When combined with technology like cloud systems, additive manufacturing, the internet of things and big data, AI is moving food and beverage ever closer to an autonomous production line. This means vastly improved efficiencies, smarter use of human labor and the assurance of a sustainable future.
But effective AI integration across any business requires strategic thinking. Instead of operating solely in terms of project-centered goals, decision makers’ strategy should be holistic across the business.
Here are some key areas to consider as you prepare your organization for AI’s awesome potential.
Managing Cultural Change
To ensure success, your AI initiative must have the support of senior business leaders who understand that business processes must be transformed to take best advantage of AI’s capabilities.
Support across the workforce is also crucial. It requires careful managing of expectations and assurances that AI is designed to augment humans’ work, not replace it. Though roles may need redefining, AI allows individual workers the chance to develop new skills as they move away from more tedious and dangerous tasks.
AI is made by humans, intended to behave like humans, and, ultimately, impact humans’ livesand society.
Dr. Fei-Fei Li, Professor of Computer Science, Stanford
Far from being rendered obsolete, human workers will always be a crucial component of the manufacturing process. For instance, although AI can deliver highly nuanced analytical reports, it is the knowledge and creativity of your people that actually contextualizes the data into the most useful insights.
Addressing skill shortfall
AI will be a significant asset in addressing the lack of skilled workers in the manufacturing industries, which are facing a growth-threatening talent deficit of 7.9 million people by 2030.
It’s becoming increasingly difficult to fill time-consuming, dangerous and manual roles with human workers. AI will be important in responding to the skills shortage whilst enabling workers to perform other in-demand roles.
Optimizing production today
While dreaming big is important, ensuring buy-in from the executive suite will necessitate a clear understanding of the ways AI is optimizing production right now. With the development of predictive and condition-based maintenance, planning more efficient repairs becomes possible, reducing unplanned downtime and unnecessary maintenance work, as well as enabling peak operating performance of the plant.
Maintenance strategies are increasingly reliant on AI’s data-driven insights, which are used to ensure continuous operations and enhanced equipment reliability. While predictive maintenance uses sensors to precisely collect data on equipment condition and overall operational state and predicts when failure will occur, utilizing AI takes it to the next level: prescriptive maintenance. Prescriptive maintenance means failures are not only anticipated, but actions are also recommended. This enables maintenance teams to excel with timely action, fewer incidents and faster issue resolution.
AI can play a vital role in optimizing power and water consumption, creating immediate benefits for the environment and reducing operating costs. For instance, AI solutions can easily recognize variances in fresh fruit and vegetables, removing contaminants without wasting whole batches and continually adjust water and energy usage according to requirements. This process, including robotics, can be fully automated, running 24-7 and active across production.
Understanding data
With the Industrial Internet of Things gathering momentum, business leaders must start thinking about their data in a completely different way.
The essential differences between consumer and industrial AI account for many of the misaligned expectations F&B leaders encounter. Unlike mainstream consumer AI, which can analyze a large range of different datasets because it is usually looking to learn from average consumer behaviors, industrial AI’s pattern recognition seeks to learn from differences.
Industrial data is sourced from a diverse range of devices and systems, which often have little in common with one another. The outcome of this is that industrial AI requires a much larger dataset than an AI designed for consumer use. Effectively communicating the essential difference in these outcomes is crucial to ensuring business-wide buy in.
Manufacturers are also embracing Edge computing as a crucial part of their hybrid data architecture. Edge computing provides a method of storing data based on factors like importance, privacy or regulatory requirements. This means that instead of being transmitted to a far-off data center, data is processed in real-time on a local computer, server or even the device that collected it. The advantages of Edge computing are compelling: vastly reduced latency, a lessening need for data centers and slashing the cost of bandwidth requirements.
Looking ahead
At the heart of modern manufacturing is the need for over-arching digital transformation. AI’s revolution will be one made in small steps. For a more in-depth guide to the awesome potential of AI, including a practical transformation checklist, download our whitepaper.
