Role of Artificial Intelligence
- Subject Code :
BMGT7021
Oxford Brookes University
The Role of Artificial Intelligence in Revolutionizing Small Business Supply Chains
BMGT7021: Research Methods
Research Proposal
Student ID: 19302297
Table of Contents
1. Introduction <!-- [if supportFields]> PAGEREF _Toc183538476 h 3 <![endif]-->
2. Research objective <!-- [if supportFields]> PAGEREF _Toc183538477 h 4 <![endif]-->
3. Capstone Project Rationale <!-- [if supportFields]> PAGEREF _Toc183538478 h 4 <![endif]-->
4. Literature Review <!-- [if supportFields]> PAGEREF _Toc183538479 h 5 <![endif]-->
5. Methodology <!-- [if supportFields]> PAGEREF _Toc183538483 h 7 <![endif]-->
5.1 Research Design <!-- [if supportFields]> PAGEREF _Toc183538484 h 7 <![endif]-->
5.2 Sampling Strategy <!-- [if supportFields]> PAGEREF _Toc183538485 h 7 <![endif]-->
5.3. Data Collection Methods <!-- [if supportFields]> PAGEREF _Toc183538486 h 8 <![endif]-->
5.4. Data Analysis Procedures <!-- [if supportFields]> PAGEREF _Toc183538487 h 8 <![endif]-->
6. Planning <!-- [if supportFields]> PAGEREF _Toc183538489 h 9 <![endif]-->
7. References <!-- [if supportFields]> PAGEREF _Toc183538490 h 9 <![endif]-->
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1.
<!--[endif]-->Introduction
The following paper explores the main challenges that affect supply management in small business activities, which are crucial for business performance but also time-consuming and challenging. Such problems include areas like inventory control, sales forecasting, management of suppliers and, more fundamentally, supply chain management (Belhadi et al., 2024). Due to resource constraints, the absence of know-how, and because of fast technological developments, in many cases, the above processes cannot be easily augmented by small businesses with superior technologies.
The introduction of Smart Systems, or Artificial Intelligence AI, has recently emerged as a new eye-opener in almost all business sectors. Experts also argue that artificial intelligence, including machine learning and predictive analytics, as well as natural language processing, have the potential to affect complex supply chains, decision-making and making supply chains more/quickly flexible (Gupta & Khan, 2024). AI can help small businesses cut costs, develop and implement efficient strategies in managing their inventory, and improve demand forecasting processes, supplier evaluation and logistics management. Although the idea is promising, the act has seen little enthusiasm from small businesses, where factors like high implementation costs, lack of technical know-how, and data security concerns have hindered the utilization of AI technology in managing supply chains.
This research aims to establish the possibility of AI as an enabler of innovations in small business models by investigating the functionality of AI in supply chain networks. Hence, the research focuses on deciphering how AI can be utilized to sustain small business supply chains and minimize cost and work to enable these businesses to increase their operations' competitiveness. To investigate this hypothesis, the study will use case studies, industry reports and other studies that analysed the impact of AI to estimate total cost reductions, bettering decision making processes, and business performance of SMEs.
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2.
<!--[endif]-->Research objective
- <!-- [if !supportLists]-->To assess the current state of AI adoption in small business supply chains.
- <!-- [if !supportLists]-->To evaluate the potential benefits and challenges of AI integration into supply chains.
- <!-- [if !supportLists]-->To identify key AI technologies that can optimize inventory management, logistics, and demand forecasting in small businesses.
- <!-- [if !supportLists]-->To provide actionable recommendations for small businesses seeking to implement AI in their supply chain operations.
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3.
<!--[endif]-->Capstone Project Rationale
The rationale for this capstone project is premised on the current and future centrality of AI in supply chain management and, more so, to small businesses, whereby such innovation could translate to critical competitive advantage. Due to the constraint of resources, particularly in finance, many SMEs have to search harder to increase efficiency, reduce cost and ensure the competitiveness of their supply chain (Dey et al., 2022). AI technologies provide potential strategies by increasing or automation workloads, streamlining the decision-making process and organizational processes. Still, the application of AI in SBs has not been paid much attention compared to large business organizations, despite the problems those SBs encounter, including a shortage of technological competencies, financial resources, and organizational culture in accepting change.
This project aims to fill this research gap by establishing the extent to which AI can be integrated within the context of the value chain of small business operations and the factors that cause its implementation. Unlike some of the earlier research studies, this research will explore the purpose, utility, issues, and AI approaches from the SBOMs perspective. Finally, in light of the study's conclusion, it will help widen knowledge of how SMBs can integrate AI to improve the supply chain and what actions stakeholders should take to help SMBs on their way to digital transformation.
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4.
