Machine Learning and artificial intelligence give manufacturers the edge to optimize their current supply chain. AI speeds up the research and development field to the next level. Since the internet came into being, all the leading production factories worldwide have digitalized their processes.
Humongous amount of data flows from every tool within the factory floor. This provides organizations with enough information to determine the next steps. Unfortunately, some companies need more resources to help translate information to increase efficiency and reduce costs. Thus firms need AI for that.
Much like AI is transforming the workflow within chemical industries, you can see the effective use of Artificial Intelligence within the manufacturing sector too. Reading till the end of this post will help you understand how world-class manufacturing units are now using the power of AI to elevate their workflows to the next level!
Some Inspiring Instances of AI Transformations
● French multinational Danone Group has been using machine learning to help improve demand forecast accuracy. This resulted in a 20% decrease in forecasting errors, a 30% decrease in lost sales, and a 50% reduction in the workload of demand planners.
● Siemens is utilizing AI in several ways, including optimizing power usage in plants during live production, machine-operated quality inspections, and the autonomous adjustment of wind turbine rotors to boost the yields of the wind farm.
● Canon is implementing advanced levels of quality control in its production facilities by integrating human skill, insight, and AI technologies. Industrial radiography is used to inspect manufacturing components thoroughly to ensure that they are structurally sound.
● Many car manufacturing companies have been using the power of robotic workers to operate factories 24 x 7. The robots are well-designed to produce major components for motors and CNCs and operate production floor machinery non-stop. It helps in the continuous monitoring of all the chosen operations.
AI Computer Vision and Object Detection
Several companies are turning to computer vision to streamline the product assembly line. For instance, manufacturers can streamline key operations that have historically been difficult for human workers with a computer vision inspection system.
When employees have to switch products many times in one day, computer vision can help with Standard Operating Procedures (SOPs). As a result, procedures can be improved upon. To aid in this process, CV-powered cameras are set up, with the captured images being sent into an AI system for fault detection.
The algorithm flags problems, and the manager is alerted immediately to address them. Besides, it gives the workers directions to follow so they can finish each task properly.
Similarly, firms use object detection and object tracking to check production lines for discrepancies. Cracks, scrapes, and other flaws fall into this category.
CellStrat developed Object Detection AI Algorithm that helped a leading multinational spectacles manufacturer to detect flaws in the frames and identify customer insurance claims.
CellStrat Hub has also developed and AI Marketplace where ready-to-integrate inference APIs are available to supercharge manufacturing units with Machine Learning. Visit CellStrat Hub to know more.
Cameras equipped with AI computer vision can identify personal protective equipment such as welding helmets, high visibility vests, face shields, and hard hats. That ensures the safety of the workers in the manufacturing unit.
Other noteworthy instances where manufacturers are using AI vision and object detection:
● Quality assurance
● Paint surface inspection
● Identify font distortions and anomalies in labels.
● Identify missing pieces after assembly.
● Identify undercooked or overcooked chips in the snack manufacturing unit.
● Inventory Management by auto-counting
Production line Monitoring and Predictive Analysis of Supply-demand Elements
Every manufacturer is on a new path to finding fresh ways to make money and save more. Each of them is trying to reduce the risks involved and improve the efficiency level of the production line.
AI tools are used to process and interpret the vast data from the production line to spot the winning patterns. These tools help analyze and predict consumers’ behavior and detect real-time anomalies within the production line.
● The outcomes of using AI techniques in predicting models are very promising. McKinsey Digital found that with the help of AI, predicting errors in supply chain networks might be cut by 30–50%. Losses in revenue from stock-outs can be cut by as much as 65%. At the same time, storage expenses can be cut by as much as 40%.
● Artificial intelligence (AI) investments for manufacturing and supply chain planning are predicted to increase by $1.2T to $2T globally by 2025.
AI has helped with the predictive analytics feature. It helps tackle the operational challenges and disruptions to all the supply chains and covers the workforce. It is one proven way to reduce the level of unplanned downtime.
Improving demand prediction accuracy yields positive results across all industries, with FMCG makers leading the way.
Machine learning and artificial intelligence are impacting how industries manufacture and what they manufacture. With the ability to recognize shifting customer tastes through data analysis and trend spotting, FMCG units may respond by changing ingredients to produce limited-time specials that resonate with consumers.
There are also significant changes to uncover potential product expansion regions while speeding up the process to ensure a more timely launch.
Renovation and Maintenance
“Predictive maintenance can help you avoid unplanned downtime.” One can see the proficient use of AI within the manufacturing unit to plan and produce floor operations and let them run smoothly enough.
Manufacturers can create an asset viability protection plan that indicates when it is the most cost-effective to upgrade equipment. Manufacturers can achieve it by responding to alerts and correcting small faults as they occur.
● Sensor data is collected from the equipment, sensing heat, vibrations, and movement, whereas PLC data tracks machine inputs and outputs.
● Computer vision data is collected from cameras around the plant, and time-series data is used to assess the machine’s state based on its history.
● External data sources, such as changing weather conditions or the effects of connected equipment, are also considered.
These findings provide a good supply of contextual data for training machine learning models. Trained AI modules can
- Detect quality defects to ease predictive measures.
- Forecast effective losses to help chalk out a better plan.
- Automate the production process.
Enhancing Human-Machine-Human Communication Channels
AI and machine learning aren’t just for running operations. Its ability to find patterns in the audio, image, and video data can help communicate better with customers and employees. Manufacturers can save a lot of time & money if they use their communication channels correctly.
An AI chatbot could take some of the load off of the call center and give the field sales teams more time to focus on getting new clients. Meanwhile, AI chatbots and voice assistants will solve customer queries most humanly.
AI improves not only human-to-human communication channels but also enhances machine-to-machine integrations. Artificial Intelligence has proved its capability to establish effective communication between machines.
This can be done in two ways:
- by analyzing data and
- by automating tasks.
Due to improvements in online network connectivity, like the recent rollout of 5G-connected devices and the continued improvement of Bluetooth, it is now easier than ever to connect the different production units and devices.
Get on with Artificial Intelligence to boost productivity, safety, and profitability
All the above-stated points advocate that AI is crucial to mark the future of the manufacturing world. Using AI technologies will help us reduce labor costs and stay resilient no matter how many disruptions occur within the supply-demand chain.
Manufacturers can now gain the visibility of the entire operation within the facilities with the help of AI tools, no matter the geographical location.
AI-powered tools can further adapt and learn new means and improve on the production and manufacturing scale daily.
They get to deploy the major predictive maintenance, which in turn, helps in reducing the downtime. Further, manufacturers can now respond to real-time changes in demand, covering the whole supply chain.
These points prove how AI is here to change the future of manufacturing processes and units.
