In the dynamic landscape of modern manufacturing, process industries are facing constant pressure to optimize their operations and stay ahead of the curve. As raw materials become increasingly scarce and expensive, and global growth slows, manufacturers must explore innovative solutions to boost productivity, profitability, and sustainability.
One powerful catalyst for this transformation is the integration of sensor networks and advanced analytics into manufacturing processes. By harnessing the vast troves of data generated by industrial Internet of Things (IoT) devices, process manufacturers can unlock unprecedented insights, uncover hidden opportunities, and revolutionize the way they operate.
Predictive Maintenance: Maximizing Asset Uptime
One of the key applications of sensor-driven analytics in manufacturing is predictive maintenance. Traditionally, manufacturers have relied on a combination of instinct and experience to anticipate and address asset failures. However, as machinery becomes increasingly complex and the demand for uptime and productivity grows, this approach is no longer sustainable.
Advanced analytics have transformed the way manufacturers can manage their critical assets. By leveraging historical data from sensors and other sources, predictive maintenance systems can identify the precursors to machine failures and proactively intervene before breakdowns occur. This not only reduces downtime by 30-50% but also increases machine life by 20-40%.
One example from the oil and gas industry illustrates the power of this approach. A major oil producer faced persistent issues with the compressors on its offshore platforms, which would result in costly production shutdowns costing $1-2 million per day. By applying advanced analytics to the vast troves of sensor data, the company was able to pinpoint the specific combinations of temperature, pressure, and other factors that correlated with the compressor failures. This allowed them to predict several weeks in advance when a compressor would go offline, enabling them to pre-position personnel and repair equipment and reduce downtime from 14 days to just 6 days.
Optimizing Yield, Energy, and Throughput
While predictive maintenance focuses on maximizing the uptime of individual assets, Yield-Energy-Throughput (YET) analytics can help manufacturers optimize the overall efficiency and profitability of their production processes. By balancing the interdependencies between yield, energy consumption, and throughput, YET analysis can deliver significant gains in EBIT (Earnings Before Interest and Taxes).
A global chemical corporation faced challenges with variable throughput and low overall output at one of its plants in Europe. By applying advanced data analysis to the 40 million data points collected from the plant’s sensors, the company was able to build a comprehensive model of the production process, quantifying the relationships between critical variables. This newfound understanding allowed the company to implement targeted experiments and adjustments, resulting in an 18-30% increase in output and a net contribution increase of around €5 million.
In some cases, the optimizations suggested by YET analysis can be relatively simple, such as tweaking a recipe. In other instances, the analysis may uncover the influence of parameters that change over time, prompting the implementation of new standard operating procedures. The most advanced approach is to integrate YET analysis into a real-time performance dashboard, enabling production personnel to dynamically adjust operating conditions based on the insights provided.
Optimizing the Entire Supply Chain
While predictive maintenance and YET analysis focus on improving the performance of individual assets and processes, Profit-per-Hour (PPH) maximization takes a holistic view, optimizing the interaction of all the components within a complex supply chain and production system.
This advanced modeling technique can encompass up to 1,000 variables and 10,000 constraints, dynamically maximizing profit generation across every step, from purchasing to production to sales. Unlike human planners, these analytics-driven models can factor in a vast array of complexity, including volatile costs and prices, multiple plants and products, and non-linear combinations of materials.
A global chemicals company was able to boost its EBIT by more than 50% in a historically low-margin industry by applying PPH maximization. The model identified immediate tactical changes, such as manufacturing an essential intermediate product on an underused line instead of buying it from a third party, as well as medium-term strategic opportunities to expand capacity and optimize sales. Crucially, the analytics also revealed counterintuitive improvements, like eliminating the production of a low-grade PVC product that was being sold at a loss.
Integrating People, Process, and Technology
While advanced analytics tools hold immense potential, realizing their full benefits requires a holistic approach that integrates people, processes, and technology. Manufacturers must invest in the necessary data infrastructure and IT expertise to aggregate, cleanse, and structure their data for effective analysis. They also need to assemble cross-functional teams of data scientists, analytics specialists, and subject matter experts to drive these transformations.
Importantly, the human element cannot be overlooked. Employees at all levels must be equipped to understand and trust the insights generated by these analytics tools, and organizational processes must be redesigned to seamlessly incorporate the new data-driven decision-making. This “analytics transformation” is not a one-off exercise, but rather an ongoing enterprise-wide effort that requires a shift in mindsets and ways of working.
Sensor-Networks.org is at the forefront of this revolution, providing a comprehensive platform for industry professionals to explore the latest advancements in sensor networks, IoT, and related technologies. By staying informed and embracing these transformative solutions, process manufacturers can unlock new levels of productivity, profitability, and sustainability in the years to come.
Unlocking the Power of Sensor-Driven Analytics
The integration of sensor networks and advanced analytics is poised to be a game-changer for process manufacturers, delivering tangible improvements in uptime, efficiency, and profitability. By harnessing the wealth of data generated by industrial IoT devices, companies can gain unprecedented insights, optimize their operations, and stay ahead of the competition.
From predictive maintenance to yield-energy-throughput optimization and supply chain-wide profit maximization, these data-driven approaches are unlocking new frontiers of productivity and competitiveness. However, realizing the full potential of these technologies requires a holistic transformation that aligns people, processes, and technology.
As manufacturers navigate the complexities of the modern industrial landscape, the integration of sensor-driven analytics will be a critical enabler of their success. By embracing these transformative solutions, they can unlock new levels of operational excellence, drive sustainable growth, and cement their position as leaders in their respective industries.