A Global Supply Chain Study for Specialty Chemicals

1. A Global Supply Chain Study for Specialty Chemicals

2. Project Participants • University of Houston • Sukran Kadipasaoglu, Associate Professor • Yavuz Acar, Ph.D. student • University of North Carolina at Wilmington • Cem Canel, Professor • Chevron-Oronite • Peter Schipperijn, Global Supply Chain Specialist

3. Purpose of the Study • Model and simulate the global Chevron-Oronite Phenate supply chain • Assess the impact of uncertainties on performance: • Demand uncertainty • Supply reliability • Lead time variability • Any others… • Analyze inventory level, cost and demand fulfillment trade-offs

4. Performance Indicators • Inventory levels • Inventory carrying costs • Transportation costs • Manufacturing costs • Demand fulfillment • % of demand filled from stock

5. Techniques to be utilized: • Simulation • Study the behavior of supply chain over time • Impact of demand/supply/lead time variability on performance • Optimization • Make periodic decisions that are to be input into simulation • Stock transfer determination among Chevron-Oronite plants (monthly) • Production scheduling (weekly)

6. Chevron-Oronite Global Supply Chain Model Inputs • 4 Plants, OP, MAUA, SMP, GV. • Demand • Production Rate • Production Costs • Maua costs • Shipment Costs • Tariffs • Inventory holding cost, 1% of production cost per month (*end-of-month balance) • Transportation Lead times among plants • Maximum inventory limit

7. Chevron-Oronite Stock Transfer Model (MIP) • Monthly • Determines stock transfer requirements among plants. • Minimizes transportation, production, inventory costs, and unmet demand. • 6 month horizon in monthly time buckets • Max inv. limit set according to monthly demand. • Stock transfer mode is MV or ISO (<=300, >300 respectively) • No bulk out of Brazil – all shipments have ISO costs. • Input into the weekly production schedule generation (another MIP)

8. Chevron-Oronite Production Scheduling Model (MIP) • Weekly • Assigns products to reactors in each of the plants. • Schedule is generated for 12 weeks. • Max 4 products are made in each plant in one week. • Minimum run length for each product is 4 days. • Max inv. limit set similarly. • Input into the simulation.

9. Simulation • Reads shipment & production schedule • Reads demand based on arrival distribution • Produces to schedule, increases inventory • Makes stock transfers as planned • Incurs costs as it runs..

10. Simulation cont’d. • When an order arrives • If inventory is available • meets demand • updates inventory level

11. Simulation cont’d. • If Inventory is not sufficient • Checks continually for availability • After 1 week, considers unmet demand to be “backlog” • Keeps checking for availability • When inv. becomes available backorders have higher priority • Checks for a max. duration of 3 weeks, after that it becomes “unmet demand” • SHOULD WE ASSIGN A COST TO THIS? • WHAT DO WE DO IF STOCK TRANSFER IS INCOMPLETE, WAIT FOR NEXT MONTH OR SHIP WHEN ITEM IS AVAILABLE?

12. Some Results for Model Verification • 6 month monthly shipment & production • 6 month, weekly production schedule • Simulation results

13. Modeling Procedure Generate Monthly Production & Stock Transfer Plans – 6 months Generate Weekly Production Schedule – 12 weeks Simulate week’s production, demand, collect statistics, record ending inventory Read ending inventory, read backlog, regenerate weekly production schedule Regenerate Monthly Production & Stock Transfer Plans – 6 months

14. Business Rules Incorporated into the Models • A machine can produce up to 2 products in one week. • Minimum production run is 2 days. (These reflect changeover limitations)

15. 1 Simulation Stage • Start with known demand – using past data, no uncertainty. • Use given lead times – no uncertainty • Use given production rates – no uncertainty • Validate the global Phenate supply chain model

16. 2 Simulation Stage • Add demand uncertainty • Experiment with various safety stock levels to see the trade-offs. • Inventory carrying costs • Transportation costs • Demand fulfillment • Manufacturing costs • Sensitivity of costs to various levels of demand uncertainty

17. 3 Simulation Stage • Add lead time uncertainty • Keep experimenting with safety stock levels • HOW TO DEFINE TRANSIT TIME UNCERTAINTY? • Assess the trade-offs with different safety stock levels • Inventory carrying costs • Transportation costs • Demand fulfillment • Manufacturing costs • Sensitivity of costs to various levels of lead-time uncertainty

18. 4 Simulation Stage • Add poduction rate uncertainty • Keep experimenting with safety stock levels • HOW TO DEFINE PRODUCTION RATE UNCERTAINTY? • Frequency and length of uplanned downtime • Assess the trade-offs with different safety stock levels • Inventory carrying costs • Transportation costs • Demand fulfillment • Manufacturing costs • Sensitivity of costs to various levels of production uncertainty

19. Final Comments • Progressively adding uncertainties help better assess the impact of each. • Simulating an “optimum” solution over time under various uncertainties reveal how much these uncertainties hamper the implementation of an “optimum” solution. • Observed simulation results will lead to better determination of operational parameters (safety stock levels for ex.) which can then be input back into the optimization.

20. Experimental Conditions - incomplete