The trend continues to grow in enterprises wanting to draw deeper and more actionable insights from their organization’s data. The days of simply analyzing information sets for superficial trends and anomalies are a thing of the past — businesses want to glean greater value across a range of functions.
Unfortunately, manufacturers still tend to be reactive and focused on historical performance for these insights. Few manufacturers are able to be proactive in terms of recognizing the impact of the ever-changing external environment on product performance and customer satisfaction, preventing them from systematically drawing actionable insights from the massive amounts of structured and unstructured information at their disposal. These challenges are not unique to manufacturers — consumer product companies, global business consultants and wealth management firms are also all vulnerable to operating at less than peak performance due to poor visibility into their most vexing business challenges.
Increasingly, artificial intelligence is brought up as a possible savior for industries that would benefit from deeper insights into market behaviors by extracting signals from both structured and unstructured content. The ability to quickly assess local and global events reported in the news, emerging research, blogs and more is essential to having an effective active investment strategy. However, the incredibly high frequency and heterogeneity of information flow far outpaces the manpower that most manufacturers have at their disposal to pursue the level of intelligence they require to make strategic change a possibility.
At this point, you might be asking yourself how artificial intelligence (and along the same lines, machine learning) can improve a manufacturing outfit for the better. Let’s walk through a few different scenarios whereby deep learning can interpret mass quantities of information to provide “signals” to a manufacturer that point to potential areas of improvement.
Customer monitoring: In manufacturing, hundreds of millions of dollars’ worth of orders can go out the door with payment hinging on a customer’s ability to pay. Machine learning can provide the real-time monitoring manufacturers need to sift through news articles, blogs and other unstructured content to determine if a client is experiencing negative financial performance or other triggering indicators that may signal an inability to pay.
Feedback loop: By using artificial intelligence to track the performance of a specific product or application in the marketplace, manufacturers can spot problems before they become a customer relations nightmare. Ongoing scanning and analysis of structured and unstructured information can piece together key indicators of a manufacturing defect or other anomaly that can be remedied before it becomes a widespread problem.
Lead generation: Using the same logic as above, artificial intelligence can also do the deep digging needed to spot signals for future product needs that would allow a manufacturer to proactively re-tool or otherwise prepare for incoming orders. This level of ongoing news and data analysis would not be productive for a human worker, but use of AI coupled with contextual machine learning can provide the steady, ongoing consumption of information needed to spot new business opportunities buried deep in stacks of information.
What’s critical to understand about the potential impact machine learning can have on a manufacturing business lies in the way data is studied. The use of artificial intelligence that relies on natural language understanding is vital for achieving the potential outcomes mentioned above — otherwise, market intelligence would be based purely on statistical patterns and not contextual meaning.
There is tremendous interest and need for timely analysis and interpretation of the massive amount of unstructured data [along with structured data] that is being created in the new digital age. By adopting an approach that utilizes fine-grained linguistic based analysis, inaccuracies and coarse-grained interpretation that is common among conventional platforms can be avoided. By focusing on all types of structured and unstructured data, enterprises can leverage rapid analysis of untold volumes of information for business advantage without tying up valuable knowledge workers in the process.
Today, artificial intelligence technology that targets precisely the challenges listed above and more is currently deployed in the some of the largest global companies in the financial services, high tech, consulting and logistics sectors, among others, enabling those organizations to extend the frontier of deep learning and machine intelligence technology to become truly strategic organizations no longer hamstrung by a reliance on historical performance.
Joy Dasgupta, SVP, Business Transformation at Rage Frameworks, Inc.
