With a high level of digitization and a need for productivity, manufacturing can serve as an early adopter of technology trends. This was the case for robotics and the Internet of Things and is occurring again with various branches of artificial intelligence.
Powerful AI algorithms have the potential to revolutionize efficiency in the manufacturing sector and general heavy industry space, but seizing this value isn't automatic. As a leader, you need to know more about the related technologies and their applications to make sure you're using them up to their fullest potential.
In times of rapid change and development, some technology solutions add more complexity than value. It's up to you to avoid these false starts and achieve real success. If you can achieve this level of clear-eyed AI leadership, you can differentiate yourself as a manufacturing executive.
Read more: See how Heineken modernized operations to unlock hidden value.
What does AI mean in the manufacturing industry?
AI technology in the manufacturing sector, which is being deployed as part of the wider Industry 4.0 movement, has already touched several types of solutions.
Industry 4.0 digital breakthroughs apply to a wide range of business scenarios, and they include:
- Digital twin technology: Complex models that simulate the whole manufacturing process. All assets therein are invaluable for their role in scenario modeling and predictive analytics.
- Sensors and the Industrial Internet of Things (IIoT): Large sensor deployments allow manufacturers to collect more production data than ever before for real-time analysis.
- Machine learning analytics: The analytics algorithms underlying Industry 4.0 deployments can gain power and effectiveness when built with machine learning (ML). This is a branch of AI technology concerned with self-improving processes.
- Efficiency-building generative AI systems: Generative AI (GenAI) is a major growth area and is the fuel behind systems such as digital agent technology. GenAI solutions can respond to natural-language prompts while helping employees complete actions such as searching large, complex databases and brainstorming new ideas.
- Augmented reality and virtual reality: Augmented reality (AR) and virtual reality (VR) enable in-depth problem-solving by displaying data in immersive new ways.
Read more: Learn the value of the Industrial Internet of Things.
Strategic AI algorithm applications within an Industry 4.0 context include:
- Deepening the application of existing methodologies by allowing companies to delve deeper into larger amounts of production information and turn it into fuel for their strategies.
- Enabling full-scale digital transformation, as the more powerful new AI tool sets enable the automation of processes that were once time-consuming manual work.
- Improving both productivity and safety by empowering intelligent digitized operations around the high-powered machinery used in manufacturing and heavy industry.
Read more: See how executives can take a systems approach to Industry 4.0.
Signs of success (and potential challenges) for AI in manufacturing
The wide variety of powerful new technology tools available to manufacturers isn't an automatic promise of success. MIT Sloan Executive Education's experts have observed that the majority of companies exploring Industry 4.0 either struggle to reach their objectives or fail to do so outright. It's worth examining what success looks like, as well as the challenges that can harm businesses' progress.
Industrial AI and Industry 4.0 success factors
Successful AI algorithm and digital solution implementation can result in practical outcomes, including:
- Enhancing performance and decision-making, allowing leaders to quickly choose their directions based on clear signals from their data.
- Integrating AI and smart technology into everyday routines to enhance human workers' productivity over the long term.
- Creating a safe and efficient workplace that operates with a reduced need for time-consuming, risky and potentially inaccurate manual processes.
See more: Watch a webinar on the convergence of Industry 4.0 and Industrial AI.
Challenges around AI in manufacturing
Achieving high-level success from an AI system or other Industry 4.0 effort can be an elusive goal for today's manufacturers. Barriers include:
- A need to engage employees and instruct them in the effective use of a variety of newly introduced technology tools.
- The risk that new technology deployments will add complexity to companies' IT stacks instead of streamlining their processes.
- The danger of misalignment between companies' needs and their purchases, with leaders potentially buying based on hype rather than their specific business use cases.
Read more: Find out where digital transformation and AI come together.
How do you successfully invest in industrial AI?
The process of investing in industrial AI is an essential consideration for manufacturers of all kinds. A successful implementation can lead to enhanced productivity, greater operational efficiency, and an improved competitive advantage, while a failed effort might lead to aimless spending.
A forward-thinking factory can find an AI application for every important job. AI-driven predictive maintenance can keep machines on the production line in working order, while a separate AI project can take care of product quality assurance. Still another can deal with supply chain management and inventory levels.
A purposeful, step-by-step approach to implementing AI initiatives can help your company remain on the right side of this divide. Such a method may consist of:
- Identifying leverage points within your existing manufacturing process where an AI system can have a positive impact.
- Using a methodology such as the Hidden Factory concept to discern the best integration approach for your organization.
- Selecting truly impactful technologies that accurately align with your needs, rather than falling for market hype.
- Engaging your human workers in the implementation process and ensuring they have the know-how to effectively operate the new solutions.
- Integrating the AI technology with the productivity methodologies you already use, potentially including Six Sigma or Lean.
Following such a straightforward and value-focused approach can help you apply industrial AI to your needs rather than ending up with a deployment that doesn't accurately reflect your business.
An era of digital transformation
Now more than ever, building a resilient framework for your company's operations is a top priority. In the years ahead, as accelerated digital transformation takes hold, that very infrastructure will play an essential role in your business.
In these conditions, it's important to make ongoing transformation a part of your mindset and commit to future-proofing your operations. From supply chain optimization to smart factory development, there's an AI model for every issue. Knowing how to assess and implement them into the production process is now a highly relevant skill.
Read more: See what it takes to intentionally increase organizational resilience and watch a webinar on the process.
Executive education on AI in manufacturing
Due to the strategic importance of getting value out of AI tool integration and other Industry 4.0 technologies, it's worth seeking out more learning on the subject. This is where the recently launched MIT Sloan Executive Education course Strategy, Survival, and Success in the Age of Industrial AI can help.
Participants in the course review real-world case studies, build their data assessment skills, and consolidate specific knowledge that will help them:
- Integrate AI into existing manufacturing business frameworks for operational efficiency.
- Apply the Hidden Factory model to gain insight into operations.
- Understand digital twins, the IoT and IIoT, ML, GenAI, AR, and VR.
Read more: Learn how AI is making its way into business, then glean systems thinking insights from Ford v. Ferrari.
Get started by updating your industrial AI knowledge
Becoming a skilled user of industrial AI and related technology tools is a relevant undertaking, whether you're hoping to use the knowledge in a current role or build your resume for a future executive position. Organizations are inevitably moving into the era of Industry 4.0, and leaders' familiarity with advanced technology can be a differentiator.
Executive education is a way to deepen your immersion in cutting-edge systems in a short period of time. The sooner you build this skill set, the quicker you can apply it to implementing AI initiatives and solving pressing manufacturing business problems.
Enroll in Strategy, Survival, and Success in the Age of Industrial AI and bring your knowledge up to date.