Case Studies & Practical Applications in OM: Real-World OM Problems & Solutions

TAKEAWAYS:

• Case studies bridge theory and practice by illustrating how OM principles (forecasting, quality, scheduling) solve real problems in companies like Nabil Bank (loan processing) or Daraz (inventory management).
• Root cause analysis in cases reveals inefficiencies (e.g., Hetauda Kapada Udhyog’s textile bottlenecks) and links them to OM tools like value stream mapping or linear programming.
• Strategic vs. tactical decisions are highlighted: BIROI’s expansion uses capacity planning, while Pathao’s driver routing relies on queuing theory.
• Comparative analysis (e.g., Toyota’s lean vs. traditional batch production) shows how OM frameworks drive competitive advantage.
• Ethical and sustainability challenges (e.g., Nepal’s textile waste) are framed through OM lenses like total quality management (TQM) or circular economy principles.
• Exam focus: Expect data-driven case questions (e.g., "Calculate EOQ for Daraz’s spare parts") and strategic recommendations (e.g., "How would you improve NTC’s call-center waiting times?").

1. Why Case Studies Matter in Operations Management

Operations Management (OM) is about transforming inputs (resources, information) into outputs (products/services) efficiently. While units 1–11 cover tools (forecasting, scheduling, quality control), Unit 12 shows how these tools are applied in messy, real-world contexts. Here’s why it’s critical:

Key Differences: Theory vs. Practice

mindmap
  root((Theory vs. Practice in OM))
    Theory
      "Clean, controlled environments (e.g., textbook EOQ models)"
      "Assumptions: constant demand, no lead-time variability"
      "Focus: Mathematical optimization"
    Practice
      "Uncertainty: e.g., **Daraz’s** demand spikes during Dashain sales"
      "Constraints: e.g., **NTC’s** limited fiber-optic capacity"
      "Trade-offs: e.g., **Pathao’s** driver pay vs. customer wait times"
    Case Studies Bridge
      "Reveal hidden complexities (e.g., **Himalayan Java’s** coffee bean sourcing delays)"
      "Test OM frameworks under pressure (e.g., **BIROI’s** supply chain disruptions)"
      "Highlight soft skills: negotiation, stakeholder management"

2. How to Analyze a Case Study: Step-by-Step Framework

Examiners expect you to systematically dissect cases using OM tools. Follow this 5-step approach:

Step 1: Identify the Core OM Problem

Classify the issue using the OM Process Framework:

Example: For Hetauda Kapada Udhyog Ltd. (textile case), the core problem is likely:

• Process inefficiency (bottlenecks in dyeing/weaving).
• Inventory issues (excess fabric waste).
• Quality control gaps (defective garments).

Step 2: Gather and Organize Data

Look for quantitative and qualitative clues in the case. Use this table to structure findings:

Step 3: Apply OM Tools to Diagnose

Map the problem to specific OM units and tools. Here’s how:

Worked Example: BIROI’s Customer Care Backlog Scenario: BIROI’s call center receives 500 calls/day with an average handling time (AHT) of 8 minutes and 3 agents per shift (8 hours/day). Customers complain of wait times >15 minutes. Steps to Solve:

1. Calculate current capacity:

   • Agents per hour: $3\text{ agents}\times 8\text{ hours}=24\text{ agent-hours}$.
   • Calls handled/hour: $24\text{ agent-hours}\times (60\text{ mins}/8\text{ mins})=180\text{ calls/hour}$.
   • Daily capacity: $180\times 8=1440\text{ calls/day}$.
   • Shortfall: $500-1440=-940$ (actually underutilized, but AHT is too high).

2. Identify root causes (use a Fishbone Diagram):

   flowchart LR
     A["High AHT"] --> B["Lack of Scripts"]
     A --> C["Agent Training Gaps"]
     A --> D["Technical Issues"]
     A --> E["Complex Customer Queries"]

3. Recommendations:

   • Short-term: Hire 1 more agent (now 4 agents → capacity = 2880 calls/day).
   • Long-term: Reduce AHT via:
     • Automated IVR for FAQs (reduces calls by 30%).
     • Cross-training agents to handle multiple issues.

Real-World Tie-In:

• Nepal Telecom’s 1000+ call-center agents use similar queuing models to manage peak hours (e.g., during festival sales). Their average speed of answer (ASA) is a KPI tied to OM efficiency.

