The Emergence of Stability in Diverse Supply Chains Self- Workshop - - PowerPoint PPT Presentation

the emergence of stability in diverse supply chains
SMART_READER_LITE
LIVE PREVIEW

The Emergence of Stability in Diverse Supply Chains Self- Workshop - - PowerPoint PPT Presentation

The Emergence of Stability in Diverse Supply Chains Self- Workshop 2004 * Owen Densmore Xerox, Apple, Sun Retired - Santa Fe http://complexityworkshop.com http://friam.org http://friam.org Talk Example Agent Based Models


slide-1
SLIDE 1

The Emergence of Stability in Diverse Supply Chains

Self- Workshop 2004 Owen Densmore Xerox, Apple, Sun “Retired” - Santa Fe

*

http://complexityworkshop.com http://friam.org http://friam.org

slide-2
SLIDE 2

2

Talk

  • Example Agent Based Models
  • Discuss SFI ValueNet Simulation
  • Self-* “Three Step Plan”
  • Future Directions

(Remember this is a workshop!)

slide-3
SLIDE 3

3

History

  • Santa Fe Institute Business Network
  • 2001: ValueNet Team
  • Project:
  • Repast Beer Game Model
  • Bullwhip Effect
  • Human Decision Making

Model

  • Goal: Explore Impact of

Mesh & Visibility on Human Decision Making

slide-4
SLIDE 4

4

The Beer Game

slide-5
SLIDE 5

5

Game Play

  • Each round of play, each Player (Supplier)
  • 1. Refresh Inventory from Received orders
  • 2. Get customer Demand
  • 3. Supply demand from Inventory
  • 4. Make Order to update Inventory
  • Delay: 1 week for order processing, 2 weeks for shipping.
  • Goal: Minimize Cost = $0.50 * stock + $2.00 * backorder

1-Received 4-Ordered 3-Supply 2-Demand Inventory

}

Three Week Delay

slide-6
SLIDE 6

6

Sterman 4 Parameter Model

Goal: Minimize Cost = $0.50 * stock + $2.00 * backorder (Note: model human behavior rather than optimize cost) Ordert = Expected Demandt + Inventory Adjustment Expected Demandt+1 = Θ Demandt + (1 - Θ) Expected Demandt Inventory Adjustment = α (Desired Inventory - Inventory) + β*α (Desired Pipeline - Pipeline) Desired Inventory = Q - β * Desired Pipeline

slide-7
SLIDE 7

7

The 4 Ordering Parameters

Θ : Controls expected demand update rate α : Controls desired inventory vs. actual inventory β : Controls desired pipeline vs. actual pipeline (Ratio of importance of pipeline vs stock) Q : Desired inventory + relative desired pipeline (Note: Inventory = stock - backorder, can be negative)

slide-8
SLIDE 8

8

Model Ordering Rules

The Beer Game agent behavior is entirely in the ordering rules, which contain 4 parameters used in two phases:

  • 1. Predict Demand (Θ)
  • 2. Create an order (Demand, α, β, Q)

1 - Expected Demandt = Θ Demandt-1 + (1 - Θ) Expected Demandt-1 2 - Ordert = Expected Demandt + α ( Q - Inventoryt - β Pipelinet ) Completely Deterministic -- no random components. Customer Orders: 4 4 4 4 8 8 . . . .

slide-9
SLIDE 9

9

Bullwhip Effect!

  • Many parameter sets lead to extreme volatility.
  • Value Net: Model Visibility (RFID) & Mesh (Internet)
slide-10
SLIDE 10

10

Visibility Creates Stability

None Adjacent

slide-11
SLIDE 11

11

Adjacent Customer Average

slide-12
SLIDE 12

12

Mesh Creates Stability

slide-13
SLIDE 13

13

Why?

Question: “Why do increased visibility and mesh topology settle into non-volatile behavior?”

  • Visibility: Increasing

Knowledge

  • Mesh: Increasing

Choice

  • Future directions: Auctions, Brokers, ..
  • Until ...
slide-14
SLIDE 14

14

Self Star!

  • Would Self-* be a good direction for project?
  • We would be interested in:
  • How measure Organization/Healing
  • How predict ways to increase Organization/Healing
  • New ABM theories and tools (Algebra, “Derivative”, Tools)
slide-15
SLIDE 15

15

Step 1: New, Flexible Model

slide-16
SLIDE 16

16

  • What is Self-*
  • How Predict?

Step 2: Read!

slide-17
SLIDE 17

17

Step 3: Add Analysis Back!

  • Model -- Ft:{parameters} -> {state space}

Resulting Properties { Xi(state space) }

  • Analyze:
  • Parameter Sweeps
  • Entropy
  • S = -Σpi log pi
  • Problem: How determine {pi}?
  • Attractors
  • Ex: In 1 hour first night added point-attractor detection.
  • Self-* Ideas: Self-P, Bounded Algorithms, ...
  • Language: System -> Modeling Language
slide-18
SLIDE 18

18

Conclusion

  • Self-* has prompted us to become more analytic.
  • Help!
  • Quiz: Which models were Self-*? Why?
  • Questions??