Manufacturing has always been a complex world. It is only getting more complex with so much data being generated everyday, and this is certainly going to increase significantly in the future. At the center of it, surviving means being able to leverage all this data and adapting your workforce to data-driven manufacturing. Two factors are converging that make big data analytics a perfect fit for manufacturing. First are growing market pressures, including global competition, regulations, thin margins, and accelerated design cycles, among others. In order to respond, manufacturers have to be able to make data-driven decisions quickly. The second factor is the growth of the Industrial Internet of Things (IIoT), referring to how everything is going digital and being networked. As a result, more data is being generated than ever before by equipment, automation, systems, and even the products themselves. This data just needs to be harnessed to support the decision-making process.
Predictive analytics will be the key enabler to best leverage all this data. Specifically, with the ability to extract meaningful insights about products, processes, production, yield, maintenance, and other manufacturing functions, as well as the ability to make decisions and take proactive action when it matters, predictive analytics can deliver tremendous growth and profitability results. Of course, the data environment and predictive analytical requirements vary across different manufacturing organizations.
However, there are some broad areas of predictive analytics that can benefit most manufacturers.
Monitor your manufacturing processes to maintain a high quality, improve product reliability, and increase yields while reducing costs. Perform root cause analysis to help you identify quality issues and fix them before they become serious problems. View alerts and receive early warnings so you can take action to address potential quality issues, reduce rework, reduce scrap rates, and preserve your quality reputation.
Manufacturers need to sell their products. Demand is often cyclical or seasonal. In such cases, knowing how external factors such as the consumer price index, oil prices, weather, and prime rate could affect your customer’s sales demand can help in resource allocation in manufacturing. While this idea is nothing new to manufacturing, predictive analytics allows manufacturers to consider many more factors than they could before. Predictive analytics takes historical sales data and applies forms of regression to predict future sales based upon past sales. Good predictive modelers find additional factors that influenced sales in the past and apply those factors to forecasted sales models.
Data mining can help in identifying the patterns that lead toward potential failure and defects in manufacturing equipment. This methodology helps in identifying not only faulty products but can also determine the significant factors that influence the success or failure of the process. The predictive analytics process for fault detection and failure prediction depends on data that is typically collected during the normal operation of machine tools. Examples of the kind of data collected in this scenario include temperature, vibration levels, acoustic information, measure of forces, deflections, and other similar technical inputs.
Maximizing equipment value
Manufacturing engineers spend much of their time maximizing the value of equipment in the factory. With predictive analytics and the kind of tools being developed to actualize it, companies can predict the points at which equipment begin to wear out, allowing them to implement preventive care sooner. This will allow the machinery to be at work for longer periods of time without intermittent shut downs for repair.
Determine the optimal time to perform regular maintenance and minimize scheduled downtime in the factory. Knowing that a machine is likely to break down in the near future means a manufacturer can perform the needed maintenance in nonemergency conditions without shutting down production.
In the last few years the application of predictive analytics to the manufacturing field has opened up vast possibilities of using it to further the business and profitability by reducing downtime. The prime mover of predictive analytics in manufacturing is the use of insights derived from large datasets regarding production, supply chain, maintenance, and other manufacturing tasks. Thus, manufacturing companies can reap great rewards by leveraging the data they already have.