Multi-Echelon Multi-product Inventory Strategy in a Steel Company

Transcription

1 Multi-Echelon Multi-product Inventory Strategy in a Steel Company Juan Iocco Advisor: Amanda J. Schmitt, PhD May 21, 2009 MIT, ESD Master of Engineering in Logistics

2 Agenda Introduction Ternium s Problem Ternium s Three-Echelon System Theory Background: Four Models Evaluated Method applied Simulation Alternative Methods Results Inventory Allocation Best Solutions Sensitivity Analysis and Method Comparisons Conclusions 2

3 Ternium s Problem Ternium : multi-product three-echelon production system. Safety Stock only first in the stage. Customers need reduced service time and increased service level Thus: Increased Safety Stock is needed However: Ternium needs reduced Safety Stocks Costs. There is no analytical solution for determining multi-echelon distribution systems. 3

4 Objective Determine where to allocate safety stocks. Compare alternative methods results. 4

5 Ternium s Three Downstream Echelons Iron ore / Coal Coated Steel Slabs /Hot Rolled Steel Thesis Objective Photographs courtesy of Ternium Co. Cold Rolled Steel Customized Coated Steel

6 Ternium s Three Downstream Echelons Iron ore / Coal Coated Steel Slabs /Hot Rolled Steel Thesis Objective Photographs courtesy of Ternium Co. Cold Rolled Steel Customized Coated Steel

7 Ternium s Three-Echelon System Echelon: Cold Rolled Coated Steel Customized Steel Coated Steel Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 Product.. Product.. Product.. Product.. Product n ( 10,000 SKU) ( 15,000 SKU) ( 25,000 SKU) Customers

8 3-Echelon System Selected Case Echelon: Cold Rolled Coated Steel Customized Steel Coated Steel Customers

9 Multi-Echelon System Parameters Lead Time: 3 weeks, 2 weeks and 1 week (constant) Service Time < 1 week Service Level >= 95% Demand: normally distributed, independent, Coefficient of Variance = Holding Cost Scenarios ( L,L,L, L,L,H and L,H,H ) 3 Bounded Demand Scenarios: 50%, 100% and

10 Theory: options evaluated Simulation Option Alternative Method Options Single-Echelon Model Echelon-Inventory Model Graves-Willems Model 11

11 Simulation Model characteristics (general for all the models) facilities distribution three-echelon system Base-stock model Model procedure Run each scenario1,000 periods (weeks) Program to solve automatically 1,000-1,400 inventory allocation scenarios Output: average period Service Level, Holding Cost and On-Hand Safety Stock 12

12 Single Echelon-Model (Split System) Model procedure Each strategy determine where to allocate inventory. The system is considered split up in sections. Output: Base-stock Levels Strategy Customers 13

13 Model procedure Echelon Inventory Model Assumes a single decision maker and access to safety-stock information. Calculate the echelon position safety stock (ESS). Output: Base-Stock Level at every echelon Customers ESS3 ESS2 ESS1 Source: Schimchi-Levi D., Chen X. & Bramel J. (2005) 14

14 Model procedure Graves-Willems Model Solve spanning tree inventory systems. Inputs: Holding Costs, Lead Time, customer Service Time, demand parameters. Output: facilities Service Time (Base-stock levels) 15 Source: Graves S. C., & Willems S. P. (2000)

15 Solution Determine Where to Allocate SS Construct a simulation model. Scenarios, Best solution and Sensitivity Analysis. Compare alternative methods results Calculate base-stock levels with alternative methods. Simulate and compare results. 16

16 Results: Allocation Strategies 18 k sim = SS LT i ij σ ij - SS = Safety Stock at Echelon i, facility j - LT i = Lead Time of Echelon i - σ ij = Demand standard deviation at echelon i, facility j.