<!--[endif]-->Literature Review
4.1. AI Technologies in Supply Chain Management
AI technologies consist of a series of solutions, including machine learning (ML), predictive analytics, natural language processing (NLP), and robotics, which are becoming more popular and widely incorporated into general business processes to increase productivity and optimization. Of all the branches of SCM, artificial intelligence is changing three branches: machine learning and predictive analytics. With the help of ML models, patterns in the big data sets can be recognized to be used in further demand forecast, inventory control or efficient route planning. , as stated by Koh et al. (2019), are used to provide ML-driven demand forecasting models causing small businesses to avoid stockouts or overstocking by boosting the predictions of the future demand. This results in improved cost and improved service to the customers.
4.2. Benefits of AI for Small Business Supply Chains
AI integration for supply chains in small businesses offers great strategic benefits. First, the potential of AI can be seen in the improvement of operational efficiency and cost-effectiveness arising from the automation of long, labour-intensive processes prone to errors. In Sharma et al., (2023) report on artificial intelligence, the author noted that while AI can be applied to undertake minor repetitive supply chain management duties within an organization, it can also help free up supply chain management resources for more strategic use. Real time data analysis can also be achieved by AI systems whereby massive data from the supply chain can be processed and used to inform the business on the appropriate actions to take due to changes that may have occurred in supply and demand.
AI also positively impacts updating stock levels used in inventory management. Youniset al. (2022) revealed that inventory management systems, through AI, provide accurate data on inventory requirements to minimize overstock and stock-out situations. AI implementation in demand forecasting can assist small business organizations in supplementing inventory with the actual demand to minimize the use of resources and guarantee product stock.
Also, AI helps small business organizations improve supplier evaluation and relationship management. For instance, AI can flag a suppliers value based on respective historical records and give insights into reliability, quality, and delivery performance. It is beneficial for business owners with low purchasing power. It may be caught in a dilemma between choosing a particular supplier exclusively or having access to more extensive and better qualities with the help of group purchasing.
4.3. Challenges of AI Adoption in Small Business Supply Chains
There are some challenges to the use of AI, especially in the supply chain of small businesses. One of the main challenges is the capital needed to introduce AI solutions, particularly in a business. The structure of costs typically related to AI means that small businesses, especially start-ups and microenterprises, may not be able to afford investment in such tools as they remain costly to either develop or acquire. Yandrapalli, (2023)established that AI adoption costs may be high, thus preventing small businesses from adopting such solutions.
A couple of potential problems relate to the overall weakness of AI and data science knowledge within small companies. A large number of small business proprietors and managers may lack sufficient technical competence to put into practice efficient AI systems. Such mismatches may result in the suboptimal application of AI or failure to realise the maximum value that can be delivered through artificial intelligence (Huang et al., 2021). Another challenge that will affect small businesses includes the problems of securing and processing large amounts of data an AI system requires for its proper functioning. One noticeable restraint to AI use is data quality, security, and privacy, where personally identifiable customer data are processed by any business organization. Moreover, compared to new implementations, adopting AI into supply chain processes may be challenging and disruptive. Small business employees may resist the change to AI-powered systems because they convey a different work experience.
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5.
<!--[endif]-->Methodology
This research will use a qualitative research design to establish how the SC of small businesses can be improved through AI by using an exploratory research design amongst small business owners and managers who are in the AI space or planning to be in it. There is no better research approach for this study than qualitative research, which focuses on the details surrounding the phenomenon being investigated and how people make decisions and act in the given circumstances.
5.1 Research Design
A qualitative research design is appropriate for this study as this work seeks to determine the attitude of small business managerial personnel toward using AI in supply chains. Instead of wanting to calculate the effectiveness of the technology, the focus is on discovering how firms understand AI and identify its opportunities and threats, as well as the measures they take to address those threats. Because of the nature of the research questions, the study will be based on semi-structured interviews to give room for self-sourcing when investigating the respondents' experience with specific issues.
5.2 Sampling Strategy
The target population for this particular research will comprise small business owners and managers involved in industries attempting or intending to integrate AI into their inventory management paradigm. The study aims to identify the experiences of the various small businesses; therefore, purposive sampling is appropriate. It is a non-probability technique where the population is chosen deliberately depending on specific essential attributes of the identified research questions. In this case, participants will be chosen based on the following criteria:
- <!-- [if !supportLists]-->Small businesses: Of course, this will define a small business based on what is typical within the industry, say, businesses with less than 100 employees or businesses that earn less than $10 million in a year.
- <!-- [if !supportLists]-->AI implementation: Interested participants should either have adopted AI solutions in their supply chain or be evaluating or planning to evaluate the options.
- <!-- [if !supportLists]-->Geographical location: While the study can involve small businesses worldwide, it will focus especially on those in urban centres where the use of AI might be more rampant and technological enablers are readily available.
- The sample size will be between 10 and 15 participants.
5.3. Data Collection Methods
Interviews will serve as the main source of primary data collection since they allow the participants to present their experiences in their own words. At the same time, they are the most appropriate technique for qualitative research since the information provided will try to answer the set questions. Interviewing is semi-structured in that it retains some form of structure and can explore additional critical problems as they emerge from the discussion.
