The six ways machine learning is driving profits in the enterprise

The six ways machine learning is driving profits in the enterprise

The six ways machine learning is driving profits in the enterprise
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.

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.

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.

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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.

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