AI model accuracy evaluation is a systematic process of measuring how accurately a machine learning model predicts and classifies data.

Accuracy is generally defined as the percentage of cases that the model correctly predicts. This is especially useful in binary classification problems and represents the percentage of correct predictions out of all predictions.

However, many small and medium-sized enterprises (SMEs) fail to evaluate model accuracy. This is because SMEs lack the resources to collect and process large amounts of data, making it difficult for AI models to obtain sufficient data for training.

In particular, manufacturers often have a defect rate of less than 1%, which means they may not have enough defect data to train their models, which can result in lower model prediction accuracy.

In addition, they may lack the infrastructure and technical expertise needed to adopt and utilize the latest AI technologies. To maintain the high accuracy of AI models, they need to perform continuous updates and maintenance, but they often lack the resources to do so.

In this article, we will introduce what decision-makers in companies need to know when evaluating model accuracy. You will find out about the importance of model accuracy evaluation, evaluation indicators by industry, and a model evaluation framework that will be helpful to practitioners.

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Importance of AI model accuracy evaluation

Improving quality and reducing costs

The accuracy of AI models in manufacturing is directly linked to a company's profitability. The case of Samsung Electronics clearly demonstrates this.

In fact, an AI inspection system introduced in a company's semiconductor process achieved a defect detection accuracy of 98%, which led to an innovative achievement of reducing inspection time by 90%. In the semiconductor industry, which requires a high degree of precision, such an improvement in accuracy can lead to improved yield and direct cost savings.

What is even more noteworthy is the case of LG Chem's battery production line. Achieving a quality prediction accuracy of 96% has produced tangible results in the form of a 30% reduction in defect rate. In particular, in the battery industry, which is a high-value-added product, such a reduction in defect rate leads to direct profitability improvements.

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Practical problems that arise when applied on site

The accuracy of the model is directly linked to stable operation in the actual field. The case of POSCO is a good example of this challenge and solution.

The quality prediction model for heavy plate products achieved an accuracy of over 95%, but this was the result of continuous model optimization that took into account various variables in the field. The annual cost savings of KRW 10 billion is the result of this field-oriented approach.

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Correlation between evaluation indicators and business performance

The accuracy of AI models is closely related to the company's key performance indicators.

In fact, Hyundai Motor Company has achieved a defect detection accuracy of 97% in the painting process through its body quality inspection system. This has led to management performance in the form of reduced quality assurance costs and improved brand value, beyond simple technical performance.

In particular, the 40% improvement in defect detection rate is a key performance that is directly linked to improved customer satisfaction.

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Differentiated evaluation criteria according to industry characteristics

The criteria for model accuracy required by each industry differ. SK Bioscience's vaccine production case shows the high level of accuracy required in the pharmaceutical industry.

The 99.5% process parameter optimization accuracy was a prerequisite for regulatory compliance and quality assurance, which led to a tangible result of a 15% improvement in production yield.

This differentiated approach by industry is a key criterion for prioritizing AI investments. While more than 99% accuracy is required in the semiconductor or pharmaceutical industries, even 95% accuracy is sufficient for general manufacturing industries.

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Model accuracy evaluation indicators by industry and how to use them

Key evaluation indicators for the pharmaceutical/bio industry

In the pharmaceutical/bio industry, the accuracy of AI models is a key factor directly linked to compliance with Good Manufacturing Practice (GMP) regulations. The quality prediction accuracy (QPA) requires a reliability of 99.9% or higher, and in particular, the false negative rate must be kept below 0.001%.

This is because, according to FDA regulations, the market circulation of defective drugs must be absolutely prevented.

Predictive models based on real-time process analysis technology (PAT) require continuous monitoring of the variability of key process parameters (CPPs). This allows the process stability index (PSI) to be evaluated and real-time control of the product's core quality attributes (CQA) to be achieved.

In addition, raw material analysis reliability (RMA) is the basis for accurately predicting the purity and impurity profile of raw materials to ensure the quality of the final product.

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Quality control indicators in the food manufacturing/sales sector

AI models in the food industry have the unique requirement of meeting HACCP standards while optimizing productivity. Safety Prediction Index (SPI) requires more than 98% accuracy in predicting microbial contamination and shelf life. This is because it is directly linked to the prevention of food safety incidents.

