Prof. Jacek ŻAK. Australia and New Zealand; July August; 2011 Sydney, August 9, PDF

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2011 ACADEMIC TOUR: Applying Advanced Methods in MULTIPLE CRITERIA DECISION MAKING/ AIDING IN TRANSPORTATION & LOGISTICS Poznan University of Technology Prof. Jacek ŻAK Australia and New Zealand; July

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2011 ACADEMIC TOUR: Applying Advanced Methods in MULTIPLE CRITERIA DECISION MAKING/ AIDING IN TRANSPORTATION & LOGISTICS Poznan University of Technology Prof. Jacek ŻAK Australia and New Zealand; July August; 2011 Sydney, August 9, 2011 CONTENTS INTRODUCTION / MOTIVATION DEFINITION OF MCDM/A; DECISION MAKING PROCESS WHY TO USE MCDM/A METHODOLOGY IN TRANSPORTATION MULTIPLE CRITERIA DECISION MAKING / AIDING METHODOLOGY HISTORICAL BACKGROUND CHARACTERISTICS OF THE MULTIOBJECTIVE DECISION PROBLEMS CLASSIFICATION OF THE MCDM/A METHODS APLICATIONS OF MCDM/A METHODS IN TRANSPORTATION/ LOGISTICS REAL LIFE CASE STUDIES OPTIMIZATION OF THE DISTRIBUTION SYSTEM EVALUATION OF LOGISTICS SERVICE PROVIDERS FINAL CONCLUSIONS Slide 2 INTRODUCTION / MOTIVATION MULTIPLE CRITERIA DECISION MAKING / AIDING MULTIPLE CRITERIA DECISION MAKING / AIDING MULTIPLE CRITERIA ANALYSIS S (FRENCH) MULTIPLE CRITERIA DECISION MAKING (AMERICAN) MCDM/A IS A DYNAMICALLY DEVELOPING FIELD WHICH AIMS AT GIVING THE DM SOME TOOLS IN ORDER TO ENABLE HIM/ HER TO SOLVE A COMPLEX DECISION PROBLEM WHERE SEVERAL (CONTRADICTORY) POINTS OF VIEW MUST BE TAKEN INTO ACCOUNT IN CONTRAST TO CLASSICAL OR TECHNIQUES MCDM/A METHODS DO NOT YIELD OBJECIVELY BEST SOLUTIONS BECAUSE IT IS IMPOSSIBLE TO GENERATE SUCH SOLUTIONS WHICH ARE THE BEST SIMULTANEOUSLY, FROM ALL POINTS OF VIEW MCDM/A CONCENTRATES C ON SUGGESTING S G COMPROMISE O SOLUTIONS O S WHICH TAKE INTO ACCOUNT THE TRADE-OFFS BETWEEN CRITERIA &THE DM S PREFERENCES Slide 3 INTRODUCTION / MOTIVATION DECISION MAKING/ AIDING PROCESS BASED ON MCDM/A ANALYST STAKEHOLDERS CONFLICTING INTERESTS REAL WORLD PHENOMENA PROCESSES LIMITATIONS DECISION MAKER CRITERIA PREFERENCES EVALUATIONS EXPERIENCE EXPERTEESE IN MATHEMATICAL MODELING MATHEMATICAL MODEL CRITERIA CONSTRAINTS PREFERENCES DECISION MAKING (OPTIMIZATION) O TOOLS DSS COMPROMISE SOLUTIONS Slide 4 INTRODUCTION / MOTIVATION MCDM/A BASED DECISION PROCESS CUSTOMIZATION TO THE CREW SIZING PROBLEM MULTIPLE CRITERIA OPTIMIZATION OF A CREW SIZE TRANSPORTATION LOGISTICS COMPANY/ SYSTEM CUSTOMER DECISION MAKER EMPLOYEE DSS DECISION MODEL DECISION MAKING METHODS ANALYST COMPROMISE SOLUTIONS