Aggregate Planning & Scheduling: Techniques, Trade-offs & Real-world Applications

Core Concepts: Definitions and Scope

Aggregate planning is the medium-term (3–18 months) process of aligning production capacity (workforce, machines, inventory) with demand forecasts to meet customer needs while minimizing costs. Unlike short-term scheduling (which assigns tasks to specific workers/machines), aggregate planning focuses on broad resource levels (e.g., "hire 50 more workers" or "increase overtime by 20%").

Key Terms

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Aggregate Planning Techniques

Three primary strategies balance costs and flexibility. Each has trade-offs in inventory levels, workforce changes, and backorders.

1. Level Strategy (Constant Workforce)

How it works:

• Maintain a fixed workforce and steady production rate.
• Absorb demand fluctuations via inventory (build up during low demand, draw down during peaks).
• Use backorders (delayed orders) if inventory runs out.

Pros:

• Low hiring/firing costs (good for labor-intensive sectors like textiles).
• Stable workforce morale (e.g., Himalayan Java maintaining barista teams year-round).

Cons:

• High inventory holding costs (e.g., perishable goods like fresh produce).
• Risk of obsolescence (e.g., Daraz overstocking last year’s smartphone models).

Example: Nabil Bank’s Loan Processing

• Scenario: Loan applications spike in January (post-budget season) but drop in July.
• Solution: Bank maintains a core team of 50 loan officers year-round.
• Trade-off:
  • Inventory: Stored "capacity" (idle officers in slow months).
  • Cost: Salaries + office space vs. training new hires during peaks.

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2. Chase Strategy (Demand-Driven Workforce)

How it works:

• Adjust workforce size (hiring/firing) or hours (overtime, part-time) to match demand exactly.
• Minimize inventory but may require frequent workforce changes.

Pros:

• No inventory holding costs (ideal for perishable goods or custom products).
• Lower labor costs in low-demand periods (e.g., NTC reducing hydropower workers in winter).

Cons:

• High hiring/firing costs (e.g., training new drivers for Pathao during Dashain).
• Workforce instability (demoralizing for employees).

Example: Daraz’s Holiday Rush

• Scenario: Orders surge by 300% during Dashain and Tihar.
• Solution:
  • Hires 1,000 temporary warehouse staff (from local agencies).
  • Uses overtime for permanent staff in customer service.
• Trade-off:
  • Cost: ₹500/month per temporary worker + overtime pay.
  • Benefit: Avoids stockouts and lost sales.

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3. Mixed Strategy (Hybrid Approach)

How it works:

• Combines level and chase by using:
  • Inventory for minor fluctuations.
  • Workforce changes for major shifts.
  • Subcontracting or backorders as needed.

Example: Toyota’s Just-in-Time (JIT) with Aggregate Planning

• Level: Maintains a core assembly line workforce (stable).
• Chase: Uses temporary agencies for seasonal model changes (e.g., more SUVs in winter).
• Inventory: Holds buffer stock of common parts (e.g., bolts, sensors) but not finished cars.

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Aggregate Planning Models

Mathematical models quantify trade-offs. Two key approaches:

1. Linear Programming (LP) Model

Objective: Minimize total cost (production, inventory, hiring/firing, backorders). Constraints:

• Demand must be met (or backordered).
• Workforce limits (e.g., max 10% overtime).
• Inventory capacity (e.g., max 500 units stored).

Example: Kathmandu Traffic Police’s Patrol Scheduling

• Variables:
  • $x_t$ = number of officers scheduled in month $t$.
  • $I_t$ = inventory of "patrol-hours" carried over.
• Objective: Minimize cost of overtime + idle time + recruitment.
• Constraint: Demand for patrols in month $t$ = $1,200$ hours (higher in festival months).

Solution Approach:

1. Define cost coefficients (e.g., ₹100/hour for overtime, ₹5,000 to hire/fire an officer).
2. Solve using Excel Solver or Python (PuLP library).

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2. Transportation Model

Used when multiple production facilities supply multiple regions. Example: Himalayan Java’s Coffee Distribution

• Facilities: 3 factories (Pokhara, Dharan, Kathmandu).
• Demand: 5 regions (KTM, LTP, BHR, BJR, DIL).
• Goal: Minimize shipping costs while meeting regional demand.

Solution: Use northwest corner rule or Vogel’s approximation method to find the optimal distribution.

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Scheduling: Short-Term Execution

Once aggregate plans are set, scheduling assigns tasks to specific resources (machines, workers, time slots).

