Global Manufacturing Research Group Meeting, July 2004, Istanbul. A Global Supply Chain Study for Specialty Chemicals

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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

Global Manufacturing Research Group Meeting, July 2004, Istanbul. Performance Indicators Inventory levels Inventory carrying costs Transportation costs Manufacturing costs Demand fulfillment –% of demand filled from stock

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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)

Global Manufacturing Research Group Meeting, July 2004, Istanbul. Chevron-Oronite Global Supply Chain Model Inputs 4 Plants, OP, MAUA, SMP, GV. Demand Production Rate Production Costs –Maua costsMaua 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

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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 respectively) No bulk out of Brazil – all shipments have ISO costs. –Input into the weekly production schedule generation (another MIP)

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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.

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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..

Global Manufacturing Research Group Meeting, July 2004, Istanbul. Simulation cont’d. When an order arrives –If inventory is available –meets demand –updates inventory level

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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?

Global Manufacturing Research Group Meeting, July 2004, Istanbul. Some Results for Model Verification 6 month monthly shipment & production6 month monthly shipment & production 6 month, weekly production schedule Simulation results

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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 Modeling Procedure

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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)

Global Manufacturing Research Group Meeting, July 2004, Istanbul. Transportation Mode Selection –Order >= 300 mt, ship bulk –20 < Order qty. < 300, ship ISO –No bulk out of Brazil

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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 1

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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 2

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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 3

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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 4

Global Manufacturing Research Group Meeting, July 2004, Istanbul. 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.

Global Manufacturing Research Group Meeting, July 2004, Istanbul. BiasForecast ErrorSafety StockLead Time Uncertainty Production Rate Uncertainty Repl wk wk % of Avg.Dem % of Avg.Dem.1 wk % of Avg.Dem.3 wk % of Avg.Dem.2*Std. FE % of Avg.Dem % of Avg.Dem.1 wk % of Avg.Dem.3 wk % of Avg.Dem.2*Std. FE0010 Experimental Conditions - incomplete