BUS5003 Information Systems and Data Analysis Report 1 Sample

BUS5003 Information Systems and Data Analysis Report 1

Assignment Brief

For this assessment, you must select a real business and develop a structured report outlining a project that requires data management and information systems to solve a specific business challenge. Your report must explain why data management and an information system are essential for the company’s success and how they will support the project.

Your assessment must include the following sections:

1. Business Selection & Overview (Maximum 150 words)

• Choose a business and provide a brief introduction, including:

• Company Name & Industry: Identify the business and the industry it operates in.

• Core Operations: Describe the company's main products/services and key business activities.

• Business History: How long has the company been in operation?

• Mission, Vision & Goals: Summarise the company’s mission, vision, and key business objectives.

Important: Your business selection should be relevant to data management and information system needs to ensure a clear connection to the project.

2. Project Description (Maximum 150 words)

Define a specific project that the business must undertake, including:

• A clear explanation of the project’s purpose and objectives.

• The business challenge the project aims to address.

• How the project will improve efficiency, decision-making, or business performance through data management and information systems.

• The key stakeholders and their roles in the project (e.g., IT department, management, data analysts).

Example Projects:

✔ Implementing a customer relationship management (CRM) system to manage customer data.
✔ Developing a data analytics platform to improve sales forecasting.
✔ Enhancing supply chain management through automated data tracking.

3. Market Analysis (Maximum 300 words)

Analyse the business environment and why data management is crucial in this market:

• Industry & Market Conditions: Overview of current trends, challenges, and opportunities in the industry.

• Need for Data & Information Systems: Explain how data-driven decision- making is essential for the business to remain competitive.

• Target Audience: Identify the company’s key customers (e.g., age, gender, income, occupation, location) and their needs.

• Competitive Analysis: Identify major competitors and discuss how they use data management or information systems to gain an advantage.

Important: You must demonstrate how data and information systems will help the business understand its market, improve efficiency, or gain a competitive edge.

4. Expected Outcomes & Success Metrics (Maximum 300 words)

Define the expected outcomes of the project and how success will be measured:

• How will data management and information systems help the business?

• What specific improvements are expected (e.g., increased sales, reduced costs, improved customer retention)?

5. Project Constraints & Considerations (Maximum 300 words)

Identify potential challenges and limitations in implementing the project, including:

• Technical Constraints: Are there limitations in existing IT infrastructure?

• Integration Challenges: Will the new system need to integrate with existing databases or software?

6. Budget Estimation (Maximum 300 words)

Provide an estimated budget for the project:

• Estimated costs for system implementation, including software, hardware, and training.

• Comparison with industry standards to justify the estimated costs.

7. Project Timeline (Maximum 300 words)

Outline the step-by-step implementation plan:

• Project Phases & Milestones (e.g., system selection, data migration, testing, training, full deployment).

• Estimated timeline for each phase

8. Distribution &Implementation Strategy (Maximum 200 words)

Explain how the business will deploy the project and ensure adoption:

• How will the system be introduced to employees/customers?

• Training and onboarding strategy for employees.

Solution

1. Business Selection and Overview

The Australian automotive company Holden functioned within the automobile manufacturing sphere and automobile sales operations (BBC News, 2020). The company began operations in 1856 by selling saddles before starting car production in 1908 before General Motors acquired it in 1931 (BBC News, 2020). Holden created and marketed passenger vehicles and utes joined with SUVs through its extensive operations in the Australian market. The manufacturing operations stopped in Australia in 2017 but the company continued as an importing business until 2020 (Ramey, 2020). The management at Holden worked to build automobile products for Australian environments while pursuing innovative automotive design standards of excellence. The primary objective of this company is to deliver dependable, high-performance automobiles.

