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The introduction of connected machines into industrial environments has raised quality standards, led to increased profits and improved the maintainability of both manufacturing equipment and end products. Manufacturers that have integrated their production floors with other aspects of the business (including design, sales and supply chain) are seeing the largest benefits as the machine learning aspect of connected networks trickles into all areas of the business.

Here is a look at six ways machine learning is impacting industrial business.

Changing the face of customer relationships

One clear indicator that machine learning and artificial intelligence are coming together to improve customer relationship management is Salesforce’s acquisition of several Machine Learning and AI companies. Since 2014, Salesforce has acquired six AI and Machine Learning companies including: RelateIQ, TempoAI, MinHash, PredicitonIO, MetaMind and Implisit Insights. As a result of these acquisitions, Salesforce has released several new products leading to an estimated new product revenue of $635 million by FY18.

Dramatically improving both product and service quality

Product quality and customer service are woven throughout every aspect of a workflow cycle. Production cell leaders impact customer service by ensuring that products move smoothly through their cell and that waste is minimized, thereby reducing costs. Sales team leaders ensure product quality by understanding customer needs and working with design teams to develop best-fit solutions. With machine learning, executive teams are gaining a better understanding of how decisions both upstream and downstream of specific points in the production cycle are impacting product and service quality.

Optimising processes with greater accuracy and better results

The fast-paced world of manufacturing requires leaders to constantly consider the impact of each decision and to make trade-offs based on schedule demands, material and machine availability and customer needs. Prioritizing each demand while simultaneously managing waste, equipment efficiencies and human resource efficiencies has always been a challenge to manufacturing floor leaders; optimizing each of these aspects to improve yields and profits is a careful balancing act. Quick access to reliable data dramatically improves the ability of leaders to make the best decisions.

Improving price competitiveness without compromising profits

With so many manufacturers available, the demand to provide high-quality products at the best possible price has never been higher. Integrated supply chains, especially those that have connected some aspects of their own internal systems with those of their vendors can provide customers with variable pricing that closes the deal while maintaining the margins the business needs.

MaaS (manufacturing as a service) and on-demand manufacturing are becoming a reality

As individual departments within the business are integrated, the next logical gaps to close are those that exist between the end customer, the OEM (original equipment manufacturer) and material suppliers. The benefits of subscription services (consistent pricing, reliable service, scalability) are trickling throughout all aspects of commercial enterprise. End-customer orders are driving demand while data collection and machine learning are making it easier to anticipate these needs. Because of this data, production runs, even those of highly-customised products, are quickly scalable.

Improving predictive maintenance analysis and driving efficiency through maintenance, repair and overhaul (MRO) stations

Maintenance has always been a cost-driver in industries that rely on expensive equipment in the field (think airlines and major shippers). In these industries, performing field maintenance at just the right time (before the equipment fails but not so early as to scrap excess product life) dramatically impacts profitability and safety. Equipment that must be returned to an MRO station is under even greater scrutiny as this may mean that part of the fleet is inaccessible until the equipment is returned. Machine learning and data gathering are dramatically improving maintenance scheduling, reducing equipment downtime and driving greater profitability.

The Industrial Internet of Things is about more than simply connecting the machines of one area of the business and even about more than connecting various departments – IIoT is bringing the benefits of iterative algorithms to the business as a whole. Data is gathered, analysed and used to make minor changes at specific points within the lifecycle of the product. Those changes are then either accepted or scrapped based on the results of additional analysis. Just as software testing involves many iterations, industrial manufacturers are finding success through the continuous improvement machine learning enables.

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In this power panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, Bradley Holt, Developer Advocate at IBM, and Esmeralda Swartz, VP, Marketing Enterprise and Cloud at Ericsson, discussed the vast toolbox of options being offered and how individual IoT project managers benefit from the choice of tools being offered to them.

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Now some unsurprising new developments since Tony Fadell, the CEO of Nest, resigned in June. Here’s what Google is trying to achieve by “re-absorbing” the troubled Nest group.

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