High product quality and efficient operation on a consistent basis are key objectives in the highly competitive manufacturing arena of today. Analytics integration along the manufacturing value chain has emerged as an important driving force in achieving the above-desired goals. This article will discuss the role played by Analytics in helping to drive Quality Control (QC) and Quality Assurance (QA) in manufacturing environments. We further explore how such data-driven insights can help optimize quality, reduce defects, increase efficiency, and eventually lead to better product reliability and customer satisfaction through analysis of sources, such as production processes, equipment performance, and product characteristics.
Challenges With Traditional QC/QA In Manufacturing
- Limited Data Utilization
While more manufacturers seek to achieve data excellence, there is a wide gap between the manufacturing data used effectively and otherwise. In fact, according to a 2021 study of more than 1,300 manufacturing executives, a disturbing trend remains: only 39% of respondents have been able to scale data-driven initiatives from a single-product production line into tangible business results. This gap emanates from not using their manufacturing data efficiently and not extracting insights that could uplift quality and efficiency.
- Increased Risk of Human Error
The other significant challenge associated with traditional QC/QA systems is human error. Human errors in manufacturing account for 23% of unplanned downtime, though it’s much higher in the pharmaceutical industry; over 80% of process deviations emanate from human errors. Such errors not only halt production but also create large financial ramifications, contributing to manufacturing scrap and rework that can consume between 5% and 30% of total costs.
- Time-Consuming Processes
Traditional QC/QA practices are time-consuming processes involving labor-intensive manual inspections and sluggish procedures that slow up production. These slow processes aside from slowing up time-to-market also reduce the efficiency of the operation itself. Using these slow processes, firms run the risk of letting key competitive advantage slip through the cracks.
- Reactive Approach
Finally, traditional QC/QA typically adopts a reactive stance. In this method, manufacturers respond to problems after they have arisen and seek a means of rectification after an event has occurred. This can allow an issue or opportunity to snowball and damage any potential gains that have been achieved.
Role of Analytics in Modernizing QC/QA and Manufacturing Methods
- Real-Time Monitoring and Alerts
Analytics screens use IoT sensors and machine data for continuous, real-time monitoring of production lines, and automated alerts are sent out when anomalies are detected against quality standards set, so it gives manufacturers a direct window of intervention that can prevent defects in large batches of products.
- Predictive Analytics for Proactive Quality Management
Predictive analytics helps manufacturers forecast potential quality issues before they develop using the historical data that is used to predict such occurrences. This, therefore, means early intervention to prevent defects, reduce downtime, and increase overall product consistency and reliability.
- Just in Time (JIT)
JIT is a manufacturing strategy of producing goods at the very moment that they are needed, hence resulting in reducing the waste and all the associated costs and waste. Analytics optimizes the JIT systems in terms of producing goods by employing good forecasting, analyzing the inventory levels, and fine-tuning the production schedule.
- Root Cause Analysis (RCA)
RCA is a problem-solving technique where one attempts to find the root cause of defects or failures in manufacturing processes. With analytics, manufacturers can process large datasets and point out the root causes of quality issues. It accelerates the troubleshooting process, reduces the problems that happen again and again, and improves overall manufacturing efficiency.
- Statistical Process Control (SPC)
SPC is the methodology to monitor/control manufacturing processes through statistical methods so that manufacturing processes are always within the specified limits of quality. Analytics supports SPC by automatically tracking the vital process variables continuously, so manufacturers can be ensured about maintaining high-quality standards.
- Total Quality Management (TQM)
TQM is an overarching management philosophy wherein long-term success is achieved through customer satisfaction; all members of an organization are involved in improving processes, products, and services. Analytics plays a very important role in TQM since it allows one to measure the data-driven incidence of improvement while helping identify areas for enhancement at all production stages.
Benefits of Using Analytics for QC/QA
- Reduction in Defects = Enhanced Product Quality
The core advantage of the use of analytics in QC/QA would be that there is a significant reduction in product defects. Analyzing data allows businesses to determine defects at an earlier production stage. This leads to improved product quality and, more importantly, enhanced customer satisfaction and loyalty.
- Savings on Rework, Waste, and Downtime
Analytics can identify inefficiencies and areas of waste in manufacturing and production processes. By leveraging data analysis, rework and waste are significantly reduced. In addition to cutting costs, it makes the manufacturing operations leaner. Additionally, warning equipment failures from data analysis enable companies to provide maintenance schedules that better reduce downtime.
- Faster Decision-Making
Decision-making must be prompt in an agile business environment. Analytics will produce real-time insights to allow for responses to quality-related issues that arise in real time. With the right information, decision-makers can evaluate options and make swift decisions to guide and ensure product quality and consistency without unduly prolonging delivery times.
- Increased Operational Efficiency
Data analytics tends to spur the spirit of continued improvement by allowing insights that directly drive effectiveness in operations. Reviewing and assessing how every production phase impacts the quality of the final product enables an organization to optimize its workflow and align and streamline processes according to the proper sources of resources. This improves quality and performance overall.
Conclusion
In today’s competitive manufacturing landscape, Analytics is becoming critically important in developing Quality Control (QC) and Quality Assurance (QA); it reduces defects, streamlines processes, and increases product reliability. Through real-time monitoring of products, predictive analytics, and advanced methodologies, manufacturers can increase their efficiency, reduce waste, and delight their customers in the long run.
At Ascentt, we are experts in leveraging advanced analytics to improve quality control and assurance in manufacturing. Our customized solutions help organizations unlock the value of their data for improvement in the production process, reduction of defects, and high operational efficiency. From predictive analytics that prevent quality issues to tools that streamline your manufacturing workflows, our team of experts is ready to help you. Contact us today to find out how Ascentt can help your business deliver superior product quality and operate with operational excellence through advanced analytics solutions.