2. Project Description

The automated data analytics system at Holden will provide accurate customer demand prediction through automated methods. The system aims to improve stock management and production scheduling as well as optimise selling methods through immediate analytical information. The main concern stems from Holden's use of outdated demand prediction strategies which results in inventory shortages together with unfulfilled customer demands. Advanced data management coupled with an information system will improve operational efficiency along with decision-making capabilities (Al-Surmi, Bashiri, & Koliousis, 2022). The essential stakeholders for this initiative are the IT team for system creation and data analysis professionals for insight generation, in addition to executives who implement the strategic plan.

3. Market Analysis

3.1. Industry and Market Conditions

The automotive sector experiences quick changes because of technological development together with changing market tastes and environmental sustainability demands. Competitive dynamics within the automotive industry have changed because of electric vehicles (EVs) independent driving systems together with digital retail models. Before its termination in 2017, Holden once controlled the market, but it ceased production because of expanding global competition and changing market preferences (Ramey, 2020). Supply chain interruptions, along with changing customer tastes and market demand changes, make up the key hurdles.

3.2. Need for Data and Information Systems

Success for Holden depends on the implementation of data management and information systems in this active business environment. By using data-driven analysis, Holden can evaluate customer buying behaviours mar, market trends, and regional market fluctuation, which generates superior resource distribution decisions. Holden obtains immediate market perspectives as well as improves advertising for MBA assignment expert approaches and product customisation through AI analytics and automated management protocols.

3.3. Target Audience

The Australian market segment that Holden geared its products toward consisted of middle-income clients who belonged to families and professions and auto enthusiasts.
Key demographics included:

• Age: 25–55 years

• Gender: Predominantly male, but increasing female interest in SUVs

• Income: Middle to upper-middle class

• Occupation: Professionals, tradespeople, and rural workers

• Location: Urban and regional Australia, with strong demand in rural areas for durable vehicles

3.4. Competitive Analysis

Montreal-Trudeau follows competitive rivalry from Toyota, Ford and Hyundai through data-based strategies real-time analytic systems and AI-controlled inventory systems. The production planning at Toyota depends on predictive analytics, while Ford makes use of AI-based forecasting to decide inventory locations (Cohen, 2021). The connected vehicle platform of Hyundai establishes improved customer engagement by processing data insights. An advanced data analytics system at Holden will allow the company to achieve competitive parity with peers through improved operational efficiency and personalised customer interactions.

4. Expected Outcomes and Success Metrics

4.1. Data Management and Information Systems Help Holden

Through automated data analytics implementation, Holden will experience a significant transformation in its demand forecasting operations, which will create better operational performance, superior inventory control and improved customer satisfaction. Duration-based data analysis combined with artificial intelligence enables Holden to execute decisions that reduce outmoded forecasting systems as well as stock discrepancies.

4.2. Expected Improvements

• Increased sales: Vehicle availability increases through better demand predictions; thus, Holden maintains continuous availability of popular models to avoid stock shortage-based lost sales.

• Reduced costs: Beyond controlling inventory expenses the organisation will save money on warehousing together with logistics fees by avoiding both overproduction and excess stock accumulation.

• Enhanced Customer Retention: The analysis of customer data enables Holden to deliver customised purchases and create superior satisfaction levels that lock customers in for extended periods.

• Improved decision-making: Real-time market and customer behaviour trends accessible through the system enable management to make decisions in advance (Okeleke et al., 2024).

4.3. Key Performance Indicators (KPIs)

The accuracy of demand forecasts will improve, leading to correct inventory management. The Inventory Efficiency will rise through a reduction in stock errors, which minimises both excess stock and shortages. The company will achieve growth in sales through better matching of customer demand during the initial year. The operational speed will be boosted by shortening order fulfilment times to make the supply chain more efficient. The implementation will boost Customer Satisfaction Scores because customers will experience better purchasing processes alongside available inventory improvements (Kakolu & Faheem, 2023).

4.4. Measurable targets

• The company will raise its forecasting accuracy by 25% before the end of six months.

• The percentage of inventory errors will decrease by 20% by the 6-month period to prevent both stockouts and excessive inventory.

