Using Big Data Analytics To Improve Production
Manufacturing remains a critically important part of the world’s economic engine, but the roles it plays in advanced and developing economies has shifted dramatically. In developing countries, manufacturing operations deliver unprecedented new employment opportunities that are transforming societies. Of course, manufacturing remains important as a job creator in the developed world too, but in more mature economies, manufacturers drive productivity and efficiency gains and innovation.
Big Data is essential in achieving productivity and efficiency gains and uncovering new insights to drive innovation. With Big Data analytics, manufacturers can discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency and identify variables that affect production.
Manufacturing enterprise leaders understand the stakes. A Honeywell Process Solutions-KRC Research studyfound that 67 percent of manufacturing executives planned to invest in Big Data analytics, even in the face of pressure to reduce costs. The majority understand that Big Data analytics are required to compete successfully in a data-driven economy, and they are making investments in data integration and management assets to achieve digital transformation and gain a competitive edge.
Since manufacturing profits rely heavily on maximizing the value of assets, asset performance gains can lead to big productivity improvements — even if asset performance is only improved on the margins. By the same token, a reduction in asset breakdowns can reduce inefficiencies and prevent losses. For these reasons, manufacturers focus on maintenance and continuously optimize asset performance.
Machine logs contain data on asset performance. The Internet of Things (IoT) adds a new dimension with connected assets and sensors capable of measuring, recording and transmitting performance in real time. This data is potentially of great value to manufacturers, but many are overwhelmed by the sheer volume of incoming information. Data analytics can help them capture, cleanse and analyze machine data to reveal insights that can help them improve performance.
In addition to enabling historical data analysis, Big Data can drive predictive analytics, which manufacturers can use to schedule predictive maintenance. This allows manufacturers to prevent costly asset breakdowns and avoid unexpected downtime. Big Data analytics can have a significant impact: The Honeywell-KRC study found that Big Data analytics can reduce breakdowns by up to 26 percent and cut unscheduled downtime by nearly a quarter.
In an increasingly global and interconnected environment, manufacturing processes and supply chains are long and complex. Efforts to streamline processes and optimize supply chains must be supported by the ability to examine every process component and supply chain link in granular detail. Big Data analytics give manufacturers this capability.
With the right analytics, manufacturers can zero in on every segment of the production process and examine supply chains in minute detail, accounting for individual activities and tasks. This ability to narrow the focus allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Big Data analytics also reveal dependencies, enabling manufacturers to enhance production processes and create alternative plans to address potential pitfalls.
Traditionally, manufacturing focused on production at scale and left product customization to enterprises serving the niche market. In the past, it didn’t make sense to customize because of the time and effort involved to appeal to a smaller group of customers.
Big Data analytics is changing that by making it possible to accurately predict the demand for customized products.