Sensory Quality Prediction Accuracy (SQPA) predicts quality characteristics directly linked to consumer experience, such as taste, aroma, and texture.

In particular, early detection of quality deterioration is a key indicator directly linked to the profitability of the food industry. The production process optimization indicator (POI) predicts yield and energy efficiency, contributing to securing cost competitiveness.

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Process optimization indicator for the chemical/materials industry

Due to the continuous process characteristics of the chemical/materials industry, process control accuracy (PCA) requires a high degree of reliability of 95% or more. Real-time quality prediction and process variable optimization are directly linked to product uniformity, and early detection of equipment abnormalities is essential for preventing major accidents.

Product Characteristic Prediction Reliability (PCP) ensures product quality through property prediction and defect prediction. Due to the nature of chemical processes, small changes in variables have a significant impact on the final product, requiring a high level of prediction accuracy.

Productivity Optimization Index (POI) is a criterion for determining the economic feasibility of a process by comprehensively evaluating yield, energy efficiency, and cost reduction effects.

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Specialized evaluation methods for agriculture, forestry, and fisheries

The agriculture, forestry, and fisheries sector requires an evaluation method that takes into account the characteristics of being greatly affected by environmental variables. The yield prediction index (YPI) predicts production by comprehensively analyzing crop conditions, pest occurrence, and climate effects.

In particular, as there is a great deal of uncertainty due to climate change, the robustness of the prediction model is very important.

The Quality Grade Prediction (QGP) contributes to maximizing profitability by predicting the grade and shelf life of agricultural and marine products. The Environmental Adaptability Index (EAI) plays a key role in establishing a stable production base by responding to climate change and predicting disaster risks.

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Practical model accuracy evaluation framework and considerations

Data quality and representativeness assurance measures

Data quality is a key factor in determining the accuracy of an AI model. In practice, the representativeness and completeness of the data should be evaluated first. If the data does not sufficiently reflect the various situations in the actual business environment, the performance of the model may deteriorate rapidly when applied in the field.

In particular, in the manufacturing field, there is a serious imbalance between normal and defective data. In this situation, it is necessary to apply data augmentation techniques or imbalance treatment methodologies to ensure the robustness of the model.

In addition, a quality control system that takes into account the temporal continuity of data and the interaction between processes is essential.

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Collaboration system with on-site experts

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The evaluation of the accuracy of an AI model should be closely linked to the domain knowledge of on-site experts. On-site experts can identify hidden patterns and correlations that are not explicitly reflected in the data.

Their empirical knowledge plays a key role in setting the evaluation criteria for the model and interpreting the prediction results.

In fact, many companies have evaluation committees composed of AI experts and field experts. This collaborative system enables a balanced assessment of technical accuracy and practical usefulness.

In particular, experts play a very important role in improving the explainability of prediction results and verifying the applicability of the field.

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Cost-effective evaluation methodology

Model accuracy evaluation is quite costly and time-consuming. Therefore, it is essential to establish an efficient evaluation methodology.

It is desirable to use a phased evaluation approach, initially verifying basic performance indicators and then gradually moving on to more in-depth evaluations.

In particular, when the model needs to be retrained or updated, the evaluation efficiency can be increased by statistically significant sampling instead of using the entire dataset. In addition, an automated evaluation pipeline should be established to enable continuous monitoring and evaluation.

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Real-time performance monitoring system

The accuracy of the model may change over time. In particular, in a manufacturing environment, various variables such as changes in raw material properties, aging of equipment, and seasonal factors affect the performance of the model. Therefore, it is essential to establish a real-time performance monitoring system.

Real-time monitoring plays an important role in tracking the prediction accuracy trend and detecting early signs of performance degradation. In particular, it is necessary to detect model drift or data drift and respond in a timely manner.

To this end, a system should be built to monitor key performance indicators (KPIs) in real time and provide immediate notifications when thresholds are exceeded.

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Building a Continuous Improvement System

Model accuracy assessment should not be a one-time activity but should be part of a continuous improvement process. Based on the assessment results, the weaknesses of the model should be identified and a specific action plan should be established to improve them.

