Despite recognizing AI’s potential, manufacturers are underinvesting and often lack a clear starting point.
Valtech’s latest study, “The Voice of Digital Leaders in Manufacturing” (VODL), reveals that more and more companies are leveraging AI to enhance operational efficiency. However, AI’s potential extends far beyond short-term gains; manufacturers anticipate it will transform their entire business models.
Despite recognizing AI’s potential, manufacturers are underinvesting and often lack a clear starting point. It’s crucial to present concrete solutions and explore promising use cases tailored to a manufacturer’s level of digital maturity.
Valtech’s latest survey of digital leaders in manufacturing revealed that only 11% are prioritizing AI in their 2024 digitization plans. Image copyright Valtech.
AI Aspirations vs. Reality: Bridging the Gap
Valtech’s VODL study highlights a stark disparity between the industry’s AI aspirations and its current reality. When asked about what digital trend will have the biggest impact on their business, many of the respondent mentioned AI. However, only 11% of respondents prioritize it in their 2024 digitization plans. This underscores an awareness of AI’s potential but also the challenges in realizing it. Progress is needed in three key areas to make AI profitable:
Making Data Available and Usable Sophisticated AI applications, such as predictive maintenance or process optimization, require a robust data foundation. Many manufacturers need to address fundamental issues first, freeing critical data from silos. Often, valuable data from different business units remains unlinked or stored in disconnected tools. Additionally, incomplete, outdated, and inconsistently formatted data cannot be reliably used for AI applications.
Customers’ equipment data availability is another challenge. Predictive maintenance, for example, requires extensive data sharing, sometimes beyond company boundaries. This means the customer must be open and incentivized to grant access to their equipment data to the equipment’s manufacturers. But customers experience an overall lack of incentive to share said factory data as they have reservations with regards to which benefits this will bring them, as opposed to the benefits that the manufacturer will gain from this. Another issue is reluctance to share the on-premise manufacturing data due to security policies, interoperability issues between different production ecosystems, and cost concerns for installing necessary sensors also limit data availability.
Where do we go from here? Manufacturers should invest in company-wide data projects to connect and consolidate data, benefiting AI projects by providing a more holistic view of customers and enhancing B2B customer portals. For high-quality operational machine data, manufacturers should analyze cases where sharing data with B2B partners offers more benefits than risks.
Overcoming Fear of AI AI can be risky because it requires large amounts of data that can be susceptible to cyberattacks or cause unexpected outcomes due to the lack of clarity on how some machine learning models arrive to certain conclusions. This means, traditional risk aversion in the manufacturing industry, driven by a zero-tolerance policy for downtime and the need to protect trade secrets, can hinder AI adoption.
Where do we go from here? To gain experience with AI, manufacturers should start with low risk, “simple” AI experiments, such as in customer service. This allows them to build knowledge and later transfer it to other projects. Additionally, experimenting with production data in sandbox environments, particularly using ML models for predictive maintenance, can be beneficial.
Embracing Organizational Change Many established manufacturing companies operate in mature ecosystems, lacking disruptors. This results in a shortage of role models and best practices. B2B manufacturers need to accelerate innovation projects on their own initiative. The lack of strategic priority and direction for embracing this change (top-down) is endangering the success of AI adoption equally much as organizational inertia and resistance to change (bottom-up). For a successful transformation both issues need to be addressed, albeit through different measures.
Where do we go from here? Digital transformation requires organizational change. Existing digitalization and AI projects should be consolidated into holistic, centrally managed initiatives with clear governance structures. AI roadmaps, strategies for recruiting and retaining specialists, and IT setups enabling easy KPI measurement and data exchange are essential. The C-suite must recognize that a data-driven approach can lead to innovative business models. Data should be viewed as a product, just like manufactured components.
AI Use Cases for the B2B Manufacturing Industry
Bringing Order to Documentation An easy-to-implement AI use case in manufacturing is using large language models (LLMs) to search through extensive documentation. This can help employees quickly find the required information, especially under time pressures. Integrating generative AI functions into B2B customer portals can set companies apart from competitors.
AI-supported documentation is also part of modern B2B commerce accelerators from third-party providers, offering external expertise when needed.
Predictive Maintenance Predictive maintenance, a pinnacle AI application in manufacturing, promises significant efficiency gains but requires high digital maturity. It involves connected, high-quality data, cross-departmental collaboration, and sophisticated IT management.
In practice, this involves sensors and cameras that provide continuous data monitoring of machine components. This data can then be analyzed with (mostly) custom-built software that uses AI/ML techniques, ultimately reducing unplanned downtime. Such software applications can also consider factors like order status and availability of maintenance staff or spare parts, paving the way for new business models within interconnected B2B ecosystems.
Manufacturers Control Their AI Future
Although many manufacturers are just beginning to harness AI, there’s no need for concern. Successful AI use cases primarily depend on organizational change. By making AI a core priority and centralizing efforts, manufacturers can achieve significant progress.
There are suitable AI use cases for every manufacturer, matching their digital maturity. Experience gained can be applied to future projects. Long-term success requires closely aligning organizational factors with a sophisticated data strategy.
In short, manufacturers have the power to turn their AI ambitions into reality, using AI to create new revenue sources and efficiency gains.
Tune in to hear from Chris Brown, Vice President of Sales at CADDi, a leading manufacturing solutions provider. We delve into Chris’ role of expanding the reach of CADDi Drawer which uses advanced AI to centralize and analyze essential production data to help manufacturers improve efficiency and quality.
