Date post: | 04-Jan-2016 |
Category: |
Documents |
Upload: | stella-hampton |
View: | 219 times |
Download: | 3 times |
Analyzing Supply Chain Performance under Different Collaborative Replenishment Strategies
AIT Masters Theses Competition
Wijitra NaowapadiwatIndustrial Systems Engineering
Asian Institute of Technology
May 18, 2009
Outline
Problem Statement
Collaborative Replenishment Operation Flow
Model Development
Results Analysis
Conclusion and Future Work
Problem Statement
• Fulfill the requirements between CPFR guideline and supply chain collaboration practice
• Study different collaborative replenishment strategies and address mechanism analysis
• Learn impacts of three strategies to the system performance
CPFR® Model (VICS Published 2004)
Collaborative Planning, Forecasting and Replenishment ( CPFR) is guideline that helps the collaborations between supply chain partners
Contribution of this thesis
Develops the sequential diagram which represents the operation flow of collaborative replenishment
Gives an analysis mechanism to approach different collaborative levels
Shows the impact of three strategies to the system performances and provides a better understanding of the supply chain collaboration
1. Develop Time Sequencing Diagram to address operation tasks >> HQ replenishment collaboration >> DC replenishment collaboration >> Store replenishment collaboration
2.Identify Analysis an Input and Expected Output data
3. Develop of the Simulation Model
4. Run the experiment on ARENA under the considered conditions
5.Analyze the impact of Supply chain performance under three Replenishment collaboration strategies on ANOVA
6.Conclusions and Recommendations for future study
Methodology
Simulation ModelProcess flowSequential diagram
Sequential diagrams for collaborative replenishment
HQ_CR DC_CR Store_CR
Three levels of replenishment collaboration between seller and buyer are demonstrated on main collaborativetasks including:Sale forecasting, order forecast, and order generation.
Supply Chain Performances
Customer Service Back Order and Fill rate Shortage Rate
Total Supply Chain Cost Inventory Cost + Transportation Cost
Carrying Shortage
Experiment Design
Experiment 1. The impact of supply chain performances under different CR
Experiment 2. The impact of supply chain performances under economic transportation consideration
Independent Variables Supply Capacity ( Cap) Lead time (LT) Demand Variation ( DV)
Parameters and Variables design
Independent factor Level
1 2 3
CR HQ DC St
CAP μ* bμ* aμ*
DV 0 2 4
LT 1 days 1 week 4 weeks
Note * : a>b>1 and μ is averaging of Customer demand
General Parameters and Basic assumptions Number of product and supplier (single product item, 1 supplier) Number of retailer store ( 1 retailer,2 DCs, 4 stores) Sale Forecast method (Exponential Moving Average, α= 0.2 ) Inventory Policy ( Periodic review weekly, Up-to-level) Simulation run length ( 2800 days, 400 wk 30 replications)
Supply Network
Lead time vs. Impact of supply chain performances under three CR Strategies
Demand Variation vs. Impact of supply chain performances under three CR Strategies
Supply Capacity vs. Impact of Supply chain performances under three CR Strategies
Summary
Level of valued information sharing during order replenishment shows significant impact for supply chain performance
Over all Store CR shows lower total supply chain cost and higher order fulfill rate from simulation results
When limited supply capacity and short lead time, the practitioner may better investment for DC_ CR
Demand uncertainty is high , Store CR is a better choice Regional DC is needed for a cluster Store replenishment
when economic of transportation influences
Future Work
Complexity with replenishment collaboration such as multiple manufacturers collaboration for multiple items
Collaborative Forecasting in supply chain network IT in Order collaborative replenishment
Q&A THANK YOU