For years, analytic models have been used in the manufacturing space—starting from simple, guess-based forecast models. Today, predictive analytics software solutions play an important role in manufacturing, and major manufacturers from around the world use predictive analytics to improve production. The data required for advanced predictive models is readily available today thanks to Industrial Internet of Things (IIoT), a system of sensors embedded in manufacturing machines. Advanced predictive algorithms comb through the data collected from the machines to uncover issues and trends quickly. When coupled with IIoT, predictive analytics leverages big data for quick, informed decision making. Following are four key benefits of predictive analytics in the manufacturing sector.
Boost in Quality
With predictive analytics, databases see faster aggregation, data cleansing becomes quicker, and information can be stored in tighter spaces. Furthermore, typical predictive analytics software—such as IBM SPSS Modeler—automates numerous processes, resulting in enhancement of the overall quality of the predictive analytics model. This allows manufacturers to create stronger plans and focus on their core deliverable.
Forecasting of Demand
Demand forecasting is an integral component of the manufacturing process, as manufacturers need to foresee the demand for different product types at different time periods. Traditional demand forecasts rely on data from previous years and are based on the knowledge that some items are in high demand during particular events or seasons. Predictive analytics solutions like Altair Knowledge Studio allow for futuristic demand forecasting by leveraging a comprehensive view of manufacturing processes to identify anomalies or trends. Predictive analytics in manufacturing combines demand forecasting and risk management to produce more accurate demand estimations.
Utilization of Manufacturing Machines
Manufacturing is a technologically-intensive discipline that relies heavily on machines. However, every machine faces breakdowns over time. Whether it is due to regular wear and tear or an accident on the factory floor, the cost of replacing modern equipment is often in the thousand-dollar range. With predictive analytics tools like RapidMiner Studio, manufacturers can foresee and prevent machine loss, automate data analysis through sensors within the equipment, and even determine if a machine needs to be shut off to prevent any issues.
The purpose of preventive maintenance is to preemptively reduce downtime and expensive servicing by locating the issues found in machinery through alerts based on captured IIoT data. For example, the preventive maintenance feature of a predictive analytics solution like Oracle Advanced Analytics might automatically signal users indicating the need for repair of a worn machinery belt, helping replace it before it snaps and brings production to a standstill. With preventive maintenance, manufacturers can rest easy knowing that all their machines are operating at optimum efficiency.
Manufacturing, like all industries, must evolve to keep up with changing technological trends. Through the use of predictive analytics in manufacturing coupled with IIoT, manufacturers can ensure their processes fulfill the customer expectations of today and tomorrow.