3. Case Study Deep Dives: Nepali and Global Examples

Case 1: Hetauda Kapada Udhyog Ltd. (Textile Industry)

Problem: Rising costs, low productivity, and quality issues threaten profitability. OM Analysis:

1. Process Bottleneck:

   • Dyeing section operates at 60% efficiency due to manual batch processing.
   • Solution: Replace batch dyeing with continuous dyeing machines (reduces setup time by 40%).

2. Inventory Waste:

   • 30% fabric waste due to poor cutting patterns.
   • Solution: Implement computer-aided design (CAD) for fabric cutting (saves 20% material).

3. Quality Control:

   • 10% defect rate in finished garments.
   • Solution: Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) to reduce defects to <3.4%.

Visual: Value Stream Map for Hetauda Kapada

flowchart TD
  A["Raw Materials: Cotton"] --> B["Spinning"]
  B --> C["Weaving"]
  C --> D["Dyeing (Bottleneck)"]
  D --> E["Cutting (30% Waste)"]
  E --> F["Sewing"]
  F --> G["Quality Check (10% Defects)"]
  G --> H["Finished Garments"]

Exam Tip: For textile cases, always check:

• Make vs. Buy: Could they outsource dyeing?
• Lean Principles: Can they apply 5S or Kaizen in warehouses?

Case 2: BIROI Electronics (Smart Home Appliances)

Problem: Rapid expansion led to supply chain disruptions and customer complaints. OM Analysis:

1. Forecasting Failure:

   • Demand for smart bulbs surged 200% post-lockdown, but BIROI ordered only 50% more stock.
   • Solution: Use exponential smoothing with a safety stock of 25% for seasonal items.

2. Supplier Lead Times:

   • PCB assembly from China takes 60 days (vs. industry avg. 30 days).
   • Solution: Dual sourcing (local supplier for 30% of components).

3. Customer Service:

   • 30% of orders delayed due to poor warehouse layout.
   • Solution: ABC analysis to prioritize fast-moving items (e.g., smart plugs) near packing stations.

Real-World Link:

• Daraz Nepal faces similar issues during Dashain sales. Their solution?
  • Pre-position inventory in regional warehouses (e.g., Kathmandu, Pokhara).
  • Use AI-driven demand forecasting (like Amazon’s tools).

Case 3: Pathao (Ride-Hailing Service)

Problem: Driver shortages and long wait times for customers. OM Analysis:

1. Driver Scheduling:

   • Peak hours (6–9 PM) have 50% more demand but only 20% more drivers.
   • Solution: Dynamic pricing + driver incentives (e.g., double pay during peak hours).

2. Route Optimization:

   • Inefficient routes increase fuel costs by 15%.
   • Solution: Vehicle routing problem (VRP) algorithm to optimize pickups/drop-offs.

3. Customer Wait Times:

   • Average wait time: 8 minutes (vs. Uber’s 3 minutes in Kathmandu).
   • Solution: Queuing theory to calculate optimal number of drivers per zone.

Mermaid: Pathao’s Driver Allocation Model

Exam Tip: For service cases (Pathao, NTC, banks), focus on:

• Service-level agreements (SLAs): e.g., "90% of calls answered in <20 seconds."
• Capacity planning: Use M/M/1 queuing model to calculate wait times.

4. Comparative Analysis: OM Strategies in Action

Not all companies solve problems the same way. Compare traditional vs. modern OM approaches:

5. Ethical and Sustainability Cases

OM isn’t just about efficiency—it’s also about responsibility. Two key areas:

A. Circular Economy in Textiles (Hetauda Kapada)

Problem: 20% of fabric becomes waste, and dyes pollute rivers. OM Solutions:

1. Closed-loop systems:
   • Recycle water in dyeing (saves 50% water).
   • Upcycle fabric scraps into insulation material.
2. Sustainable sourcing:
   • Partner with organic cotton farmers (reduces pesticide use).
3. Cradle-to-Cradle Design:
   • Use biodegradable dyes (e.g., natural indigo).

