Robust Global Supply Chains

Transcription

1 Strategic t Design of Robust Global Supply Chains Marc Goetschalckx Georgia Institute of Technology Tel. (404) , fax (404) marc.goetschalckx@isye.gatech.edu Credits Interdisciplinary systems engineering Virtual Factory Lab (VFL), Leon McGinnis Manufacturing Research center (MARC), Russell Peak Ph.D. students, Huang, Megahed, Tang Copyright , Marc Goetschalckx, All rights reserved. 1

2 Overview Supply Chains Modeling Based Systems Engineering (MBSE) Strategic Supply Chain Design Strategic and tactical model Algorithm Conclusions Supply Chain Supply Manufacturing Assembly Distribution Customers Copyright , Marc Goetschalckx, All rights reserved. 2

3 US Logistics Costs by Activity: Costs (B$) Year Inventory Transport Administration Total US Logistics Costs as Fraction of GDP: % 16.0% 14.0% Cost as % of GDP 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Year Inventory Transport Logistics Copyright , Marc Goetschalckx, All rights reserved. 3

4 Global Supply Chain Example: Boeing 787 Dreamliner Global Supply Chains Nearly all supply chains are global Longer chains in space and time Influenced by policies of two or more governments Exchange rates Local content Protectionism of goods and jobs Political and property rights stability Copyright , Marc Goetschalckx, All rights reserved. 4

5 Modeling Based Systems Engineering (MBSE) Design and management of systems Formalized, holistic, interdisciplinary Systems have structure and behavior Large variety of systems international space station, Florida wetlands management, healthcare system, supply chains Systems Modeling Language (SysML) Open standard, commercial implementation Descriptive INCOSE, UML, Mathematical Models in Strategic Design Process Copyright , Marc Goetschalckx, All rights reserved. 5

6 Logistics Meta Model (1) Decide Structure Network / Facilities + Channels IT support Systems Decide Behavior Transportation Inventory Transformation Logistics Meta Model (2) With as Objective Minimize total system cost Maximize total system NPV(NCF) Maximize expected profit Tradeoff expected profit and profit variability Copyright , Marc Goetschalckx, All rights reserved. 6

7 Logistics Meta Model (3) Subject to Capacity p y constraints (infrastructure, budget, demand, time) Service level constraints (demand) Conservation of flow constraints (time, space, BOM) Linkage or consistency constraints Substitution constraints (calculation of intermediate variables) Extraneous special conditions Program Lifecycle Cash Flow Diagram (+) $ Flow Breakeven point Total cumulative profit RDT&E time Sunk Cost (-) $ Flow Production Setup Capital investment period Delivery start Capital recovery period Profitable period End of production Copyright , Marc Goetschalckx, All rights reserved. 7

8 Matrix Organization of Planning Modules Strategic Strategic Enterprise Planning Strategic Demand Planning Tactical Master Production and Distribution Planning Tactical Demand Planning Operational Material Requirem. Planning Production Planning Distribution Planning Operational Demand Planning Execution Purchasing Scheduling Vehicle Dispatching Demand Monitoring Strategic Planning and Design Strategic Decisions (configure, structure) Supply chain capital assets, capacity, and technology configuration 5-15 year schedule (planning horizon) Tactical Decisions (evaluate, behavior) Purchasing, production allocations, transportation t ti flow quantities, inventory Transfer prices Yearly or seasonal flows Copyright , Marc Goetschalckx, All rights reserved. 8

9 Common Goal for Supply Chain Design Maximize the World Wide Long Term After Tax Net Cash Flow Net Cash Flow Calculations EBITDA SALES FIXEDCOSTS VARIABLECOSTS NIBT EBITDA INTEREST DEPRECIATION NCFct NIBTct (1 TAXRATEct ) DEPRECIATIONct AMORTIZATIONct C NCFct NCFt c 1 EXRATEct T NCFt NPV NCF t t1 1 df j1 j Copyright , Marc Goetschalckx, All rights reserved. 9

10 Objectives Incorporating Uncertainty Ignore uncertainty mean value problem (MVP), best guess, deterministic Expected value Expected value problem (EVP), stochastic problem (deterministic equivalent problem DEP) max Robust configuration Minimize worst case value (WCVP) max NPV NCF E NPV NCF max min NPV NCF Objective Incorporating uncertainty Bi-criteria: central tendency minus dispersion times penalty Pareto optimality Penalty from risk profile Risk analysis graph Central tendency = expected value, dispersion = standard d deviation ENPV NCF max STDEV NPV NCF Copyright , Marc Goetschalckx, All rights reserved. 10

11 Risk Analysis Graph Robust Supply Chain Example Millions Standard Deviation Millions Expected Value Multi-objective Criteria and Efficiency Envelope Efficiency Frontier Copyright , Marc Goetschalckx, All rights reserved. 11

