Model paper

Dean's Office Official Model Question Paper

IT 233 · Business Information Systems

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Programme
BBA
Academic year
Semester 5
Paper type
Official Model Question
Sitting
Dean's Office Blueprint
Full marks
60
Duration
180 minutes

Tribhuvan University

Faculty of Management

Office of the Dean

Official Model Question Paper / Dean's Office Blueprint

Course: IT 233 · Business Information Systems

Level: Bachelor of Business Administration (BBA) · Semester 5

Full Marks: 60

Time: 3 hrs.

Candidates are required to give their answers in their own words as far as practicable. The figures in the margin indicate full marks.

Group A

Brief Answer Questions. Attempt ALL questions.

[5 × 2 = 10]

  1. Define a Management Information System (MIS) and state its primary role in managerial decision making.

    [2]
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    Answer: MIS: An integrated, computer-based system that provides past, present, and predictive information regarding internal operations and corporate environment to support planning, control, and operational decision-making functions of managers in an organization.

  2. Distinguish between a Transaction Processing System (TPS) and a Decision Support System (DSS).

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    Answer:

    • TPS: Operational-level system that records and processes routine, daily business transactions (e.g., payroll, sales order entry) with high speed and accuracy.
    • DSS: Management-level analytical system that combines data and sophisticated mathematical models to support semi-structured and unstructured decision making (e.g., pricing simulations, financial forecasting).

  3. What is Enterprise Resource Planning (ERP)? Name two globally recognized ERP systems.

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    Answer: ERP: An enterprise-wide software suite that integrates all core business functions (finance, HR, manufacturing, supply chain, sales) into a single unified system sharing a centralized database. Systems: SAP S/4HANA and Oracle Cloud ERP.

  4. Define Data Mining and name two common data mining techniques.

    [2]
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    Answer: Data Mining: The computational process of discovering valid, novel, potentially useful, and understandable patterns and knowledge from large repositories of data (data warehouses). Techniques: Association Rule Mining (Market Basket Analysis) and Classification / Clustering.

  5. What is a Supply Chain Management (SCM) System?

    [2]
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    Answer: SCM System: An inter-organizational enterprise system that coordinates and optimizes the flow of materials, information, and finances across all entities involved in transforming raw materials into finished products delivered to the end customer.

Group B

Descriptive Answer Questions. Attempt any THREE questions.

[3 × 10 = 30]

  1. Explain Anthony’s Pyramid Framework of organizational hierarchy and map the corresponding types of Information Systems (TPS, MIS, DSS, ESS/EIS) to each organizational level.

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    Anthony’s Framework and Organizational Information Systems

    Robert Anthony categorized organizational management into three distinct operational and decision-making tiers:

    1. Operational Level (Front-Line Supervisors):

      • Nature of Decisions: Structured, repetitive, day-to-day routine tasks with established rules.
      • Information System: Transaction Processing Systems (TPS). Records operational events (e.g., barcode scans, ATM cash withdrawals, order billing). Focuses on data accuracy and real-time processing.

    2. Tactical / Management Level (Middle Managers):

      • Nature of Decisions: Semi-structured, periodic planning, monitoring, and resource allocation (monthly sales targets, inventory replenishment).
      • Information Systems:
        • Management Information Systems (MIS): Generates periodic summary, exception, and drill-down reports from underlying TPS databases.
        • Decision Support Systems (DSS): Utilizes interactive analytical models (What-If analysis, goal seeking) to evaluate alternatives under changing conditions.

    3. Strategic Level (Senior Executives & Board of Directors):

      • Nature of Decisions: Unstructured, long-term strategic direction (mergers, new market entry, 5-year capital budgeting) characterized by high uncertainty.
      • Information System: Executive Support Systems (ESS) / Executive Information Systems (EIS). Aggregates internal MIS/DSS data with external competitive intelligence, economic indicators, and regulatory feeds displayed on intuitive executive digital dashboards.

  2. Analyze the architecture and benefits of Customer Relationship Management (CRM) Systems. Contrast Operational CRM with Analytical CRM.

    [10]
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    Customer Relationship Management (CRM) Systems

    1. Concept and Strategic Purpose

    CRM is an enterprise-wide business strategy backed by integrated software applications designed to identify, acquire, retain, and nurture profitable long-term relationships with customers throughout the customer lifecycle.

