Application of Artificial Intelligence at Samsung

Application of Artificial Intelligence at Samsung

Introduction

The telecommunications industry is rapidly growing, leading to stiff competition for companies that manufacture and sell hardware and software. One of the companies that have been impacted by the change is Samsung Company, a global electronics company that specializes in the production of consumer electronic devices. The company has been in the technology industry since the 1970s, when it began exporting home electronic products worldwide. The company relies on the Samsung Data Systems department to support the development of the systems required to sustain operations. Over the years, the company has been experiencing quality management challenges, leading to reduced competitive advantage. This project report will review the application of artificial intelligence in improving quality management at Samsung based on the company’s technology, operational environment, and competitive value.

Technology

According to Kaplan (2016), artificial intelligence is the development of computer systems that can complete tasks that need the application of human intelligence. The Artificial Intelligence technology that will be applied in Samsung Company’s quality management process is machine learning. Wittek (2014) defines machine learning as a type of Artificial Intelligence that enables software applications to be more precise in forecasting results without the need to program them. Machine learning can be applied to help robots manipulate physical objects with more agility and flexibility, leading to automated distribution and production. The technology in quality management will also include combining robotics and Artificial Intelligence to build, design, and control the hardware used in the production process. Computer vision technology will be used alongside Artificial Intelligence to control quality. According to Murthy & Jadon (2011), computer vision is mainly applied in quality management because the quality of many products in the manufacturing process is defined by the surface and dimension features. Another technology that will be implemented to improve quality assurance is automated machine vision systems that can analyze the surface and geometric features related to product quality. An intelligent quality management system with the ability to adapt and learn the requirements of flexible manufacturing will also be used to increase efficiency in quality management.

Operational Environment

The application of Artificial Intelligence in quality management covers different areas. One of the areas in which Artificial Intelligence will be applied in Samsung Company’s quality management is designing quality. Artificial Intelligence and machine learning will be applied to determine the optimum values of the quality features that should be considered and help optimize the quality design. The quality function deployment tool will be used to determine product specifications based on customer needs and preferences. According to Bossert (2021), the quality function deployment tool can also be used to automatically predict the quality of a product based on customer expectations. The tool will also be used to assess the impact of changes in customer demand on manufacturing, removing existing requirements, adding new requirements, and changing the design characteristics. Another area of application is in assessing suppliers. According to Schmidtke (2022), the fuzzy logic theory is applied in selecting the best suppliers based on the quality of supplies needed. Neural networking and evolutionary modeling will be used to ensure that the products produced by the company are of high quality. The third area is decision-making. Computer-based decision-making will be used to generate effective implementation of quality standards.

Artificial intelligence will also be applied in the production part’s approval process. According to Doshi & Desai (2016), the production part approval process can be supported by intelligent tools to offer evidence that all specifications related to customer demand are understood and that the right manufacturing approach is used. The process also helps ensure that the manufacturing process flows at the required rate and that the products produced are of acceptable quality to customers. The production part approval process will also be used to create quality plan assurance, prevent the production of nonconforming parts, provide early detection of problems in the manufacturing process, and support the integrity of the selected designs. Samsung Company can also use the production part approval process to ensure that all its suppliers understand their suppliers’ engineering design specifications and attributes. Artificial Intelligence will also be applied in analyzing the effects of failure and failure models. Artificial intelligence systems will handle information on various failures in the manufacturing process and interpret them from different viewpoints to improve it. They may include knowledge and model base and functional models to predict failures.

The application of Artificial Intelligence in Samsung Company’s quality management will include the integration of Artificial Intelligence into quality assurance through data and pilot annotation, validation, and testing. In data and pilot annotation, the quality assurance team will identify the tests that should be completed and what the team should accomplish. The team will use the information on the required tests and the company’s expectations to set the scope and objectives of testing. Numan (2019) argues that the testing methods should be selected based on the project’s scope to enable the application of Artificial Intelligence models and algorithms in product testing. Validation and testing include developing algorithms and selecting a part of the training data to validate tests. The training data is then applied to evaluate the performance of the artificial intelligence models to get accurate and consistent predictive results (Numan, 2019). Therefore, Artificial Intelligence meets Samsung Company’s needs because it will help determine product defects before they are launched, leading to a reduction in quality-related complaints. Artificial Intelligence can also help Samsung upgrade its software by applying machine learning to detect changes in functionality.

