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Using AI and Edge Computing for Supply Chain Engineering

July 3, 2023
With AI and edge computing in the toolbelt, engineers are teasing out actionable insights and applying intelligent automation to increase supply chain agility.

Supply chain engineering is an essential process for any goods-based business today. Repeated significant disruptions over the past few years have revealed how most supply chains need optimization, but achieving that is far from easy. New technologies, namely artificial intelligence (AI) and edge computing, can help.

What Are AI and Edge Computing?

AI is a broad category covering software that can think and adapt similarly to the way the human brain operates. It takes data and looks for patterns and trends, using what it learns to interpret new information. Machine learning, deep learning and neural networks are all subsets of this technology that build on this basic premise.

Edge computing is a less familiar technology for many but is equally important for supply chain engineering. This practice involves distributing computing tasks across a network of devices. It’s similar to cloud computing, but instead of processing information in far-away data centers, it does so across nearby devices, reducing latency and improving performance.

Both technologies can benefit supply chain engineers, but they’re even more impactful together. The edge AI market could become a $107 billion industry before 2030 as networks make advanced processes more accessible and AI capitalizes on the data.

The Role of AI in Supply Chain Engineering

Supply chain engineers can apply AI across a wide range of processes. Predictive analytics, AI that makes predictions based on past trends, is one of the most helpful of these use cases. Organizations can use predictive models to accurately forecast demand shifts or anticipate disruptions, such as stock shortages, and adapt accordingly.

The same type of analytics can also improve equipment maintenance schedules. Predictive maintenance uses AI to alert workers when a machine needs service, preventing breakdowns and avoiding unnecessary repairs. As a result, it can increase productivity by 25% and lower maintenance costs by the same amount, according to estimates quoted by Deloitte.

READ MORE: AI Makes a Deep Impression on Industrial Manufacturing

AI can also analyze supply chain structures and workflows to find inefficiencies and suggest improvements. Engineers can use these models to create digital twins of their supply chains and simulate various changes to find the optimal path forward. AI optimization can also happen on a smaller scale, such as using machine learning to route deliveries more efficiently or to reorganize warehouse layouts for improved productivity.

AI can bridge gaps between partners in different countries as supply chains expand globally. Translation services can produce the same effect as a 35% reduction in the distance between nations by preventing miscommunication. Even basic AI software can help by automating routine data entry or administrative tasks to improve team productivity and cooperation.

The Role of Edge Computing in Supply Chain Engineering

Edge computing is similarly versatile. Many supply chains already embrace Internet of Things (IoT) systems, and edge computing unlocks these device networks’ full potential.

Many of AI’s most impactful use cases are more accessible and reliable when supported by edge computing. Take predictive maintenance, for example. The sensors monitoring equipment health factors must be relatively small, but this limits their on-device computing power. Edge computing provides a solution by distributing compute tasks across multiple nearby endpoints, enabling compute-heavy AI analytics despite each device having minimal processing power.

READ MORE: A Successful Digital Transformation Hinges on Values

Similarly, edge computing makes IoT systems more practical by reducing network latency. Instead of IoT data traveling to remote data centers for processing, it goes to devices in the same physical location. As a result, supply chain engineers can analyze information from their facilities faster. This latency enables real-time functionality, such as up-to-the-second inventory tracking and autonomous guided vehicle (AGV) collision warnings.

Edge networks can also improve supply chain cybersecurity under the right circumstances. Traditional cloud computing keeps data in large, centralized databases, so attackers only need to break into one system to access everything. Because edge computing distributes data, there’s no single point of failure. That advantage is crucial, considering manufacturers are the victims of 30% of extortion attacks.

Best Practices to Consider

Like any technology, AI and edge computing are only as effective as end-users’ ability to implement them. Supply chain engineers hoping to fully capitalize on these innovations should remember a few best practices.

First, it’s important to recognize that these initiatives can be expensive and challenging to implement. About 60%-80% of AI projects fail, and edge computing faces similar changes thanks to its complexity and organizations’ lack of understanding. 

READ MORE: AI-Based Machine Health Solutions Promise Fast Time to Value

Engineers can avoid these outcomes by approaching technologies slowly and carefully. They should define clear use cases and goals for each, and start by applying them where they’re most needed. Leaders can monitor KPIs to see what worked and what didn’t to inform more effective projects in the future.

Cybersecurity is another pressing concern. Edge networks can include hundreds of devices and AI consumes large amounts of data, expanding companies’ attack surfaces. Employee security training, zero-trust network architecture and continuous system monitoring may be necessary to use these technologies safely.

Finally, remember that these tools should always serve a business-related purpose. Use AI and edge computing to solve existing, relevant issues, and keep these goals in mind throughout the process. Implementing technology for technology’s sake may create more problems than it solves.

Modern Supply Chains Need AI and Edge Computing

Supply chains face increasingly high demands and are growing more complex. In light of this shift, conventional, manual approaches to supply chain engineering are no longer sufficient. Businesses need AI and edge computing.

Successfully implementing these technologies can be challenging, but it’s an essential step forward. Engineers who know how to use AI and edge computing effectively can significantly improve their supply chains.

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