Potential of AI within the Quality Management

Potential of AI within the Quality Management

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Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines. AI has a profound impact on organizations across various sectors. The goals of artificial intelligence include computer-enhanced learning, reasoning, and perception. AI is being used today across different industries and areas of human life. It enhances decision-making processes, automates routine tasks, and facilitates efficiency gains. AI can be of two types: (1) Generative AI, and (2) Predictive AI.

 

Courtesy - Image Created with the help of AI tool. 

Let us understand both the terms. Generative AI refers to systems and processes that can create new content, data or outputs that mimic human-like patterns. This is possible because AI systems and processes are trained on large datasets and learn to generate outputs that look real. A notable example of generative AI is OpenAI’s ChatGPT models. These generate human-like realistic text, images, or music based on the input it gets. On the other hand, predictive AI involves using algorithms and models to make predictions or forecasts based on the historic data provided. Predictive AI systems and processes analyze patterns and trends to forecast future outcomes. It can be used to check the quality of produced product. In predictive maintenance, AI algorithms can analyze equipment performance data and can predict when the equipment may likely to fail. To have an example of predictive AI, consider a scenario in supply chain management of an organization. Predictive AI algorithms can analyze historical data on factors like demand patterns, supplier performance, and logistic efficiency. By identifying trends and correlations, the system can forecast future demand, optimize inventory levels and anticipate potential disruptions. This allows organizations to proactively adjust their supply chain strategies, minimizing delays and reducing costs.

 

AI in Quality Management

 

Both generative AI and predictive AI have potentials within the quality management. Generative AI can be used to create synthetic or simulated datasets for testing and validating quality control processes. For example, generative AI may generate realistic product defect images to train computer vision models for quality inspection. Predictive AI can enhance quality control by predicting or forecasting potential defects before they occur. For example, predictive AI can analyze production data to forecast issues in manufacturing process and allow adjustments to prevent defects.

 

Challenges for Organizations using AI

 

There are several challenges for organizations using AI.

 

Data Quality – Providing high-quality data is a big challenge as reliable AI models require high-quality data. Ensuring the accuracy and relevance of data is a significance challenge.

 

High Implementation Cost – Initial setup costs, including acquiring the AI technology and training personnel, can be substantial.

 

Ethical Consideration – Bias in Algorithms – There are chances of bias in algorithms that means presence of systematic and unfair favouritism towards or against particular groups or individuals in the outcomes produced by the algorithm. If historic data used in the AI model contains inherent bias, the algorithm may learn and perpetuate those biases. Addressing bias is a crucial ethical consideration in AI development. There may be several essential steps, such as, careful data curation, transparency in algorithms, ongoing monitoring that can mitigate bias.

 

Ethical Consideration – Job Displacement – Automation and AI technologies replace certain human tasks that leads to job losses in organizations. It is due to the fact that as AI and automation technologies advance, they can take over routine, repetitive, manual tasks leading to increased efficiency but potentially reducing manpower demand in those specific tasks thus creating job displacement. Managing job displacement involves consideration of many factors, such as, workforce reskilling, retraining, policies that support the transition of human workforce to new roles in organizations.

 

AI developers and organizations using AI need to be aware of above issues and work towards creating AI systems and processes as fair, transparent, and considerate of their broader societal implications.

 

Opportunities for Organizations using AI

 

There are several opportunities for organizations using AI.

 

Efficiency – AI is useful in optimizing production processes, reducing waste, and enhancing overall operational efficiency. Thus, organizations can improve their efficiency by using AI tools.

 

Predictive maintenance – With the help of AI, organizations can implement predictive maintenance that can anticipate equipment failure and organizations can take proactive maintenance steps thus leading to cost savings.

 

Customization – AI enables producing product based on customer preferences, needs and expectations.

 

AI can be applied to check the quality of produced products by utilizing computer vision and machine learning techniques. For example, in a manufacturing setting, AI-powered visual inspection systems can analyze images of products in real time. The system learns from a dataset of acceptable and defective products, identifying patterns associated with defects. This allows it to flag potential issues during production, ensuring that only high-quality products move forward in the manufacturing process. In essence, AI acts as a real-time quality control tool.

 

We can conclude, both generative AI and predictive AI have significant potential in quality management, offering ways to enhance efficiency, reduce defects, carry out predictive maintenance, and optimize organization’s processes. However, organizations must suitably address challenges related to data quality, costs and ethical considerations of bias in algorithms and job displacement to take the optimum benefits from the opportunities AI presents to the organizations.

 

Best wishes,

Keshav Ram Singhal