BM9718 / LD9718 / AT9718 Assignment Sample – Research Methods and Analytics for Business Practice 2022
World is moving towards digital transformation over the last two decades and industry 4.0 are some of the advances for long term benefit (Kumar et. al. 2020). One of the foremost conspicuous of the advances is Artificial Intelligence (AI) integrating with Cloud Computing, IoT, and block chain. AI includes an endless set of applications, making features adjusting forms of various differing zones, depending on supply chain management (SCM).
Based on AI on SCM, a number of companies are moving from inaccessible checking to controlled optimisation and frameworks to move forward to frame efficient SCM (Kohtamäki et. al. 2019).
Due to its rising importance in industry, AI appears to be expanding and reaching broader perspective within the scope of organizational functioning. AI is presently looked for many applications (e.g. Canhoto and Clear, 2020, Soni et. al. 2020). SCM is perceived as an important segment that is most likely to benefit from AI applications.
The counterfeit of human intelligence in machine that are lined up to think like human and mock their actions. The present advancement of technology is witnessing devices which are providing superior experience in internet on things. Mobile Applications are finding a substantial space in the market and are becoming the high choice of customers today.
The growth of Artificial Intelligence has a great impact on the automotive industry. Starting with driver less cars, taxis, buses, and trucks to robots that work on the factory floor, AI technology has transformed the automotive sector completely.
Manufacturers working in the automotive industry are realizing the importance of AI applications. In this article, we would like to describe the benefits and current state of AI in automotive sectors and also discuss the influence of AI on automobiles.
Main problem/ reason for conducting this research/this research conducted previously/ Outcome:
In area of supply chain management, there are different types of issue that are being faced by businesses. There many reasons for such cause, like it includes unskilled labour, lack of employees to perform the roles, etc. In such type of issues AI is considered to be an effective source that can support the business to reduce errors that occur in supply chain.
Main reason for conducting this research is that it enable to understand the effectiveness of AI in reducing the errors that occur in automotive sector. The outcome showed that AI is effective enough for companies to improve their supply chain management. This research is different as it is conducted on overall industry in the world.
“To determine impact of Artificial intelligence on supply chain management in the automotive sector”
My research in the field of supply chain management focuses on the objectives:
- To understand the importance of AI in supply Chain Management
- To bring out implementation of AI in SCM
- To review the benefits and challenges of AI in SCM.
- This study similarly presents what types of computerized rational practices are used today in supply chain management.
This study similarly presents what types of computerized rational practices are used today in supply chain management. The main purpose of this study is to review automobile industries current distribution chain. This study involves reviewing how to implement AI in automobile sector’s SCM and reviewing the advantages and challenges of AI in SCM (Acemoglu, 2019).
The notion that AI could potentially be applied in innovation settings is further supported by the rapid development of AI and machine learning, which points to significant and intriguing changes to come (Lu, 2019). Most of the works are carried out by employees and their situations in which there are errors occurred (Wuest et. al. 2020). As per Paschen et. al. (2020) is systematic aspect that supports the business to make the business operations run more smoothly.
Apart from all these it has smart decision making in which companies are able to determine the needs and requirement as per their customer’s preferences (Cioffi et. al. 2020).
At the VietAI Summit 2019, Dr. James J. Kuffner gave an insightful presentation about automotive sector and artificial intelligence. As technology rapidly developed, especially in recent decades, automotive industry has been contributing to the transportation sectors, allowing people to move further and faster (Varian, 2019).
The advance in AI is bringing unfaltering results, e.g. killing occupations by applying work computerisation. Such a scenario can be seen within the Industry 4.0 system, which is utilised within the vehicle industry (Chesterman, 2020). The current state of the craftsmanship in AI investigation makes it more competitive in a few spaces than people like Siri (Wang et. al. 2019).
All the work for supply chain management is being done by employees and there are high chances that faults can occur. Apart from this Mondal, (2020) stated that the amount of time take by AI is less when same task is performance by employees. Further, to perform certain set of roles it requires having a team of people and if any one of them does not perform their roles properly, then it affects service quality of the business (Lu, 2019).
Supply Chain Management is an organised system of arranging raw materials, transport them to various locations, make semi finished components and finally distribute the end product to final user. It ranges obtaining, fabricating and dispersion (Rizvi, 2021) of goods right from raw material to the finished product.
Fundamental objective of supply chain management is to “optimise execution of the chain to include as much esteem as conceivable for the slightest fetched possible” (Vaishya et. al. 2020). Futhermore, Rizvi et. al. (2021), gave excellent review on supply chain management and its practical implementation. The work characterise concept, principles, nature, and advancement of SCM.
