Strategic Applications of IoT and Big Data JUNE 2026

Description

Strategic Applications of IoT and Big Data

Jun 2026 Examination

 

 

Q1. A leading retail chain has launched smart shelf technology in its stores. Each shelf is equipped with weight sensors, RFID readers, and motion detectors to provide real-time inventory levels and consumer interaction data. However, the company faces issues with inaccurate data, sensor malfunctions, and unauthorized access to sensitive sales information. The IT director is pushing for a comprehensive IoT data lifecycle management plan that addresses accurate data acquisition, secure data transmission, processing, and responsible data archiving or deletion.

Apply the IoT data lifecycle model to create a step-by-step management plan for the retail chain’s smart shelf system. How will you ensure data accuracy, integrity, security, and compliance at each stage from generation to deletion? (10 Marks)

Ans 1.

Introduction

Smart shelf technology represents a significant operational advance for retail chains, enabling real-time inventory visibility and consumer behavior analysis that was previously impossible at this scale. When weight sensors detect that a product category is running low, when RFID readers track individual item movement, and when motion detectors capture consumer interaction patterns, the resulting data stream has enormous business value. But this value is entirely dependent on the quality, security, and responsible governance of that data throughout its entire lifecycle. A smart shelf system generating inaccurate readings, transmitting data over unsecured channels, or storing sensitive sales information beyond its useful life creates operational risk rather than competitive advantage. A structured IoT data lifecycle management plan is therefore not optional infrastructure but the foundation on

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Q2 (A). A leading electronics manufacturer plans to transform its conventional factory into a smart factory using IoT and big data analytics. However, the company’s legacy equipment is deeply integrated into its workflow, making digital retrofitting challenging and costly. Leadership must decide whether to fully upgrade to smart machinery or pursue gradual integration via IoT gateways. Both approaches have implications for operational disruption, ROI, employee adaptability, and competitive agility.

Evaluate the strategic merits and drawbacks of a complete versus phased IoT integration approach in this scenario. Considering factors such as operational efficiency, implementation cost, cultural resistance, and market responsiveness, justify which method you recommend and how it addresses both immediate and long-term business goals. (5 Marks)

Ans 2(A).

Introduction

Transforming a conventional manufacturing facility into a smart factory is one of the most operationally complex technology decisions a company can make. When legacy equipment is deeply embedded in production workflows, the stakes of the integration approach are high: choose wrong and the company either over-invests in disruption it cannot absorb, or under-invests in change and loses competitive ground to more agile manufacturers. The choice between complete

 

Q2 (B). A regional logistics company uses IoT and big data to track shipments across road, rail, and sea, employing geofencing, real-time diagnostics, and predictive route planning. Recently, an industry-wide push for data standardisation has presented both a challenge and an opportunity: their legacy devices are not fully compatible with new industry standards, risking data silos and integration issues with partners. Simultaneously, the company faces pressure to remain competitive and interoperable in the market.

Assess the implications of legacy system incompatibility with industry-standard IoT protocols for this logistics firm. Evaluate the possible strategies such as immediate system overhaul, phased upgrades, or middleware solutions and justify the most effective path forward considering cost, risk, and competitive positioning. (5 Marks)

Ans 2(B).

Introduction

Legacy IoT device incompatibility with emerging industry standards is a problem that logistics companies cannot afford to ignore but equally cannot afford to solve recklessly. For a company already using geofencing, real-time diagnostics, and predictive route planning, the risk is not starting from scratch but protecting and extending existing operational capabilities while aligning with the interoperability standards that partners and regulators are increasingly demanding.

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