What is Predictive Quality Analytics?
The software testing landscape largely focuses on tracking and fixing bugs through automation and manual testing. However, predictability is the biggest challenge while getting the code shipped. This challenge seems to find a solution in the form of Predictive Quality Analytics that works on the concept of Artificial Intelligence (AI) and Machine Learning (ML). This process of extracting useful insights from test data and applying statistical algorithms to them helps organizations predict the patterns and outcomes of the trend. As a result of this emerging AI-enabled technology support manufacturers in decreasing losses by identifying the root causes. The common statistical algorithm used for the purpose is – Regression Algorithms, Time Series Analysis, and Machine Learning.
Benefits of Predictive Quality Analytics
Many manufacturing and production units have incorporated Predictive Quality Analytics in their day-to-day working to ensure effective quality control. The ways in which Quality Analytics has helped these organizations are as follows.
| Benefit | Description |
| Prevent Quality Failures | The real-time alerts and notifications allow manufacturers to adjust processes on receiving the indication about quality failure. It further helps them to maintain the process parameter to improve first-pass yield and maintain compliances. |
| Optimizing Material Usage | The tools provide detail about the live production conditions by using the scrap rates. Alongside, it generates alerts if the desired rates are about to go beyond the fixed limits. As a result, companies experience an optimal use of their raw material. |
| Improve Contribution Margins | By including the practice of Predictive Quality Analytics in their operations many food factories have overcome the quality control hindrances. Furthermore, manufacturing sectors have minimized losses occurring due to quality issues. As a result, Quality Analytics creates better contribution margins for the industries. |
| Generating Predictive Recommendations | As a key component of Predictive Quality Analytics as they help organizations identify the process and quality issues. For this purpose, the analytics use the continuous and multivariate analysis of production data and defines the optimal range. |
Examples of Predictive Quality Analytics
There are several companies that have adopted Predictive Quality Analytics to streamline their business operations. Some of the known companies have been the early adopters of this advanced analytics and have seen significant results within their organizations.
| Example | Description |
| Amazon | This E-commerce giant has used data analytics to identify fraudulent transactions and save customers from being the victim of such frauds. This resulted in better quality and customized experience for the users which in turn attracted optimized sales for the organization. |
| Nissan Motor | This automaker used Google analytics to track E-commerce activities and understand the product preferences of the customers. In the long run, it helped the company to develop better marketing strategies for delivering quality services to the customers. |
| Shell | It is a Netherland-based Oil and Gas company that used Predictive Quality Analytics to foresee the downtime in the drilling machine. Based on this prediction either the repair or servicing processes are fast-tracked or new machinery is purchased. Hence, helped to create a seamless inventory and ensure effective delivery. |
Metrics Used for Predictive Quality Analytics
The business organization evaluates the performance models generally by categorizing them into two broad categories – Classification and Regression problems.
Classification and Evaluation Model
Here, metrics focus on predicting the instances and events belonging to some specific categories. These categories could be medical data, finance data, technological data, etc. Some of the metrics used as a part of the classification model are as follows.
| Classification & Evaluation Metric | Description |
| Percent Correction Classification | Also known as PCC it measures the overall accuracy of the data and gives equal weight to teach error. As a result, turns out useful in providing great insights into improving quality control practices. |
| Confusion Matrix | This metric measures the errors but establishes a clear distinction between each error. Here, errors are defined as false positive, false negative, and correct predictions. Hence this metric helps organizations to clearly identify the type of error and work accordingly. |
| Area Under ROC curve (AUC-ROC) | It is the widely used evaluation metric that ranks the positive prediction higher and the negative lower. As a result, it helps to define the change in the proportion of the respondents on the website and marketing campaign. |
| Lift and Gain Charts | The chart measures the effectiveness of the quality control measures and examines their effects on the stakeholders of the company. Precisely, customers are the major stakeholders and this chart helps to define their quality preferences. |
Regression Model
This model predicts the quantity of the events such as the selling price of the real estate property and customer-oriented products. The metrics that help an organization develop these quantifiable measures are Average Error, Mean Square Error (MSE), Median Error, and Median Absolute Error. Alongside, R- squared helps organizations with each variable involved in quality control processes.
Companies Enabling Predictive Quality Analytics
Various startups across the world have powered the idea of Predictive Quality Analytics b using advanced technology. Most of these companies have come up with a product or process-specific technology to help industries work on their quality control.
| Company | Description |
| Smartia –Process Failure Prediction | This UK-based company utilizes Artificial Intelligence (AI) to create predictive models and automate quality control. It collects data from machines and other such sources using the Machine Learning (ML) classifier. As a result, this solution assists in predicting the quality issues even before they occur. |
| Cognexa – Quality Compliance | It is a Slovakian-based startup that detects surface defects and anomalies at the early production stage. They also create digital twins and predictive quality models to forecast control methods. It recognizes the pattern and provides insights generated from sensors and other sources. Thus, it helps to maximize efficiency and minimize resource wastage. |
| dotData – Warrant Planning | This company has developed AI-powered data analytics solution to enable smart manufacturing. It considers the combines the significance of operation and sensor data to create failure prediction models. As a result, it reduces maintenance costs and enhances customer satisfaction. The Machine Learning models perform predictive maintenance and optimize the supply chain. |
| 40 Factory – Product Quality Forecasts | The company focuses on developing Machine Learning tools for managing and maintaining a healthy relationship between the manufacturing variables. 40actory further offers the tools to evaluate these variations and examine the products’ performance &quality parameters. |
| innoSEP – Design Level Quality Control | This company supports the process of optimizing product designs by collecting data from knowledge-based systems which further helps to identify the possible defects in the product’s lifecycle. The products are developed keeping in mind the improved quality, reduced testing, and simulation time. |
Predictive Quality Analytics and Digital Transformation
Digital transformation is the core of the competitive business landscape that we see today. According to the 2017 reports of Forrester Research 42% of CEOs are already heading towards digital transformation processes in their organization. One of the crucial parts of such digital transformation is the adoption of fact-driven analytics culture that proves effective in reducing waste and increasing the velocity and quality of the software/tools. In general, 17% of the decision-makers have an analytical budget of $ 10 million, and 48% of the organizations are planning to increase their spending on advanced analytics for effective management. Predictive Quality Analytics is one such advanced analytical concept that is potent to develop useful predictions and insights from the incredible amount of unstructured data.
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