The COVID-19 eruption may be the most important event affecting the economy from generation to generation. Accordingly, OEMs and suppliers from all layers may need to reconsider the supply chain assembly line model, from the source of raw materials to the production of finished products and everything in between (Wang and Siau, 2019).
In fact, the “Big Three” automakers, along with their European and Asian counterparts, have realized the immediate effects of the virus, not only to protect their workforce, but also as a result of disrupted market and supply chain disruptions (Varian, 2019).
Figure 1: Users for automated Warehousing
(Source: 15 AI Applications/ Use Cases / Examples in Logistics in 2021. 2021)
In fact, the domino effect of plant closure and supply shortages across the extended distribution network will soon lead to significant supply chain disruption (Pató and Herczeg, 2020).
Figure 2: AI application in Logistics
(Source: 15 AI Applications/ Use Cases / Examples in Logistics in 2021. (2021)
Supply-chain management at an automobile industry is a main component of company’s operational norms and methodology which is wholly based on its Production System (Aghion et. al. 2019). Production System was taken as framework by other companies, the standards of which are communicated by the term “lean manufacturing.” Kumar (2020) records on automobiles Production Systems: shared understanding and belief, interrelated department, controlling in frameworks, congruous capabilities, data transparency, and improvement in process through its productions in use.
Artificial Intelligence (AI) is undergoing drastic changes in the field of technology, where it can be implemented to automate the system for greater efficiency and effectiveness (Menon et.al. 2020). Here we will mainly discuss about the fields or industries where AI plays a key role in enabling humans to perform better and efficiently without human assistance (Annamalai et. al. 2019).
Currently AI uses topics / fields that follow:
- Virtual assistant or chatpots
- Agriculture and Agriculture
- Retail, Shopping and Fashion
- Security and surveillance
- Production and production
- Self-driving cars or autonomous vehicles
- Health and clinical imaging analysis
- Warehousing and logistics supply chain
- Recognise who or what will utilise to get information or data
- Clarify how will collect and get to this information/data
- Distinguish how are getting to analyse information/information
An acceptable approach to investigation is used when the data are based on evidence, models and hypotheses. On the other hand, the flush investigation into the approach to coherent exclusion is integrated into the investigation. In expansion, the exclusionary approach to investigating points to classify persistent discrepancies with explicit considerations (Pató et. al. 2020).
The data collected will be analysed and summarised by the examiner, enhancing the only solid and sensible conclusion almost the impact of supply chain management utilising manufactured insights (Snyder, 2019)..
The apparent reasoning of consideration is consolidated when inquiring about points that cover human intrigued. In any case, if the title of considering is based on an everyday wonder, it makes a difference to draw conclusions based on the characteristics and handle of the ordinary circumstance (Hughes and Barlo, 2021). Positivism, on the other hand, is based on science.
There are numerous sorts of investigating, expressive, expository, quantitative, conceptual and observational. The investigation sort is based on information collection and expository apparatus chosen by the researcher (Snyder, 2019). In order to gather information, there are two methods that can be used by the investigator: Primary and secondary. Among these two, the researcher will make use of both these methods.
More specifically, primary method will be gathered with the help questionnaire. On the other hand, secondary method will be collected through books, journals, online sources, etc. (Mkandawire, 2019). For the current investigation exercises, the secondary information collection is chosen by the activity apparatus to utilise on a few diaries, yearly report of organisation, magazines, etc.
Appropriate examination of investigating information will investigate, prepare and make a sub-site or structure that can accomplish the correct inquire about comes about (Mkandawire, 2019). Models, tables and diagrams will be utilised to improve the investigation of the proper information introduction apparatuses. Gathered data can be analysed with the help of two methods which are qualitative and quantitative.
Among these two the researcher will make use of qualitative methods. More specifically, use of thematic will be done as it enables to present the information gathered in the form of tables, charts, graphs, etc. (Mkandawire, 2019). The arrangement of the introduction will assist you to oversee the data correctly.
The ethical concerns of this research are awareness about the purpose of this research. It is essential to know about the survey conducted and the unimpaired presentation of the secondary data that is collected from various journals and online resources. Information collected will be used for this educational research only. Analysis and Findings will not be used to harass any of its participants or to offend any organisation (Hughes and Barlo, 2021).
The investigator will make use that all the information gathered from respondents will be protected and will not be shared to anyone else. Further, the personal information will be used for the purpose of research only. Secondary data may be data collected as part of large-scale studies or individual research.
While the fundamental ethical issues related to the secondary use of research data remain, they have become more pressing with the advent of new technologies (Hughes and Barlo, 2021). Data sharing, compilation and storage have become much faster and easier. At the same time, there are new concerns about data confidentiality and security.