Slide 5 INTRODUCTION / MOTIVATION WHY TO USE MCDM/AM/A IN TRANSPORTATION/ LOGISTICS? COMPLEXITY OF TRANSPORTATION/ LOGISTICS PROCESSES/SYSTEMS; MANY EVALUATION MEASURES ( ECONOMICAL, TECHNICAL, ENVIRONMENTAL & SOCIAL) MANY STAKEHOLDERS (CUSTOMERS, OPERATORS, EMPLOYEES, LOCAL COMMUNITIES & AUTHORITIES) TRADE OFFS COST VS. QUALITY RESULTS OF THE SURVEY RESEARCH (121 COMPANIES, DIFFERENT SCOPE, DIFFERENT SIZE & LOCATION) 21 MOST IMPORTANT DECISION PROBLEMS MULTIOBJECTIVE CHARACTER - 80% RESPONDENTS 89% OF RESPONDENTS RECOGNIZES TRADE OFFS AND CONTRADICTORY INTERESTS DIFFERENT GROUPS OF STAKEHOLDERS (SHAREHOLDERS & TOP MANAGEMENT 76%, EMPLOYEES 54%, CUSTOMERS 52%) Slide 6 INTRODUCTION / MOTIVATION WHY TO USE MCDM/AM/A IN? Peter F. Drucker (Management, 1974): To manage a business is to balance a variety of needs and goals. And this requires multiple objectives Jimmy Carter (US President; mid 1970s): I have been guided by four objectives for the United States economy: employment, economic growth, inflation, international harmony Henry Ford (Beginning of 20th century): By introducing the moving assembly line we were trying to satisfy different groups: customers (affordable car), employees (work comfort), designers (new challanges), investors (profit) Herbert A. Simon (Nobel laureate in economic science, 1978): The choice to satisfice or to accept the good enough is generally more realistic Slide 7 INTRODUCTION / MOTIVATION MULTIPLE CRITERIA IN TRANSPORTATION/ LOGISTICS? Portfolio selection analysis of alternative transportation services Selecting alternative transportation services Designing satisfactory portfolio Transportation projects evaluation (network extension; highway construction) Designing g and ranking the proposed p solutions/ projects Transportation job assignment and pricing Decision accept / reject the incoming order Price definition Facility location problem (depots; terminals; hubs; logistics centers) Selecting the most desired location; Satisfying different interests; Crew selection, assignment and scheduling Selecting alternative eployees for a certain position; balancing different interests Fleet composition / selection and replacement Analysis of different vehicles evaluated by different measures Technical / economical diagnosis of their utility Evaluation and ranking of common carriers/ logistics service providers Multidimensional analysis of different companies Slide 8 MCDM/A M/A METHODOLOGY HISTORICAL BACKGROUND AMERICAN SCHOOL Making optimal decisions under several criteria 1951 T. KOOPMANS; H. KUHN + A. TUCKER - NON-DOMINATED SOLUTION EUROPEAN SCHOOL Supporting the DM in the process of solving complex, multiple objective decision problems 1960-s BEGINNINGS - R.BENAYOUN, B.ROY, B. SUSSMAN (ELECTRE I) 1961 A. CHARNES; W. COOPER - GOAL PROGRAMMING MULTI ATTRIBUTE UTILITY THEORY H. RAIFFA & R. KEENEY AHP, UTA METHODS Slide R. BENAYOUN, et al. POP (PROGRESIVE ORIENTATION PROCEDURE) FIRST MULTIOBJECTIVE INTERACTIVE ALGORITHM 1970-s E.JACQUETE-LAGREZE, BROY B.ROY, R.BENAYOUN, P. BERTIER EXTENSIVE DEVELOPMENT OF THE CONCEPT OF THE OUTRANKING RELATION FAMILY OF ELECTRE METHODS MCDM/AM/A METHODOLOGY CHARACTERISTICS OF MULTIPLE CRITERIA DECISION PROBLEMS MULTIPLE CRITERIA DECISION PROBLEM MULTIPLE CRITERIA DECISION PROBLEM IS A SITUATION IN WHICH, HAVING DEFINED A SET A OF ACTIONS AND A CONSISTENT FAMILY OF CRITERIA F ONE WHISHES TO: DETERMINE A SUBSET OF ACTIONS CONSIDERED TO BE THE BEST WITH RESPECT TO F (CHOICE PROBLEM) DIVIDE A INTO SUBSETS ACCORDING TO SOME NORMS (SORTING PROBLEM) RANK THE ACTIONS OF A FROM BEST TO WORST (RANKING PROBLEM) MULTIPLE CRITERIA DECISION PROBLEM - ILL DEFINED MATHEMATICAL PROBLEM SEARCHING FOR A SOLUTION x THAT MAXIMIZES MULTIPLE OBJECTIVE FUNCTION MaxF( x) = Max f1( x), f2( x),..., f J ( x) Subject to: x A MULTIPLE CRITERIA DECISION PROBLEM IS DEFINED BY: A SET A OF ACTIONS A CONSISTENT FAMILY OF CRITERIA F Slide 10 MCDA/M METHODOLOGY CHARACTERISTICS OF MULTIPLE CRITERIA DECISION PROBLEM A SET A OF OBJECTS / SOLUTIONS A SET A IS A COLLECTION OF OBJECTS, CANDIDADTES, VARIANTS, DECISIONS, SOLUTIONS THAT ARE TO BE ANALYZED AND EVALUTED DURING THE DECISION PROCESS; A CAN BE DEFINED: DIRECTLY BY DENOMINATING ALL ITS ELEMENTS (FINITE SET, RELATIVELY SMALL) INDIRECTLY BY DEFINING CERTAIN FEATURES OF ITS COMPONENTS AND / OR CONSTRAINTS (INFINITE SET, FINITE SET BUT RELATIVELY LARGE) A SET A CAN BE: CONSTANT, A PRIORI DEFINED; NOT CHANGING DURING THE DECISION PROCESS EVOLVING, BEING MODIFIED IN THE DECISION PROCESS Slide 11 MCDM/AM/A METHODOLOGY CHARACTERISTICS OF MULTIPLE CRITERIA DECISION PROBLEMS A SET OF CRITERIA F A CONSISTENT FAMILY OF CRITERIA F IS A SET OF FUNCTIONS f DEFINED ON A AND REPRESENTING THE DM S PREFERENCES TOWARDS A SPECIFIC ASPECT (DIMENSION) OF THE DECISION PROBLEM. A SET OF CRITERIA F SHOULD GUARANTEE: COMPREHENSIVE AND COMPLETE EVALUATION OF VARIANTS (CONSIDERATION OF ALL ASPECTS OF THE DECISION PROBLEM) CONSISTENCY OF THE EVALUATION (EACH CRITERION SHOULD CORRESPOND TO THE DM S GLOBAL PREFERENCES) NON-REDUNDANCY OF CRITERIA (REPETITIONS SHOULD BE ELIMINATED; MEANINGS