Top 3 KPIs for data-driven quality performance evaluation

By November 19, 2020

What information do quality executives truly need to keep a finger on the pulse of their supply chains? What type of data would not only give them a holistic yet detailed overview of their quality operations but also enable them to make fast, effective business decisions when the next crisis strikes?

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“Operational risk to supply chains has been growing over the last several years — compounded by the ongoing impact from COVID-19,” writes McKinsey & Company. Quality assurance operations are no exception to this trend. Businesses around the globe are undergoing a significant stress test, working hard to maintain quality across widespread supply chains despite severe disruptions to their operations. According to Quality Magazine, “Companies in which quality was already fragile and inefficient have seen their processes go from lacking to completely broken during the COVID-19 crisis.” Many are also finding common key performance indicators (KPIs) to be inadequate in assessing and fixing the problems they face. 

As companies adapt to this new normal and attempt to manage the unique obstacles and risks that it brings, conversations have shifted to supply chain visibility — as well as digital systems that can provide it with consistent, accurate, and remote access to real-time operational data.

In responding to COVID-19, “the immediate focus for most companies needs to be on improving visibility to supply chain risk—in your own facilities, in your direct suppliers, and beyond,” writes Deloitte. Pen-and-paper and manual reporting are increasingly disconnected and inadequate, with Quality Magazine writing that companies relying on them “lack real-time visibility into current and accurate quality data and related activities across their ecosystems and therefore have no way of sustaining ‘pandemic proof’ quality.” 

Although the need for such data is clear, the question remains: what data? What information do quality executives truly need to be able to keep a finger on the pulse of their supply chains? Which type of data would not only give them a holistic yet detailed overview of their quality operations, but also enable them to make fast, effective business decisions when the next crisis strikes?

TOP 3 KPIs for Data Driven Quality Performance Evaluation Inspectorio Sight Top 3 KPIs

Below, we have honed in on three of the most important metrics quality executives need in order to do just that:

1. Fail Rate and Defective Rate

Fail rate and defective rate are two of the most powerful performance indicators available to quality executives. 

TOP 3 KPIs for Data Driven Quality Performance Evaluation

However, even more powerful is comparing these two metrics to one another. 

As a pair, fail rate and defective rate take you beyond merely examining the effectiveness of your existing acceptable quality level (AQL). Too great an emphasis is often placed on fail rate (and hence AQL) alone. SgT Group writes that AQLs “aren’t 100% effective in avoiding quality defects altogether” — and because it tracks performance purely on whether goods are meeting AQL tolerance, it follows that fail rate is a limited benchmark for measuring quality. Teams basing their decision making on fail rate often rely on guesswork to identify the true cause of problems, leading to ineffective or wasteful modifications to their AQL. Examining the correlation between the overall fail rate and defective rate can provide actionable paths forward when dealing with underperformance. 

To identify your top and lowest performers, create a graph with 4 quadrants:

TOP 3 KPIs for Data Driven Quality Performance Evaluation

Now analyze an X-Y scatter plot of your factories or suppliers, with defective rate on the Y-axis and fail rate on the X-axis

TOP 3 KPIs for Data Driven Quality Performance Evaluation

By overlaying your 4-quadrant graph on this X-Y plot, it suddenly becomes trivial to group your supply chain partners by these performance metrics:

1: High fail rate, high defective rate

These are the worst performers in your supply chain.

2: Low fail rate, high defective rate

Landing in this quadrant indicates that, although the factories are consistently passing inspections, there are still high numbers of products with defects in any given pull sample. A possible mitigation tactic is examining how strict you want to be with your AQL, which could help catch these defects, according to inTouch. Optimizing AQL is much easier with risk analytics provided by data-driven digital services.

3: High fail rate, low defective rate

This quadrant is a good indicator of other problems aside from product quality — if high numbers of defects are not being found, why would factories have a high fail rate? These problems could include missing paperwork, being unprepared for inspections or unavailability of tagged samples, to name a few. 

4: Low fail rate, low defective rate

These are your highest performers.

Leveraging analysis of fail rate vs. defective rate requires the use of a network platform aggregating data in real-time. Such a digital platform not only captures and incorporates all incoming data into insights but also allows organizations to maintain full transparency over their supply chain ecosystems — a capability that becomes prohibitively complex with manual reporting, particularly as supply chains scale upward. Maintaining this visibility prevents common ailments like quality fade from creeping into a brand or retailer’s products.  

