It is important to understand what big data analytics in manufacturing entails to be able to understand big data for manufacturing. This is nothing hard, big data analytics simply focuses on common data model usage to assist when making manufacturing decisions. Financial and inventory transactions, structured operational systems data like process parameters, quality events alarms, unstructured internal and external data, machine data and web are the components for manufacturer big data. All these are important when uncovering new production insights using advanced analytical tools.
So what is the starting point of big data solutions?
To effectively adopt or incorporate big data solution into a manufacturing system, the first step is hiring qualified experts. There is a lot to be done to establish a reliable database. That is why in numerous cases you will see manufactures struggling to invest in visibility and data collection. The experts can use various ways to gather data about your business. They achieve that with the help of data historians, EMI and MES. Ideally, for a data to be considered valid it must be able to assist in decision making towards achieving the companies best interest.
Your experts must understand structured and unstructured data
Enterprise systems and IT are the two aspects that support big data analytics solution. That means any service provider you decide to hire must have a great understanding of the IT background to flawlessly understand data management. They should be able to operate the available analytical tools to incorporate structured and unstructured data to get real time results that the business so much needs when making decisions.
What needs to be in place for data tobe considered big data for manufacturing?
- The data needs to be able to interrelate with the company’s big interest.
- It needs to be compatible with the current technologies.
- The firm needs to invest in a data model that has the potential to handle unstructured and structured data from any point in the system whether inside or outside the factory.
- The data model must be able to let in new analytical tools with ease to facilitate the generations of newer possible insights. Example of such analytical tool could be Geospatial, Video, Image, Predictive Modeling, Time Series, Optimization, Machine Learning, Statistical Process Control and Simulation.
How big data and machine learning is transforming the manufacturing process
Of late manufacturing businesses have discovered the benefits of automation and are always on the look out to boost it. It is evident that machine learning and big data are set to completely transform the future of production. Companies are actively collecting data from resources at a higher rate before including industrial machinery. All these help in monitoring the precise details of production, improve, and possibly boost the quality.
After enough correct data is gathered, the experts use predictive technology to manage the servicing of machinery.Especially those that are sensor based. That means no need for the traditionally fixed maintenance schedules, as they may not be that necessary. The technology helps to predict how and when the machinery is likely to breakdown.
The whole concept behind big data for manufacturing seems to be cost effective and that is what companies are looking for currently.