AND SCOPES OF CRITERIA MUST BE CLEARLY DEFINED) A SET OF CRITERIA SHOUD BE MANAGABLE: MAGIC NUMBER 7 +/- 2 Slide 12 MCDM/AM/A METHODOLOGY MCDM/AM/A METHODS CLASSIFICATION OF MCDM/A METHODS DIFFERENT CLASSIFICATION CRITERIA (DECISION PROCESS OBJECTIVES, MANNER OF SYNTHETIZING PREFERENCES, ACCURACY OF SOLUTIONS) DECISION PROCESS OBJECTIVES MULTIPLE CRITERIA CHOICE (OPTIMIZATION) METHODS (INTERACTIVE METHODS) MULTIPLE CRITERIA SORTING METHODS (ELECTRE TRI) MULTIPLE CRITERIA RANKING METHODS (ELECTRE, AHP) MANNER OF SYNTHETIZING (AGGREGATING) THE DM S GLOBAL PREFERENCES MULTIOBJECTIVE METHODS BASED ON THE UTILITY FUNCTION (UTA, AHP ) MULTIOBJECTIVE METHODS BASED ON THE OUTRANKING RELATION (ELECTRE, PROMETHEE) Slide 13 MCDM/AM/A METHODOLOGY MCDM/AM/A METHODS METHODS BASED ON THE UTILITY FUNCTION UTILIZE THE MULTIPLE ATTRIBUTE UTILITY THEORY (R. KEENEY, H. RAIFFA; 1976) DIFFERENT POINTS OF VIEW ARE AGGREGATED INTO ONE UTILITY FUNCTION, WHICH IS MAXIMIZED U = U(g 1,g 2,...,g n ) ALL ACTIONS ARE COMPARABLE a P b IFF U(z a ) U(z b ) a I b IFF U(z a ) = U(z b ) Slide 14 MCDA/M METHODOLOGY MCDA/M METHODS METHODS BASED ON THE OUTRANKING RELATION INTRODUCE THE CONCEPT OF THE INCOMPARABILITY BETWEEN ACTIONS OUTRANKING REALATION IS A BINARY RELATION S DEFINED IN A, SUCH THAT asb IF, GIVEN WHAT IS KNOWN ABOUT THE DECISION MAKER S PREFERENCES AND GIVEN THE QUALITY OF THE EVALUATIONS OF THE ACTIONS AND THE NATURE OF THE PROBLEM, THERE ARE ENOUGH ARGUMENTS TO DECIDE THAT a IS AT LEAST AS GOOD AS b, WHILE THERE IS NO ESSENTIAL REASON TO REFUTE THAT STATEMENT. Slide 15 MCDA/M METHODOLOGY MCDA/M METHODS OUTRANKING RELATION S IS A SUM OF THE INDIFFERENCE I AND PREFERENCE P RELATIONS S = P I SOME ACTIONS ARE INCOMPARABLE apb IFF asb bsa aib IFF asb bsa a? b IFF asb bsa Slide 16 CASE STUDY I SINGLE CRITERION & BI-CRITERIA OPTIMIZATION OF THE DISTRIBUTION SYSTEM EXISTING DISTRIBUTION SYSTEM C 4A C 5A C 3A CUSTOMERS SERVED BY WAREHOUSE A Slide 17 A C 2A C 1B C 6A C 1A C 2B B C 3B CUSTOMERS SERVED BY WAREHOUSE B C 9A C 4B C 5B 2 PRODUCTION PLANTS & WAREHOUSES DIFFERENT PRODUCT PORTFOLIOS IN PRODUCTION PLANTS (45% TRUNKING) ORDER FULFILLMENT PROCESS IN B; FLEET IN A&B WAREHOUSING AND MATERIAL HANDLING IS CARRIED OUT BY THE COMPANY ITSELF, TRANSPORTATION IS OUTSOURCED EACH WAREHOUSE HAS A CERTAIN AREA TO COVER DIAGONAL LINE 400 CUSTOMERS C 1A,...; C 1B,... C 7A C 8A DISTRIBUTION COSTS 10 MLN ZL DELVERY TIME HOURS = RIDING TIME 9 12 HOURS (AVG. 9.5 HOURS) CASE STUDY I TWO MATHEMATICAL MODELS DECISION VARIABLES { 1 WAREHOUSE i IS