Big Data in Manufacturing
What is big data?
Big data has been an exciting new trend in the business and industry ecosystems for a number of years now. There’s some disagreement about when the term was coined, but it entered the Oxford dictionary in 2013. The big data era was enabled by the development of smartphones and connected networks, which underpinned the explosion of data generated and collected by smart devices.
Process plants generate an enormous amount of data, even in big data terms. Every part, process, and system has multiple sensors tracking numerous metrics, generating millions of data points every minute, making it an ideal arena for big data collection and tools.
Big data is measured by what’s called the “three Vs”:
- Volume, or the amount of data produced
- Velocity, the speed at which datasets grow
- Variety, or the range of different data types collected
Each of these Vs is growing at an astonishing rate. Big data is far too large for human intelligence to process and understand, and it’s constantly growing, with real-time data gathering that continuously adds more datasets. Only artificial intelligence (AI) solutions that use machine learning (ML) and deep learning (DL) algorithms are able to handle big data and convert it into valuable insights and predictions.
Why is big data important for process manufacturing plants?
Big data is the fuel that powers ML tools in process manufacturing plants. These include:
- Predictive maintenance to enable parts to be repaired or replaced before they cause widespread equipment failure and plant downtime
- Predictive monitoring to spot and clear up bottlenecks and inefficiencies within the plant
- Demand forecasting to help process companies stay ahead of fluctuating customer requests
- Supply chain optimization so that raw materials arrive on time and in the right quantities
- Smart factories that can communicate machine to machine to streamline processes and self-heal minor problems in the system
- Integrated logistics to ensure that the final product reaches the right person at the right place and the right time
- Root cause analysis to speed up the process of investigating and correcting part failures and process incidents
How can process plants apply big data?
Streamline data collection
Process plants generate enormous amounts of data, but that data is of no use if it’s not collected and stored. It’s important to establish data gathering policies that collect data and keep it in a way that makes it easily accessible to ML tools.
Monitor data quality
Big data is easily corrupted if the datasets aren’t collected and stored correctly. Set data policies that ensure that data is always stored in full datasets, with all the relevant tags and attributes.
Choose the right tools
There’s an overwhelming number of different ML-powered tools for crunching big datasets and mining them for predictions. Take the time to explore the capabilities of different vendors. Look for solutions that include self-service interfaces so that big data insights are accessible for all your employees and teams.
Define your goals
Big data can be used to generate value in numerous use cases, but if you try to implement them all at once you’ll run out of momentum. Prioritize your goals so that you can apply big data analysis in a way which best drives profitability.
How do process plants benefit from big data?
With a combination of big data and ML tools, process plants can improve safety and reduce environmental damage, increase product quality, reduce unplanned downtime, and boost profitability in a competitive market.