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Industrial Analytics

Impact of Industrial Analytics in Fostering Manufacturing Transformation

If data is oil, manufacturing is the brightest lamp powered from it. Manufacturing is expected to generate 1812 Petabytes (PB) of data every year, a…

Impact of Industrial Analytics in Fostering Manufacturing Transformation

If data is oil, manufacturing is the brightest lamp powered from it. Manufacturing is expected to generate 1812 Petabytes (PB) of data every year, a lot more than BFSI, healthcare & many other industries, according to Deloitte. Industrial Analytics today are helping optimize every facet of manufacturing by enabling proactive decision making & automation across organizations through the access of the right data to the right people on time.

What does Industrial Analytics exactly do?

Industrial Analytics collects, analyses, and uses data generated in industrial operations through machines, processes, and people.

Traditionally Manufacturers have always been using data to improve their efficiency & machine health for years. But what has changed now with technologies like IoT is how the data is captured. In the past, data collection was done manually, with plant operators recording data or feeding it in a machine. But these approaches are flawed- they are time-consuming and prone to human errors and biases. This data is still grassroots and not actionable for decision-making, particularly at a senior level. With digital transformation, tons of strategically placed sensors capture every critical machine data, recorded and analyzed in real-time. The level of insights that emerge is actionable for every level.
Here is how industrial analytics is transforming various use cases in manufacturing, improving the efficiency and productivity of machines, processes & people:
  • Improving Manufacturing Supply Chain
  • In a hyperconnected world, manufacturing processes and supply chains are getting increasingly extensive and complicated. Industrial Analytics enable manufacturers to hone in on every stage of the manufacturing process and study supply chains in minute detail, accounting for individual activities and tasks. Based on machine health, inventory status, forecasting of orders, preparation, and choice of suppliers can be made in advance.


    In the current scenario of a dynamically changing environment, a resilient supply chain can make or break your enterprise.
  • IReduce Downtime
Manufacturers can extend the life of critical assets by using data to predict when they will fail. Predictive maintenance systems today collect past data to produce insights that aren’t visible using traditional methods. For example, companies may utilize industrial analytics to identify the conditions that may cause a machine to malfunction and monitor input parameters to act before the equipment breaks or be prepared to replace it when it does, reducing downtime. Factors like Misaligned shafts, lubricant oil contamination and excessive vibrations can result in unplanned downtime if not controlled in time. Technologies like Infinite Uptime’s IDAP can allow plant managers to track these in real-time and predict anomalies with a prescribed solution, effectively minimizing planned and unplanned downtime.

Since machine downtime can cause a loss of around $260,000 an hour per hour for a manufacturing company, this is one of the most critical use cases for industrial analytics.
  • Productivity & Production process improvement
Improved workforce productivity and processes can be measured, monitored and optimized with suitable parameters via industrial analytics. With the efficiency of machines and processes in place, operator efficiency performance can be mapped to benchmarks to identify techniques that cause a decrease in operator performance at various stages of production.

On the other hand, Manufacturers can detect bottlenecks and inefficient processes and components that are causing them. Industrial analytics also reveals interdependence between different processes and their outputs, allowing producers to consider each process separately, improve manufacturing processes, and devise predictive maintenance procedures to address any stumbling blocks.
  • Drive machine OEE improvement
OEE improvement is a crucial metric for shopfloor performance. With effective Manufacturing analytics in place, components like asset utilization, efficiency, product quality rating and runtime for every machine can be tracked. This information in real-time can enable manufacturers to figure out the machines causing the bottlenecks in reaching the planned OEE. Mapping these key performance metrics at a plant level can help top-level decision-makers to make changes at an asset and process level to take the OEE to the planned level.

For the plant head & manufacturing head for multiple plants, it is easy to track performance vis-a-vis assets and entire plants to find what is performing well and what can be improved.
  • Reduce Manufacturing Errors
Industrial analytics can assist in reducing errors in manufacturing processes, operators and machines, improving the quality of the output.

For example, a perfectly aligned machine can perform at its best, producing quality output. Infinite Uptime’s IDAP helps ensure the machine is correctly aligned at all times and functioning at its optimal capacity.

With predictive & prescriptive industrial analytics, any potential downstream quality or equipment issues can be detected. Corrective action can be taken to salvage the quality, and in the case of discrete manufacturing, an intermediate product can be discarded to save further losses.

Conclusion

Industrial Analytics can thus be the key to unlocking hidden potential and business value in various parts of your manufacturing process.

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