INCLUDED IN THE PLAN y i = 0 OTHERWISE { 1 REGION j IS ASSIGNED TO WAREHOUSE i x ij = 0 OTHERWISE CONSTRAINTS REGIONS ARE ASSIGNED ONLY TO WAREHOUSES INCLUDED IN THE PLAN x ij y i i=1 1,...I ; j=1 1,...J Slide 18 EACH REGION IS ASSIGNED TO 1 WAREHOUSE I i = 1 x ij = 1 j = 1,...J CASE STUDY I TWO MATHEMATICAL MODELS TTC OBJECTIVES one in model 1; two in model 2 TDC TOTAL (ANNUAL) DISTRIBUTION COSTS MRT MAXIMUM RIDING TIME TDC = TTC + TPHC + TCC TTC TOTAL TRANSPORTATION COSTS TPHC TOTAL PALLETS HANDLING COSTS TCC TOTAL LOCKED-UP CAPITAL COSTS = TPHC TCC= Slide 19 I I y TCA J x DA + TCB i i ij j i i= 1 j= 1 j= 1 = y I y PHC J x DA J x ij DB i i ij j ij j i = 1 j = 1 j = 1 max MCC J + J x DB j + I J i= 1 j= 1 x ij TC ij ( DA j + DB J ( CRT+ DHA ) + x DB CCB ( CRT+ DHB ) x DACCA ij j = i i i ij j i i= 1 j 1 j= 1 DHA i, DHB i AVG. HEADWAYS OF DELIVERIES FOR PLANTS A & B j ) CASE STUDY I TWO MATHEMATICAL MODELS MAXIMUM RIDING TIME { } MRT = max x ijtt ij SOME TRANSFORMATION OF OBJECTIVE FUNCTIONS WAS REQUIRED TO OBTAIN A LINEAR FORMULATION OF THE PROBLEM FINALY ONE OBTAINS: MIXED BINARY SINGLE CRITERION (TDC) & BI CRITERIA (TDC + MRT) LINEAR PROGRAMING PROBLEMS WITH IxJ+1 BINARY VARIABLES & I+1 CONTINUOUS VARIABLES THE PROBLEM IS SOLVED BY AN EXTENDED VERSION OF MS EXCEL SOLVER PREMIUM SOLVER PLUS BY FRONTLINE SYSTEM Slide 20 CASE STUDY I COMPUTATIONAL EXPERIMENTS SINGLE CRITERION OPTIMIZATION MINIMIZATION OF THE TOTAL DISTRIBUTION COSTS - 6% IMPROVEMENT NUMBER OF WAREHOUSES 7 NEW ASSIGNMENT OF 49 REGIONS TO 7 WAREHOUSES COMPARISON OF TWO DISTRIBUTION SYSTEMS CURRENT OPTIMAL Slide 21 CASE STUDY I - COMPUTATIONAL EXPERIMENTS SINGLE CRITERION OPTIMIZATION Distribution Number of Total annual distribution Ridind time [h:mm] system warehouses costs [PLN] Existing :22 Optimal :09 REDUCTION OF TOTAL DISTRIBUTION COSTS BY 6% - ANNUAL SAVINGS MLN ZL REDUCTION OF RIDING TIME BY 34% - MORE THAN 3 HOUR REDUCTION CHANGES IN THE STRUCTURE OF THE DS. 2 WAREHOUSES REPLACED BY 7 WAREHOUSES NEW ASSIGNMENT OF 49 REGIONS TO 7 WAREHOUSES (ELIMINATION OF THE DIAGONAL LINE) FROM THE MULTIPLE OBJECTIVE POINT OF VIEW THE OPTIMAL DISTRIBUTION SYSTEM DOMINATES THE EXISTING ONE Slide 22 CASE STUDY I - COMPUTATIONAL EXPERIMENTS Riding time Total distribution No. of [h:mm] costs [PLN] warehouses 2: : : : : : : : : BI - CRITERION OPTIMIZATION APPLICATION OF ε - CONSTRAINTS METHOD TO GENERATE A SAMPLE OF PARETO OPTIMAL SOLUTIONS; RIDING TIME CONSTRAINED FROM 6 TO 2 HOURS; COST TIME TRADE-OFFS RIDING TIME REDUCTION BY 46 MIN. ; +2 WAREHOUSES; DISTRIBUTION