Supply chain management hinges on demand forecasting. One critical aspect that often trips up professionals is dependent demand. Unlike independent demand, which stems from customer orders, dependent demand ties directly to the production of another item. If I assemble a car, the demand for tires depends on how many cars I produce. This article dives deep into dependent demand, its mathematical foundations, real-world applications, and how businesses optimize it.
Table of Contents
What Is Dependent Demand?
Dependent demand occurs when the need for an item derives from the demand for a higher-level product. For example, if a bicycle manufacturer receives an order for 100 bikes, the demand for wheels, pedals, and chains depends on that order. This contrasts with independent demand, where items sell directly to consumers without reliance on another product.
Key Differences Between Dependent and Independent Demand
| Aspect | Dependent Demand | Independent Demand |
|---|---|---|
| Source | Derived from another product | Comes from customer orders |
| Forecasting Method | MRP (Material Requirements Planning) | Statistical forecasting |
| Example | Car engines for an automobile plant | Retail sales of smartphones |
The Role of MRP in Managing Dependent Demand
Material Requirements Planning (MRP) systems calculate dependent demand by breaking down production schedules into component needs. If I produce 500 laptops, MRP determines how many motherboards, screens, and keyboards I require.
The MRP Calculation Process
- Master Production Schedule (MPS): Defines how many end products to manufacture.
- Bill of Materials (BOM): Lists all components needed for each product.
- Inventory Status: Checks current stock levels.
- Net Requirements Calculation: Determines what must be ordered or produced.
The formula for net requirements is:
Net\:Requirement = Gross\:Requirement - On\:Hand\:Inventory - Scheduled\:ReceiptsExample Calculation
Suppose I need to produce 200 tables, and each table requires 4 legs. If I already have 300 legs in stock and 100 arriving next week:
Gross\:Requirement = 200 \times 4 = 800\:legs Net\:Requirement = 800 - 300 - 100 = 400\:legsI must order 400 more legs to meet production needs.
Dependent Demand vs. Just-in-Time (JIT)
Some argue that Just-in-Time (JIT) systems eliminate the need for dependent demand planning. However, JIT still relies on knowing component dependencies—it just minimizes inventory. If I use JIT for car manufacturing, I still need to know how many engines per car. The difference lies in timing and stock levels.
Common Pitfalls in Dependent Demand Management
- Inaccurate BOMs: If my Bill of Materials is wrong, all dependent demand calculations fail.
- Lead Time Misestimation: Delays in component deliveries disrupt production.
- Demand Volatility: Sudden changes in independent demand cascade down.
Advanced Techniques: Dynamic Lot Sizing
Economic Order Quantity (EOQ) doesn’t always fit dependent demand. Instead, I might use:
- Wagner-Whitin Algorithm: Optimizes ordering over multiple periods.
- Silver-Meal Heuristic: Balances setup and holding costs dynamically.
The Wagner-Whitin model minimizes total costs over time:
C_t = \min_{1 \leq k \leq t} \left( S + h \sum_{i=k}^{t-1} (i - k) D_i + C_{k-1} \right)Where:
- C_t = Minimum cost up to period t
- S = Setup cost
- h = Holding cost per unit
- D_i = Demand in period i
Real-World Case: Automotive Manufacturing
A U.S. carmaker needs 2,000 sedans next month. Each sedan requires:
- 1 engine
- 4 tires
- 1 transmission
If suppliers take two weeks to deliver engines, MRP must account for this lead time. Missing this detail halts production.
Conclusion
Dependent demand is the backbone of efficient supply chains. By mastering MRP, avoiding common mistakes, and using advanced lot-sizing techniques, I ensure smooth operations. Whether in automotive or electronics, understanding these principles keeps production lines moving.