The interview questions will cover several crucial aspects of AI use in SMB supply chains. Depending on the participants' convenience, the interviews will be face-to-face or online.
5.4. Data Analysis Procedures
Semi-structured interviews will also be conducted to gather data that will be analysed using thematic analysis, the standard qualitative method. Thematic analysis is a qualitative method of analysing and reporting patterns in the collected data through themes. This method is particularly useful in gaining an understanding of participants perceptions, attitudes and experiences, all of which form the focus of this study.
5.5. Ethical Considerations and limitation
In any qualitative research, it is essential to consider some principles of ethics to protect participant's rights and identity. Participants will also be aware of the study's objectives, its anonymity, voluntariness and their right to withdraw from the study at any time without any consequences. Informed consent will be sought before the actual interview in writing. Participants' identities will be encrypted before providing the report, and all data collected will be kept secret. All personal data will be kept in a safe and retrievable only by the researcher involved in the study.
One of the study's main strengths is its main limitation. Due to the methodological approach adopted and the number of subjects investigated, the results cannot be considered entirely generalizable. However, the objective of this research is to explore the specific experiences of small business owners and managers rather than to look for statistical averages. Purposive sampling also has the disadvantage of the possibility of selection bias because respondents' experiences may not be general to others in small businesses
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6.
<!--[endif]-->Planning
The study will be carried out from December 2024 until June 2025. Figure 1 illustrates the anticipated schedule of each task using a Gantt chart.
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7.
<!--[endif]-->References
Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation.Annals of Operations Research,333(2), 627-652. https://link.springer.com/article/10.1007/s10479-021-03956-x
Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management.Journal of Strategic Innovation and Sustainability,14(3). https://articlearchives.co/index.php/JSIS/article/view/4867
Dey, P. K., Chowdhury, S., & Malesios, C. (2022).Supply chain sustainability in small and medium sized enterprises. Routledge.
Eyo-Udo, N. (2024). Leveraging artificial intelligence for enhanced supply chain optimization.Open Access Research Journal of Multidisciplinary Studies,7(2), 001-015. https://text2fa.ir/wp-content/uploads/Text2fa.ir-Leveraging-artificial-intelligence-for-enhanced-supply.pdf
Gupta, Y., & Khan, F. M. (2024). Role of artificial intelligence in customer engagement: a systematic review and future research directions.Journal of Modelling in Management.
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics.Sensors,21(19), 6340. https://www.mdpi.com/1424-8220/21/19/6340
Koh, L., Orzes, G., & Jia, F. (2019). The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management.International Journal of Operations & Production Management,39(6/7/8), 817-828. https://www.emerald.com/insight/content/doi/10.1108/ijopm-08-2019-788/full/html
Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management.International Journal of Production Economics,241, 108250. https://www.sciencedirect.com/science/article/abs/pii/S0925527321002267
Sharma, M., Gupta, R., Sehrawat, R., Jain, K., & Dhir, A. (2023). The assessment of factors influencing Big data adoption and firm performance: Evidences from emerging economy.Enterprise Information Systems,17(12), 2218160.https://www.tandfonline.com/doi/full/10.1080/17517575.2023.2218160
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review.Journal of Business Research,122, 502-517. https://www.sciencedirect.com/science/article/pii/S014829632030583X
Younis, H., Sundarakani, B., & Alsharairi, M. (2022). Applications of artificial intelligence and machine learning within supply chains: systematic review and future research directions.Journal of Modelling in Management,17(3), 916-940.https://www.emerald.com/insight/content/doi/10.1108/jm2-12-2020-0322/full/html
Yandrapalli, V. (2023). Revolutionizing supply chains using power of generative ai.International Journal of Research Publication and Reviews,4(12), 1556-1562. https://d1wqtxts1xzle7.cloudfront.net/108912120/IJRPR20255-libre.pdf?1702491033=&response-content-disposition=inline;+filename=Revolutionizing_Supply_Chains_Using_Powe.pdf&Expires=1732631873&Signature=QXN23eHFn79dcL5HrPL4SHXqhcAEELBHIAJRvhur6bvjONSCPx~dOEJvGqX-0gNVRsWPEm3K8aNvzRpz36OccEXb6qDWuior8nITTD-Iy0UUu0zsaeLeBKiwL0Pn9DFj6UCkp1mQwBmEvCURlYdN26v2q1gIVXpilDHTjSfIJD6HSTvyc3XdVMGxby1N6n8I729SNU05MuZzt0OLlF8e4gJRtp5zIjkL91OVm9jOmZgWj84-kXr5ARvctdM~nSNSH4wW2GOfqPWpxWpIUGWb0FpXsJuHhNKT-p6ZcoqXqkxXI46ak1oAXFSaNPqxeq20A5l8NYB7c0fBS9~2WOsD6g__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