COSTS INCREASE BY 0.19 MLN ZL RIDING TIME REDUCTION BY 3 MIN, ; +2 WAREHOUSES; DISTRIBUTION COSTS INCREASE BY 0.55 MLN ZL GENERATED DISTRIBUTION SYSTEMS 7 TO 23 WAREHOUSES EXISTING DS (2 WAREHOUSES) VS. PARETO OPTIMAL DS (10 WAREHOUSES) SIMILAR LEVEL OF DISTRIBUTION COSTS 10 MLN ZL RIDING TIME REDUCTION FROM 9:22 TO 4:20 (BY 5 HOURS) 55% REDUCTION Slide 23 CASE STUDY I SOLUTION PROCEDURE & COMPUTATIONAL EXPERIMENTS EXPLANATIONS / DEFINITIONS DOMINANCE RELATION - GIVEN TWO ELEMENTS a AND b OF A, a DOMINANTES b (a D b) IFF f j (a) f j (b) ; j = 1,2,,n WHERE AT LEAST ONE OF THE INEQUALITIES IS STRICT f 1 f 1max THE IDEAL POINT PARETO OPTIMAL/ EFFICIENT SOLUTIONS ACTION a IS EFFICIENT IFF NO ACTION OF A DOMINATES IT A f 1min THE NADIR POINT Slide 24 f 2min f 2max f 2 CASE STUDY I EXPLANATIONS / DEFINITIONS THE IMAGE OF A IN THE CRITERIA SPACE IS THE SET Z a OF POINTS IN R n a ONE OBTAINS WHEN EACH ACTION a IS REPRESETED BY THE POINT WHOSE COORDINATES ARE: {g 1 (a),,g n (a)} {g 1 (a),...,g n (a)} a b c Z a Z b Z c Set of actions; decision space Set of evaluations; criteria space IN MULTIPLE OBJECTIVE DECISION PROBLEMS THE CRITERIA SPACE IS VERY IMPORTANT FOR MAKING GOOD CHOICES AND SELECTING APPROPRIATE MOST RATIONAL SOLUTIONS Slide 25 CASE STUDY I EXPLANATIONS / DEFINITIONS Slide 26 PAY OFF MATRIX IS THE MATRIX G(nxn) DEFINED BY G kl = g k (â l ), k,l = 1,2,,n IT IS THUS THE MATRIX CONTAINING, FOR EACH ACTION â l, ITS EVALUATIONS ACCORDING TO ALL THE CRITERIA IN PARTICULAR G ll = Z * l G ll = Z l * k l SOLUTION 1 SOLUTION 2 SOLUTION 3 SOLUTION n CRITERION 1 ( Max) G 11 = 250 G 12 = 150 G 13 = 125 G 1n = 175 CRITERION 2 G 21 = 0.60 G 22 = 0.95 G 23 = 0.80 G 2n = 0.75 (Max) CRITERION 3 G 31 = 67 G 32 = 44 G 33 = 29 G 3n = 58 (Min) CRITERION n G n1 = 0.12 G n2 = 0.09 G n3 = 0.05 G nn = 0.16 (Max) CASE STUDY I COMPUTATIONAL EXPERIMENTS COMPUTATIONAL EXPERIMENTS PHASE II A SAMPLE OF SOLUTIONS IS EVALUATED WITH AN APPLICATION OF LIGHT BEAM SEARCH METHOD (A. JASZKIEWICZ, R. SLOWINSKI 1995) THE RANGES OF CRITERIA VALUES ARE AS FOLLOWS: CRITERIA TDC [MLN ZL] MRT [H:MIN] IDEAL POINT :41 NADIR POINT :09 THE LBS METHOD HELPS THE DM TO CARRY OUT A GRAPHICAL & NUMERICAL ANALYSIS OF THE SOLUTIONS Slide 27 CASE STUDY I COMPUTATIONAL EXPERIMENTS SOFTWARE LBS (LIGHT BEAM SEARCH) Slide 28 CASE STUDY I COMPUTATIONAL EXPERIMENTS REVIEW OF THE SOLUTIONS MRT [H:MIN] 2:41 THE IDEAL POINT A 10; 4:00 REFERENCE POINT 609 6:09 THE NADIR POINT Slide TDC [MLN ZL] CASE STUDY I COMPUTATIONAL EXPERIMENTS SET OF 20 SELECTED (FILTERED) SOLUTIONS (LP) (EP) (PZP) (KRP) Slide 30 CASE STUDY I RESULTS & RECOMMENDATIONS RESULTS TDC REDUCTION INCREASED NUMBER OF WAREHOUSES (SINGLE CRITERION OPTIMIZATION) TDC INTERRELATED WITH MRT (BI-CRITERION OPTIMIZATION) RECOMMENDATIONS 9 10 WAREHOUSES; 1% TO 4% REDUCTION OF TDC AND 43% TO 54% REDUCTION OF MRT SUBSTANTIAL TIME REDUCTION SHOULD RESULT IN THE INCREASE OF THE MARKET SHARE OUTPUT ORIGINAL MODEL DESCRIPTION OF THE OPERATIONS OF THE DISTRIBUTION SYSTEM RESULTS INTERESTING FOR THE DM; TRADE-OFFS ANALYSIS UNIVERSAL APPROACH FLEXIBILITY OF THE DECISION PROCESS Slide 31 CASE STUDY II PROBLEM DESCRIPTION SELECTION OF LOGISTICS SERVICE PROVIDERS MULTIOBJECTIVE RANKING OF CARRIERS FOR A LARGE MANUFACTURER OF CONSUMER GOODS INTERNATIONAL COMPANY LOCATED IN WARSAW, POLAND IS SEARCHING FOR A NEW CARRIER COMPANY S PROFILE ENTERED POLISH MARKET IN 1991 PRODUCTION & SALES OF COSMETICS, DETERGENTS & WASHING GARTICLES ANNUAL TURNOVER $ 130 MLN (400 MLN PLN) 85% POLAND 15% EXPORT IN POLAND 60% OF SALES WHOLESALERS 20% OF SALES SUPERMARKETS (LARGE CHAINS) 15% OTHERS 15% MARKET SHARE LOW PROFITABILITY Slide 32 CASE STUDY II II PROBLEM DESCRIPTION PROBLEM DESCRIPTION THE COMPANY CONDUCTED THE ANALYSIS OF ITS TRANSPORTATION / LOGISTICS OPERATIONS AND THE MANAGEMENT TEAM CAME TO THE FOLLOWING CONCLUSIONS IN COMPANY WAREHOUSING IS SUBSTANTIALLY CHEAPER THAT EXTERNAL WAREHOUSING SERVICES THE CONTRACT WITH THE EXISTING PROVIDER OF TRANSPORTATION SERVICES IS NOT SATISFACTORY THE COMPANY WANTS A NEW TRANSPORTATION SERVICE PROVIDER AND DECIDES TO CARRY OUT ITS OWN WAREHOUSING OPERATIONS Slide 33 CASE STUDY II PROBLEM DESCRIPTION SCOPE OF TRANSPORTATION OPERATIONS ( EXISTING SITUATION) ANNUAL MILEAGE 5 MLN KM (74% COVERED BY THE OPERATOR S OWN FLEET) FLEET 25 TRACTORS & SEMITRAILOR UNITS (33 EURO PALLETS) + SUBCONTRUCTED TRUCKS WITH TRAILORS SHIPMENTS PALLETS PER MONTHS (45% TRUNKING = SHIPMENTS BETWEEN WAREHOUSES; 55% DIRECT DELIVERIES TO CUSTOMERS) AVERAGE SHIPMENT : 8 PALLETS BY TRUCKS; 22 PALETS TO CUSTOMERS, 33 PALLETS TRUNKING CUSTOMERS 400, DISPERSED ALL OVER POLAND; AVERAGE NUMBER OF CUSTOMERS SERVED ON EACH ROUTE 2 TRANSPORTATION MARKET VERY COMPETITIVE CARRIERS ( 99% VERY SMALL) MOST OF THE TRANSPORTATION COMPANIES FOCUSED ON FREIGHT TRANSPORTATION SIZE 3.3 BLN ZL Slide 34 CASE STUDY II PROBLEM DESCRIPTION CARRIERS CONSIDERED ERD POLISH CARRIER, FOUNDED 1990, ANNUAL SALES 40 MLN ZL, FIXED ASSETS 15 MLN ZL, EMPLOYEES 190, FLEET 100 TRACTORS & TRAILORS (33 EURO PALLETS) TRUCKS (15-18 EURO PALLETS), AVG. FLEET AGE 2 YEARS, DELIVERY