Artificial intelligence redefines supply chain, especially in automotive industry. In spite of these benefits, there are some challenges that companies will have to face to get the best output out of the new technology. The work is expected to bring out the working of AI in SCM in automotive with focus on automobile industry.
The work aims to understand how AI is used for SCM and how it is integrated in the present SCM models. The work is also expected to highlight the challenges in adopting AI in SCM and recommendations to overcome the challenges. Companies can bring about improvement in customer service, strengthening the relationship between consumer and OEM, encouraging repeated customer service. Strong relationship can be established between OEM and their suppliers, which can promote overall growth.
|Planning Steps||Month 0||Month 1||Month 2||Month 3||Month 4||Month 5||Month 5||Month 6|
|Topic Selection Process|
|Framing of aims and objective|
|Review of Literature|
|Methodology and Data collection|
|Drawing conclusion and framing recommendation|
|Addressing Feedback from the tutor and doing necessary corrections|
|Final Thesis Submission|
Books and Journals
Acemoglu, D. and Restrepo, P., (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), pp.3-30.
Aghion, P., Antonin, C. and Bunel, S., (2019). Artificial intelligence, growth and employment: The role of policy. Economie et Statistique, 510(1), pp.149-164.
Annamalai, S., Udendhran, R. and Vimal, S., (2019). Cloud-based predictive maintenance and machine monitoring for intelligent manufacturing for automobile industry. In Novel Practices and Trends in Grid and Cloud Computing (pp. 74-89). IGI Global.
Canhoto, A.I. and Clear, F., (2020). Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), pp.183-193.
Chesterman, S., (2020). Artificial intelligence and the problem of autonomy. Notre Dame Journal on Emerging Technologies, 1, pp.210-250.
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A. and De Felice, F., (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), p.492.
Hughes, M. and Barlo, S., (2021). Yarning with country: An indigenist research methodology. Qualitative Inquiry, 27(3-4), pp.353-363.
Kohtamäki, M., Parida, V., Oghazi, P., Gebauer, H. and Baines, T., (2019). Digital servitisation business models in ecosystems: A theory of the firm. Journal of Business Research, 104, pp.380-392.
Kumar, A., Luthra, S., Mangla, S. K. and Kazançoğlu, Y., (2020). COVID-19 impact on sustainable production and operations management. Sustainable Operations and Computers, 1, 1-7.
Kumar, V.A., Kumar, A., Batth, R.S., Rashid, M., Gupta, S.K. and Raghuraman, M., (2020). Efficient data transfer in edge envisioned environment using artificial intelligence-based edge node algorithm. Transactions on Emerging Telecommunications Technologies, p.e4110.
Lu, Y., (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), pp.1-29.
Menon, V.G., Jacob, S., Joseph, S., Sehdev, P., Khosravi, M.R. and Al-Turjman, F., (2020). An IoT-enabled intelligent automobile system for smart cities. Internet of Things, p.100213.
Mkandawire, S. B., (2019). Selected common methods and tools for data collection in research. Selected Readings in Education, 2, 143-153.
Mondal, B., (2020). Artificial intelligence: state of the art. Recent Trends and Advances in Artificial Intelligence and Internet of Things, pp.389-425.
Paschen, U., Pitt, C. and Kietzmann, J., (2020). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), pp.147-155.
Pató, B. S. G. and Herczeg, M., (2020). The effect of the Covid-19 on the automotive supply chains. StudiaUniversitatis Babes-Bolyai, 65(2), 1-11.
Rizvi, A. T., Haleem, A., Bahl, S. and Javaid, M., (2021). Artificial Intelligence (AI) and Its Applications in Indian Manufacturing: A Review. Current Advances in Mechanical Engineering, 825.
Snyder, H., (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, pp.333-339.
Soni, N., Sharma, E.K., Singh, N. and Kapoor, A., (2020). Artificial intelligence in business: from research and innovation to market deployment. Procedia Computer Science, 167, pp.2200-2210.
Vaishya, R., Javaid, M., Khan, I. H. and Haleem, A., (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339.
Varian, H., (2019). 16. Artificial Intelligence, Economics, and Industrial Organization (pp. 399-422). University of Chicago Press.
Wang, W. and Siau, K., (2019). Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: a review and research agenda. Journal of Database Management (JDM), 30(1), 61-79.
Wuest, T., Kusiak, A., Dai, T. and Tayur, S. R., (2020). Impact of COVID-19 on manufacturing and supply networks—The case for AI-inspired digital transformation. Available at SSRN 3593540.
15 AI Applications/ Use Cases / Examples in Logistics in 2021. (2021) [Online]. Accessed through:< https://research.aimultiple.com/logistics-ai/ >
Know more about UniqueSubmission’s other writing services: