G 

 

 

 

 
Improving sustainability of agriculture supplies 

distribution 

- A case study of Foria AB 

Master of Science Thesis in the Master Degree Programme Supply Chain 

Management  

Adrian Ruiz de la Llata & Oscar Kjellberg 

Department of Technology Management and Economics 

Division of Logistics and Transportation 

Technical report no E2012:065 

CHALMERS UNIVERSITY OF TECHNOLOGY 

Gothenburg, Sweden, 2012 

 



 

 

 

 

 

 

 

 

 

 

 

Improving sustainability of agriculture supplies distribution 

- A case study of Foria AB 

ADRIAN RUIZ DE LA LLATA & OSCAR KJELLBERG 

© ADRIAN RUIZ DE LA LLATA & OSCAR KJELLBERG, 2012.  

Technical report no E2012:065 

Department of Technology Management and Economics 

Chalmers University of Technology 

SE-412 96 Göteborg 

Sweden 

Telephone + 46 (0)31-772 1000 

 

 

 

 

Cover: 

A collage of some of the equipment used and cargo delivered in the analyzed transports. See 

pages 28-32.  

Department of Technology Management and Economics 

Gothenburg, Sweden 2012  



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ABSTRACT 

This master thesis analyzes the distribution of general cargo from a centralized 

warehouse to a large number of widespread drop-off points. The research approach is 

practical and by analyzing a real-world case through both a financial analysis and by 

mimicking their operations in a proprietary simulation program, the authors were able to 

analyze how transport efficiency is affected by changes to the distribution setup. 

The case used for the study is the distribution of agriculture supplies performed by Foria 

from Lantmännen’s central warehouse in Västerås to farmers in the counties of Östergötland, 

Södermanland, Närke, northern Småland, Västmanland and Uppland. This distribution could 

be thought of as the distribution of general cargo, from a central warehouse to a large number 

of widespread drop-off points. 

The aim of the thesis is twofold: (1) propose efficiency improvements for the focal 

company Foria so that their operations could improve both financially and from an 

environmental point of view. (2) Generalize the case specific results and draw general 

conclusions on which distribution efficiency improvements renders the best sustainability 

outcomes from both a financial perspective as well as an environmental perspective.  

After an introduction, method description and literature review, the thesis describes the 

studied operations in detail and the algorithm describing their work operations which are the 

base for the simulation program are presented.  

The analysis part starts with an in-depth problem analysis of focal company´s current 

way of working. Possible solutions to the identified problems are presented and two methods 

for attacking the issues, vehicle differentiation of the trucks and routing differentiation, are 

quantified in a financial analysis and through using the simulation program to analyze it both 

from a hauler´s perspective and an environmental perspective. 

The results from this thesis confirm previous research assumptions and indications that 

there exist many inefficient and unprofitable transports due to poor choice of distribution 

strategy. Furthermore, the simulation analyses indicate a discrepancy in incentives for 

improving transport efficiency from a financial perspective and the incentives for improving 

from an environmental perspective. 

Keywords: Transportation, Distribution Network Design, Simulation, Scenario Analysis.  

  



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ACKNOWLEDGEMENTS 

This Master of Science thesis was conducted between September of 2011 and April of 

2012 as part of the Master Degree Program in Supply Chain Management at Chalmers 

University of Technology in Gothenburg, Sweden. The thesis was carried out as a case study 

and a project for Foria AB’s office in Nyköping.   

We would first of all like to send our gratitude to all the people working at Foria AB. 

This thesis would not have been possible without their help and cooperation. Thanks to the 

management team for their insights on the problem, thanks to the transport planners for 

helping us understand their work process, and thanks to the drivers for their guiding in an 

inspirational field study. We would especially like to thank Mr. Stefan Palmgren, our 

supervisor and tutor at Foria AB. Thank you for your support, guidance and feedback through 

the entire work process of this thesis. 

We also want to thank our Chalmers supervisor Dr. Henrik Sternberg for his constant 

encouragement to pursuit excellence. We also want to acknowledge the staff at the division of 

Logistics and Transport at Chalmers University of Technology who has contributed to our 

learning along the two year master programme. 

Last but not least, we want to recognize our computer systems engineering friends, 

Manuel Espinoza, Salvador Melendez and Guillermo Leon. Their help and support during the 

work of coding our algorithm into a functioning simulation program saved us a great deal of 

time. 

  

Gothenburg, April 2012. 

Adrian Ruiz de la Llata and Oscar Kjellberg 

 

 

 

 

 

 

 

 

 



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TABLE OF CONTENTS 

ABSTRACT ................................................................................................................................................... I 

ACKNOWLEDGEMENTS ........................................................................................................................ II 

TABLE OF CONTENTS ........................................................................................................................... III 

ABBREVIATIONS .................................................................................................................................. VII 

LIST OF FIGURES ................................................................................................................................ VIII 

LIST OF TABLES ..................................................................................................................................... IX 

1 INTRODUCTION ................................................................................................................................ 1 

1.1 Background and motivation .......................................................................................................... 1 

1.2 Problem Area .................................................................................................................................... 2 

1.3 Purpose .............................................................................................................................................. 3 

1.4 Research questions .......................................................................................................................... 4 

1.5 Delimitations .................................................................................................................................... 4 

1.6 Outline of the thesis ......................................................................................................................... 4 

1.7 The focal company - Foria AB ........................................................................................................ 6 

2 RESEARCH APPROACH ................................................................................................................ 7 

2.1 Research strategy for the thesis ........................................................................................................ 7 

2.2 Methodology ....................................................................................................................................... 8 
2.2.1 Literature review ........................................................................................................................ 8 

2.3 The problem analysis models .......................................................................................................... 12 
2.3.1 Qualitative analysis methods .................................................................................................... 12 
2.3.2 How was the financial analysis tool created and analyzed? ..................................................... 13 
2.3.3 How was the simulation model created and analyzed? ............................................................ 13 

2.4 Validation ......................................................................................................................................... 13 
2.4.1 Financial analysis ..................................................................................................................... 13 
2.4.2 Simulation model ..................................................................................................................... 13 

2.5 Reliability .......................................................................................................................................... 14 

3 LITERATURE REVIEW ................................................................................................................ 15 

3.1 Logistic performance ....................................................................................................................... 15 
3.1.1 Logistics Efficiency and Effectiveness ..................................................................................... 15 

3.2 Distribution and Transportation Network Design ........................................................................ 18 
3.2.1 Distribution network design (DND) ......................................................................................... 18 
3.2.2 Transportation network design (TND) ..................................................................................... 20 

3.3 Distribution and Transportation Optimization ............................................................................. 21 
3.3.1 Traveler Sales Problem (TSP) .................................................................................................. 21 



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3.3.2 Greedy Algorithm ..................................................................................................................... 22 

3.4 Environmental implication of transportation ................................................................................ 23 
3.4.1 Approaches to Reduce emissions ............................................................................................. 23 
3.4.2 Environmental performance in transportation .......................................................................... 24 

4 EMPIRICAL FINDINGS ................................................................................................................ 26 

4.1 Operations of interest for this project ............................................................................................ 26 
4.1.1 Historically ............................................................................................................................... 26 
4.1.2 The new contract – a shift in responsibilities ........................................................................... 26 

4.2 The involved actors .......................................................................................................................... 27 
4.2.1 Transport planners at Foria ....................................................................................................... 27 
4.2.2 Foria associated haulers ............................................................................................................ 28 
4.2.3 The supplier of cargo - Lantmännen AB, Division Lantbruk ................................................... 29 
4.2.4 The end customers - The farmers ............................................................................................. 29 

4.3 What they are transporting and order information ...................................................................... 30 

4.4 Case Fleet .......................................................................................................................................... 31 

4.5 Transportations costs ....................................................................................................................... 32 
4.5.1 Costs for Foria using their own drivers .................................................................................... 32 
4.5.2 Costs to use and external network provider .............................................................................. 33 
4.5.3 Freight calculation and volumetric weight ............................................................................... 33 
4.5.4 Costs for the Foria associated haulers ...................................................................................... 33 

5 THE SIMULATION MODEL ........................................................................................................ 34 

5.1 How the program works .................................................................................................................. 34 
5.1.1 Input to the program ................................................................................................................. 34 
5.1.2 Output from the program .......................................................................................................... 35 
5.1.3 Working conditions and assumptions for the simulation model ............................................... 35 

5.2 The algorithm and its parts ............................................................................................................. 36 
5.2.1 The start of the simulation ........................................................................................................ 36 
5.2.2 Choosing one of two different LTL-building processes ........................................................... 37 
5.2.3 Calculation of the distance traveled by the truck ...................................................................... 40 
5.2.4 How alpha was estimated ......................................................................................................... 41 
5.2.5 Validation of model .................................................................................................................. 43 
5.2.6 Delimitations to the simulation model ...................................................................................... 45 

6 ANALYSES ...................................................................................................................................... 46 

6.1 Root cause problem analysis of the agriculture supplies distribution ......................................... 47 
6.1.1 Physical distribution activities restraining profitability ............................................................ 49 
6.1.2 Planning activities are restraining profitability ......................................................................... 50 
6.1.3 External activities and factors are limiting profitability ........................................................... 51 

6.2 Visualization framework ................................................................................................................. 51 

6.3 How to address the case company´s problems ............................................................................... 53 
6.3.1 Proposals of different distributions models .............................................................................. 53 
6.3.2 How would the focal company be able to implement these solutions? .................................... 54 

6.4 Financial analysis ............................................................................................................................. 56 
6.4.1 Foria Price Structure ................................................................................................................. 56 



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6.4.2 EDN price Structure ................................................................................................................. 56 
6.4.3 Comparison between Foria prices and EDN prices .................................................................. 56 
6.4.4 The breaking Point ................................................................................................................... 57 
6.4.5 The Breaking point with volumetric weight adjustment ........................................................... 58 

6.5 Simulation analysis – Hauler’s perspective ................................................................................... 64 
6.5.1 Outsource to an EDN all orders up to 450 kg ........................................................................... 64 
6.5.2 Outsource to an EDN all orders up to 780 kg ........................................................................... 65 
6.5.3 Outsource to an EDN all orders up to 1000 kg ......................................................................... 65 
6.5.4 Outsource to an EDN all orders up to 1500 kg ......................................................................... 66 
6.5.5 Outsource to an EDN all orders up to 2000 kg ......................................................................... 66 
6.5.6 Outsource to an EDN all orders up to 3000 kg ......................................................................... 66 

6.6 Environmental analysis ................................................................................................................... 68 
6.6.1 Assumptions and Specifications ............................................................................................... 68 
6.6.2 Pollutant Emission in different transportation setups scenarios ............................................... 68 
6.6.3 Environmental Qualitative Analysis ......................................................................................... 75 

7 RESULTS ......................................................................................................................................... 76 

7.1 Results from the analyses ................................................................................................................ 76 
7.1.1 Results from the case study analysis ........................................................................................ 76 
7.1.2 Results From the financial analysis .......................................................................................... 77 
7.1.3 Results From the simulation analysis – Hauler´s perspective .................................................. 77 
7.1.4 Results From the environmental analysis ................................................................................. 78 
7.1.5 Combined results from the analyses ......................................................................................... 78 

7.2 Answers to research questions ........................................................................................................ 79 
7.2.1 RQ1 .......................................................................................................................................... 79 
7.2.2 RQ2 .......................................................................................................................................... 80 

8 RECOMMENDATIONS TO FORIA ............................................................................................ 81 

8.1 Short term ......................................................................................................................................... 81 

8.2 Long term ......................................................................................................................................... 82 

9 CONCLUSIONS .............................................................................................................................. 83 

10 REFERENCE LIST ......................................................................................................................... 84 

APPENDIX A – THE SIMULATION ALGORITHM .............................................................................. I 

APPENDIX B – IMAGES AND TABLES ............................................................................................... IV 

APPENDIX C – INITIAL TOPICS FOR THE OPEN INTERVIEWS ............................................. VIII 

APPENDIX D – SIMULATION RESULTS HAULERS PERSPECTIVE ............................................. X 

APPENDIX E – ENVIRONMENTAL TABLES AND FIGURES ..................................................... XVI 

APPENDIX F – FINANCIAL ANALYSIS (QLIKVIEW) .................................................................. XXI 

APPENDIX G - SCALED MAP OF SWEDEN ................................................................................. XXVI 



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APPENDIX H –SUMMARIES OF INTERVIEWS, MEETINGS AND FIELD STUDIES ......... XXVII 

APPENDIX I – FINANCIAL ANALYSIS OF VEHICLE DIFFERENTIATION SCENARIOS .. XLII 

APPENDIX J – ESTIMATION OF COST FOR UNREIMBURSED WORK ............................... XLIII 

APPENDIX K – VISUAL VALIDATION OF SIMULATION MODEL AND DISTANCES .......XLIV 

APPENDIX L – ALPHA SENSITIVITY ANALYSIS ......................................................................... LIV 

 



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ABBREVIATIONS 

 

API Application Programming Interface 

BI Business Intelligence  

CCU Cargo Capacity Utilization 

DND Distribution Network Design 

EDN External Distribution Network 

EU European Union 

GHG Greenhouse Gas 

GPS Geographical Positioning System 

HDV Heavy Duty Vehicle 

ICT Information and Communication Technology 

IS Information Systems 

IT Information and Technology 

LCV Light Cargo Vehicle 

LTL Less Than Truckload 

MDV Medium Duty Vehicle 

NTM Network for Transport and Environment 

SCM Supply Chain Management 

SCP Shipping Consolidation Problem 

SEK Swedish Kronor 

TND Transportation Network Design 

TT Transit Time 

TSP Traveler Sales Problem 

T&D Transportation and Distribution 

US United States 

VSM Value Stream Mapping 

WMS Warehouse Management Inventory 

 

 

 

 

  



viii 

 

LIST OF FIGURES 
Figure 1 Region where Foria is responsible for Lantmännen´s agriculture supplies distribution ........................... 3 

Figure 2 Organizational chart of Foria AB ............................................................................................................. 6 

Figure 3 Visualization model (Allenström and Linger, 2010) .............................................................................. 12 

Figure 4 Logistics Performance Model (Fugate et al., 2010) ................................................................................ 15 

Figure 5. Designing  the Optimal Distribution Network (Sharma et al., 2008) .................................................... 19 

Figure 6 Foliated transportation network (Persson and Lumsden, 2006) .............................................................. 21 

Figure 7 The four greedy principles with implications (Curtis, 2003) .................................................................. 22 

Figure 8 Value stream mapping of current operations .......................................................................................... 26 

Figure 9 Service Area and sub regions with actual customer visits during 2010-2011 ......................................... 29 

Figure 10 1: The truck, 2: The trailer, 3-4: The crane, 5-6: The portable forklift ................................................. 32 

Figure 11 Interface of the simulation model ......................................................................................................... 34 

Figure 12 Start of simulation algorithm ................................................................................................................ 36 

Figure 13 Simulation algorithm, one or several "Prio1" orders ............................................................................ 37 

Figure 14 Simulation model available orders........................................................................................................ 37 

Figure 15 Simulation algorithm, LTL-shipment building process 1 ..................................................................... 39 

Figure 16 Simulation algorithm, LTL-shipment building process 2 ..................................................................... 40 

Figure 17 Simulation Algorithm, distance calculation .......................................................................................... 41 

Figure 18 Simulation Algorithm, alpha calculation .............................................................................................. 41 

Figure 19 Top 20 sub-regions with unique visits .................................................................................................. 42 

Figure 20 Relationship of the three numerical analyses ........................................................................................ 46 

Figure 21 Root cause analysis of problems for the case studied ........................................................................... 48 

Figure 23 Vehicle differentiation .......................................................................................................................... 53 

Figure 23 Distribution setup with a transshipment terminal ................................................................................. 54 

Figure 24. Outline of the volumetric weight analysis ........................................................................................... 59 

Figure 25 Errors in weight column in the historic order lines data ....................................................................... IV 

Figure 26 Typical order, pallets with animal feed ................................................................................................. IV 

Figure 27 Foria associated haulers trucks .............................................................................................................. V 

Figure 28 Screenshot from the transport planners IS interface. ............................................................................. V 

Figure 29 Screenshot from a few of the original raw orderliness from the year of shipping data......................... VI 

Figure 30 Price matrix combined below 1000 kg (part of) ................................................................................... VI 

Figure 31 Price matrix combined above 1000 kg (part of) .................................................................................. VII 

Figure 32. Qlikview overview: Regions by Volume (Weight and Order) ......................................................... XXI 

Figure 33 Qlikview overview: Weight Range occurrence ................................................................................. XXI 

Figure 34. County and Sub-region prices comparison between Foria and EDN ............................................... XXII 

Figure 35. The Breaking Point (Foria vs. EDN) ............................................................................................... XXII 

Figure 36. Breaking point overview with the different sensitive volumetric weight adjustments ................... XXIII 

Figure 37. The Breaking Point volumetric adjustment (Conservative Scenario) ............................................ XXIV 

Figure 38. The Breaking Point volumetric adjustment (Max Profit Scenario) ................................................ XXIV 

Figure 39. Conservative Scenario price comparison by county (0 kg – 299 kg) ............................................... XXV 

Figure 40. Conservative Scenario price comparison by county (350 kg – 449 kg) ........................................... XXV 

Figure 41. Max Profit Scenario price comparison by county (0 kg – 299 kg) .................................................. XXV 

Figure 42. Max Profit Scenario price comparison by county (350 kg – 449 kg) .............................................. XXV 

 
  

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LIST OF TABLES 
Table 1 Summary of meetings and interviews ...................................................................................................... 10 

Table 2 Summary of field studies and orientation visits ....................................................................................... 11 

Table 3 Summary of quantitative data collection .................................................................................................. 11 

Table 4 Summary of articles relating to logistic efficiency and effectiveness ...................................................... 16 

Table 5 Proposed Distribution Networks Designs (Chopra, 2003) ....................................................................... 18 

Table 6 Proposed Transportation Networks Designs (Chopra and Meindl, 2007) ................................................ 20 

Table 7 Greedy Algorithms Classification (Curtis, 2003) ..................................................................................... 22 

Table 8. Link between emissions from transport and environmental impact (Thomas and Harrison, 2004) ........ 23 

Table 9. Vehicle concepts/types and cargo capacity (NTM-Road, 2008) ............................................................. 25 

Table 10 Calculation of alpha value ...................................................................................................................... 42 

Table 11 Simulation distances and real world distances ....................................................................................... 44 

Table 12 Confidence interval calculation of simulation distances ........................................................................ 44 

Table 13 Visualization framework adapted from Allenström & Linger (2010) .................................................... 52 

Table 14 Solutions to the issues identified in the root cause analysis presented through Allenström & Linger 

(2010) visualization framework ...................................................................................................... 55 

Table 15. Foria price structure obtained in Qlikview. ........................................................................................... 56 

Table 16. EDN price structure obtained in Qlikview. ........................................................................................... 56 

Table 17. Difference between Foria prices and EDN prices ................................................................................. 57 

Table 18. Breakpoint 450 kg (EDN 0-449 kg and Foria 450 kg– up) ................................................................... 57 

Table 19. Safety factor 1,5X (Breakpoint 299kg) ................................................................................................. 59 

Table 20. Safety factor 2X (Breakpoint 249kg) .................................................................................................... 60 

Table 21. Safety factor 3X (Breakpoint 149kg) .................................................................................................... 60 

Table 22. Safety factor 5X (Breakpoint 79kg) ...................................................................................................... 60 

Table 23. Conservative Scenario results overview ............................................................................................... 61 

Table 24. Order weight adjustment for Max Profit Scenario ................................................................................ 62 

Table 25. Max Profit Scenario overview .............................................................................................................. 63 

Table 26. Pre-defined data for the environmental performance calculations in scenario 1 (Simulation) .............. 68 

Table 27. Summary of the fuel consumption for scenario 1 ................................................................................. 69 

Table 28. Total pollutant emissions (Scenario 1 simulation with 574 trucks). ..................................................... 69 

Table 29. Pre-defined data for the environmental performance calculations in scenario 2 (Simulation) .............. 70 

Table 30. Summary of the fuel consumption for scenario 2 ................................................................................. 70 

Table 31. Total pollutant emissions (Scenario 2 simulation with 340 MDV Lorry trucks and 515 HDV + trailer).

 ........................................................................................................................................................ 71 

Table 32. Total pollutant emissions (Scenario 2 simulation with 855 trucks). ..................................................... 71 

Table 33. Pre-defined data for the environmental performance calculations in scenario 3 (Simulation) .............. 72 

Table 34. Summary of the fuel consumption for scenario 3 ................................................................................. 72 

Table 35. Total pollutant emissions (Scenario 3 simulation with 429 MDV Lorry trucks and 478 HDV + trailer).

 ........................................................................................................................................................ 73 

Table 36. Total pollutant emissions (Scenario 3 simulation with 907 trucks). ..................................................... 73 

Table 37. Total pollutant emissions Scenario 1 versus Scenario 2 ....................................................................... 74 

Table 38. Total pollutant emissions Scenario 1 versus Scenario 3 ....................................................................... 74 

Table 39 Comparison of simulation results, outsourcing all orders up to 450 kg .................................................. X 

Table 40 Comparison of simulation results, outsourcing all orders up to 780 kg ................................................. XI 

Table 41 Comparison of simulation results, outsourcing all orders up to 1000 kg .............................................. XII 

Table 42 Comparison of simulation results, outsourcing all orders up to 1500 kg ............................................ XIII 

Table 43 Comparison of simulation results, outsourcing all orders up to 2000 kg ............................................ XIV 

Table 44 Comparison of simulation results, outsourcing all orders up to 3000 kg .............................................. XV 

Table 45. Fuel consumption for the vehicles concepts/types (NTM, 2008) ....................................................... XVI 

Table 46. Data used to calculate fuel consumption based on Cargo Capacity and type of road (Scenario 1) .... XVI 



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Table 47. Data used to calculate fuel consumption based on Cargo Capacity and type of road (Scenario 2) ... XVII 