1. Gantt Charts

Visual tool to track project timelines and resource allocation. Example: NTC’s Power Plant Maintenance Schedule

Key Features:

• Horizontal bars = tasks.
• Color coding = departments (e.g., red = mechanical, blue = electrical).
• Critical path = longest sequence of dependent tasks (identifies delays).

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2. Critical Path Method (CPM) and PERT

Used for complex projects with uncertain durations.

Example: Pathao’s Bike Fleet Expansion

• Tasks:
  1. Secure funding (3 months).
  2. Procure bikes (2 months, uncertain due to supplier delays).
  3. Train drivers (1 month).
  4. Launch marketing (1 month).
• Critical Path: Funding → Procurement → Training (total 6 months).
• Slack: Marketing can start later (1 month buffer).

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In the Real World

1. Daraz: Mixed Strategy for E-Commerce Peaks

• Problem: Demand spikes 5x during Dashain/Tihar (Nepal’s biggest shopping festivals).
• Solution:
  • Level: Maintains a core warehouse team (2,000 FTEs).
  • Chase: Hires 5,000 temporary workers (from local agencies).
  • Inventory: Stocks high-demand items (e.g., diyas, sweets) in advance.
  • Scheduling: Uses Gantt charts to coordinate packing/shipping.
• Cost Trade-off:
  • Temporary labor: ₹500/worker/month.
  • Inventory holding: 15% of revenue.
  • Result: 98% order fulfillment rate during peaks.

2. Nabil Bank: Chase Strategy for Loan Processing

• Problem: Loan applications vary by season (high in Jan–Mar, low in Jul–Sep).
• Solution:
  • Core team: 50 loan officers (year-round).
  • Peak season: Hires 20 temporary officers (from CA campuses).
  • Off-peak: Reduces overtime; uses automated pre-screening to filter simple loans.
• Impact:
  • Processing time drops from 45 to 20 days during peaks.
  • Cost per loan: ₹1,200 (vs. ₹1,800 with only permanent staff).

3. Toyota’s Just-in-Time (JIT) with Aggregate Planning

• Global Challenge: Fluctuating car demand across regions.
• Solution:
  • Level: Maintains core assembly lines (stable workforce).
  • Chase: Uses flexible shifts (e.g., 3 shifts in Q4, 2 shifts in Q1).
  • Inventory: Zero finished cars (only parts; cars built to order).
• Result:
  • Inventory turnover: 50x/year (vs. 5x for traditional automakers).
  • Cost savings: $2 billion/year (2019 data).

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Exam Tip: How This Unit is Tested

1. Problem-Solving Questions (40–50%)

• Format: Given demand data, workforce constraints, and cost parameters, calculate the optimal aggregate plan using level/chase/mixed strategies.

• Example Question:

  "A Kathmandu-based bakery forecasts monthly demand (units): Jan=500, Feb=300, Mar=700. Hiring cost=₹2,000/worker, firing=₹1,500, inventory cost=₹5/unit/month, backorder cost=₹10/unit. Current workforce=10 (each can produce 50 units/month). Use a level strategy with 10% overtime allowed. Calculate total cost for Q1."

• How to Score Full Marks:

  • Step 1: Define variables (e.g., $x$ = production level, $I_t$ = inventory).
  • Step 2: Write the cost function (e.g., $\text{Total Cost}=\text{Hiring}+\text{Firing}+\text{Inventory}+\text{Backorders}$).
  • Step 3: Solve for each month, showing workforce adjustments and inventory levels.
  • Step 4: Sum costs and compare with chase strategy (briefly).

2. Short-Answer Definitions (20–30%)

• Key Terms to Memorize:
  • Aggregate planning horizon: 3–18 months.
  • Chase strategy: "Demand-chasing" via workforce changes.
  • Gantt chart: "Bar chart for project scheduling."
  • Critical path: "Longest duration path in a project network."
  • Transportation model: "Minimizing cost of shipping from sources to destinations."

3. Case Analysis (20–30%)

• Format: Given a real or hypothetical scenario (e.g., a Nepali hotel, a Daraz warehouse), identify the aggregate planning strategy used and suggest improvements.

• Example Scenario:

  "Hotel Himalaya in Pokhara faces 60% occupancy in monsoon (Jun–Sep) but 90% in winter (Dec–Feb). Labor costs are ₹20,000/month per employee. Inventory (food) costs ₹500/unit/month. Current policy: fires 20% staff in monsoon. Should they switch to a level strategy?"

