Inventory Management & Just-in-Time (JIT): EOQ, ABC, JIT, Safety Stocks

TAKEAWAYS:

• EOQ balances ordering costs vs. holding costs to minimize total inventory costs using the formula $EOQ=\sqrt{\frac{2DS}{H}}$, where $D$ = demand, $S$ = ordering cost, $H$ = holding cost.
• ABC analysis classifies inventory by value (A = 70-80% value, 10-20% items; C = 5-10% value, 50-60% items) to prioritize control efforts.
• JIT eliminates waste by producing only what is needed, when it is needed, using pull systems (e.g., Kanban cards) and supplier partnerships.
• Safety stock prevents stockouts but increases holding costs; its level depends on demand variability and lead time.
• Inventory turnover ratio ($\frac{\text{Cost of Goods Sold}}{\text{Average Inventory}}$) measures efficiency—higher ratios (e.g., 8–12 for retail) indicate better management.
• Real-world trade-offs: JIT reduces holding costs but requires reliable suppliers (e.g., Daraz’s warehouse automation) and flexible production (e.g., NTC’s spare parts inventory).

1. Inventory Management Fundamentals

Inventory is an asset that ties up cash but enables sales. Poor management leads to stockouts (lost sales) or overstocking (high storage costs). Key metrics:

• Inventory Turnover: Measures how quickly inventory is sold. $$\text{Turnover}=\frac{\text{Cost of Goods Sold (COGS)}}{\text{Average Inventory}}$$ Example: A Kathmandu electronics shop sells goods worth Rs. 5,000,000 annually with average inventory of Rs. 625,000. Its turnover is $\frac{5,000,000}{625,000}=8$ times/year.

• Days Sales of Inventory (DSI): Average days inventory sits before sale. $$DSI=\frac{365}{\text{Turnover}}=\frac{365}{8}=45.6\text{ days}$$ Goal: Reduce DSI without causing stockouts (e.g., Daraz aims for <30 days).

IMAGE: warehouse inventory racking system | Labelled diagram showing FIFO/LIFO flow, safety stock zone, and ABC classification zones

2. ABC Analysis: Prioritizing Inventory

Not all items require equal attention. ABC analysis categorizes inventory by annual consumption value (not quantity) into three classes:

Example: For a Nepali textile shop (e.g., Fashion Xpress in Thapathali):

• A-items: Silk fabrics (Rs. 2,000,000/year, 15% of items).
• B-items: Cotton blends (Rs. 300,000/year, 30% of items).
• C-items: Buttons (Rs. 50,000/year, 55% of items).

Action: Focus JIT on silk fabrics; order buttons in bulk.

3. Economic Order Quantity (EOQ) Model

The EOQ model determines the optimal order quantity that minimizes:

1. Ordering costs (fixed cost per purchase order, e.g., Rs. 500 for Daraz supplier coordination).
2. Holding costs (storage, insurance, obsolescence; typically 10–25% of inventory value/year).

Formula:

$$EOQ=\sqrt{\frac{2DS}{H}}$$ Where:

• $D$ = Annual demand (units).
• $S$ = Ordering cost per order (Rs.).
• $H$ = Holding cost per unit per year (Rs.).

Worked Example: NTC’s Spare Parts Inventory

NTC needs 3,600 units/year of a specific cable for its Kathmandu exchange. Data:

• Ordering cost ($S$): Rs. 400 per order (supplier setup).
• Holding cost ($H$): 10% of Rs. 20/unit = Rs. 2/unit/year.
• Demand ($D$): 3,600 units.

Calculation: $$EOQ=\sqrt{\frac{2\times 3,600\times 400}{2}}=\sqrt{1,440,000}=1,200\text{ units}$$ Total Cost at EOQ:

• Ordering cost: $\frac{3,600}{1,200}\times 400=1,200$ Rs.
• Holding cost: $\frac{1,200}{2}\times 2=1,200$ Rs.
• Total: Rs. 2,400 (minimum possible).

Mermaid Diagram: EOQ Cost Curve

graph TD
    A["Total Cost"] --> B["Ordering Cost\n(Decreases with larger orders)"]
    A --> C["Holding Cost\n(Increases with larger orders)"]
    B -->|"Minimized at EOQ"| D["Optimal Order Quantity"]
    C -->|"Minimized at EOQ"| D

4. Safety Stock and Reorder Points

Safety stock is extra inventory held to prevent stockouts due to:

• Demand uncertainty (e.g., monsoon season sales spikes for NTC batteries).
• Lead time variability (supplier delays).

Reorder Point (ROP): $$ROP=\text{Average Daily Demand}\times \text{Lead Time}+\text{Safety Stock}$$

Example: A Kathmandu grocery store orders rice with:

• Daily demand: 50 kg.
• Lead time: 7 days.
• Safety stock: 100 kg (to cover supplier delays).

$$ROP=50\times 7+100=450\text{ kg}$$ Action: Place an order when inventory drops to 450 kg.

5. Just-in-Time (JIT) Inventory System

JIT, pioneered by Toyota, aims to eliminate waste by producing only what is needed, when it is needed. Core principles:

1. Pull System: Production triggered by actual demand (e.g., Daraz’s "order now, ship now" model).
2. Zero Defects: Quality built into processes (e.g., NTC’s defect-free cable production).
3. Supplier Partnerships: Long-term relationships with reliable suppliers (e.g., Pathao’s bike rental inventory).
4. Small Lot Sizes: Frequent, small deliveries reduce holding costs.

