Data-driven Quality Management Can Optimize Your Customer Satisfaction

Blog: The Tibco Blog

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Quality management procedures are vital in any manufacturing setting. They ensure outputs are standardized, defect-free, and compliant with all health, safety, and industry regulations. When products tick these boxes, they consistently satisfy customers and help the business gain a solid footing in the market.

Manufacturing companies are well aware of the importance of proper quality management. According to Grand View Research, the global quality management software market is expected to grow at a compound annual growth rate (CAGR) of 9.7 percent until 2028. This growth is primarily driven by more focus on customer-centric production.

Despite their best efforts, many manufacturers are still struggling to maintain high-quality products. The primary reason is the overreliance on reactive quality management methods, which results from an implicit concession that quality can’t be built into processes and must instead be inspected after production.

Moving from a reactive to a proactive strategy is critical for quality assurance success. By leveraging advanced, AI-driven data integration and analytics, manufacturing companies can manage quality continuously during production and benefit from higher customer satisfaction and retention.

Here’s how data-driven, AI-powered quality management can help manufacturers improve product quality and garner better customer satisfaction and net promoter scores.

Why Is Data-driven Quality Management Important in Manufacturing?

Conventionally, manufacturers conduct quality inspections after a production cycle or when defects are spotted during production. As a result, issues are only addressed after they occur, resulting in significant (yet avoidable) losses.

Moreover, it is often difficult to ascertain where the defect occurred by looking at the end product. Instead, staff must inspect various production stages and components to find the root cause of the defect. This manual, time-intensive process is inefficient and subjective, leading to potentially costly errors.

Data-driven quality management enables you to pre-emptively identify possible defect points before and during as opposed to after production. This approach involves:

Systematically collecting and analyzing real-time quality data
Using data to build quality models and profiles
Comparing product samples and machinery status to the quality models and profiles in real time

As an upgrade to traditional methods, data-driven quality management replaces the numerous tests performed on individual products post-production with an integrated system that connects to modules tracking product and machinery parameters at relevant points of the production line.

These parameters are then used to create the quality models and profiles that manufacturers can compare with the real-time status of production. A mismatch between the models and the real-time product or equipment status is a clear indication that a defect has, or may, occur.

How Can Data-Driven Quality Management Boost Customer Satisfaction?

Data-driven quality management delivers a more accurate and efficient way to identify the exact point of potential defects, improving problem solving and resulting in products that adhere more closely to predetermined standards.

High-quality products have a direct impact on customer satisfaction (CSAT) and net promoter scores (NPS). With data-driven quality management, you can identify problems at the earliest possible stage and implement corrective actions before defects occur. This proactive quality management leads to more satisfied customers who are likely to return for future purchases and advocate for your product in the market.

Additionally, data-driven quality management accommodates real-time integration of numerous internal and external data sources to deliver more insights for improving product quality. For instance, data integration can connect quality management with buyer feedback tools to capture responses about a particular product and get ahead of reported defects. This approach makes customers part of the quality processes and demonstrates your commitment to addressing their feedback.

Bolstering Data-Driven Quality Management with Artificial Intelligence and Machine Learning

An increasingly apparent problem with conventional analytics solutions is that they merely help data analysts uncover what they expect to find.

For instance, a traditional business intelligence (BI) setup might have a dashboard with several graphs, each tracking quality metrics such as nonconforming incidents over time or materials intake. These graphs can reveal a spike in nonconformities coinciding with an intake of a particular material.

Although it’s easy to conclude that these two events are correlated, the data itself does not present definitive proof. It also doesn’t suggest a clear action.

Do you call your supplier? Do you audit your materials handling procedures? Or do you wait and see if the same material causes another incident? Answering these questions without concrete proof from data and analytics becomes a game of assumptions that can result in costly mistakes and delays.

Artificial intelligence (AI) adds value to BI setups by providing more context to data. Because AI and machine learning (ML) algorithms can uncover previously hidden relationships and patterns across various data sets, they can paint a complete picture of the issue and improve the accuracy of problem-solving efforts.

In the example above, rather than merely showing that a spike in nonconformities coincided with a material intake, an AI-powered BI system could reveal more variables—humidity, temperature, pH levels, and so on—and show their trend when the defect was reported.

Moreover, when integrated with enterprise resource management (ERP), the system could retrieve data, such as where the material was stored and the staff member who handled it, revealing even more potential nonconformity causes.

Therefore, rather than wondering whether the material intake caused the nonconformity, you can more closely gauge the variable responsible and the action to take.

Reinforce Quality Management with TIBCO’s Manufacturing Intelligence Solutions

Today’s highly dynamic and competitive manufacturing landscape has rendered conventional quality management and statistical process control solutions ineffective at keeping customers happy and maintaining market share.

Instead, you need a system that can combine historical and real-time data from relevant internal and external sources and use it to create AI models that help you detect and prevent quality issues before they occur.

TIBCO’s AI-powered data integration solutions are all you need to unify your data and convert it into comprehensive insights that facilitate proactive detection, classification, and resolution of quality issues.

TIBCO can help you leverage unrivaled integrated data, AI, and ML algorithms to ferret out anomalies and predict future states before failures. With TIBCO, you can consistently maintain high-quality levels, boost customer satisfaction, and gain a formidable edge above the competition.

Want to know more about TIBCO’s intelligence options for manufacturing? Contact a TIBCO expert today.

The post Data-driven Quality Management Can Optimize Your Customer Satisfaction first appeared on The TIBCO Blog.