Setting the Stage
Against the backdrop of Industry 4.0, manufacturers are getting better at leveraging vast troves of operational data to improve shop floor efficiency and decision making. However, competition has stiffened markedly. As artificial intelligence becomes increasingly democratized, leading organizations have integrated new tools and methods to further the benefits of Industry 4.0. AI-centric digitalization is no longer just an accelerant to operational excellence, but an absolute must-have to protecting market share in a volatile environment. As we delve into this transformative force, we explore how AI plays an integral role in digitalizing and optimizing the manufacturing sector, and why this trend will continue to accelerate in coming years.
A Look through History
Industry 4.0 marks the fourth revolution in manufacturing, where factories are transformed into intelligent environments that are digitally interconnected. Unlike previous eras, Industry 4.0 capitalizes on technologies such as IoT, big data, and AI to achieve unprecedented levels of automation and efficiency, reducing the reliance on human labor while improving decision making.
However, many organizations continue to struggle with systematically organizing, managing, protecting, and analyzing data to achieve these outcomes. From inconsistent data quality to cumbersome reports and dashboards, many leaders are recruiting outside consultants and creating new departments that are accountable for data & analytics transformation goals. Organizations that have the necessary culture, processes, and tools in place to digitalize their production facilities are also best positioned to take advantage of new AI tools. These innovative companies have seen gains in operational excellence, including waste reduction, higher product quality, and shorter time-to-market for new products when compared to their lagging peers. And the gap is widening.
The Age of AI
The most impactful AI use cases on the factory floor typically involve high-frequency decision making and a need for more seamless human-machine interaction:
- Predictive Maintenance: Leveraging AI algorithms, factories can anticipate when machinery is likely to fail, enabling preemptive action that minimizes downtime and maximizes production efficiency. Generative AI now allows for intelligent assistants to communicate these recommendations quickly and accurately.
- Quality Assurance: Advanced machine learning models can analyze data in real time to detect anomalies or quality issues, thus ensuring the consistency of products rolling off the production line. AI chatbots can streamline the communication and dissemination of this information, not to mention draw causal inferences from vast troves of internal and external quality data.
- Resource Optimization: The ability of AI systems to analyze complex patterns allows for a more judicious use of resources, be it materials, energy, or human capital, thereby driving sustainability and efficiency. World-class organizations leverage an ensemble of AI models, dynamic simulation, and digital twins to proactively address changing resource needs and constraints.
- Adaptive Manufacturing: Through the use of AI algorithms, production lines can automatically adapt to changes in the environment or requirements, allowing for a more flexible and responsive manufacturing process. The ability to remove the “human in the loop” is at the core of this trend, requiring the ability to incorporate contextual knowledge from suppliers, vendors, competitors, and macroeconomic factors, something that has been largely out of reach until recently.
“AI is the perfect complement to new IIoT hardware systems, unlocking the ability to remove the human in the loop while increasing visibility and efficiency."
Putting it into Practice
Adopting AI on the shop floor is not a “plug-and-play” affair. It requires a strategic approach that often involves re-working an organization’s operating model to include the treatment and analysis of data as a critical business function, empowering human knowledge workers to flourish. This begins with a clear set of desired business outcomes, such as boosting average production line efficiency across facilities by 10%, and an understanding of where current processes and tools deviate from best practices. These gaps often exist in the areas of data management and governance, operationalizing AI & machine learning models, and drawing proper lines of accountability to ensure that data-driven insights are democratized and thereby harnessed by the entirety of the organization. With our clients, we advocate for an iterative, outcome-centric approach to clearly define and execute pilot projects that put AI in position to quickly demonstrate measurable business value. With such an approach, we ensure that executive decision makers and data & AI practitioners are aligned before undertaking enterprise-scale implementation, reducing costs and boosting adoption. With the momentum of early trust and adoption, leaders are best suited to drive large-scale implementation of new shop floor analytics platforms and automated processes to attain true operational excellence.
Solutions
In our work as trusted advisors to leading manufacturing, retail, and CPG companies, we have developed and implemented a plethora of AI-driven Industry 4.0 platforms. While challenges and goals often rhyme, each organization presents a unique set of challenges and opportunities to cultivate competitive advantage with the help of data-driven tools. As such, AI & analytics platforms must cater to the needs of each group of business stakeholders, communicating insights and recommendations intuitively.
In this example, our client struggled to operationalize and maintain the machine learning models they had built to predict machine failures, leaving money on the table. Our team worked to define and implement a revamped production operating model, one with data at the core that prioritized a consistent approach from facility to facility and a continuous improvement mentality to managing AI & analytics tools.
Getting Started
If you are on the verge of diving into the AI wave, we recommend starting small. Define several candidate pilot projects with clear success criteria and a measurable ROI and take inventory of which data sources may be useful. By partnering with a knowledgeable and experienced solutions provider, you can navigate the complexities of AI implementation and realize its true potential without significant financial or technical risk.
Contact Us to learn how you can begin reaping the benefits of AI and analytics within your production environments, and which tools are right for your organization.
About the Author
Chris Andrassy is an entrepreneur and managing partner at Astral Insights, focused on transforming data into sustainable business value on a global scale. He began his career at PwC in New York City, supporting the digital transformation of mature organizations struggling to innovate in a hyper-competitive world. After experiencing the limitations of traditional analytics practices, he decided to begin a new chapter alongside colleagues and industry veterans. His departure from New York marked the inception of Astral Insights, a Raleigh-based AI & analytics solutions firm helping mid-market and enterprise clients transform data into profit. Chris is also an investor focused on innovative technologies including synthetic biology, sustainable energy, and artificial intelligence. Outside of work, he is an avid musician, skier, traveler, and fitness enthusiast.
