With mountains of data at their disposal and the pressure to drive efficiencies, organizations across sectors are eager to jump on the artificial intelligence (AI) and machine learning (ML) bandwagon. But the progress of AI and ML has not happened equally in all areas. In January 2021, we conducted a study of 1,870 IT decision makers, which found that AI and ML increased productivity and decreased costs in manufacturing more than any other industry. Our survey also made clear that data quality is a major differentiator between AI and ML leaders and laggards, regardless of industry.
Productivity gains
It was recently reported that 92 percent of senior manufacturing executives view AI as an essential tool for increasing productivity. AI is increasingly being applied to provide business intelligence in the supply chain and manufacturing process, enabling better planning and driving efficiency in a number of key areas, including predictive maintenance and raw material availability, reducing downtime Could Extending critical life span. machinery.
Why has AI become such a ubiquitous tool in construction? Perhaps more than any other industry, the vast amount of data created by modern devices through IoT sensors provides a rich vein of data from which organizations can draw insights.
At the same time, though, manufacturers relying on human power alone to draw conclusions from this data are trying to drink from a firehose. Machine learning models, in contrast, can provide data insights in real time, enabling companies to adjust on the fly, reduce waste, and ultimately optimize operations. With each passing year, machines are becoming more efficient at capturing micro bits of data and are increasingly training machine learning models to discover new insights.
Medical device company Cerapedics is using IoT and AI to reduce its production losses and improve the yield of manufacturing batches. By utilizing predictive maintenance, real-time alerts and analytics capabilities, Serapedics is able to reduce costs, reduce potential risks and resolve issues in a timely manner.
Responding to product demand can also significantly reduce supply-chain costs. According to a Gartner survey, demand forecasting is the most widely used machine learning application in supply chain planning. The study reports that 45 percent of companies are already using the technology and 43 percent of them plan to use AI-powered demand forecasting within two years.
Still, barriers to widespread adoption of AI remain. According to our study results, a majority of respondents (82 percent) said they are still discovering how to implement AI or are struggling to operationalize AI and ML models. Top reasons for failure include lack of data quality (34 percent), lack of expertise within the organization (34 percent), lack of production-ready data (31 percent) and poorly conceived strategy (31 percent).
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Clear your data
To make effective use of data, it must be made available in a consumable, properly structured format. It may come as no surprise then, that our survey respondents identified poor data quality as the top cause of failure in their AI and ML initiatives. The success of any data initiative depends on having clean data. Data must be accurately labeled, free of duplicate records, and highly curated in order to generate accurate and meaningful results.
This finding underscores the need for data quality management to be institutionalized across the organization. It should be an integral part of the way we do business - just like everything on the factory floor is measured on quality with a goal of "zero defects". The data an organization is taking and feeding into its AI and ML systems deserves the same rigorous analysis as the products flowing off the manufacturing line.
In addition, the right stakeholders should be involved. An effective AI strategy can reduce the manpower needed to crunch the data, but that doesn't mean human oversight isn't necessary. Organizations that have AI right have data management teams responsible for ensuring data accuracy and determining how insights are being applied to day-to-day operations.
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The bottom line message for manufacturers is this: The potential benefits of AI and machine learning across industries are massive, but they require thoughtful planning, ongoing quality assessment and buy-in from the top of the organization. Organizations that can bring all of these elements together will not only make better and more informed business decisions today, but will be better positioned for the future, as machines become more sophisticated than ever and new data sources proliferate. Stayed
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