• How to Answer:

  1. Current Strategy: Chase (hiring/firing).
  2. Problems: High turnover, training costs, guest dissatisfaction.
  3. Alternative: Mixed strategy:
     • Level: Keep 80% core staff.
     • Chase: Hire only 10% temps in winter (instead of 20%).
     • Inventory: Stock non-perishable items (e.g., spices) in advance.
  4. Cost Comparison: Show ₹50,000 savings in labor vs. ₹30,000 extra in inventory.

4. Diagram-Based Questions (10–20%)

• Expectations:
  • Draw a Gantt chart for a given project.
  • Sketch a transportation model table and solve using northwest corner rule.
  • Illustrate level vs. chase strategies with demand graphs.

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Worked Example: Aggregate Planning for a Nepali Textile Factory

Scenario: Sano Textiles in Biratnagar produces ready-made garments with seasonal demand:

• Winter (Nov–Feb): 5,000 units/month (peak).
• Summer (Mar–Oct): 2,000 units/month (low).
• Costs:
  • Hiring/firing: ₹10,000/worker.
  • Overtime: ₹500/worker/month.
  • Inventory: ₹20/garment/month.
  • Backorders: ₹50/garment.
• Current Workforce: 50 workers (each produces 100 units/month).

Task: Compare level and chase strategies for 6 months (Mar–Aug).

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Step 1: Level Strategy

• Production Rate: Fixed at 3,000 units/month (average demand).
• Workforce: 30 workers (3,000 units / 100 units/worker).
• Inventory/Backorders:
  • Mar–Apr: Demand=2,000 → Inventory = +1,000.
  • May–Aug: Demand=2,000 → Inventory depletes to 0.
  • No backorders (sufficient inventory).

Cost Calculation:

Workforce Cost: ₹30 workers × ₹10,000/month × 6 = ₹180,000.

Total Level Cost: ₹180,000 (labor) + ₹30,000 (inventory) = ₹210,000.

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Step 2: Chase Strategy

• Workforce Adjusts to Demand:
  • Mar–Oct: 20 workers (2,000 units/month).
  • Hiring Cost: (30–20) × ₹10,000 = ₹100,000 (if starting from 30).
  • No Inventory Needed.

Cost Calculation:

• Labor: 20 workers × ₹10,000 × 6 = ₹120,000.
• Hiring: ₹100,000 (one-time).
• Total Chase Cost: ₹120,000 + ₹100,000 = ₹220,000.

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Step 3: Mixed Strategy (Optimal)

• Base Workforce: 25 workers (2,500 units/month).
• Peak Months (Nov–Feb): Use overtime (no hiring).
  • Overtime cost: (5,000–2,500) × ₹500 = ₹1,250/worker/month.
  • Total overtime for 4 months: 25 workers × ₹1,250 × 4 = ₹125,000.
• Inventory: Build 500 units in Mar (for buffer).
  • Holding cost: 500 × ₹20 × 4 months = ₹40,000.

Total Mixed Cost:

• Labor: 25 × ₹10,000 × 6 = ₹150,000.
• Overtime: ₹125,000.
• Inventory: ₹40,000.
• Total: ₹315,000 (worse than level in this case—re-evaluate).

Revised Mixed Strategy:

• Base Workforce: 20 workers (2,000 units/month).
• Peak Coverage: Hire 10 temps (1,000 units) for Nov–Feb.
  • Hiring cost: 10 × ₹10,000 = ₹100,000.
• Inventory: 0 (no buffer needed).
• Total Cost:
  • Labor: 20 × ₹10,000 × 6 = ₹120,000.
  • Hiring: ₹100,000.
  • Total: ₹220,000 (same as chase).

Conclusion: Level strategy (₹210,000) is cheapest for Sano Textiles, despite inventory costs, because hiring/firing costs are high.

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Key Takeaways for Exams

1. Always compare strategies: Level vs. chase vs. mixed—calculate total costs to decide.
2. Graph demand vs. production: Visuals (even rough sketches) double your marks.
3. Memorize cost formulas:
   • Inventory cost = (Units) × (Holding cost/unit) × (Time).
   • Hiring/firing cost = (Workers changed) × (Cost/worker).
4. Real-world tie-ins: Link to Daraz (e-commerce), Nabil Bank (loans), or NTC (energy) in answers.
5. Gantt charts > words: For scheduling, draw a timeline even if not asked.
6. Transportation model: Know northwest corner rule for quick calculations.