Table 48. Data used to calculate fuel consumption based on Cargo Capacity and type of road (Scenario 3) ... XVII 

Table 49. Emissions for HVD + trailer truck in urban road type (NTM-Road, 2008) ..................................... XVIII 

Table 50. Emissions for HVD + trailer truck in rural road type (NTM-Road, 2008) ....................................... XVIII 

Table 51. Emissions for HVD + trailer truck in motorway road type (NTM-Road, 2008) ................................ XIX 

Table 52. Emission for MVD Lorry truck in urban road type (NTM-Road, 2008) ............................................ XIX 

Table 53. Emission for MVD Lorry truck in rural road type (NTM-Road, 2008) ............................................... XX 

Table 54. Emission for MVD Lorry truck in motorway road type (NTM-Road, 2008) ...................................... XX 



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1 INTRODUCTION 

This chapter starts with a brief background description and the underlying reasons for this 

thesis. Following this the purpose, the research questions, and the delimitations for this research are 

presented. 

1.1 BACKGROUND AND MOTIVATION 

The demand for transportation is increasing on a yearly basis (Piecyk and McKinnon, 

2011) and it is not only the shipped weight that is increasing. Supply chain efficiency 

measures such as reducing local warehouse levels increase the demand for more frequent, 

timely and small deliveries. Most industries, as well as the public, depend heavily on trucking 

companies to solve their demand for cargo transportation and motor hauler transportation is 

the predominantly used mode of inland transportation. In Sweden this compose  around 60% 

of the total inland freight transported weight (International Transportation Forum, 2010). 

Truck hauling ventures is normally a low margin business with fierce competition, 

usually margins lie within the range of 2-4% (SIKA Statistik, 2009). The   transportation 

execution is often easy to copy which means that most of the actors mainly compete on price 

and relationships have often been at arm’s length (Belman et al., 2005). An example of this is 

the recent market penetration of low-cost eastern European haulers on the Nordic market 

(Sternberg, 2011a), they are pushing an already low price down further and putting pressure 

on local haulers to increase their operation efficiency. However, many of actors the actors 

involved in distribution have realized the potential with closer relationships and logistic 

alliances have formed where one actor are providing a larger package of a value adding 

services (Lumsden, 2006b) and not only the actual transportation, e.g. planning processes or 

outsourcing the entire distribution process and handing over control and responsibility to an 

external expert (Esper and Williams, 2003).  

Another important reason is to improve transportation efficiency is to reduce the 

environmental impact of transportation. Thomas and Harrison (2004) agree on major impacts 

such as human health implications, dilution of the ozone layer, greenhouse effect, hyper 

fertilization, acidification and destruction of landscapes. The transportation sector in general 

stands for 19% of the greenhouse gases (GHG) emitted in the European Union (EU-27) and 

out of this, road transportation share is up to 90%  (Huggins, 2009). Reducing air pollution is 

already a priority for policymakers and through the establishment of regulations and 

environmental policies (Thomas and Harrison, 2004), policymakers are increasing pressure on 

Transportation and Distribution (T&D) companies to improve operating efficiency to reduce 

their environmental impact. Moreover, society is also pressuring companies towards being 

“green” and becoming environmental friendly has increased its importance as an order 

qualifier in the market (Jonsson, 2008, McKinnon, 2003). 

The above-mentioned developments are the underlying motives for this thesis. A lot of 

research has previously been done in the field of transportation efficiency. E.g. Samuelsson 



2 

 

and Tilanus (1997) described a general framework for measuring the physical efficiency of 

Less-than-truckload (LTL) transports, Crainic and Roy (1988) developed a mathematical 

model for the tactical planning of freight transportation, Chapman et al. (2003) discussed how 

innovation on logistics firms can help to re-design their structures and enhance relationships 

through information sharing and coordination, and Kalantari and Sternberg (2009) described 

the conceptual model of foliated transportation networks, which aims at increasing the 

efficiency of transportation networks by increasing resource utilization. However, an 

identified gap in previous research is the study and numerical analysis of real world problem 

based on actual transportation and not an optimization of an ideal situation or research limited 

to qualitative analysis and suggestions. Sternberg (2011b) states that there exist a lot of 

wasteful transport operations due to inefficient strategies and lack of knowledge about what 

drives the revenues and what drives the costs. This leads road transport operators to carry 

many unprofitable assignments. The close relationship and correlation between transport 

efficiency, profitability and environmental sustainability makes addressing unprofitable an 

interesting area of research since it will also likely improve environmental sustainability. 

This thesis has its base in a case study of the Swedish logistics firm Foria AB. More 

specifically, the part of their operations which is responsible for distributing agriculture 

supplies to farmers in mid-eastern Sweden from Lantmännen´s central warehouse in Västerås. 

To analyze improvements to their operations a financial sustainability analysis is made 

comparing the impact of using an External Distribution Network (EDN) for part of this 

distribution. Secondly, a simulation model that analyzes the financial impact to local haulage 

companies and impact to transportation efficiency and environmental impact from different 

distribution design scenarios are developed. From this case study general conclusions are 

drawn.  

1.2 PROBLEM AREA 
The distribution of agricultural supplies is essentially the distribution of general cargo on 

pallets in a LTL setup. Our focal company shares the above described need for efficiency 

improvements, as most T&D companies have in order to stay competitive. Furthermore, Foria 

as the sponsors of this thesis has a request to receive suggestions that would improve the 

financial and environmental performance for their agriculture supplies distribution, which is 

an important aspect of this thesis. This means that the general research area for this thesis is 

methods for improving financial and environmental performance in the distribution of general 

cargo.  

