Description

Strategic Applications of IoT and Big Data

Dec 2025 Examination

 

 

Q1. An agri-tech startup has developed a smart agriculture platform that collects real-time data from soil sensors, weather stations, and drones across hundreds of farms. As the user base grows, the volume and variety of data increase exponentially. The startup must choose between cloud storage, edge storage, and traditional databases to balance scalability, cost, performance, and security. The project manager must recommend a storage architecture that supports advanced analytics, remote access, and compliance with data protection laws. In this scenario, how should the project manager apply IoT data storage and processing models to select the most suitable storage solution for a rapidly growing smart agriculture platform? (10 Marks)

Ans 1.

Introduction

The adoption of IoT in agriculture is transforming farming into a data-driven practice where real-time information from soil sensors, drones, and weather stations enables precise decision-making. An agri-tech startup managing hundreds of farms faces the challenge of handling exponential growth in the volume, velocity, and variety of data. The choice of storage architecture—cloud storage, edge storage, or traditional databases—becomes critical for ensuring scalability, security, performance, and compliance with regulations. At the same time, the system must support advanced analytics to deliver actionable insights to farmers and allow remote access for geographically dispersed users. The project manager, therefore, must evaluate IoT data storage and processing models strategically and

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Q2(A). BMW, responding to rising demand for customised vehicles, adopted Industry 4.0 technologies including IoT sensors, robotics, and automated guided vehicles. These innovations enabled real-time data collection, predictive maintenance, and flexible production systems, resulting in increased production flexibility, reduced downtime, and improved quality control. Despite these gains, BMW faces ongoing challenges in managing vast data streams and ensuring consistent quality as customer expectations and market conditions evolve. Assess the effectiveness of BMW’s transition to a smart factory model using Industry 4.0 technologies. From the perspectives of customisation, production flexibility, and quality assurance, what improvements or alternative strategies could BMW consider to address the challenges of managing complex data and maintaining high standards in a rapidly changing market? (5 Marks)

Ans 2a.

Introduction

BMW’s adoption of Industry 4.0 technologies marks a strategic move to align with customer demand for customised vehicles and enhance operational efficiency. By deploying IoT sensors, robotics, and automated guided vehicles, BMW has achieved predictive maintenance, real-time monitoring, and greater production flexibility. These efforts have reduced downtime and improved quality control, strengthening the company’s competitiveness. However, managing the vast flow of complex data while meeting ever-

 

 

Q2(B). A mid-sized logistics company is exploring IoT-based route optimization to improve delivery efficiency. The proposed system would use real-time GPS, telematics, and weather data to dynamically adjust routes, aiming to cut fuel costs, reduce emissions, and provide accurate delivery estimates to customers. However, the company faces dilemmas such as the upfront investment, possible resistance from drivers, and the challenge of aligning sustainability goals with tight delivery deadlines. Management must decide how to balance these priorities to achieve both operational and reputational benefits. Evaluate the strategic implications for a logistics company considering the adoption of IoT-enabled route optimization systems, especially in balancing cost reduction, environmental sustainability, and customer satisfaction. What potential dilemmas might arise, and how should management prioritize competing objectives? (5 Marks)

Ans 2b.

Introduction

The logistics industry is under increasing pressure to deliver efficiency while meeting sustainability goals and customer expectations. A mid-sized logistics company considering IoT-based route optimisation seeks to integrate GPS, telematics, and weather data to enhance delivery accuracy and cut costs. While such technology promises reduced emissions, fuel savings, and improved service quality, it also poses dilemmas around investment, employee resistance, and operational trade-offs. Management must carefully weigh these priorities to achieve both operational efficiency and reputational strength in