Advantages of JIT:

• Reduced holding costs (no excess inventory).
• Lower storage space needs (critical for urban businesses like Kathmandu cafes).
• Higher quality (defects exposed immediately).

Disadvantages:

• Vulnerable to disruptions (e.g., COVID-19 supply chain breaks).
• Requires supplier reliability (e.g., NTC’s JIT for spare parts failed during the 2015 earthquake).
• High setup costs for automation (e.g., Daraz’s warehouse robots).

IMAGE: JIT production line | Labelled diagram showing Kanban cards, pull system arrows, and supplier integration

6. JIT vs. Traditional Inventory Systems

7. Real-World Applications in Nepal

Example 1: Daraz’s Warehouse Automation

• Idea Used: JIT + ABC Analysis
• How: Daraz uses robotics and AI to:
  • Classify inventory into A/B/C (e.g., electronics = A, household items = C).
  • Pull orders from suppliers only when a customer buys (reducing holding costs by 30%).
  • Dynamic safety stock: Adjusts based on monsoon season demand spikes.

Example 2: NTC’s Spare Parts Inventory

• Idea Used: EOQ + Safety Stock
• Problem: Before 2015, NTC stored 6 months’ worth of spare parts, tying up Rs. 200 million.
• Solution: Applied EOQ to high-value parts (e.g., fiber optic cables) and set safety stock for 15 days of demand.
• Result: Reduced inventory by 40% while maintaining 99.8% service levels.

Example 3: Kathmandu Traffic Management (Indirect JIT)

• Idea Used: Flow Efficiency (Like JIT Pull)
• How: The Kathmandu Metropolitan City uses real-time traffic data to:
  • Adjust signal timings (like a JIT production line balancing flow).
  • Reduce "waiting time" (inventory) for vehicles at intersections.
• Outcome: Cut average travel time by 20% on busy routes (e.g., Thapathali to Kantipath).

8. Worked Example: EOQ for a Nepali Retailer

Scenario: Sano Retail in Lalitpur sells 5,000 units/year of a popular snack. Data:

• Cost per unit: Rs. 100.
• Ordering cost: Rs. 200 per order.
• Holding cost: 20% of inventory value/year.

Step 1: Calculate EOQ $$H=20\%\times 100=\text{Rs. }20/\text{unit/year}$$ $$EOQ=\sqrt{\frac{2\times 5,000\times 200}{20}}=\sqrt{100,000}=316\text{ units (round to 300)}$$

Step 2: Calculate Total Cost at EOQ

• Number of orders: $\frac{5,000}{300}=16.67$ (17 orders).
• Ordering cost: $17\times 200=\text{Rs. }3,400$.
• Average inventory: $\frac{300}{2}=150$ units.
• Holding cost: $150\times 20=\text{Rs. }3,000$.
• Total cost: Rs. 6,400.

Step 3: Compare with Bulk Ordering (1,000 units)

• Orders: 5.
• Ordering cost: $5\times 200=\text{Rs. }1,000$.
• Holding cost: $\frac{1,000}{2}\times 20=\text{Rs. }10,000$.
• Total cost: Rs. 11,000 (higher than EOQ).

Conclusion: Ordering 300 units at a time saves Rs. 4,600 annually.

9. Inventory Management in Budgeting

Inventory appears in master budgets as:

1. Production Budget: Links sales forecasts to raw material needs.
2. Cash Flow Budget: Inventory purchases affect cash outflows.
3. Budgeted Income Statement: COGS depends on inventory levels.

Example: Himalayan Beverages forecasts:

• Sales: 20,000 bottles/month.
• Desired ending inventory: 5,000 bottles.
• Beginning inventory: 3,000 bottles.

Required Production: $$\text{Production}=\text{Sales}+\text{Ending Inventory}-\text{Beginning Inventory}=20,000+5,000-3,000=22,000\text{ bottles}$$

Exam Tip: How This Unit is Tested

1. Calculations (40% of marks):

   • EOQ, ROP, safety stock, inventory turnover.
   • Tip: Always show all steps in calculations (e.g., break down $H$ into unit cost × holding rate).

2. Definitions (20%):

   • Differentiate JIT vs. traditional inventory, ABC vs. EOQ, safety stock vs. buffer stock.
   • Tip: Use real examples (e.g., "Like Daraz’s pull system vs. NTC’s old push system").

3. Scenario Analysis (30%):

   • Given sales data, calculate optimal order quantities or budgeted inventory.
   • Tip: Draw a simple table for ABC analysis or a flowchart for JIT steps.

4. Advantages/Disadvantages (10%):

   • Compare JIT vs. traditional systems or EOQ vs. bulk ordering.
   • Tip: Use Nepali business examples (e.g., "NTC’s JIT failure during earthquakes").

Common Mistakes to Avoid:

• Ignoring units in EOQ (e.g., mixing Rs. and units).
• Forgetting safety stock in ROP calculations.
• Confusing holding cost (per unit/year) with purchase cost (per unit).

Final Mermaid: Accounting Cycle with Inventory

flowchart TD
    A["1. Sales Forecast"] --> B["2. Production Budget\n(Links to inventory needs)"]
    B --> C["3. Purchase Budget\n(EOQ determines order size)"]
    C --> D["4. Cash Flow Budget\n(Inventory purchases = cash outflow)"]
    D --> E["5. Budgeted Income Statement\n(COGS = Opening + Purchases - Closing)"]
    E --> F["6. Actual Results\n(Compare to budget for variances)"]