Visual: Circular Economy for Textiles

flowchart LR
  A["Raw Materials: Organic Cotton"] --> B["Spinning"]
  B --> C["Weaving"]
  C --> D["Dyeing (Water Recycled)"]
  D --> E["Cutting (Zero Waste Patterns)"]
  E --> F["Sewing"]
  F --> G["Finished Garment"]
  G --> H["End of Life"]
  H --> I["Recycle into Insulation"]
  H --> J["Biodegradable Disposal"]

B. E-Waste Management (BIROI Electronics)

Problem: 50 tons/year of e-waste from obsolete smart home devices. OM Solutions:

1. Take-back programs:
   • Offer discounts on new purchases if old devices are returned.
2. Reverse logistics:
   • Partner with Nepal’s e-waste recyclers (e.g., Green Hub Nepal).
3. Design for Disassembly:
   • Use modular components (e.g., detachable smart bulb bases).

Real-World Example:

• Apple’s "GiveBack" program uses OM principles like reverse logistics optimization to recycle 95% of iPhone materials.

6. How Companies Use OM Tools in Practice

Here’s how real Nepali companies apply OM concepts:

In the Real World

Here’s how OM case studies appear in apps and companies students use daily:

1. eSewa (Digital Payments)

   • OM Idea: Queuing Theory & Service Design
   • How it’s used:
     • During Dashain, eSewa’s servers handle 50,000 transactions/hour.
     • Solution: Load balancing across multiple data centers (e.g., Nepal Telecom’s servers in Kathmandu and Chitwan).
     • Exam Link: Calculate M/M/c queuing model for eSewa’s payment processing (where c = number of servers).

2. Khalti (Digital Wallet)

   • OM Idea: Inventory Management (Digital "Inventory")
   • How it’s used:
     • Khalti’s "balance" is like cash inventory—too much idle cash earns low interest, but too little causes failed transactions.
     • Solution: EOQ model adapted for liquidity management (optimize cash reserves vs. investment returns).
     • Real Example: During Tihar, Khalti holds NPR 2 billion in user balances but invests only 60% to meet liquidity needs.

3. Daraz (E-Commerce)

   • OM Idea: Transportation & Logistics
   • How it’s used:
     • Vehicle routing problem (VRP): Daraz’s 100+ delivery vans in Kathmandu use AI to optimize routes, saving 15% fuel.
     • Case Study: During Black Friday 2023, Daraz processed 50,000 orders/day—OM tools like simulation modeling predicted warehouse bottlenecks.
     • Exam Link: "Daraz’s warehouse has 3 loading docks. If 200 orders arrive every 10 mins, how many docks are needed to keep wait time <5 mins?" (Use M/M/c queuing).

4. Pathao (Ride-Hailing)

   • OM Idea: Scheduling & Sequencing
   • How it’s used:
     • Driver assignment problem: Pathao uses linear programming to match 10,000+ drivers to 15,000 daily rides in Kathmandu.
     • Surge pricing: During monsoon floods, Pathao increases prices by 30% to balance supply-demand (like airline yield management).
     • Exam Link: "Pathao has 50 drivers in Thamel. If demand is Poisson with λ=10 rides/hour, what’s the avg. wait time?" (Use M/M/1 queuing).

5. Nepal Telecom (NTC)

   • OM Idea: Capacity Planning & Queuing
   • How it’s used:
     • Network congestion: During COVID-19, NTC’s 4G data traffic spiked by 400%. They added 100+ new cell towers using capacity planning models.
     • Customer service: NTC’s 1000+ call-center agents are scheduled using workforce optimization software (like Amazon’s contact-center tools).
     • Exam Link: "NTC’s call center has 5 agents, avg. call time=4 mins, arrivals=Poisson (λ=10/hour). What’s the avg. wait time?" (Answer: ~20 mins—this is why NTC hires more agents!).

6. Nabil Bank (Loan Processing)

   • OM Idea: Process Reengineering & Automation
   • How it’s used:
     • Traditional: Loan approval took 15 days (manual checks, paper).
     • Modern: RPA bots now handle 80% of routine checks (e.g., credit score verification), reducing time to 3 days.
     • Exam Link: "Nabil Bank processes 500 loans/month. If automation reduces processing time from 15 to 3 days, what’s the new capacity?" (Answer: 5x higher throughput).

Exam Tip: How to Score Full Marks in Case Study Questions

Examiners follow a strict marking scheme. Here’s how to structure your answer for 10–15 marks:

Step 1: Understand the Case (2 marks)

• Paraphrase the problem in OM terms.
  • ❌ Weak: "The company has high costs."
  • ✅ Strong: "Hetauda Kapada’s high fabric waste (30%) and machine downtime (30%) lead to excess inventory costs and poor customer satisfaction."

