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AI-augmented process decisions: Beyond automation and into independence

Manufacturers can use AI as a holistic solution that goes beyond automating specific tasks and into making sound decisions.

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Quantumrun Foresight
August 11, 2023

Insight highlights

The advent of Industry 4.0 and AI, powered by cloud computing and advanced technologies like computer vision, has transformed manufacturing and supply chain operations, enabling improved control, efficiency, and defect detection. Companies are using AI for quality assurance and streamlined warehouse operations. However, these AI-augmented processes require workforce adaptation, increased technological investment, and ethical guidelines while offering potential solutions for environmental concerns and targeted consumer engagement.

AI-augmented process decisions context

As manufacturing methods have improved, the technology needed to manage them has also had to grow. Industry 4.0 has brought promising options for connecting machines and tools to the internet. This feature has allowed manufacturers to have better control and understanding of all the work happening on the factory floor.

The introduction of AI into manufacturing has become possible thanks to the power of cloud computing. Manufacturers use a mix of computer vision technology on the production line with AI-driven digital tools based in the cloud. The capabilities of these tools extend beyond singular tasks such as analytics, predictions, or replacing human labor. It also holds the potential to enhance and streamline operations within the supply chain.

The implications of deploying AI for independent decision-making and boosting production procedures are substantial. BMW trains its AI system using 100 input images to recognize acceptable standards for various car features, enabling it to identify installations that don't align with the company's standards. Meanwhile, Amazon employs its Sagemaker image classification algorithm to determine if a product has flaws and decide the appropriate corrective measures.

In 2023, ABB Robotics introduced the Robotic Item Picker, which uses AI and vision technology. This new solution can precisely identify and pick up items in disorderly surroundings within warehouses and fulfillment centers. The company said that the machine's high-precision picking is 99.5 percent efficient.

Disruptive impact

Predicting demand and analyzing data are critical ways AI is used in the supply chain and logistics industry, but it can also check the regularity and quality of products. It can even make decisions and processes on its own. AI can change many parts of the supply chain through defect detection, influencing how products are received and shipped. These modifications can be included in the tracking process so customers can see how their orders are protected and managed.

Better defect detection systems could sway what consumers buy based on the quality of products scanned and made by machines. As 3D printing grows, AI can spot and fix problems before they occur and even choose what products to make without human intervention. This development could lead to more consistent outcomes, meeting customer expectations. In the future, an AI chatbot could be responsible for telling customers where their items are and if there are any issues. If a problem arises, the chatbot could automatically send a replacement, making returns quicker and easier.

Additionally, with real-time data analysis, AI can predict demand, manage inventory better, and identify potential disruptions before they become critical. AI-powered systems can also provide insights into supplier performance and customer behavior patterns, enabling businesses to be more strategic in planning and execution.

Implications of AI-augmented process decisions

Wider implications of AI-augmented process decisions may include:

Workers adapting to new skill sets, such as robot collaboration and maintenance, data analysis, and automation troubleshooting.

AI systems, particularly machine learning models, requiring substantial computational resources and energy, potentially contributing to environmental degradation. However, AI could develop efficient routing of deliveries and optimization of warehouse operations, reducing energy consumption.

AI optimizing inventory management, demand forecasting, and transportation logistics, leading to significant cost savings.

Businesses responding to changing consumer demographics more effectively. For example, using AI to predict shifts in consumer behavior or demand patterns could allow companies to target products and services toward different demographic groups.

Increased investments in data storage, the Internet of Things (IoT), digital twins, networking, and computation capabilities.

Increased reliance on AI and data in supply chains making supply chains targets for cyberattacks. This development would require stronger cybersecurity measures and regulations to protect these crucial systems.