A Strategic Empty Container Logistics Optimization in a Major Shipping Company

Transcription

1 A Strategic Empty Container Logistics Optimization in a Major Shipping Company

2 ANDRES WEINTRAUB INDUSTRIAL ENGINEERING DEPARTMENT UNIVERSITY OF CHILE 2

3 OVERVIEW CSAV and the Shipping Business The Empty Container Logistics Process The Optimization System and its Implementation Impact and Benefits 3

4 The Company FERNANDO VALENZUELA SENIOR VICE PRESIDENT OF OPERATIONS & LOGISTICS 4

5 STRATEGIC PLAN Strengthen Capital Base and Asset Structure Improve Commercial & Operational Efficiency 5

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7 7 REGIONAL OFFICES New Jersey Barcelona Hamburg Hong Kong Mumbai Valparaiso Sao Paulo 7

8 CSAV SERVICE NETWORK EUROANDES ASIAM AMEX NACSA SAMEX ECS THL MNS ABS ADL BSL SGLX ASIAM JCS AMEX JCS ANDEX SLING 1 ANDEX SLING 2 AMERICAS GULF BRIDGE MAYA MED MEX CUBA SHUTTLE TANGO 1 & 2 ORINOCO US GULF INCA MEDITERRANEAN FEEDER LA PLATA MEDECSA FEM WEST AFRICA EXPLORER INTRA GULF IMEX MARCO POLO NEW DISCOVERY ASAX INDUS ANDEX SLING 1 ANDEX SLING 2 ANDINO EUROATLAN BRAZILIAN & LA PLATA STRINGS DOÑA PAULA PATAGONIA CONOSUR 57 Services 184 Vessels 100+ Countries 8

9 CONTAINER FLEET 700,000 TEUs Dry & Reefer Valued at $2 billion Dry Van 20 ft Dimensions: Tare Weight: Max. Load: 20' x 8' x 8'6 2,330 Ton 28,150 Ton 1 TEU Twenty-foot Equivalent Unit 9

10 CONTAINER TYPES Dry Van 20 ft Dry Van 40 ft High Cube 40 ft Flat Rack 20 ft Flat Rack 40 ft Reefer 40 ft Open Top 20 ft Others: Reefer 20 ft Ventilated 20 ft Open Top 40 ft Reefer High Cube 40 ft 10

11 CONTAINER FLEET GROWTH 30,000,000 CSAV World 900, ,000 TEUs (for World fleet) 25,000,000 20,000,000 15,000,000 10,000, , , , , , ,000 5,000, , TEUs (for CSAV fleet) 11

12 CSAV GLOBALIZATION 12

13 UNIVERSITY OF CHILE Forest Operations (Edelman Prize 1998) First in Interfaces Publications Ranking in Applied Operations Research Outside US School Meals Combinatorial Auctions (IFORS Prize 2002) Mine Planning 13

14 EMPTY CONTAINER LOGISTICS Complex Network Uncertainty Size Information Availability 14

15 2008 FINANCIAL CRISIS Sales Dropped 38% CSAV s Market Value Fell 66% CSAV was in a Critical Situation Strategic Change: Excellence in Operations 15

16 Empty Container Logistics Problem ANDRES WEINTRAUB INDUSTRIAL ENGINEERING DEPARTMENT UNIVERSITY OF CHILE 16

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18 EMPTY CONTAINER LOGISTICS CHALLENGES IMBALANCE UNCERTAINTY INFORMATION HANDLING AND SHARING COORDINATION OF INTERRELATED DECISIONS WORLDWIDE 18

19 CONTAINER IMBALANCE Exports Imports 19

20 EMPTY CONTAINER LOGISTICS CHALLENGES IMBALANCE UNCERTAINTY INFORMATION HANDLING AND SHARING COORDINATION OF INTERRELATED DECISIONS WORLDWIDE 20

21 UNCERTAINTY Demand Return of Empty Containers Travel Times Ships Capacity for Transporting Empty Containers 21

22 EMPTY CONTAINER LOGISTICS CHALLENGES IMBALANCE UNCERTAINTY INFORMATION HANDLING AND SHARING COORDINATION OF INTERRELATED DECISIONS WORLDWIDE 22

23 INFORMATION HANDLING 400,000 Container Activity Transactions per Day Timely Decisions need Accurate Information 23

24 EMPTY CONTAINER LOGISTICS CHALLENGES IMBALANCE UNCERTAINTY INFORMATION HANDLING AND SHARING COORDINATION OF INTERRELATED DECISIONS WORLDWIDE 24

25 2006 EMPTY CONTAINER LOGISTICS Imbalance Uncertainty Information Coordination High Quality Service High Levels of Stocks Inefficient Repositioning 25

26 Solution and Methodology RAFAEL EPSTEIN INDUSTRIAL ENGINEERING DEPARTMENT UNIVERSITY OF CHILE 26

27 E MPTY C ONTAINER LOGISTICS O PTIMIZATION

28 Regional Offices Web Interface ECO Safety Stocks MC Network Flow Model Inventory Model Container Positions Local Forecasting Forecasting Model Travel Times Vessel Capacity Expected Demand & Return Standard Deviation Historical Data Transaction Database CSAV 28