    2. Comparison: Operational CRM vs. Analytical CRM

    Dimension Operational CRM Analytical CRM
    Focus Customer-facing front-office business processes and daily touchpoints. Back-office analysis of customer data to identify behavioral patterns.
    Primary Tools Sales Force Automation (SFA), Customer Service & Support ticketing, Marketing Campaign Automation. Data Warehouses, OLAP cubes, predictive customer analytics, machine learning churn models.
    Primary Users Call center agents, sales representatives, customer support staff. Marketing strategists, business analysts, Chief Revenue Officers.
    Key Output Streamlined call resolution, automated lead capture, calendar scheduling. Customer Lifetime Value (CLV) calculation, customer segmentation, churn prediction.

    3. Core Enterprise Benefits

    • Higher Customer Retention Rates: Tracking customer preferences prevents customer dissatisfaction and defection.
    • Cross-Selling and Up-Selling: SFA suggests relevant product upgrades based on past purchase history.
    • Optimized Marketing Spend: Targeted campaigns yield significantly higher ROI than broadcast marketing.

  3. Explain the stages of the System Development Life Cycle (SDLC). Compare the traditional Waterfall Model with the Agile Methodology in business software development.

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    System Development Life Cycle (SDLC) and Methodologies

    1. Stages of SDLC

    1. Planning & Feasibility Analysis: Evaluates technical, economic (cost-benefit), legal, and operational feasibility.
    2. System Analysis & Requirements Engineering: Identifies user requirements through interviews and documents them in a Software Requirements Specification (SRS).
    3. System Design: Architecture design, database schema, user interface (UI/UX), and network topologies.
    4. Development / Coding: Software engineers write, document, and unit-test the source code.
    5. Testing: Integration testing, system testing, security vulnerability scanning, and User Acceptance Testing (UAT).
    6. Deployment & Implementation: Direct cutover, parallel running, pilot, or phased rollout into production.
    7. Maintenance & Evolution: Bug patching, system upgrades, and performance tuning.

    2. Waterfall Model vs. Agile Methodology

    Characteristic Traditional Waterfall Model Agile Methodology (Scrum)
    Process Flow Sequential, linear; phase must finish completely before next begins. Iterative, incremental cycles (1-4 week sprints).
    Flexibility to Change Highly rigid; scope changes are costly and resisted. Welcomes changing customer requirements even late in development.
    Working Software Delivery Delivered only at the very end of the project life cycle. Working software increments delivered at the end of every sprint.
    Customer Involvement High at beginning (requirements) and end (testing); minimal during coding. Continuous daily customer/Product Owner collaboration.
    Best Suited For Well-defined, stable requirements with strict safety standards. Fast-evolving business environments, mobile apps, and digital platforms.

  4. Discuss the architecture of a Data Warehouse. Explain the ETL (Extract, Transform, Load) process and contrast Online Analytical Processing (OLAP) with Online Transaction Processing (OLTP).

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    Data Warehousing, ETL, and OLAP vs. OLTP

    1. Data Warehouse Architecture

    A Data Warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data organized to support executive managerial decision making.

    2. The ETL Process

    1. Extract: Gathers raw structured and unstructured data from heterogeneous operational source systems (ERP, CRM, flat logs, POS terminals).
    2. Transform: Cleanses, standardizes, deduplicates, and converts raw data into a consistent reporting schema (handling null values, converting currencies, applying business calculations).
    3. Load: Bulk loads the processed dimensional data into fact tables and dimension tables (Star Schema / Snowflake Schema) within the enterprise data warehouse.

    3. Comparison: OLTP vs. OLAP

    Criterion OLTP (Online Transaction Processing) OLAP (Online Analytical Processing)
    Primary Purpose Day-to-day transactional business execution. Historical analysis and complex strategic decision making.
    Data Schema Highly normalized (3NF) to maximize write speed and eliminate redundancy. Denormalized Star or Snowflake schemas to optimize fast read queries.
    Query Type Short, atomic INSERT, UPDATE, simple SELECT statements. Complex queries involving multi-table joins, aggregations (SUM, AVG), and multi-year time trends.
    Response Time Milliseconds. Seconds to minutes.
    Data View Current snapshot of operational state. Multi-year historical snapshot across business dimensions.

Group C

Comprehensive Answer / Case Analysis Question. Attempt ALL questions.

