- Organizations in the global industrial products industry face significant challenges.
- New AI technologies have the capacity to make sense of the abundance of data through systems that can adapt and learn.
- 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 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.
To understand how organizations can better plan for AI adoption, the IBM Institute for Business Value (IBV), in collaboration with Oxford Economics, surveyed more than 6,000 C-suite members and heads of functions worldwide – including 300 industrial products respondents. The goal was to better understand their considerations, expectations and objectives in applying AI solutions to the most pressing business challenges and opportunities.
This report explores how industrial products executives perceive the readiness of the technology, the industry and their organizations for AI adoption.
It identifies how companies are currently applying AI and their plans in the next few years.
We also identified a select group of outperformers that are ahead of others in AI adoption and examined what they’re doing differently compared to similar organizations.
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Why AI and why now?
AI enables organizations to synthesize vast amounts of structured and unstructured data, query results in natural language and apply machine-learning capabilities to data analysis. Together, these capabilities can significantly enhance insights, efficiency and speed. 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.
Each of these three priorities presents significant opportunities to improve efficiency and decision making. For quality control, AI systems can analyze data from raw materials, production lines, finished products, maintenance records and customer complaints to identify causal factors that led to quality problems.
In production operations, AI systems can continuously learn from process data and actions taken by top-line operators. Similarly, it helps predict and identify impacts and recommend actions to improve production. For machine maintenance, AI can identify anomalies, assess their criticality, determine the root cause and help maintenance technicians correctly perform the repair the first time.
Using AI and analytics to find valuable answers to questions not yet asked
To remain competitive, a US-based retailer and manufacturer of pre-engineered metal buildings and metal roofing products must be as nimble as small companies, and as scalable as larger competitors. Yet its business intelligence platform lacked the ability to derive insight from unstructured data.
An AI analytics solution helps business leaders analyze new datasets for unrecognized trends and patterns, providing insight and answers to questions not yet considered. It has implemented AI systems to assist with revenue forecasting, supply chain management, marketing, employee health and safety and talent management. By accelerating analysis by a factor of ten, the company can unlock new marketing opportunities, improve supply chain management and virtually eliminate worker safety incidents.
Creating 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
Insights can help stakeholders make decisions to better manage compliance initiatives. Plant-level compliance can use AI capabilities to explore and evaluate current procedures, complaints, upcoming marketplace changes and environmental changes that may have direct or indirect impacts on products.
With aftermarket repair services, each solution requires expert product repair. AI can assist technicians in performing the repair correctly the first time. The technology helps product technicians sort through product usage questions and provide remedies faster.
For safety, AI technologies can analyze worker movement in real-time and predict unsafe situations that could lead to an accident. Furthermore, it can identify near-misses where an accident could have happened to recommend preventive actions.
Are you ready to start using AI technologies?
- Which areas within your organization do you think could benefit from AI?
- What is your plan to encourage and support revenue growth, including the expansion of AI technologies?
- How effective is your organization in bringing together data from various sources to solve important business problems? In what ways can effectiveness be improved?
- What new skills or competencies would be required in your organization to take advantage of AI?