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Manufacturing Analytics

With the advent of Industry 4.0 and the Industrial Internet of Things (IIoT), manufacturers are expected to embrace advanced data analytics for making better-informed decisions and achieving automation of all processes in the long run. With machines connected to sensors, cloud-based manufacturing analytics platforms are fed with a consistent barrage of data that can analyze and generate insights that are developed higher than human capabilities. 

What is manufacturing analytics?

Manufacturing analytics is the use of data from machines, systems and operations to optimize production and other important functions like planning, maintenance, quality checks, supply chain, etc. that can benefit from higher efficiency. 

Data has always been used to a certain extent for a long time, but the most significant change has been in the way data is collected. The traditional method of data collection is often complex and tedious, with operators manually recording changes and filling out forms for maintenance records. This method is severely prone to human error, and it would take days or weeks to figure out the reason for breakdowns in the production process. The time-consuming and fragmented nature of this data is not suitable for analytics and subsequently, the decision-making from this data would also be inaccurate. 

Manufacturing units are increasingly connecting their equipment with sensors and IoT devices to improve data collection and remove labor effort for the same work. Data captured through modern techniques is more reliable for advanced analytics and the insights generated as a result will lead to more accurate decision making.

Manufacturing analytics can induce a variety of benefits ranging from increased utilization of resources, streamlining processes, reducing costs, achieving complete visibility of the entire operation and eliminating human-based mistakes. Manufacturing analytics is designed to collect and analyze data from multiple sources to pin down areas of improvement. 

Benefits of Manufacturing Analytics

  1. Increased Revenue – With the assistance of analytics in manufacturing, real-time insights are available in all aspects like production management, inventory and stock management and planning for demand and supply. If the data indicates that the production unit is about to reach maximum capacity, the managers can put in place changes that would help meet delivery schedules and eradicate delays.
  2. Reduced Costs – Since processes in manufacturing can be optimized with the insights generated from analytics, in-the-process costs can be saved on multiple fronts. The work from laborers can be minimized with the use of robotics and autonomous machine decision-making. Similarly, through the application of predictive maintenance programs, the massive costs incurred for repair work can be saved and with reduced downtime, there is a boost in productivity.
  3. Better Quality Services – Data analytics ensures that manufacturing companies are generating high-profit yields and that the supply chain is optimized. This is an essential outcome of manufacturing analytics as meeting consumer expectations becomes easier. Advanced tools help in studying market trends and figures, providing insights into how services and outputs can be improved. 

Manufacturing Analytics Use Cases

Manufacturing analytics help in identifying where the opportunities for growth and improvement lie. Manufacturing is a multi-dimensional process and various operations are a part of it like the sourcing of raw materials, production planning to meet the changing demand in the market, etc. Listed below are a few examples of how manufacturing analytics has become indispensable for the manufacturers of today. 

  1.  Predictive Maintenance – This technology provides the ability to predict when a piece of equipment might be out of service and plan the maintenance schedule accordingly. Downtimes in production are very harmful for the output and productivity of the manufacturing unit. Predictive maintenance helps avoid such production outages by collecting and analyzing historical as well as real-time data to determine the potential for failure and the root cause of the problem.
  2. Supply Chain Optimization – For the production process to run on time and meet tight delivery deadlines, it is important that the supply chain is without unwarranted bumps. Data analytics software can study massive volumes of complex data to quickly identify any bottlenecks in the supply chain and thus, manufacturers can have contingencies planned out to minimize the impact of these issues on production. 
  3. Forecasting Demand – If an organization is aware of which products are in demand and when they can give a significant competitive advantage in the market, production can be planned to meet this demand to achieve higher profitability
  4. Warehouse Management – Streamlining warehouse operations can be achieved with data analytics. Workers can be appointed because production was better planned, and operations of the warehouse can be more efficient. 

Conclusion

Before the current digital transformation was achieved, it was difficult for manufacturers to harness the full potential of all their data. With advancements in analytics and its unique use cases in the manufacturing industry, data-driven decision-making has become more accessible. It is imperative that the top management change their attitude toward analytics because it is an ongoing process to embrace this technology.

Marco Polo
Marco Polo
Marco Polo is the admin of sparebusiness.com. He is dedicated to provide informative news about all kind of business, finance, technology, digital marketing, real estate etc.
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