AI adoption by large enterprises has increased steadily for the last few years as business leaders look for ways to adapt to drive efficiency at optimal costs. The manufacturing sector particularly has seen the application of AI mostly in large enterprises. Small and medium manufacturers, however, can benefit just as much from AI. The hesitation of some SMBs may stem from the assumption that AI is expensive and difficult to implement. While this may have been true a few years ago, the rapid development in the field of AI is making it increasingly accessible to businesses of all sizes. AI can be applied in small and medium enterprises, especially in the manufacturing sector, to improve productivity, reduce costs and increase operational efficiency and profitability.
Ways AI Improves Efficiency of Small and Medium Scale Manufacturers
Predictive Maintenance
AI monitoring of machinery helps reduce machine downtime by 30% to 50% and increases machine life by 20% to 40%."
- McKinsey
Machines fail all the time. Traditionally, the industry required experienced workers to monitor machines for signs of trouble and fix them before any disruption occurs. This was not an optimal approach and with new machinery, it is no longer even viable. Furthermore, manufacturers cannot afford downtime and decreased productivity. All manufacturers whether big or small, possess data that can be used to predict machinery breakdown and identify the cause proactively. The availability of AI computational power and advanced analytics at lower costs can help small manufacturers analyze multiple data points and historical data to anticipate machinery breakdown and enable maintenance before it happens.
Quality Assurance
Quality assurance is an integral part of the manufacturing industry. However, traditional quality assurance processes are not 100% accurate. A defective product delivered to the customer can negatively impact the brand image, moreover, detecting defects late in the production process can increase the production cost. AI-enabled visual inspection systems such as computer vision defect detection systems can ensure defects are detected and reported at the right time with accuracy. The computer vision system captures images of parts and products, the machine-learning algorithm compares these with predefined quality parameters, identifies the defective ones, and sends them for repair or discards them. The use of computer vision ensures a strong quality assurance at a much lower cost.
Stages of Quality Control in Manufacturing
- Detect defects in raw material pre-production
- Continuously monitor production line for defects
- Final product QA check off
- Detect defects in packaging
Yield Enhancement
Manufacturing equipment creates a lot of data, but manufacturing organizations don't use the data efficiently even if it is a well-known fact that big data can offer unique insights and intelligence. With AI engines, manufacturers can use data to gather insights and detect patterns to identify causes of low yields and areas that need attention. Based on this data, performing timely and optimal changes to production processes can increase yields.
Inventory Management
1/3 of Businesses
will miss a shipment deadline because they’ve sold an item that wasn’t actually in stock."
Assessing stock across manufacturing sites according to demand is a costly exercise that needs to be carried out continuously to ensure that there is no needless stockpiling at the wrong location or critical shortages of needed materials. Held up assets in the wrong location raises logistics costs. AI can forecast market and production demands and respond to them with agile and optimized supply chain management. Automated inventory management can alert managers in case there is a shortage. AI can not only reduce the cost of maintaining inventory but also ensure that the inventory is maintained in response to demand.
Employee Safety Management
In manufacturing, employees operate in sometimes hazardous conditions. Employees must adhere to safety standards. It is not possible to manually monitor if employees are following safety standards throughout the entire site 24/7. AI-powered computer vision systems can monitor employees while on the job site and send notifications to managers if any violation is observed.
Conclusion
AI has several more advantages in the manufacturing sector such as AI chatbots to support customer service and provide insights to the company’s sales and marketing teams. AI can transform and improve every department of an organization regardless of its size. In the manufacturing sector especially, AI offers enormous advantages. The Industry 4.0 revolution will not be complete without the adoption of AI.