If new data is accumulated, the model should be re-trained and performance verified periodically. This continuous improvement system should work throughout the model's life cycle.

Successful AI adoption requires an integrated governance system involving the technical team, field experts, and management. This will lay the foundation for continuously maintaining and improving the accuracy of the model.

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Future prospects and preparations for AI model accuracy evaluation

The rise of automated evaluation systems

The biggest change in AI model accuracy evaluation is the emergence of automated evaluation systems. Moving away from the traditional manual evaluation method, systems that monitor model performance in real time and automatically detect performance degradation will become the norm.

In particular, in a continuous learning environment, the ability to automatically detect and respond to data drift and model drift will become a key competitive advantage.

This automated evaluation system will be closely integrated with the Machine Learning Operations (MLOps) platform. The training, deployment, monitoring, and retraining of models will be configured as a single automated pipeline, allowing the accuracy of the models to be continuously optimized.

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Increasing the importance of explainable AI

Explainability will emerge as a key factor in evaluating the accuracy of models. Beyond simply showing high accuracy, the ability to explain why such predictions were made is required.

This will become more important especially in regulated industries, and the use of advanced explanation techniques such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) will become standard.

In addition, advanced tools that visualize and interpret the decision-making process of the model will be developed. This will make it easier for field experts to understand and validate the predictions of AI models.

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The spread of multimodal assessment approaches

The AI models of the future will process various types of data, not just a single type of data, in an integrated manner. Accordingly, a multi-modal approach is required for evaluation. An integrated evaluation methodology for various types of data, such as text, images, and sensor data, will be developed.

In particular, an integrated evaluation system that comprehensively analyzes process data, quality data, and equipment status data will be established in industrial sites. This will enable more accurate evaluation of the actual performance of the model.

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Institutionalization of ethical AI assessment

Assessment of the fairness and ethics of AI models will become an essential element of accuracy assessment. Standardized methodologies for detecting and measuring model bias will be developed, and this will be reflected as an ESG assessment factor for companies.

In particular, as the social impact of AI models increases, an integrated framework is needed to assess not only the accuracy of the model but also its ethical impact. This will be an important factor directly related to corporate reputation management.

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Building a real-time adaptive evaluation system

The evaluation system of the future will have the ability to adapt to environmental changes in real time. Intelligent systems that automatically adjust evaluation criteria in response to concept drift or data drift and relearn models when necessary will become the norm.

This requires an advanced monitoring system and a flexible evaluation framework that can respond quickly to changing environments. In particular, the ability to evaluate in real time in edge computing environments will become important.

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What companies should do to evaluate the accuracy of AI models

The evaluation of the accuracy of AI models is becoming more advanced with technological innovation. In this changing environment, building a high-performance evaluation infrastructure is no longer an option, but a necessity.

In particular, automated monitoring systems and integrated MLOps platforms play a key role in tracking and optimizing model performance in real time. Without this technological foundation, it would be difficult to effectively operate AI models in a rapidly changing business environment.

Along with the advancement of technological infrastructure, it is also essential to strengthen human capabilities. Along with the cultivation of AI model evaluation experts, it is important to establish a close collaboration system between domain experts and AI experts.

Therefore, the overall AI capabilities of the organization must be enhanced through continuous education and training programs. In particular, the convergence of domain knowledge and AI technology will be a key factor in improving the accuracy and effectiveness of model evaluation.

A systematic institutional foundation must be established to support the development of these technologies and personnel. The establishment of an AI governance system and the enactment of ethical AI guidelines are essential conditions for the sustainable operation of models.

In particular, the establishment of evaluation criteria that reflect the characteristics of each industry is essential for the practical creation of business value for AI models.

The evaluation of the accuracy of AI models should be recognized as a strategic task for companies, rather than a simple technical task. A system should be established to continuously ensure and optimize the accuracy of AI models through the balanced development of technology, personnel, and systems.

This will soon lead to the creation of practical business value through AI and the securing of sustainable competitiveness for companies. Future competitive advantage will depend on how systematically such comprehensive preparations are carried out.