29 FORECASTING MODULE Combination of 3 Methodologies: Moving Average Trended and Seasonal Time Series Sales Force Forecasts (CFM) 29

30 FORECASTING MODULE 1.5 Million Forecasts per Day Container Demand Space in Vessels for Empty Containers Container Client s Returns 30

31 FORECAST ERROR AND SAFETY STOCK High Forecast Std. Dev. 100 Safety Stock TEU Medium Forecast Std. Dev. Low Forecast Std. Dev. 0 80% 95% 99% 99.99% Service Level 31

32 INVENTORY MODEL Inventory = Expected ( Demand & Return) + Safety Stock z σ α Forecast σ z α Forecast : : Forecast Standard Deviation Safety Factor with a Service Level of α% 32

33 Regional Offices Web Interface ECO Safety Stocks MC Network Flow Model Inventory Model Container Positions Local Forecasting Forecasting Model Travel Times Vessel Capacity Expected Demand & Return Standard Deviation Historical Data Transaction Database CSAV 33

34 MC NETWORK FLOW MODEL: MAIN DECISIONS Inventory Levels per Type, Location, and Day Empty Container Repositioning Flows When and Where to Lease & Return Containers 34

35 OBJECTIVE FUNCTION Minimize Empty Container Logistics Costs Empty Container Cost Elements: Leasing Repositioning Storage Loading Discharge 35

36 EMPTY CONTAINER INVENTORY BALANCE Per Container Type, Location, and Day: Arrivals New Leasing Returns Inventory Demand Departures 36

37 CAPACITY CONSTRAINTS Origins CAPACITY Destinations 37

38 MC NETWORK FLOW MODEL: SAFETY STOCK? Uncertainty Safety Stock 38

39 Regional Offices Web Interface ECO Safety Stocks MC Network Flow Model Inventory Model Container Positions Local Forecasting Forecasting Model Travel Times Vessel Capacity Expected Demand & Return Standard Deviation Historical Data Cleansing Module Transaction Database CSAV 39

40 MC NETWORK FLOW MODEL: RESOLUTION 3.7 Million Parameters, 1.2 Million Variables, and 600,000 Equations 3 Runs per Day Solution in 2 Hours: Warm Starts Size Reduction Integer Friendly 40

41 INFORMATION QUALITY IMPROVEMENTS Projects: Data Gathering, Reliability, and Transmission Company-Wide Improvements Example: Avg. Activity Tracking Time Before: +48 Hours Today: -24 Hours 41

42 CHALLENGE The most difficult part was still to come 42

43 COLLABORATIVE WEB INTERFACE

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45 Implementation SERGIO HURTADO VICE PRESIDENT OF LOGISTICS AND GLOBAL OPERATIONAL PROCESSES 45

46 IMPLEMENTATION CHALLENGES Different Processes Data Quality Regional Structures Coordination Parameters 46

47 CSAV FREIGHT TRANSPORTATION 3,500,000 3,000,000 2,500,000 TEU 2,000,000 1,500,000 1,000, ,

48 REGIONAL LOGISTICS Hamburg New Jersey Barcelona Hong Kong Mumbai Sao Paulo Valparaiso 48

49 STEP 1 - PROTOTYPE DEVELOPMENT Development Sequence: Analyze Design Prototype with Historical Data Support Develop Prototype for Chile Deploy Test Prototype for Chile-Brazil Global Prototype 49

50 STEP 2 - GLOBAL PROCESS IMPLEMENTATION Global Process for Empty Container Logistics: Weekly Coordination Meetings Best Practices in use of ECO Target Inventories Coordinated Flows System Validation 50

51 IMPLEMENTATION HISTORY 2006: Project Start 2007: Prototype in Chile & Brazil 2008: Global Prototype Validation 2009: Global Implementation Plan Jan 2010: ECO Goes Live 51

52 GLOBAL LOGISTICS Hamburg New Jersey Barcelona Hong Kong Mumbai Sao Paulo Valparaiso 52

53 Impacts & Benefits FERNANDO VALENZUELA SENIOR VICE PRESIDENT OF OPERATIONS & LOGISTICS 53

54 OPTIMIZATION Flexibility Coordination Creativity 54

55 INTEGRATION 55

56 INFORMATION VALUE Data + Intelligence = Efficiency 56

57 EMPTY TIME PER CONTAINER CYCLE (Days) Empty Time [Days] Average Empty Container Turnover Increased 60%

58 CONTAINER CYCLES PER YEAR Voyages per Year

59 REQUIRED DRY CONTAINERS FOR 2010 DEMAND 740, , ,450 TEU 700, , , % 640, , , , ,000 Without ECO Avg With ECO

60 EXCESS COST PER FULL VOYAGE RESPECT TO $ USD Avg: +$ Base Year

61 SAVINGS 2010 Per Voyage Excess Cost : $35 Voyages 2010: 2.9 million Total Savings: $ = $101 million Estimated ECO Effect: 80% Total ECO Savings: $81 million 61

62 CSAV NET INCOME 300 US$ million ECO Impact

63 FUTURE SAVINGS $ USD million million 100 million 100 million

64 Testimony ARTURO RICKE CHIEF EXECUTIVE OFFICER 64

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