12 Number of Scenarios and Configuration Robustness A robust supply chain configuration has a high quality for (many) simultaneous scenarios The more scenarios, the better Every scenario grows the model size Scenario probabilities (sampling, black swans) Tactical Model: Problem Assumptions Medium term, master planning Material Flow and Storage Decisions Continuous variables Given Supply Chain Configuration Given capacities and resource capacities Copyright , Marc Goetschalckx, All rights reserved. 12

13 Tactical Model: Objective Variable Costs = Sum of sales revenue minus procurement, transportation, production, resource, inventory, and backorder costs maxsrkpt sqkpt kc p t pc pq tc x ac aq ipt ipt ijpt ijpt jpt jpt is p t io jd p t jtr p t arc ares aq irc ires iq jrt jprt jpt jrt jprt jpt j TR p r AR t j TR p r IR t rc res otq j p rr t jrt jprt jpt bc bq hc iq kptu kptu jpt jpt kc p tt, t2 ut, ut jtr p t Tactical Model: Suppliers Individual and joint-product capacities Procurement = outgoing flow Supplier selection decision p res pq cap i, t ip ipt it pq cap i, p, t ipt ipt pq x i, p, t ipt j ijpt Copyright , Marc Goetschalckx, All rights reserved. 13

14 Tactical Model: Transformers Component product inventory and incoming i flow BOM manufacturing flow balance Individual and joint-product capacities Assembled product inventory and outgoing flow Tactical Model: Transformers Conservation of Flow Graph General conservation of flow with transportation, inventory, production Copyright , Marc Goetschalckx, All rights reserved. 14

15 Tactical Model: Transformers Conservation of Flow Equations i x ijpt itq jpt itq aq init _ inv iq cq otq 0 t 1 jpt jpt jp jpt jpt jpt itq aq iq iq cq otq 0 t 2.. T 1 jpt jpt jpt1 jpt jpt jpt itq aq iq cq otq 0 t T jpt jpt jpt1 jpt jpt x jkpt otq jpt k cq 1bom aq p 1BOM jpt jpvt jvt v v Tactical Model: Transformers Production with 1BOM 1BOM = One-level BOM 1 aircraft has 2 wings as immediate components Copyright , Marc Goetschalckx, All rights reserved. 15

16 Tactical Model: Transformer Capacities Capacities for throughput, inventory, and production p p ares aq acap y jprt jpt jrt j aq jpt acap jpt y j tres otq tcap y p jprt jpt jrt j otq tcap y jpt jpt j ires iq icap y jprt jpt jrt j iq icap y jpt jpt j Tactical Model: Customers Deliveries = incoming flow Deliveries i balanced with demand d through h backorders j x jkpt dq kpt dq bq bq dem kpt kput kptu kpt tu ut Copyright , Marc Goetschalckx, All rights reserved. 16

17 Solution Algorithm Work in progress St. Dev. Pareto points subset of variance Pareto points Solve Mean-Variance Robust Design Problem (MV-RDP) for various values of penalty Currently monolithic MIQO # iterations (also) dependent on # scenarios Solution Algorithm continued MV-RDP Bound to limit of # penalty values to be examined Few non-trivial test cases available Industrial Case with 30 scenarios 2 minutes computing time 2 optimal configurations (including expected value) If necessary primal Benders decomposition Copyright , Marc Goetschalckx, All rights reserved. 17

18 Extensions: Local Content Required local content per customer order Required data Component costs Local content fraction Assumption of one level of components Easy single additional constraint Problem becomes hard if any assumptions are relaxed Challenges for Robust Design by MBSE Immediate Modeling Challenges Local value-adding adding Supply chains for strategic partnerships Long-Term Discipline Challenges Holistic approach and interfacing with other disciplines Economic justification of robust design Copyright , Marc Goetschalckx, All rights reserved. 18

19 Boeing 747-LCF LCF: Large Cargo Freighter Transport B-787 fuselage sections 8 m internal height 150 ton payload Interactions and Trade-offs between Disciplines Finance Marketing Design Production Finance Marketing Design Cost and Revenue Stream Estimated and actual Expenditure (RDTE) Estimated and actual 1 Feasibility Requirements Operating Budget COO Acquisition Cost 2 Budgets Mission Requirements Budgets Production Ramp Outsourcing Requirements Design Specifications Production Expenditure Estimated and Actual Key Tradeoffs: Production Cost Investment Cost Purchase Cost Cost Models Cost Models 3 1. Financial Discussion 3. Product design /Process Cost & Risk Trade-off 4 2. Mission/COO & Production Trade-off 4. Marketing/Manufacturing Network Trade-off Copyright , Marc Goetschalckx, All rights reserved. 19

20 Thank You Can I Answer Any Questions? Copyright , Marc Goetschalckx, All rights reserved. 20