Competitive Value of the Solution

Over the past decade, Samsung has been experiencing quality issues, resulting in a decrease in its customer base, especially in markets dominated by competitors such as Apple and Nokia. Customer complaints about the company’s products are related to the poor quality of electronics. For instance, the company suspended Galaxy Note 7 phone shipments because of complaints about the quality of the phones (Jung-a, 2016). The company is also under investigation by the United States government because of concerns raised by customers about the quality of the company’s refrigerators. A report by IANS (2022) indicates that Samsung refrigerator customers in the United States have complained about the refrigerators’ safety. Therefore, there is a need to improve the quality management process to increase product quality and customer loyalty and satisfaction.

According to Cheremisinoff (2020), testing product quality is time-consuming, and by the time the test codes are applied, customer requirements may have changed. Quality assurance teams also experience challenges testing the quality of products and are unable to complete the quality testing process because of human error. Therefore, Samsung can use Artificial Intelligence to reduce human error and improve the quality management process by prioritizing quality tests based on existing test logs and test cases so that quality assurance engineers can focus on investigating the issues that could impact product quality. Artificial Intelligence agents can also develop and learn to find new quality management approaches throughout the testing process. Therefore, the main competitive value of using Artificial Intelligence in the quality management process guarantees the manufacturing of quality products that meet customers’ needs and preferences, thus reducing complaints.

Samsung Company can also enjoy the competitive value of increased customer satisfaction and loyalty by producing high-quality products. A detailed quality analysis may be conducted after receiving customer feedback. For example, Samsung can use negative feedback about its phones and refrigerators to identify quality improvement areas. Additional feedback can be received through satisfaction surveys and customer information shared on the company’s social media platforms. The information shared by customers may be used to update quality expectations and standards within the manufacturing systems. Samsung can also use Artificial Intelligence in quality management to analyze the quality costs that offer useful information for the operations relating to quality management and their effectiveness. The company may use an intelligent cost analyzer to gather cost data and establish the true pricing and costing strategies. Another competitive value of the solution is enabling the company to identify product defects and correct them before release. Samsung can focus on eliminating the identified quality issues and offer warranties that customers can use to replace defective products. Artificial Intelligence will also help the company release well-researched products by enabling the production team and engineers to compare software and similar apps to determine the strengths that can be leveraged to increase market success and value proposition. Producing high-quality products will also help the company increase its competitive advantage and profitability.

References

Bossert, J. L. (2021). What is quality function deployment? Quality Function Deployment, 1–8. https://doi.org/10.1201/9781003066545-2

Cheremisinoff, N. P. (2020). Quality in product development. Product Design and Testing of Polymeric Materials, 313–363. https://doi.org/10.1201/9780367812881-6

Doshi, J. A., & Desai, D. A. (2016). Role of production part approval process in continuous quality improvement and customer satisfaction. International Journal of Engineering Research in Africa, 22, 174–183. https://doi.org/10.4028/www.scientific.net/jera.22.174

IANS. (2022). Customer complaints put Samsung under probe for refrigerators in US: Report. Business News, Finance News, India News, BSE/NSE News, Stock Markets News, Sensex NIFTY, Budget 2022. https://www.business-standard.com/article/international/customer-complaints-put-samsung-under-probe-for-refrigerators-in-us-report-122110500615_1.html.

Jung-a, S. (2016). Quality issues hit the Samsung Galaxy Note 7. Financial Times. https://www.ft.com/content/1b116d36-6fec-11e6-a0c9-1365ce54b926.

Kaplan, J. (2016). Defining artificial intelligence. Artificial Intelligence. https://doi.org/10.1093/wentk/9780190602383.003.0001

Murthy, G. R. S., & Jadon, R. S. (2011). Computer Vision-based Human-Computer Interaction. Journal of Artificial Intelligence, 4(4), 245–256. https://doi.org/10.3923/jai.2011.245.256

Numan, G. (2019). Testing artificial intelligence. The Future of Software Quality Assurance, 123-136. https://doi.org/10.1007/978-3-030-29509-7_10.

Schmidtke, H. (2022). Towards a fuzzy context logic. Fuzzy Systems – Theory and Applications. https://doi.org/10.5772/intechopen.95624

Wittek, P. (2014). Machine learning. Quantum Machine Learning, 11–24. https://doi.org/10.1016/b978-0-12-800953-6.00002-5.

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Application of Artificial Intelligence at Samsung

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