• The system will generate a minimum 10% growth in vehicle sales starting from its implementation year.

• The organisation will reach its supply chain coordination goal by improving order fulfilment speed by 15% overall.

• The customer rating performance will advance by 10% because consumers will give better reviews.

5. Project Constraints and Considerations

5.1. Technical Constraints

The current IT infrastructure at Holden does not possess enough advanced functionalities to implement automated data analytics systems. Systems that rely on previous technology do not handle data processing in real-time thus creating the need for updates in hardware along with software. Enhancements to cybersecurity systems need implementation to safeguard both business data along client information (Ogborigbo et al., 2024).

5.2. Integration Challenges

The implemented system needs to connect with all current database systems and enterprise platforms without causing any disruptions. The system requires complete compatibility between its functionality and CRM systems inventory management systems, and sales tracking solutions (Mittal, 2024). The process of moving and synchronising data will pose technical difficulties which need step-by-step testing protocols to avoid operational interruptions.

5.3. Budget or Time Restrictions

Conducting system development, alongside cloud resources and artificial intelligence analytics tool implementation, needs substantial budgetary support (Yathiraju, 2022). The budgetary demands could force Holden to perform the system deployment in stages instead of implementing it in its entirety. The company needs to follow a quick timeline because it needs to respond promptly to market trends alongside consumer requirements.

5.4. Stakeholder Involvement

Employee acceptance stands as the essential requirement for project triumph. The staff will need training programs that help them master system usage skills while acquiring data interpretation abilities. The system will be better utilised when staff gets proper support from management as well as effective clear communication to address possible resistance to new technology (Mittal, 2024).

6. Budget Estimation

Estimated Costs

• Software Development and Licensing: The costs amount to $250,000 for creating the AI analytics platform and cloud storage, which integrates with the current database structure.

• Hardware Upgrades: The expenditure for improved hardware consists of $100,000 for servers, along with high-speed processors and storage solutions.

• Training & Employee Development: Training and Employee Development Programming will receive $50,000 for data interpretation and system usage teaching purposes.

• Cybersecurity Enhancements: $30,000

• Ongoing Maintenance and Support: $70,000 annually

• Total Estimated Budget: $500,000

Distinct project costs for enterprise-level AI analytics vary from $400,000 to $800,000 based on their size and implementation needs. The budget for Holden matches up with standards found in the industry to offer an affordable system implementation that provides robust performance.

Return on Investment (ROI)

The project will deliver an estimated annual return of $1 million because it will boost sales by 10% while raising inventory efficiency by 20% and decreasing order fulfilment times by 15%. Holden expects to receive a financial return on investment within 12–18 months thanks to these enhancements, which benefit the company operationally.

7. Project Timeline

Table 1: Project Timeline
Source: (Developed by the author)

8. Distribution and Implementation Strategy

The automation of data analytics systems at Holden uses structured deployment to enable departments to adopt it smoothly.

System Introduction

The implementation will commence with an initial roll-out in vital business areas, particularly sales and inventory divisions. The system benefits will receive public announcements throughout the entire company and demonstration sessions to gain stakeholder agreement.

Employee Training and Onboarding

• Workshops and Online Modules for hands-on learning.

• Training programs will be delivered to specific organisational units to fulfil unique user requirements.

• Ongoing Support with IT specialists available for assistance.

Monitoring and Evaluation

The system performance tracker will use KPIs to monitor demand forecast accuracy along with inventory efficiency. Organisation-wide support staff will manage instant problem fixes along with quarterly analysis events to drive ongoing development progress (Liao et al., 2024). The organised methodology makes sure that the transition runs smoothly without causing many interruptions while resulting in lasting operational success.

