Machine learning drives the future of industry

HSO, a leading provider of enterprise business solutions, explores the many different ways machine learning is helping to make manufacturing more agile and productive.

HSO Machine Learning Technology Industry 4
Machine learning has the potential to deliver greater predictive accuracy to each phase of production, – image courtesy of HSO.

What is machine learning? 

Machine learning refers to a method of data analysis that enables computer programs to grow and learn by studying predictive and statistical analytics, rather than by being explicitly programmed.

This type of artificial intelligence is similar to that of data mining as it involves the process of searching through data to look for patterns. However, in the case of machine learning, the computer program uses the data to adjust its own actions accordingly, therefore reducing the need for human intervention.

Machine learning in manufacturing

The sophistication of algorithms is certainly making the manufacturing industry sit up and take note. This new technology has the potential to deliver greater predictive accuracy to each phase of production, as well as:

  • Predictive maintenance or condition monitoring
  • Warranty reserve estimation
  • Demand forecasting
  • Process optimisation
  • Telematics

It is now within the grasp of every manufacturer to assimilate machine learning into their operations and become more competitive by gaining predictive insights into production.

New insights and intelligence

Machine learning is set to bring new dimensions of insight and intelligence to manufacturing operations. From supply chain through to finance, each department will be able to benefit from access to more relevant data.

A common problem in the past has been the lack of integration between departments, making it difficult for manufacturing companies to achieve shared goals. One of the advantages of machine learning is that access to predictive analytics can help teams optimise production workflows and inventories to better manage factory needs and customer demands.

Increasing production capacity

According to a report by General Electric on improving manufacturing efficiency through predictive analysis:

  • Up to 20% of production capacity is recovered as equipment is proactively tuned for reliability.
  • Utility infrastructure is optimised against process needs, improving efficiency by 2% and lowering material consumption by 4%.
  • Reliable, predictable production capacity allows finished goods buffers to be reduced by 30% or more.
  • Comprehensive quality data can be shipped alongside product, reducing rework by 20% and satisfying customer traceability needs.

Three ways machine learning can transform manufacturing

Machine learning and predictive data analytics have the potential to improve yield rates for manufacturers at the machine, production cell, and plant level.

  • Preventative maintenance – the enhanced predictive accuracy of machine learning can have a big impact on maintenance costs for manufacturers. With data that drills down to component and part-level, preventative maintenance is now possible across the factory floor, enabling time and energy to be spent where it is needed and before equipment develops faults.
  • Optimised supply chains – the insights generated by machine learning provide exactly the right information for optimising the supply chain and creating greater economies of scale. The data produced allows buyers and suppliers to collaborate more effectively to improve forecast accuracy and meet delivery dates.
  • Improved product and service quality – algorithms can determine the factors that have the highest and lowest impact on quality. This helps manufacturers to create workflows and internal processes that will be most effective in ensuring quality standards in products and services are met.

Machine learning improves ERP

Although ERP can already provide predictive analytics, with machine learning it can improve the accuracy of analysis over time. As a result, forecasting is improved and manufacturers can target investments more effectively.

For more manufacturing specific whitepapers from HSO, click here.

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Another advantage of incorporating machine learning into ERP is the ability to tailor insights.

This allows manufacturers to gain a level of understanding of their processes, customers and workflows; which becomes more accurate over time, as machine learning applications adjust to target specific elements based on the results generated.

Finally, the combined technologies of ERP and machine learning can also highlight new opportunities. Patterns that emerge from the data can show product preferences and customer trends that would otherwise be unrecognisable. This knowledge helps manufacturers to capitalise on sales, improve service and even create new products.

HSO is a silver sponsor of the Connect ERP event taking place at the Jaguar Visitor Centre, Caslte Bromwich on 23 November, 2016.