Title:Utilizing Technology to Navigate Challenges in Three Key Manufacturing Operations During Peak Seasons
Since 1999, Bill Rokos has spearheaded the development of Parsec's manufacturing operations management (MOM) platform, TrakSYS. Managing peak seasons, when many organizations strive to meet annual revenue goals within a few months, can be challenging due to various factors such as supply chain disruptions, labor shortages, and economic volatility.
The solution to strengthening operations during these times lies in three digitally enhanced areas: predictive maintenance, quality management, and production planning. Integrating technology into these critical areas can help manufacturers remain smooth and efficient, even during a rush of peak season demand.
1. Predictive Maintenance
Machine learning, a subset of AI, has significant potential in the industry. One remarkable area where machine learning and AI can shine is predictive maintenance. Instead of leaving downtime or machine failure to chance, predictive maintenance tools enable manufacturers to anticipate potential delays and damages. This forward-thinking approach saves manufacturers time and money by allowing them to address issues before they escalate.
Imagine you're a food manufacturer producing holiday-themed baked goods. One of your machines, which cuts dough into shaped treats, suddenly starts producing irregular blobs instead of crisp, clean cuts during a period of high demand. Thankfully, AI could have detected that unusual, unseasonably warm temperature, which could have caused the machine to malfunction, in time to prevent delays and costly mistakes.
2. Production Planning
Preparing for future peak seasons means studying past successes and mishaps to make improvements. Manufacturers can rely on technology to help understand previous seasons' output, quality concerns, and other production data insights. This data analysis reduces workload and offers invaluable insights to improve quality and optimize processes.
When your software is integrated with an enterprise resource planning (ERP) system, it serves as a personal assistant that remembers even the smallest details of your end-to-end operations during peak seasons. This interconnectedness ensures that your entire business is informed about the status and progress of holiday production.
3. Quality Management
During peak periods, ensuring quality management can help manufacturers navigate the chaos and stick to their customer demands. Overall equipment effectiveness (OEE) is a popular and reliable metric for evaluating factory machinery performance and is essential during busy times. By closely monitoring OEE and making adjustments as needed, manufacturers can prevent slowdowns, minimize scrapped products, and fulfill orders accurately.
During the holiday season, computer vision technology can also help manage and improve quality. Monitoring visual data in real-time, this technology detects defects that human eyes miss, ensuring that products maintain high standards of quality. By aiding human capabilities, computer vision transforms the quality management process—enabling manufacturers to produce holiday items with precision and excellence.
Peak seasons may bring supply chain disruptions, labor issues, and economic volatility. But manufacturers can take confidence in the control they have over their operations by focusing on predictive maintenance, production planning, and quality management technologies. The integration of AI-powered tools into these areas can significantly improve a manufacturer's ability to manage high-demand periods effectively.
- Incorporating innovations like AI and machine learning into predictive maintenance strategies can revolutionize the way manufacturers address potential machine failures or delays, leading to reduced downtime and costs.
- advancing production planning through the use of technology, including seamless integration with ERP systems, allows manufacturers to preemptively plan for peak seasons, making accurate demand forecasts and ensuring efficient operations.