TOP 3 KPIs for Data Driven Quality Performance Evaluation

2. Ratio of “self-inspection” results to third-party inspection results


Empowering factories and vendors to complete their own self-inspections is an excellent long-term goal, saving resources and promoting partnerships based on trust instead of policing. However, a common problem persists within the industry: very few people trust self-inspections. 

Factories and vendors conducting them may gradually relax their standards until their “pass” rate settles conspicuously near 100%. This presents a challenge for quality executives, who must then rely on third-party inspection companies to enter the factory and inspect the same goods or purchase orders, thereby providing a clearer picture of product quality in these factories.

This is why the metric ratio — that is, the ratio of self-inspection results to those of third-party inspections — is such a valuable quality KPI in a post-COVID-19 world. The closer this ratio is to 1:1, the better indication that factories are conducting each internal audit accurately and fairly. 

You can also use this KPI to rank your vendors in order of the accuracy of their self-inspections. Those with ratios indicating more trustworthy self-inspections can continue conducting them, avoiding the need to hire a third-party inspection company. This saves you time and resources that you can then allocate toward higher-risk vendors. In addition, it improves your company’s resiliency against future disruptions like the current pandemic. When another crisis strikes and you are unable to send third-party inspectors to your factories, you know from this ranking who can be trusted to conduct their own self-inspections. In this way, your organization will be better prepared to remain resilient throughout a COVID-like global disruption.

A digital solution is necessary to ensure the accuracy of self-inspections and the trustworthiness of data. One of the primary advantages of a data-driven digital solution is that it provides the same visibility for all stakeholders, in real-time. Brands and retailers can use the platform as their sole communications avenue for sharing defect lists, AQLs, self-inspection checklists, and any other standards with their factories and vendors conducting these inspections. This allows for high gains in efficiency and ensures that the brand’s quality and self-inspection guidelines are being used instead of those of the factory or vendor. 

3. Return Material Authorization (RMA) insights


RMAs are the third most valuable KPI to help ensure quality in a post-COVID-19 world. Because they require the supplier to issue some form of refund or credit for the faulty merchandise, this represents a significant loss in profits — particularly if the defects reach a large number of customers. 

Therefore, RMAs represent “a direct measure of product quality and a product’s nonconformance to customers’ specifications and requirements,” according to Diginomica. Performing a monthly Pareto Analysis will help identify the top 20% of factors that cause 80% of the returns, letting your organization identify the root causes of RMAs and allocate resources to them. In this way, RMAs serve as an invaluable tool for analyzing patterns that could indicate serious anomalies in your supply chain. By leveraging them as a KPI, your brand can “gain greater cost and operational efficiencies while maximizing brand customer satisfaction and loyalty,” says Louis Columbus of IQMS

The greater a supply chain’s transparency and ability to collaborate and share data, the more effectively they can act in a concerted way to reduce their RMAs. Investing in a digital solution to accomplish this pays dividends; reducing RMAs has been shown to have a positive impact on improving Net Promoter Scores (NPS), improving a brand’s reputation over time. 



“Companies need an understanding of their exposure, vulnerabilities, and potential losses to inform resilience strategies,” writes McKinsey & Company. For brands and retailers, maintaining product quality is paramount to success in the “era of customer-driven manufacturing” (Diginomica). Analyzing these 3 KPIs provides an unparalleled level of insight into supplier and factory performance and quality indicators, showing where suppliers are excelling, and more importantly, where immediate improvement or corrective actions are needed. 

However, organizations need effective tools capable of collecting and analyzing this data in real-time. Beyond that, they must “move beyond reactive and even proactive quality management and toward predictive quality management,” writes Quality Magazine. Technologies powered by machine learning and AI, such as those pioneered by Inspectorio, already have the capability to facilitate this kind of predictive quality control. “Any company that can harness the power of predictive technology today will have an edge over competitors tomorrow.”

The Inspectorio Sight platform creates a level playing field for every member of a production network, creating a common source of data, a bounty of intuitive analytics, risk prediction, and a space for continuous improvement for every member of a production network. Find out more about how Inspectorio Sight can give you access to insightful, actionable, and predictive quality data. 

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