More specifically, it examines how distribution of general cargo from a central 

warehouse could be made more financially and environmentally sustainable through the use 

of fleet optimization techniques and/or the use transshipment terminals. The basis for 

suggestions and conclusions is the case study done at Foria’s operations for Lantmännen in 

mid-eastern Sweden.  

This thesis investigates the economic and environmental impact of modifying, in terms 



3 

 

of fleet selection and adding transshipment terminals, Foria´s distribution model for 

agriculture products from Lantmännen´s central warehouse in Västerås to farmers in mid-

eastern Sweden. The changes are then compared to the current distribution model and one 

year of shipment data for these shipments will be used as input to the analysis models and as 

reference for comparison. 

From this case study a recommendation to focal company along with general conclusions 

on transport efficiency improvements regarding LTL-shipments of general cargo will be 

presented.  

1.3 PURPOSE 
This thesis analyzes the current distribution setup, in terms of fleet performance and 

delivery routes, the distribution of agriculture supplies from Lantmännen terminals in 

Västerås to the end user in the counties of Östergötland, Södermanland, Närke, northern 

Småland, Västmanland and Uppland as is shown in Figure 1.  

With this analysis as a reference, the purpose is to propose distribution efficiency 

improvements so Foria will gain a better financial performance along with a reduced 

environmental impact from these shipments, without having a negative effect on the current 

service level. Moreover, these results are then to be analyzed to form general 

recommendations on how the distribution of general cargo from a central warehouse could be 

made more efficient and sustainable. 

 

Figure 1 Region where Foria is responsible for Lantmännen´s agriculture supplies distribution 

 



4 

 

1.4 RESEARCH QUESTIONS  
Derived from the problem identification and the purpose two sets of research questions 

were formulated. These research questions are the starting point from where the literature 

review, empirical data collection and analysis were carried out. 

RQ1 are derived from the focal company´s need and the specific problem identification 

from the analyzed transport operations. RQ1 is split into four parts and presented below. 

RQ1-1. What transport efficiency improvements should be implemented at Foria to increase 

the financial sustainability for the agriculture supplies distribution?  

RQ1-2. How high are the possible financial gains for Foria?  

RQ1-3. What environmental effects will the proposed transport efficiency improvements 

render? 

RQ1-4. How will proposed changes affect the local hauler companies that today perform 

these transports? 

Derived from the RQ1, which is targeted directly towards the focal company´s situation, 

RQ2 generalizes the results from the case study and puts them into a broader context.  

RQ2. Given the conclusions from RQ1: How can distribution of general cargo from a central 

warehouse to a wide array of drop-of points become more sustainable?  

1.5 DELIMITATIONS  
This thesis focuses on improvements on the T&D activities that the focal company will 

perform in the new arrangement. All other activities performed by other external actors are 

out of the scope for potential improvements. Therefore, Lantmännen’s distribution warehouse 

in Västerås, the customers (farmers) location, and the products are fixed external factors that 

are not feasible to change and they set the boundaries for any realistic improvement. 

1.6 OUTLINE OF THE THESIS 
This section presents how the thesis is structured and under what headings different 

segments will be found. 

1 Introduction  

This chapter starts with a brief background description and the underlying reasons for 

this thesis. Following this the purpose, the research questions, and the delimitations for this 

research are presented. 

2 Research approach 

In this chapter the research method this thesis is presented. It starts with a description of 



5 

 

the general strategy for solving Foria’s problems, following this is a description of how data 

collection, problem analysis, and data analysis was done. Finally the reliability and validity of 

the results are discussed. 

3 Literature review 

This chapter describes and explains the academic literature that supports and function as 

reference in the analysis part of the master thesis. Three main areas will be presented and 

broken down into different subareas: Efficiency in LTL Transportation, DND and 

Fleet/Vehicle Differentiation. 

4 Empirical findings  

In this chapter the empirical findings are presented. This includes the roles, 

responsibilities and activities of the involved actors. Furthermore, the nature of the goods that 

is being transported as well as the properties of the vehicles being used for these transports 

today. 

5 The simulation model 

This chapter starts with describing the reasons for the constructed simulation model; 

following this, a through description of how the simulation model works in detail in the most 

important parts is presented. How it tries to mimic the behavior of a transport planner, how it 

creates shipments and simulates a year of transportation. Finally the limitations to the model 

and how it was validated is presented.   

6 Analyses 

In this chapter theory and empirical findings form the base for analysis. An initial 

problem analysis of the focal company´s situation within the agriculture supplies distribution 

identifies thirteen problems. Possible solutions to the identified problems are examined with 

three analyses from three perspectives; financial perspective, haulers perspective, and 

environmental perspective. These analyses will then acts as a reference from which general 

efficiency improvements can be sought in the research area of improving transport efficiency 

of general cargo from a central warehouse. 

7 Results 

This chapter presents the results from the different analyses made. First the qualitative 

general results from the case study analysis are presented; following this the quantitative 

results from the financial analysis as well as the two simulation analyses are presented. 

Finally the last subchapter answers research question 1 and research question 2.  

  



6 

 

8 Recommendations to Foria 

In this chapter we provide our recommendations to the company based on the analysis 

and conclusions drawn. Recommendations are given on both a short-term and long-term 

perspective. 

 9 Conclusions 

In this chapter the results of the research are discussed from a managerial and theoretical 

perspective. Lastly possibilities for future research and improvements are highlighted. 

1.7 THE FOCAL COMPANY - FORIA AB 
Foria is one of the biggest transport- and heavy equipment service companies in Sweden. 