TIME 24 HOURS; IN THE PROCESS OF INTRODUCING QUALITY STANDARDS ISO 9000 HARTKAT INTERNATIONAL CARRIER, LONG TRADITION ON THE POLISH MARKET- POLISH DEVISION FOUNDED 1958, ANNUAL SALES 91 MLN ZL, FIXED ASSETS 10 MLN ZL, EMPLOYEES 850, FLEET 45 TRACTORS & TRAILORS (33 EURO PALLETS) + 10 TRUCKS (15-18 EURO PALLETS) + 10 VANS (UP TO 8 EURO PALLETS), AVG. FLEET AGE 3 YEARS, DELIVERY TIME 24 HOURS TRANS-UNI - POLISH CARRIER, FOUNDED 1990, ANNUAL SALES 28 MLN ZL, FIXED ASSETS 6.5 MLN ZL, EMPLOYEES 180, FLEET 84 TRACTORS & TRAILORS (33 EURO PALLETS) + 3 TRUCKS (15-18 EURO PALLETS) + 3 VANS (UP TO 8 EURO PALLETS), AVG. FLEET AGE 4 YEARS, DELIVERY TIME 24 HOURS Slide 35 CASE STUDY II PROBLEM DESCRIPTION CARRIERS CONSIDERED NOLIM POLISH CARRIER, FOUNDED 1990, ANNUAL SALES 7.5 MLN ZL, FIXED ASSETS 3.5 MLN ZL, EMPLOYEES 65, FLEET 10 TRACTORS & TRAILORS (33 EURO PALLETS) + 8 TRUCKS (15-18 EURO PALLETS) + 10 VANS( UP TO 8 EURO PALLETS), AVG. FLEET AGE 8 YEARS, DELIVERY TIME 48 HOURS; ;QUALITY CERTIFICATE ISO 9000 POLBI POLISH CARRIER, FOUNDED 1991, ANNUAL SALES 25 MLN ZL, FIXED ASSETS 7.5 MLN ZL, EMPLOYEES 53, FLEET 5 TRACTORS & TRAILORS (33 EURO PALLETS) + 1 VAN( UP TO 8 EURO PALLETS), AVG. FLEET AGE 7 YEARS, DELIVERY TIME 72 HOURS SPOL POLISH CARRIER, FOUNDED 1991, ANNUAL SALES 182 MLN ZL, FIXED ASSETS 41 MLN ZL, EMPLOYEES 990, FLEET ALL VEHICLES ARE SUBCONTRUCTED, AVG. FLEET AGE 5 YEARS, DELIVERY TIME 24 HOURS; QUALITY CERTIFICATE ISO 9000 RIDPOL (EXISTING CARRIER) INTERNATIONAL CARRIER, POLISH DIVISION FOUNDED 1997, ANNUAL SALES 22 MLN ZL, FIXED ASSETS 1.3 MLN ZL, EMPLOYEES 65, FLEET 24 TRACTORS & TRAILORS (33 EURO PALLETS), AVG. FLEET AGE 2 YEARS, DELIVERY TIME 24 HOURS Slide 36 CASE STUDY II PROBLEM DESCRIPTION CARRIERS CONSIDERED POTRANS INTERNATIONAL CARRIER, POLISH DIVISION FOUNDED 1995, ANNUAL SALES 25 MLN ZL, FIXED ASSETS 33 MLN ZL, EMPLOYEES 130, FLEET 300 TRACTORS & TRAILORS (33 EURO PALLETS) + 83 TRUCKS (15-18 EURO PALLETS) 28 VANS ( UP TO 8 EURO PALLETS), AVG. FLEET AGE 5 YEARS, ALL VEHICLES SUBCONTRUCTED LONG TERM CONTRACTS, USUALLY 30% OF FLEET USED FOR THE POLISH MARKET, DELIVERY TIME 24 HOURS; QUALITY CERTIFICATE ISO 9000 Slide 37 CASE STUDY II PROBLEM DESCRIPTION ADDITIONAL INFORMATION BASED ON EXPERT OPINIONS AND ANALYSES ADDITIONAL EVALUATIONS OF THE CARRIERS HAS BEEN CARRIED OUT EXPERTS ESTIMATED TOTAL ANNUAL COSTS OF TRANSPORTATION BASED ON THE DELIVERY SCHEME PROPOSED BY EACH CARRIER AND DIFFERENT UNIT COSTS PER TKM AND VKM IN EACH VEHICLE CATEG
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