The Rise of Advanced Analytics in Manufacturing
In today’s increasingly complex manufacturing environment, companies are facing mounting pressure to maximize the productivity and profitability of their operations. Process manufacturers have been grappling with rising raw material costs, supply chain challenges, and sluggish growth, leading to a decline in productivity growth across the sector. To address these challenges, manufacturers are turning to advanced analytics as a powerful tool for unlocking new levels of efficiency and performance.
The vast troves of data generated by modern manufacturing processes have long remained underutilized, with many companies lagging behind other industries in their ability to leverage information technology and data-driven insights. However, the confluence of cheaper computational power and rapidly advancing machine learning capabilities has opened up new frontiers in manufacturing analytics.
Predictive Maintenance: Anticipating and Preventing Breakdowns
One of the key applications of advanced analytics in manufacturing is predictive maintenance. The traditional approach to maintenance, relying on human intuition and reactive repairs, is no longer viable in the face of increasingly sophisticated and expensive machinery. Manufacturers need a more proactive and data-driven strategy to maximize the uptime of their critical assets and minimize the impact of unexpected breakdowns.
Predictive maintenance systems harness historical data from sensors and other sources to generate insights that cannot be observed through conventional techniques. By analyzing patterns and anomalies in machine performance data, these systems can predict when a piece of equipment is likely to fail, enabling manufacturers to intervene before the breakdown occurs. This approach has been shown to reduce machine downtime by 30 to 50 percent and increase machine life by 20 to 40 percent.
One oil producer, for example, faced recurring issues with the compressors on its offshore production platforms. Advanced analytics revealed that a combination of high pressure, high temperature, and several other factors were correlated with the compressor failures. By using this predictive model, the company was able to decrease downtime from 14 days to just six by pre-positioning personnel and repair equipment, saving millions of dollars for each occurrence.
Yield-Energy-Throughput (YET) Optimization
While predictive maintenance focuses on the uptime and reliability of individual assets, Yield-Energy-Throughput (YET) optimization aims to maximize the efficiency and profitability of manufacturing processes as a whole. By analyzing the interdependencies of key variables, such as yield, throughput, and material costs, YET analytics can help manufacturers identify opportunities to boost output and reduce energy consumption without significant capital investments.
A global chemical corporation, for example, faced variable throughput and low overall output at one of its plants. Advanced data analysis identified critical throughput drivers and enabled the development of a model that quantified the relationships between key variables. This led to an 18 to 30 percent increase in output, representing a net contribution increase of around €5 million.
The power of YET optimization lies in its ability to provide real-time insights to production personnel, enabling them to dynamically adjust operating conditions based on the analysis. By integrating YET dashboards into the control room, manufacturers can make data-driven decisions to optimize their processes and maximize profitability.
Profit-per-Hour (PPH) Maximization
While predictive maintenance and YET optimization focus on improving the performance of individual assets and processes, Profit-per-Hour (PPH) maximization takes a holistic view of the entire manufacturing operation, from raw materials purchasing to final sales.
This advanced modeling technique factors in thousands of variables and constraints to determine the optimal production and distribution decisions that will yield the highest profitability in each period. For large, complex manufacturers, such as global chemical companies, PPH maximization can uncover counterintuitive improvements that human planners might never have considered.
One global chemical company, for example, identified an opportunity to increase profitability by eliminating the production of a low-grade PVC product that it had been selling to China, a commoditized market with high logistical costs. By shifting the intermediate material required to produce PVC to manufacture another, more profitable product, the company was able to boost its EBIT by more than 50 percent in a commodity industry historically marked by low returns on sales.
Integrating Advanced Analytics into Manufacturing Operations
The transformative potential of advanced analytics in manufacturing is clear, but realizing these benefits requires more than just implementing the technology. Manufacturers must also address the people, process, and technology challenges that come with such a significant shift in the way they operate.
Data management is a critical first step, as advanced analytics require the retrieval, cleansing, and structuring of data from a variety of sources, including sensors, production logs, and maintenance records. This process can be time-consuming and resource-intensive, often accounting for up to half of a data scientist’s time.
Moreover, the successful deployment of these analytics solutions requires a multidisciplinary team of data scientists, advanced analytics platform specialists, and subject matter experts in areas such as process technology, asset maintenance, and supply chain management. Establishing cross-functional collaboration and effective communication between these diverse constituencies is essential for translating data insights into tangible business impacts.
Sensor-Networks.org highlights the importance of organizational transformation in unlocking the full potential of advanced analytics. Manufacturers must be prepared to rework decades-old processes and change mindsets at all levels, from the shop floor to the executive suite, to ensure that the insights generated by these tools can be effectively integrated into day-to-day operations.
The Future of Predictive Maintenance and Asset Optimization
As the manufacturing industry continues to grapple with complex challenges, the adoption of advanced analytics promises to be a key driver of productivity and profitability in the years to come. Predictive maintenance, YET optimization, and PPH maximization are just the beginning, as the convergence of IoT, AI, and data science opens up new frontiers in intelligent, data-driven manufacturing.
Looking ahead, the integration of real-time performance visualization and condition monitoring in operators’ stations will enable manufacturers to increase production rates and improve reliability through continuous improvement programs. Industry 4.0 initiatives, such as smart manufacturing and the Industrial Internet of Things (IIoT), will further accelerate the adoption of these transformative technologies.
However, the journey towards predictive maintenance and asset optimization is not without its challenges. Manufacturers must address data privacy and security concerns, as well as cultural shifts within their organizations, to ensure the successful integration of these advanced analytics solutions. By embracing the power of data-driven insights and fostering a culture of continuous improvement, manufacturers can unlock new levels of efficiency, productivity, and profitability in the years to come.