9. Conclusion

The implementation of the automated data analytics system at Holden is a crucial opportunity at transformation in terms of demand forecasting, inventory management and customer satisfaction. Holden takes advantage of the potential of AI driven insights to correct inefficiencies, cut costs and stay ahead in the developing automotive industry. While there are challenges like integration complexities and budget constraints, the structured rollout plan and stakeholder engagement, as well as continuous performance monitoring, will help to achieve a successful implementation. Thus, it can be stated that Holden is ready to operationalise efficiency, position its market better, and grow the company in a sustainable manner with measurable targets.

References

Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464-4486.https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1966540

Kakolu, S., & Faheem, M. A. (2023). Digitisation and automation in mobile applications: A catalyst for operational efficiency and user engagement.https://www.researchgate.net/profile/Muhammad-Ashraf-
Faheem/publication/387334682_Digitization_and_automation_in_mobile_applications_A_catalyst_for_operational

_efficiency_and_user_engagement/links/67697179117f340ec3d29df7/Digitization-and-automation-in-mobile-applications-A-catalyst-for-operational-efficiency-and-user-engagement.pdf

Liao, Y. Y., Soltani, E., Iqbal, A., & van der Meer, R. (2024). The utility of performance review systems: A total quality management perspective. Strategic Change, 33(4), 287-310.https://doi.org/10.1002/jsc.2580

Mittal, S. (2024). Framework for optimised sales and inventory control: a comprehensive approach for intelligent order management application. International journal of computer trends and technology, 72(3), 61-65.https://www.researchgate.net/profile/Sumit-Mittal-10/publication/379544391_Framework_for_Optimized_Sales_and_Inventory_Control_A_Comprehensive_

Approach_for_Intelligent_Order_Management_Application/links/6633bb0635243041535b22a4/Framework-for-Optimized-Sales-and-Inventory-Control-A-Comprehensive-Approach-for-Intelligent-Order-Management-Application.pdf

Ogborigbo, J. C., Sobowale, O. S., Amienwalen, E. I., Owoade, Y., Samson, A. T., & Egerson, J. (2024). Strategic integration of cyber security in business intelligence systems for data protection and competitive advantage. World Journal of Advanced Research and Reviews, 23(1), 081-096.https://www.researchgate.net/profile/Joshua-Egerson/publication/382052270_Strategic_integration_of_cyber_security_in_business_intelligence_systems_

for_data_protection_and_competitive_advantage/links/668bf60dc1cf0d77ffc361b9/Strategic-integration-of-cyber-security-in-business-intelligence-systems-for-data-protection-and-competitive-advantage.pdf

Okeleke, P. A., Ajiga, D., Folorunsho, S. O., & Ezeigweneme, C. (2024). Predictive analytics for market trends using AI: A study in consumer behavior. International Journal of Engineering Research Updates, 7(1), 36-49.https://www.researchgate.net/profile/Daniel-Ajiga/publication/383410055_Predictive_analytics_for_market_trends_using_AI_A_study_in_consumer_behavior/

links/66cb61b375613475fe7b68ef/Predictive-analytics-for-market-trends-using-AI-A-study-in-consumer-behavior.pdf

Ramey, J. (2020). Holden Brand Axed in Australia as GM Pulls Back from Region. Retrieved February 27, 2025, from https://www.autoweek.com/news/industry-news/a30978884/holden-brand-axed-in-australia-as-gm-pulls-back-from-region/

Yathiraju, N. (2022). Investigating the use of an artificial intelligence model in an ERP cloud-based system. International Journal of Electrical, Electronics and
Computers, 7(2), 1-26.https://www.academia.edu/download/85514837/IJEEC_01_march_april_2022.pdf

BBC News. (2020, February 17). GM scraps historic Holden car brand in Australia. https://www.bbc.com/news/business-51499775

Cohen, R. B. (2021, July). Manufacturers, AI Models and Machine Learning, Value Chains, and 5th Generation Wireless Networks. In CS & IT Conference Proceedings (Vol. 11, No. 10, pp. 17-31). CS & IT Conference Proceedings.https://csitcp.com/paper/11/1110csit03.pdf