17 19 Service Level-Holding Cost Efficient Frontier

18 20 Service Level - On-Hand Inventory

19 21 Bounded Demand Scenarios

20 Model Comparison Difference in holding cost against best solutions found with simulation. Alternative Methods % Variation Holding Cost Echelon Inventory Model 8.2% Graves-Willems Model 9.7% Single-Echelon % Single-Echelon % Single-Echelon % 22

21 Conclusions: Allocation Strategy Simulation worked well, but results are sensitive to cost parameter. Best results presented. General behavior related to cost. Similar behavior changing with risk-pooling factors (more correlation or serial 3-Echelon systems). Deep understanding of the demand is critical. Strong assumptions made about cost and correlation. Characterization of demand is a must. Keep safety stock just in the first stage is a bad decision. Strategy showed to be 35% more expensive. 23

22 Conclusions: Allocation Strategy Bounded demand strategies are good alternatives. We showed that cost reductions ranging 20% are possible. Important to explore other contract clause alternatives (buy-back, cost shearing, etc). On-hand inventory as only metric is not good to manage inventories. Inventory turnover and total inventories are classic metrics. It tend to make people thinks that On-Hand inventory and Service Level are proportionally related. 24

23 Conclusions: Method Comparison Simulation allowed to find the best results. Build the Efficient Frontier (and find the best results). Compare the different methods. But, find the best results was not easy, could be complicated to apply in bigger ME systems. Echelon-inventory and Graves Willems method produced the lowest alternative holding-cost results. In average, were the closest method to the best solution. In the case of the echelon-inventory model, it was needed to tuned the parameters with the simulation to get SL close to 95%. 25

24 Conclusions: Method Comparison Single-Echelon methods do not perform well as a standalone method. We showed that average extra cost was far from other simulation (16 to 35%). Decide which single-echelon strategy apply is difficult. 26

25 References Bertsimas, D., and Freund, R. (2004). Data, Models, and Decisions: the Fundamentals of Management Science. Belmont, MA: Dynamic Ideas. Chacon, G., and Terwiesch, C. (2006). Matching Supply with Demand: An Introduction to Operations Management (2nd ed.). New York, NY: McGraw Hill Companies. Chopra, S., and Meindl, P. (2007). Supply Chain Management (3rd ed.). Upper Saddle River, NJ: Pearson Education, Inc. Clark, A. J., and Scarf, H. (1960). Policies for a Multi-Echelon Problem. Management Science, 6 (4), Graves, S. C., and Willems, S. P. (2000). Optimizing Strategic Safety Stock Placement in Supply Chains. Manufacturing & Services Operations Management, 2 (1), Graves S. C., and Willems, S. P. (2003). Erratum: Optimizing Strategic Safety Stock Placement in Supply Chains. Manufacturing & Service Operations Management, 5 (2), Hillier, F., and Lieberman, G. (2005). Introduction to Operations Research (8th ed.). New York, NY: McGraw Hill Companies. Pagh, J.D., and Cooper, M. (1998). Supply Chain postponement and speculation strategies: How to choose the right strategy. Journal of Business Logistics, 19 (2). Silver, E., Pyke, D. and Peterson, R. (1998). Inventory Management and Production Planning and Scheduling (3rd ed.). Hoboken, NJ: John Wiley & Sons, Inc. Simchi-Levi, D., Chen X. and Bramel, J. (2005). The Logic of Logistics (2nd ed.). New York, NY: Springer. Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E. (2008). Designing and Managing the Supply Chain: Concepts, Strategies, and case studies (3rd ed.). New York, NY: McGraw Hill Companies. Snyder, L. V. (2006). User s Guide for BaseStockSim. Software Version 2.4. [WWW document]. URL (visited 2009, May 2). PowerChain Inventory Academic version 3.0 Manual. [WWW document]. URL (visited 2009, May 2). Taylor, D. A., (2004). Supply Chains: A Manager s Guide. Boston, MA: Addison-Wesley. van Houtum, G. J. (2006). Multi-Echelon Production/Inventory Systems: Optimal Policies, Heuristics, and Algorithms, INFORMS Tutorials in Operations Research. Winston, W. L. (2004). Operations Research: Applications and Algorithms (4 27 th ed.). Belmont, CA: Thomson Brooks/Cole.

26 Any questions? Thank you! 28 Photograph courtesy of Ternium Co.