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A Global Supply Chain Study for Specialty Chemicals. 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

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Project participants
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


Purpose of the study
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


  • Performance indicators
    Performance Indicators

    • Inventory levels

    • Inventory carrying costs

    • Transportation costs

    • Manufacturing costs

    • Demand fulfillment

      • % of demand filled from stock


    Techniques to be utilized
    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)


  • Chevron oronite global supply chain model inputs
    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


    Chevron oronite stock transfer model mip
    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)


    Chevron oronite production scheduling model mip
    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.


    Simulation
    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..


    Simulation cont d
    Simulation cont’d.

    • When an order arrives

      • If inventory is available

        • meets demand

        • updates inventory level


    Simulation cont d1
    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?


    Some results for model verification
    Some Results for Model Verification

    • 6 month monthly shipment & production

    • 6 month, weekly production schedule

    • Simulation results


    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


    Business rules incorporated into the models
    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)


    Simulation stage

    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


    Simulation stage1

    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


    Simulation stage2

    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


    Simulation stage3

    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


    Final comments
    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.



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