Step 2: Identify OM Tools to Apply (3 marks)

• List 2–3 relevant OM units and tools.
  • Example for BIROI’s supply chain:
    • Unit 5 (Forecasting): "Use exponential smoothing to predict demand spikes."
    • Unit 6 (Inventory): "Apply EOQ model to optimize safety stock."
    • Unit 11 (Queuing): "Analyze customer service wait times using M/M/c."

Step 3: Calculate/Analyze (4 marks)

• Show calculations (even if not asked).
  • Example for Pathao’s wait times:
    • Given: λ=10 rides/hour, μ=15 rides/hour (since avg. handling time = 60/15=4 mins).
    • L = λ/μ(μ−λ) = 10/5 = 2 rides waiting.
    • W = L/λ = 2/10 = 0.2 hours = 12 mins (but this is Wq, so add service time: 16 mins total).

Step 4: Recommend Solutions (4 marks)

• Prioritize 2–3 actionable steps with OM justification.
  • Example for NTC’s network congestion:
    1. Short-term: Add 50 more cell towers (uses capacity planning).
    2. Medium-term: Implement SD-WAN to optimize traffic routing (uses transportation models).
    3. Long-term: Deploy predictive analytics to preempt outages (uses data-driven decision making).

Step 5: Ethical/Sustainability Consideration (2 marks)

• Always include a social/environmental angle.
  • Example for Hetauda Kapada:
    • "Switch to organic cotton to reduce pesticide use (aligns with UN SDG 12: Responsible Consumption)."

Step 6: Conclusion (1 mark)

• Summarize impact in business terms.
  • Example: "Implementing JIT inventory and lean manufacturing could reduce costs by 25% and improve delivery times by 40%, boosting customer retention."

Common Mistakes to Avoid

1. Ignoring numbers: Always calculate even if the question doesn’t ask. Examiners reward quantitative analysis.
2. Vague recommendations: Don’t say "improve quality"—say "train workers in Six Sigma methods to reduce defects from 10% to <3.4%."
3. Overlooking trade-offs: If you recommend more inventory, mention the opportunity cost of storage.
4. Copy-pasting theory: Cases require application, not definitions. Example:
   • ❌ "EOQ is used to minimize inventory costs."
   • ✅ "Daraz should use EOQ to order 500 smartphone spare parts/month (D=6000/year, H=$20/unit/year, S=$50/order) → Q = √(2DS/H) = √(2×6000×50/20) ≈ 245 units*."

Practice Question (Exam-Style)

Case: Nepal’s Textile Industry *"Nepal exports 50% of its textiles to India, but faces high lead times (45 days) and quality rejections (15%). Local firms like Hetauda Kapada Udhyog struggle with machine downtime (30%) and fabric waste (25%)."*

Question: Analyze the OM challenges and recommend three solutions using relevant tools.

Model Answer:

1. Supply Chain Delays (Unit 4: Process Selection)

   • Problem: 45-day lead time for fabric imports.
   • Solution: Dual sourcing (30% from India, 70% from Bangladesh’s faster suppliers) + just-in-time (JIT) ordering.
   • Tool: Transportation model to optimize shipping routes (e.g., sea vs. air freight).

2. Machine Downtime (Unit 8: Productivity Improvement)

   • Problem: 30% downtime in weaving section.
   • Solution: Total Productive Maintenance (TPM) to reduce breakdowns by 50% via predictive maintenance.
   • Tool: OEE (Overall Equipment Effectiveness) to track improvements.

3. Fabric Waste (Unit 7: Quality Management)

   • Problem: 25% waste in cutting.
   • Solution: Computer-aided design (CAD) for zero-waste patterns + training workers in lean cutting techniques.
   • Tool: Pareto analysis to identify top 20% of cutting errors causing 80% of waste.

Ethical Note: "Partner with Fair Wear Foundation-certified suppliers to ensure ethical labor practices (SDG 8: Decent Work)."

Final Checklist for Cases

Before submitting, ask: ✅ Did I paraphrase the problem in OM terms? ✅ Did I apply 2–3 OM tools with calculations? ✅ Did I prioritize solutions (short-term vs. long-term)? ✅ Did I include a sustainability/ethics angle? ✅ Did I use real-world examples (e.g., Daraz, NTC) to justify my approach?