February 26, 2018 | Written by: Spencer Lin
Categorized: Industry Insights | Manufacturing
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Organizations in the global industrial products industry face significant challenges: cost pressures, increased regulations, disruptive technologies and the increasingly costly delivery of raw resources. High volatility in commodity prices has put severe pressure on company margins and can quickly expose inefficient operations.
Processes, workflows and the understanding of performance are dramatically changing. Operations can no longer work in linear execution, or in isolation of other functional work streams such as engineering, maintenance and planning. Instead, the value chain needs to perform as an integrated whole to support the fluctuating demand cycles and higher cost supply activities.
New artificial intelligence (AI) technologies have the capacity to make sense of the abundance of data through systems that can adapt and learn. By expanding digital intelligence adoption, AI technologies can help executives translate data into insights to drive greater innovation, and better operational and financial decisions.
The IBM Institute for Business Value (IBV) examined the responses of 300 executives in the industrial products industry who participated in a global study of more than 6,000 cross-industry executives on artificial intelligence / cognitive computing. In this research, we found out that industrial products companies are at a critical inflection point in their adoption of AI. Surveyed executives recognize that the technology is market-ready, and well over half say the industry and organizations are ready to adopt it.
Industrial products companies see the impact of artificial intelligence / cognitive computing
Source: IBM Institute for Business Value, “IBM Cognitive Computing Survey”, 2016
From our research, we identified three things that successful industrial products outperformers do that organizations can learn from.
Build an AI data foundation –An AI data foundation starts at the top with a clear view of what they want to achieve with their data and governance around the data. Seventy-six percent of our outperformers use an enterprise-wide system for managing data versus 52 percent of others.
Focus on new skills –The rapid growth of AI demand in the industry has created a much greater need for data science and applied engineering talent. An overwhelming 86 percent of industrial products outperformers recognize employee roles and skills will need to change to support AI, compared to 70 percent of all others. And these outperformers are targeting specific skills, including data visualization, advanced data analysis and advanced mathematical modeling.
Create a new level of intelligence –AI systems require the ability to ingest a wide variety of both internal and external data sources. Ninety-two percent of outperformers utilize both internal and external data versus 64 percent of all others. Outperformers go beyond by collecting customer data from multiple sources much more than the rest of our sample.
To join the ranks of the outperformers, industrial products executives can take specific actions outlined in the full report.