UTILIZATION OF ARTIFICIAL INTELLIGENCE TO SOLVE HIGHER RESOURCE WASTAGE AND HUMAN ERRORS IN THE MANUFACTURING DEPARTMENT OF ORGANIZATIONS
The manufacturing sector is essential to promoting economic growth and satisfying consumer demand in the quickly changing world of today. However, it is frequently hampered by greater rates of resource loss and human errors, which can reduce production and efficiency. Organizations are increasingly looking to artificial intelligence as a potent remedy to address these issues (Acerbi and Taisch 2020). By automating activities, maximizing resource allocation, and reducing human error, AI technologies, such as machine learning and robotic process automation, have the potential to revolutionize manufacturing processes. Organizations may boost productivity, save costs, and improve accuracy and quality in their manufacturing processes by utilizing AI's capabilities.
The aim of this study is to investigate how artificial intelligence might be used to solve increased resource wastage and lower human errors in organizations' manufacturing divisions.
Objectives
● To upgrade efficiency and mitigate the resource wastage
● To minimize the errors of humans and enhance quality control
● To streamline the maintenance and equipment optimization
● How might artificial intelligence be used to improve productivity and reduce resource waste in businesses' manufacturing departments?
● What techniques may be used to improve quality control in the manufacturing process and reduce human error using artificial intelligence?
● How can the use of artificial intelligence in manufacturing departments of organizations speed up maintenance and optimize equipment?
According to Ahmad et al., (2021), the manufacturing sector is undergoing a revolution thanks to artificial intelligence (AI), which is offering ground-breaking approaches to resource optimization and efficiency improvement. In many stages of the manufacturing process, AI-powered applications are being used, which has significantly improved productivity, quality, and cost-effectiveness. Predictive maintenance is a key way that AI is used in manufacturing. AI algorithms can predict possible equipment breakdowns by examining past maintenance records and real-time sensor data. As opined by Ghoreishi and Happonen (2020), this enables producers to plan maintenance tasks in advance, reducing unanticipated downtime and maximizing resource efficiency. AI is now being used for scheduling and planning manufacturing. AI algorithms can create optimized production schedules that maximize throughput and reduce production lead times by taking into account variables like client demand, equipment capacity, and material availability. As a result, resource allocation is improved for manufacturers, and inventory holding expenses are decreased.
According to Lee and Yoon (2021) In manufacturing processes, artificial intelligence has a substantial impact on quality assurance and error reduction. Manufacturers may increase efficiency, decrease errors, and improve overall product quality by utilizing AI technologies. Massive volumes of data from sensors, production lines, and quality control systems may be instantly analyzed by AI algorithms. This makes it possible to proactively identify probable mistakes or quality standard deviations. As opined by Langlotz et al., (2019), It is possible to train machine learning models to spot patterns and anomalies, enabling early detection of flaws or deviations that human operators might overlook. A timely intervention is made possible by such early identification, minimizing the effect on production and decreasing the possibility that defective products would reach customers. AI-driven solutions can also automate inspection procedures to improve quality control practices. By accurately detecting and categorizing flaws, computer vision techniques, and machine learning algorithms can replace manual inspections.
According to Andronie et al., (2021), The manufacturing sector is undergoing a transformation thanks to artificial intelligence (AI), which makes it possible to streamline maintenance and optimize equipment. Manufacturers can gather and analyze massive volumes of data from equipment sensors, production lines, and maintenance logs in real-time using AI-powered systems. To find patterns, forecast problems, and improve maintenance schedules, use this data. AI algorithms are able to identify anomalies and anticipate equipment failures before they happen, enabling preventative maintenance measures. As opined by Rathore (2023), Manufacturers can decrease unexpected downtime, optimize spare parts inventories, and boost overall equipment effectiveness by employing predictive maintenance procedures. By continuously analyzing data from several sources, such as operational parameters, ambient conditions, and previous performance, AI can improve the performance of equipment. In order to maximize effectiveness and reduce energy consumption, this enables real-time modifications and fine-tuning of equipment settings.
Researchers can use a variety of research methodologies to gather information and provide practical recommendations in order to address the problems of increased resource loss and human errors in the manufacturing division of organizations. The interpretivism research philosophy, inductive research approach, explanatory research design, and qualitative research strategy are four research methodologies that will be used. Every approach has its own advantages and viewpoints that add to our understanding of the problem at hand.
The subjective interpretation of social processes is emphasized by the interpretivism research philosophy. Understanding the perspectives, experiences, and motivations of those working in the industrial sector is a key component of this approach. To identify the fundamental causes of resource waste and human mistakes, researchers using this approach would interview participants, make observations, and analyze documents. Interpretivism offers a comprehensive knowledge of the various reasons causing industrial process inefficiencies by digging into the employees' subjective experiences and views.
The inductive research approach entails gathering and examining particular evidence in order to create more general ideas or generalizations. Researchers would gather information on instances of resource waste and human error in manufacturing and look for patterns, trends, and causal connections. Using this approach, researchers can develop ideas or hypotheses based on empirical data, which ultimately results in workable solutions. Researchers can discover common sources of resource waste and human error by using the inductive approach, and they can suggest targeted solutions to reduce these problems.
