avoid or resolve, and then provide concrete steps for collecting and analyzing data to be able to effectively use that insight to solve business-critical issues. Cloud-based analytic tools have enabled greater access to cost-effective computing power required to iterate the data models until a successful scenario is achieved, so there’s no reason traditional factories shouldn’t use data science to monetize their process data. Effective use of this data provides true cause-effect explanations and enables focused proactive action, helping to avoid negative outcomes and waste while optimizing for output and cost efficiency.
- Identify issues related to diagnostics, performance, cost, yield, headcount and smarter production, in order to begin the journey to a successful predictive project deployment
- Utilize the expertise of a cross-functional team to create a deployable and financially viable solution with predictive and prescriptive elements
- Avoid negative outcomes and waste while optimizing for output and cost efficiency
Director of Product ManagementBright Machines