[1 × 20 = 20]

  1. Enterprise Information Systems Case Study: Failed ERP Implementation at Himalayan Beverage Industries

    Himalayan Beverage Industries is Nepal’s leading brewer and juice manufacturer, operating 3 manufacturing bottling plants and 45 distribution depots nationwide with annual turnover exceeding Rs. 8 billion:

    • The Modernization Initiative: To eliminate fragmented standalone accounting software and spreadsheet inventory logs, the board contracted a multinational vendor to implement a Tier-1 ERP system (covering Supply Chain, Finance, Production Planning, and Quality Control) with a $1.2 million budget over 14 months.
    • Implementation Failures:
      • Change Resistance & Lack of Training: Plant floor supervisors and warehouse clerks were not consulted during system blueprinting. When the ERP went live, staff found the interface confusing and continued logging dispatches on offline paper chits.
      • Data Cleansing Disaster: Historical legacy inventory data was migrated into the ERP without validation. Over 40% of warehouse item SKUs had incorrect unit-of-measure mappings (e.g., cartons entered as individual bottles), causing automated replenishment algorithms to falsely report stockouts.
      • Operational Gridlock: Bottling lines shut down for 12 days due to missing raw ingredients that the ERP system claimed were in stock. Over Rs. 200 million in festival sales were lost, customer orders went unfulfilled, and the CFO halted further vendor milestone payments.

    Questions: a) Identify and analyze the critical failure factors (technical, organizational, human) that precipitated the ERP implementation crisis at Himalayan Beverage. (6 Marks) b) Apply Kotter’s 8-Step Change Management Model to explain how leadership should have managed human and cultural resistance during the digital transformation. (7 Marks) c) Formulate an emergency turnaround plan for Himalayan Beverage to clean data, retrain the workforce, and successfully stabilize the ERP system without scrapping the capital investment. (7 Marks)

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    Comprehensive Case Analysis: Himalayan Beverage Industries ERP Crisis

    a) Critical Failure Factors Analysis

    1. Human & Cultural Factors (The Primary Cause):
      • Severe Change Resistance: Frontline employees viewed the ERP as an alien surveillance tool rather than a productivity enhancer.
      • Inadequate Training and End-User Inclusion: Training was treated as a hurried afterthought rather than an ongoing competency program. End-users were excluded during blueprint workshops.
    2. Data Governance and Technical Failures:
      • ‘Garbage In, Garbage Out’ (GIGO): Failure to cleanse legacy data prior to cutover introduced corrupted SKU units of measure into production tables, rendering automated Materials Requirement Planning (MRP) algorithms useless.
      • Big-Bang Cutover Flaw: The organization attempted an abrupt all-at-once cutover instead of a phased or parallel rollout, leaving no fallback safety net when errors erupted.
    3. Organizational & Executive Sponsorship Failures:
      • Project management lacked executive cross-functional leadership bridging IT and plant operations.

    b) Kotter’s 8-Step Change Management Framework Applied

    1. Create Urgency: Transparently communicate to employees why legacy spreadsheets threatened company survival and market leadership.
    2. Build a Guiding Coalition: Form a cross-departmental transition committee comprising respected plant supervisors, warehouse heads, and financial controllers alongside IT leaders.
    3. Form a Strategic Vision: Clarify how the ERP will eliminate manual late-night paper reconciliation and make workers’ jobs easier.
    4. Enlist a Volunteer Army: Identify tech-savvy younger warehouse supervisors as ‘Super-Users’ to champion peer adoption.
    5. Enable Action by Removing Barriers: Reconfigure cumbersome screen layouts and eliminate legacy performance penalties during the learning curve.
    6. Generate Short-Term Wins: Focus first on stabilizing one simple, visible module (e.g., Finished Goods Dispatch) and publicly celebrate departmental milestones.
    7. Sustain Acceleration: Use early wins to tackle complex modules (advanced MRP and predictive maintenance).
    8. Institute Change: Anchor new digital procedures into standard operating procedures (SOPs) and employee KPIs, strictly abolishing paper logbooks.

    c) Emergency Turnaround and Stabilization Roadmap

    1. Immediate Firefighting and Data Audit Sprint (Weeks 1-3):
      • Halt automatic replenishment ordering temporarily. Perform a 100% physical inventory stocktake across all 3 bottling plants and 45 depots.
      • Run automated data cleansing scripts to reconcile SKU definitions, unit-of-measure multipliers, and warehouse bin locations before re-enabling automated planning.
    2. Super-User ‘Train-the-Trainer’ Immersion (Weeks 4-6):
      • Pull key operational leads from each depot for intensive, hands-on simulation training.
      • Embed technical support personnel physically inside factory dispatch offices to provide elbow-to-elbow coaching during live shifts.
    3. Phased Module Re-Commissioning with Parallel Running:
      • Operate essential modules in parallel with manual reconciliation for 30 days until variance between physical stock and ERP stock drops below 0.5%.
      • Re-establish vendor milestone renegotiations tied strictly to data reconciliation accuracy and operational plant throughput benchmarks.