The explanatory research design seeks to uncover causal connections and offer explanations for certain events. This strategy entails looking into the fundamental causes of resource waste and human error in the context of manufacturing. To identify the underlying causes of inefficiency, researchers would examine the body of previous research, conduct surveys, and apply statistical analysis. An explanatory study assists in determining the critical areas for manufacturing department improvement by revealing the links between variables like training procedures, equipment upkeep, and error rates.
In the qualitative research strategy, non-numerical data such as observation, and textual analyses are gathered and analyzed. Qualitative research can be utilized in the manufacturing setting to investigate employees' individual perceptions and experiences with resource waste and human error. To acquire detailed insights into the perspectives of manufacturing people on the topic, researchers would interview or hold focus groups with them. A thorough comprehension of the intricacies and contextual elements impacting resource utilization and human errors is made possible by this method's extensive and detailed data.
Secondary research is the method used to gather the data for this subject. Collecting data from sources that have previously been compiled by other people or organizations is known as secondary research. The researcher would in this instance rely on already published studies, reports, articles, and other pertinent literature linked to the use of artificial intelligence to solve resource wastage and human errors in the manufacturing department of organizations. Academic journals, business periodicals, white papers, and online databases are a few examples of these secondary sources. The researcher can develop a thorough understanding of the subject and incorporate the material into their study or analysis by looking over and analyzing the conclusions and insights from these current sources.
Every research study or project needs data analysis because it offers insightful information and helps guide decision-making. A common qualitative data analysis method is thematic analysis, which involves finding and examining patterns or themes within a dataset. It is especially well suited for delving into ambiguous and complex subjects, including how artificial intelligence (AI) might be used to alleviate resource waste and human error in an organization's manufacturing division. Thematic analysis was chosen in this instance because it makes it possible to spot recurrent themes and patterns in the data pertaining to the application of AI in manufacturing. Researchers can better grasp the problems, difficulties, and potential solutions related to this topic by methodically classifying and analyzing the information gathered. By organizing and interpreting the data in an organized way, thematic analysis enables researchers to derive relevant insights from qualitative data, such as interviews, surveys, or observations.
The use of AI in manufacturing and its effects on resource waste and human mistakes can be fully understood by using thematic analysis to find common themes, sub-themes, and variations within the data. By revealing patterns and trends that would not be visible using other quantitative analysis techniques, this technique clarifies the intricacies and complexities of the subject at issue.
Gaining a thorough grasp of how artificial intelligence can be used to effectively handle the issues of increasing resource wastage and decreased human error in manufacturing divisions of organizations is the study's anticipated end result. The goal of the study is to uncover and propose useful methods and suggestions for enhancing effectiveness, reducing mistakes, enhancing quality control, expediting maintenance, and improving equipment through the use of AI technology. The study's findings will add to the body of knowledge already available on AI in manufacturing and offer guidance to businesses looking to maximize the potential of AI in their production lines.
Task | Start Date | End Date | Duration |
Literature Review | 1-Jul-23 | 10-Jul-23 | 10 days |
Develop Research Questions | 11-Jul-23 | 18-Jul-23 | 8 days |
Develop Research Methodology | 18-Jul-23 | 25-Jul-23 | 7 days |
Ethics Approval | 25-Jul-23 | 30-Jul-23 | 5 days |
Data Collection | 1-Aug-23 | 12-Jul-23 | 12 days |
Data Analysis | 12-Aug-23 | 20-Aug-23 | 8 days |
Draft Findings and Discussion | 20-Aug-23 | 31-Aug-23 | 9 days |
Draft Conclusion and Recommendations | 1-Sep-23 | 8-Sep-23 | 8 days |
Draft Abstract | 8-Aug-23 | 12-Aug-23 | 4 days |
Proofreading and Editing | 12-Aug-23 | 20-Aug-23 | 8 days |
Final Submission | 20-Aug-23 | 27-Aug-23 | 7 days |
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Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y. and Chen, H., 2021. Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, p.125834. https://www.sciencedirect.com/science/article/am/pii/S0959652621000548
Andronie, M., Lăzăroiu, G., Ștefănescu, R., Uță, C. and Dijmărescu, I., 2021. Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: A systematic literature review. Sustainability, 13(10), p.5495. https://www.mdpi.com/2071-1050/13/10/5495/pdf
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Langlotz, C.P., Allen, B., Erickson, B.J., Kalpathy-Cramer, J., Bigelow, K., Cook, T.S., Flanders, A.E., Lungren, M.P., Mendelson, D.S., Rudie, J.D. and Wang, G., 2019. A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology, 291(3), pp.781-791. https://pubs.rsna.org/doi/pdf/10.1148/radiol.2019190613
Lee, D. and Yoon, S.N., 2021. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), p.271. https://www.mdpi.com/1660-4601/18/1/271/pdf
Rathore, B., 2023. Digital Transformation 4.0: Integration of Artificial Intelligence & Metaverse in Marketing. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 12(1), pp.42-48. https://www.eduzonejournal.com/index.php/eiprmj/article/download/248/208