They are mainly active in the middle of Sweden on the east coast; however, through 

partnerships with other actors they are able to offer services all over Sweden. During 2010, 

they had a turnover of 1.237 billion SEK, and made a profit of 9 million SEK. They have 

approximately 1000 units in their fleet of vehicles that are operating in their four different 

business areas, “Civil engineering services”, “Logistic services”, “Industry services” and 

“Environmental services”. (See Figure 2) 

 

This master thesis project has collaborated with Foria´s Business Development & Traffic 

Control in their business area Logistics Services.  Within Logistics Services, they work with 

distribution services, long-haul traffic services, terminal and warehouse services, courier 

services, moving, relocation services, and total logistics solutions with outsourcing.  

CEO 

Civil Engineering 
Services  

Logistcs Services Industry Services 
Environmental 

Services 

Finance 
Business 

Development & 
Traffic Control 

Quality and 
Environment 

Sales & 
Marketing 

Human Resources Properties 

Figure 2 Organizational chart of Foria AB 



7 

 

2 RESEARCH APPROACH 

In this chapter the research method this thesis is presented. It starts with a description of the 

general strategy for solving Foria’s problems, following this is a description of how data collection, 

problem analysis, and data analysis was done. Finally the reliability and validity of the results are 

discussed. 

2.1 RESEARCH STRATEGY FOR THE THESIS 
The starting point for this thesis was Foria’s problem since it is written for, and in 

cooperation with Foria AB. A general way of describing the research methodology, different 

steps and parts of this thesis are: 

 PROBLEM FORMULATION 

The problem at the focal company is specific and practical in its nature. This practical 

problem was generalized and broken down to its core to formulate research questions and 

to define the purpose of the thesis. After an initial orientation of the focal company’s 

operations an overview of relevant literature were carried out to so a direction for the 

research could be formulated.  

 LITERATURE REVIEW  

After an initial orientation, a deeper search of articles relating to the field was 

conducted and connected to our problem. However, continuously through the work with 

the thesis, study and review of articles, books and other media that related to the field of 

research have been done as the problem evolved over time. 

 EMPIRICAL DATA COLLECTION 

The thesis aims at contributing to theory through solving a real world problem; 

therefore gathering of empirical data has been crucial to the thesis. Empirical data was 

collected both qualitatively and quantitatively.  

 SIMULATION AND BUSINESS INTELLIGENCE ANALYSES 

As the understanding of the problems developed, two separate solutions to Foria’s 

problems emerged. In order to answer Foria’s two main questions, what is the best 

distribution network alternative and how should they motivate associated hauler 

companies’ to change according to this?  The first question was approached through a 

financial analysis through building an analysis tool in the Business Intelligence (BI) 

program Qlikview. The haulers’ perspective through analyzing the impact in a simulation 

analysis created for this purpose. Environmental analyses were finally made through 

assessing the pollutant emissions impact of different distribution scenarios. 

 VALIDATION OF RESULTS 

The results are validated both quantitatively through a statistical analysis and 

qualitatively through examination and scrutiny of the models and its results to identify 

possible errors.  

 THEORETICAL DICSUSSION OF RESULTS 

Finally the results from analysis were related to previous reviewed literature in a 

theoretical discussion of the results. 



8 

 

 CONCLUSIONS AND RECOMMENDATIONS TO FORIA 

A visual and numerical financial analysis was done with Qlikview and a transport 

efficiency analysis through our simulation model. Conclusions were drawn based on these 

results and considering these results; recommendations to Foria were given both from a 

short-term perspective and a long-term perspective.   

2.2 METHODOLOGY 
Both a literature review and an empirical data gathering were carried out. The following 

subchapter will describe in detail how these were performed. 

2.2.1 LITERATURE REVIEW 
After an initial background orientation and problem description by Foria a deep literature 

review and general search within the fields of “Efficiency and Effectiveness in transportation” 

“Distribution and Transportation Network” and “Route and Fleet optimization” was done. 

A selection of different e-journals was made based on significance to the topics 

Transportation/Logistic/Supply Chain Management from the pool of e-journals available at 

the Chalmers library through their licensing agreements. The following e-journals were 

identified to be connected with these topics, available at Chalmers and initially searched:  

 Journal of Business Logistics (JBL) (ISSN: 0735-3766) 

 International Journal of Logistics (IJL) (ISSN: 1367-5567) 

 International Journal of Logistics Management (IJLM) (ISSN: 0957-4093) 

 International Journal of Retail and Distribution Management (IJRDM)(ISSN: 0959-

0522) 

 International Journal of Physical Distribution and Logistics Management (IJPDLM) 

(ISSN: 0960-0035) 

 Light and medium truck (LMT) (ISSN: 1091-9651) 

 Logistic and transportation review (LTR) (ISSN: 0047-4991) 

 Logistic Management (LM) (ISSN: 1540-3890) 

 Professional Distributor (PD) (ISSN: 1553-6211) 

 Transport Reviews (TR) (ISSN: 0144-1647) 

 Transportation Journal (TJ) (ISSN: 0041-1612) 

 Transportation Research Part B: Methodological (TRB) (ISSN: 0191-2615) 

 Transportation Research Part D: Transport and Environment (TRD) (ISSN: 1361-

9209) 

 Transportation Research Part E: Logistics and Transportation Review (TRE) (ISSN: 

1366-5545) 

 International Journal of Integrated Supply Management (IJISM) (ISSN: 1477-5360) 

 

A complementary search at Google Scholar was also performed.  

When articles of interest were found the method of ancestry approach was implemented 

where searched through the reference lists of relevant articles narrowing the search net related 

to the topics of this thesis. Finally as the project progressed, information relevant to issues 



9 

 

that rose was searched for at Google and Google Scholar. 

