The quality of the product is an important aspect that cannot be overlooked or neglected, regardless of the motive, in any manufacturing venture. It is the question of how manufacturers can improve their products’ quality to ensure security and long-term sales of their products worldwide? Many industries have created and developed their own quality frameworks in order to ensure strict compliance with quality standards specific to their particular business.
Recalls of certain products could cause a huge cost to businesses. Do you recall the popular Note 7 smartphone battery explosion incident that Samsung was confronted with in the year 2016? The twist of events, when paired and the recall on the product , has been claimed to have earned Samsung Korean giant an estimated USD 5.3 billion in the world.
However most manufacturers don’t realize that technological advances can assist in enhancing their quality standards significantly and help them adopt the latest business techniques quicker.
As more businesses adopt an autonomous and digital-first method of operation, using more sophisticated equipment and connected technology and system, it’s a huge amount of information available within the manufacturing process. By utilizing the information and using analytics, manufacturers can uncover the information hidden that will assist in ensuring better quality production, and greater popularity of the products on the biggest markets.
Let’s take a look at the importance of analytics for improving the quality of manufacturing:
- ACCELERATE QUALITY Check
Quality checks can’t be avoided at all costs to ensure sustainability of the business However, there is a huge opportunity to improve the quality process dramatically. Analyzing and performing manual inspections of the data collected from the manufacturing industry will take time and energy. However, by using analytics, it becomes much easier to automate quality inspections.
- Identify RISKY Elements IN ADVANCE
Analytics systems continually monitor the latest data from the plant machinery and information about products. With this information they can anticipate potential dangers, including upcoming breakdown scenarios, imminent downstream quality issues with processes or products which can result in negative impacts that accumulate in the back end, and many more. It can prevent major interruptions to business operations in the near future by making sure that quality issues are not setbacks that could occur if warning signals were not communicated on time.
- HIGH QUALITY INSIGHTS FROM PRESCRIBE
There may be occasions where analytical systems are able to model effective solutions to identified problems or issues with quality in the framework of production or product. The systems are able to extract and analyze data from workarounds and successful implementations in the past. They can suggest actions such as process corrections or changes to production parameters that, if done correctly can assist in resolving errors from the standard quality on manufacturing.
- UNCOVER UPSTREAM QUALITY PROBLEMS
There’s a good chance that your vendors or suppliers are the source for issues with quality that occur within the manufacturing process. Data analytics can help identify the root cause by modeling failure or defect circumstances in each possible scenario using real-time data. It is able to identify the possible sources of errors or defects are triggered by repeated analyses of information from several historical timelines. Thus, analytics help to expand quality coverage to manufacturing upstream processes, which is an essential component of quality improvement efforts that span from end-to-end.
- BETTER PRODUCTIVITY
With the help of analytics, manufacturers are able to identify pertinent insights about utilization of raw materials, or the efficiency of equipment and machines and operational environment optimization which will help to deliver higher output from factories without sacrificing quality. Analytics systems can provide immediate insights into the the expected production effort, demands for raw materials, labor and machine deployment times as well as coordinated logistics to increase the efficiency of factories. Patterns of data in processes and workflows are analyzed to determine the best options for optimization – be it machinery or processes, or schedules of staff – to ensure that increases while ensuring quality.
