Class 3

• as you get bigger faster and faster products should move closer to the customer
• Middle mile = Whatever precedes the last stop before the customer; more scale and predictability. I don’t know what last address I’ll drop off, but I aggregate to the last point of certainty.

Why is today different than WebVan?

• The transpo and labor is mostly the same
• The tech infrastructure is different and the customer behavior and product is slightly different.
• Diminusment and tax arbitrage

Why doesn’t Amazon mimic Dominoes?

• Dominoes reduces PPE and increases transportation investment, so every takeout order decreases per order PPE ratio
• Delivery as a need of scaling.

Disruption: Probability + length (time to recovery) + impact

• Don’t ignore disruption; a little over estimation helps you w/ recurrent risk (resilience also assists w/ recurrent risk)
• Time to recovery

Risk

• Re-insurers, pool risk at a higher level. Where and what you pool is a function of risk.

Supply chain margin of $19 is divided across two players, 11 to the publisher and smaller to retailer to 8… so the retailer becomes too conservative. The publisher wants the retailer to be aggressive. My real problem is missed out opportunities.

Let's analyze the publisher-retailer partnership through a supply chain optimization lens

The Supply Chain Coordination Problem

Consider a specific example with these parameters:

• Retail price (p) = $30
• Publisher's cost (c) = $8
• Retailer's cost (handling, shelf space) = $2
• Total margin available = $20 ($30 - $8 - $2)

Current Suboptimal State

In the current scenario ($11 to publisher, $8 to retailer):

• Publisher receives: $11 per book
• Retailer receives: $8 per book
• This creates risk-averse behavior by the retailer, leading to under-ordering

The Double Marginalization Problem

When each party optimizes independently:

• Retailer orders conservatively to minimize risk (e.g., 100 units instead of optimal 150)
• Lost sales opportunity = 50 units × $20 margin = $1,000 potential profit lost

Optimal Solution: Risk-Sharing Contract

A better arrangement would include:

• Lower wholesale price (e.g., $15 instead of $19)
• Buyback provision: Publisher buys unsold books at 80% of wholesale price
• Revenue sharing: Publisher gets 60% of retail revenue, retailer gets 40%

Numerical Example of Optimal Structure

Under this new arrangement:

• Retailer orders 150 units (optimal quantity)
• If 120 units sell:
• Revenue: 120 × $30 = $3,600
• Publisher receives: $2,160 (60% of revenue)
• Retailer receives: $1,440 (40% of revenue)
• Unsold units (30) are bought back at $12 each

This structure aligns incentives because:

• Retailer is protected against downside risk through buyback provision
• Publisher benefits from increased orders and shares in upside
• Both parties are incentivized to maximize total supply chain profit

The result is higher total profits and better market service levels, demonstrating the power of coordinated supply chain contracts.

Academic Perspectives on Supply Chain Coordination

Several notable economists and business scholars have studied similar coordination problems:

• Gerald Cachon (Wharton) - Pioneered research on supply chain contracts and coordination mechanisms, particularly focusing on revenue-sharing contracts in the video rental industry
• Paul Milgrom (Stanford) - Contributed to contract theory and mechanism design, showing how proper incentive structures can align different parties' interests
• Jean Tirole (Nobel Laureate) - Developed theories on vertical relationships and how firms can optimize contracts to reduce inefficiencies

Key Academic Insights

• The "newsvendor problem" is a classical model that helps determine optimal order quantities under uncertainty
• Contract theory suggests that well-designed incentive structures can help achieve supply chain coordination
• Game theory applications show how different contract types (revenue-sharing, buyback, quantity discount) can achieve optimal outcomes

Practical Lessons

• Supply chain contracts should balance risk-sharing with incentives for optimal behavior
• Information sharing between parties can significantly reduce inefficiencies
• Flexible contracts that adapt to market conditions often outperform rigid arrangements

Moral Hazard and Risk-Sharing Considerations

Moral hazard emerges as a critical factor in supply chain contracts when risk-sharing mechanisms are implemented:

• Overprotection Risk: If retailers are too protected from downside risk, they might order recklessly or mismanage inventory
• Effort Reduction: High protection levels could reduce retailer's motivation to:
• Market products effectively
• Maintain optimal store displays
• Provide quality customer service

Risk-Sharing Based on Margins

The appropriate level of risk-sharing should be proportional to margins and control:

• High-margin scenarios: Parties can absorb more risk, enabling more aggressive growth strategies
• Low-margin scenarios: Risk-sharing becomes critical as parties have less buffer for losses

Behavioral Impact of Risk Distribution

When risk allocation is imbalanced:

• Too much risk on low-margin party:
• Leads to ultra-conservative decisions
• May cause market exit or reduced participation
• Too much protection for high-margin party:
• Can create operational inefficiencies
• May lead to opportunistic behavior

The optimal contract design must balance risk-sharing with accountability to prevent moral hazard while maintaining operational efficiency.

