Tesla CEO Elon Musk announced that the company would use genuinely useful robots in its factories by 2025.[1] Airbus has been using NLP to improve its maintenance operations.[2] Is AI only for the big players in Manufacturing? We would say No. Both medium and large enterprises are embracing AI alike on their production floors and back offices. Wondering how you can use AI in your business? In this blog, you will explore some of the popular AI use cases in Manufacturing that can help you bring a shift in your trajectory.
Why use AI in Manufacturing?
Artificial intelligence plays a crucial role in the manoeuvres of manufacturers in Industry 4.0 era. A KPMG report stated that two-thirds of manufacturers expect that AI would help them achieve their short-term ambitions. Every manufacturer aims to accelerate innovation, improve product quality, boost profit margin, and enhance compliance. When done right, AI can help manufacturers in each of these objectives.
Artificial technology solves some of the critical challenges facing manufacturers. For example, modern factory floors generate colossal volumes of data through IIOT systems. To tap into the potential of this data, manufacturers need to analyze and extract actionable insights. Traditional approaches in this practice won’t yield the desired results. To uncover hidden patterns, discover anomalies, and gain predictive analytics, manufacturers can use tailored machine learning algorithms.
AI not only solves challenges but also enables you to learn, adapt, and improve continuously.
Did you know? We empowered a remanufacturing company to accurately identify used spare parts and save costs using AI and Computer Vision.
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Top 10 AI Use Cases in Manufacturing
- AI in supply chain management
Manufacturers can leverage AI-enabled systems to assess different scenarios in the supply chain to impact time, cost, and revenue. With AI, you can predict the optimal routes for delivery, track human performance in real-time, predict delivery times with historical data and append traffic and weather reports for better route planning. All this is about product delivery to the end customers.
AI can also bring more visibility to the supply chain with capacity planning and inventory optimization. You can set up a supplier assessment model, wherein failure from a supplier can trigger alternate options without any disruption. Also, the production, demand, and supply numbers are analyzed accurately in real-time to improve inventory tracking and management.
As per McKinsey, an AI-enhanced supply chain can help in:
- Reduction of forecasting errors by 20-50%
- Decreasing the lost revenue by around 65%
- Reduce over-stocking inventories by 20-50%
- Factory automation with AI
Manufacturing workers depend primarily on their experience and intuition to monitor many equipment settings and adjust them manually. It strains their operating efficiency as well as they need to run tests and do some troubleshooting activities as well. This can impact the OEE as the operators may take shortcuts and focus on aspects other than the economic value.
Manufacturers can improve their equipment effectiveness and reduce labor costs by leveraging AI. It includes:
- Continuous tracking and monitoring of operations to identify any anomalies quickly
- A central repository of operations data to predict the optimal equipment settings
- Automate complex tasks so that scaling up according to demand becomes easy
Manufacturing giant Siemens teamed up with Google to use AI, computer vision, and analytics to improve shop floor productivity.
- AI-based design
Before designing any product, manufacturers must test various scenarios and arrive at the best possible outcome. By leveraging AI, the generative design software can consider multiple input parameters like size, weight, manufacturing methodologies, raw materials, and other cost constraints to generate various design combinations. Car manufacturer Nissan leveraged AI assistance in designing its new car. The company reported that using AI allowed them many iterations that consider the cooling and aerodynamic needs of the car while ensuring the looks were aesthetically pleasing.
- AI and IoT
Industrial IoT (IIoT) enables manufacturers to collect real-time data from their facilities, assets, and environment. Adding a layer of artificial intelligence to the IoT network amplifies the power of your data. You can extract deeper insights that will enhance precision and productivity in your manufacturing operations. With AI and IoT, manufacturers can:
- Monitor equipment performance, temperature and machine settings, and workplace safety
- Leverage HVAC and smart lighting for optimized energy consumption
- Use advanced analytics with edge devices on the production floor
- Predictive Maintenance with AI
Global management consulting firm BCG says that predictive maintenance is essential in Industry 4.0. Also, McKinsey says that predictive maintenance provides excellent value to manufacturers. For example, Siemens uses artificial intelligence to analyze their equipment data for predictive maintenance. The insights extracted using AI help the company ensure high asset availability and reliability by preventing potential equipment failures and unexpected downtimes. With AI-powered systems, you can:
- Prevent unplanned equipment breakdown by spotting any anomalies and inefficiencies.
- Reduce equipment downtime by predicting the maintenance of various spare parts and the overall equipment
- Analyze individual components to reduce the cost of replacing the overall machinery
- Process Automation
Manufacturers can optimize their production workflows by leveraging AI-powered process mining tools. Additionally, the current intelligent automation practices evolving can help in invoice processing, customer service, document management, and other vital business functions.
Usually, manufacturers operate in different physical locations. You can compare the performance across various operational facilities to optimize processes and resources.
- Quality Control and Inspections
Inspecting every production process for defects and quality conformance is essential for manufacturers as it affects their revenue and product recall. Manufacturers can leverage computer vision and image recognition technologies to inspect every aspect of the production process. Usually, AI-powered systems can detect the defects that a human eye can miss and suggest corrective measures accordingly.
Another use of AI in manufacturing is to compare the actual assembly parts to the ones provided by the suppliers to find any quality deviations.
- Reimagining Sales and Support
Manufacturers’ sales support is not very tech-savvy. By leveraging NLP and conversational AI, they can engage their leads while also answering the basic queries about the products before the sale. Information access is becoming key in this digital era.
AI can help manufacturers build customer loyalty, enhance customer support, learn from feedback and in turn generate more revenue.
- Connected Factory
Connected Factories powered by data, IoT, and cloud are the way forward for manufacturers in the future. It can help manufacturers to:
- Track multiple assets utilization
- Provide real-time visibility into the production equipment and shop floor
- Create a single source of truth for all the data through the processes
- Scale capacity without many interventions as per the demand
- Order Management
Order Management systems must be agile for manufacturers so that they can adjust according to the demand fluctuations, market changes, and customer preferences. Using AI, manufacturers can:
- Automatically create order entries
- Create purchase requests automatically by using sensors to track the inventories
- Seamlessly manage inventory planning
The AI Use cases in Manufacturing listed above are not the complete list. As per the need and problem statement, you can leverage AI to overcome challenges and drive consistent growth.
Dodging roadblocks in using AI in Manufacturing
As per Harvard Business Review, 84% of C-suite executives understand the need for adopting AI, but only 16% moved ahead for further steps to realize the value of AI. Why?
- Most organizations focus on building advanced analytics models to prove a point rather than resolving business challenges
- In most cases, technical folks are obsessed with model viability and do not understand the critical aspects of scaling the use case
- Instead of focusing on multiple PoCs, organizations should strategically plan the priorities, analytics techniques, governance, and business value
The right strategic partner with a consulting-led approach can help you succeed with AI initiatives. At Saxon AI, we help manufacturers create innovative AI-powered digital solutions to enhance operational efficiency and boost profitability.
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