Here's a visual representation of the supply chain coordination dynamics:

And here's a visualization of the risk-sharing dynamics:

This illustrates how finding the right balance in risk-sharing leads to optimal outcomes, while imbalances in either direction can create inefficiencies.

Detailed Numerical Models

Unoptimized Model Example (Current State)

Consider a specific book with:

• Retail price: $29.99
• Publisher's production cost: $7.50
• Retailer's handling cost: $2.25
• Current wholesale price to retailer: $18.99

With 1000 units:

• Publisher revenue: $18.99 × 1000 = $18,990
• Publisher profit: ($18.99 - $7.50) × 1000 = $11,490
• Retailer revenue: $29.99 × 700 (typical sell-through) = $20,993
• Retailer profit: ($29.99 - $18.99 - $2.25) × 700 = $6,125
• Unsold inventory cost: $18.99 × 300 = $5,697 loss
• Net retailer profit: $428 ($6,125 - $5,697)

Optimized Model Example (Risk-Sharing)

Revised structure:

• Lower wholesale price: $15.99
• Buyback at 80%: $12.79 per unit
• Revenue share: 65% publisher, 35% retailer

With 1000 units:

• Total sales: 850 units (higher due to more aggressive ordering)
• Total revenue: $29.99 × 850 = $25,492
• Publisher's share: $16,570 (65%)
• Retailer's share: $8,922 (35%)
• Buyback cost: $12.79 × 150 = $1,919
• Net publisher profit: $14,651
• Net retailer profit: $7,003

Key Performance Metrics Comparison

This optimized model demonstrates:

• 81.7% increase in total supply chain profit
• 21.4% improvement in sell-through rate
• 16.7% increase in publisher profit margin
• 25.5% increase in retailer profit margin

These specific numbers illustrate how the optimized risk-sharing model creates substantially better outcomes for both parties while increasing overall market efficiency.

Detailed Financial Flow Analysis

1. Initial Cost Structure (Per Unit)

2. Volume Analysis (1000 Unit Order)

Traditional Model:

• Total Order: 1000 units at $18.99 = $18,990 initial cost
• Units Sold: 700 (70%) at $29.99 = $20,993 revenue
• Units Unsold: 300 (30%) = $5,697 loss ($18.99 × 300)
• Net Retailer Revenue: $20,993 - $18,990 - ($2.25 × 700) = $428

Optimized Model:

• Total Order: 1000 units at $15.99 = $15,990 initial cost
• Units Sold: 850 (85%) at $29.99 = $25,492 revenue
• Units Unsold: 150 (15%) = $1,919 buyback cost ($12.79 × 150)
• Revenue Share: Publisher (65%) = $16,570, Retailer (35%) = $8,922

3. Revenue Flow Diagram

4. Profit Comparison

5. Process Flow

Strategic Operations Theories in Benetton's Model

1. Postponement Strategy

• Delayed Differentiation:
• Kept garments in undyed state until later stages
• Allowed for mass customization while maintaining economies of scale
• Reduced forecast errors by delaying color decisions

2. Network Manufacturing

• Distributed Production Network:
• Utilized multiple small workshops instead of large factories
• Created flexible capacity through contractor relationships
• Maintained central control while distributing production risk

3. Quick Response System

• Information Technology Integration:
• Advanced POS data collection
• Real-time inventory tracking
• Automated reordering systems

4. Vertical Integration Theory

• Strategic Control Points:
• Owned critical dyeing facilities
• Controlled design and distribution
• Maintained quality standards through process ownership

5. Supply Chain Optimization

• Operational Flexibility:
• Variable batch sizes
• Dynamic production scheduling
• Rapid response to market changes

This model demonstrates how Benetton integrated multiple operations theories to create a responsive and efficient supply chain system.

Results of Implementation

These innovative approaches fundamentally changed the fashion industry's operational paradigm and created a new model for fast-fashion supply chains.