To start with the literature review the following six search strings were used: 

 “Transportation efficiency + effectiveness”  

 “Fleet optimization” 

 “Route optimization”,  

 +Distribution +”milk-runs” 

 +Distribution +”direct deliveries” 

 +Distribution +”environmental impact” 

 

First, for each search term it was looked at the first 30 results for every journal and also 

the 50 first results at Google Scholar. Based on the title of the article it was decided whether 

to read the abstract or disregard the article directly. 

 

The second filter was based on the abstract. After the abstract had been read a decision 

was made whether further reading was of interest or if the article can be discarded at this 

stage. 

2.2.1 Empirical data 

Empirical data was collected in both qualitative forms from interviews, meetings and 

field studies as well as through quantitative form from shipping data and pricelists. The 

gathering, understanding and analysis of the empirical data were one of the central parts of 

this thesis.  

Structuring the empirical data in a process map enabled the creation of an algorithm for 

representing the T&D activities. The historic shipping and sales data along with prices was 

the input for the cost calculations for the financial analysis that was performed to find the 

optimum mix of own drivers versus an EDN. The historic data was also the input to the 

simulation model emulating a year of shipments. 

Qualitative empirical data collection 

A two-day study visit to Foria´s office in Nyköping and Lantmännen´s warehouse in 

Västerås was held to broaden our understanding. The aim was to understand the perspectives 

of the different actors involved in case. Gather empirical data through interviews and also get 

an overview of the operations. During the two days several meetings and interviews were held 

with different actors involved in these shipments, see Table 1.  

The method of choice for conducting these meetings and interviews were in a semi-

structured style. Questions and topics were prepared in advance (see Appendix C) however 

the flow of conversation was flexible and new questions were allowed to rise during the 

interview and meetings and the topics were mainly used as support and starting points. To not 

miss any information or hinder the flow of ideas and thoughts with intensive note taking the 

conversations were recorded and revised afterwards. 



10 

 

To further deepen the authors understanding of the day-to-day operations, how these 

transports are performed, and the difficulties the drivers encounter and gain insight to possible 

improvements, field studies and orientation visits were held, see Table 2.  

This gave the author’s valuable insights and understanding of the operations that would have 

been hard to learn otherwise. The extent of such problems as e.g. not contracted and 

unreimbursed work activities were experienced firsthand. When creating and validating the 

simulation model this enhanced understanding was of great value. 

Table 1 Summary of meetings and interviews 

 Meetings and interviews 

 With Type Objective 

1 Foria´s management 

accompanied with a 

management 

representative from 

Lantmännen 

Meeting and a 

semi-structured 

group interview  

Get a better understanding of Foria, who they and their 

operations.  

Get a better understanding of Foria’s problem from a 

management perspective.  

Make sure that the authors and the management group was on 

the same page regarding the projects purpose and goals. 

2 Foria´s transport 

planners 

Meeting and a 

semi-structured 

group interview  

Get an understanding of how the transport planners work with 

these shipments. 

Get a better understanding of Foria’s problem from a 

transport planner perspective. 

3 A Foria associated 

hauler and driver 

Meeting and a 

semi-structured 

interview  

Get an understanding of how the drivers and local haulers 

perceive these shipments 

Get a better understanding of Foria’s problem from a driver 

perspective. 

4 A farmer at the 

receiving end of these 

shipments. 

Meeting and a 

semi-structured 

interview  

Get an understanding of the receiver of these shipments 

perceive them. 

Get knowledge of any problems that the farmers might have 

regarding these shipments. 

 

  



11 

 

Table 2 Summary of field studies and orientation visits 

 Field studies and orientation visits 

 Where With Objective 

1 Foria´s operation office at 

Nyköping 

Transport planners and 

Foria management 

Get an orientation of the day-to-day work with these 

shipments at Foria 

2 

 

Lantmännen’s warehouse 

in Västerås 

Foria management, 

transport planners and 

Lantmännen 

representatives 

Get an orientation of the day-to-day work with these 

shipments at Lantmännen and possible limitations at 

the starting end for these shipments. 

3 Started with loading in 

Västerås and riding with a 

hauler during one day and 

unloading at various farms. 

A Foria associated 

driver for a local 

hauler.  

Experience how these shipments are performed and 

get a real world understanding of the problems 

facing the drivers on a day-to-day basis.  

4 

 

Started with loading in 

Västerås and riding with a 

hauler during one day and 

unloading at various farms. 

A Foria associated 

driver for a local 

hauler.  

Experience how these shipments are performed and 

get a real world understanding of the problems 

facing the drivers on a day-to-day basis.  

 

Quantitative empirical data collection 

The following quantitative data files describing the shipments and the restrictions for 

analysis were received from Foria, see Table 3. 

Table 3 Summary of quantitative data collection 

Quantitative data 

Received from Foria 

 

Type  Description 

Excel file with one year of 

raw order data. 

Order data for these shipments from 2010-2011 containing over 25000 order 

lines. 

Excel file with distance and 

time data 

The distance and expected travel time from Västerås to 3128 different customers 

based on the customer number. 

Excel file with price matrix Pricelist and reimbursement matrix per order based on weight from Västerås for 

different distances for the Foria associated drivers. 

Excel file with price 

matrixes 

Pricelist and reimbursement matrix per order based on weight from Västerås for 

different distances for an external network provider. One based on postal code for 

order below 1000kg and one based purely on distance for order above 1000. 

Excel file with cost 

calculations. 

Costs calculations for the trucks currently used based on the Swedish transport 

industry standard cost calculation tool, SåCalc. 



12 

 

2.3 THE PROBLEM ANALYSIS MODELS 
To start the problem analysis a basic 5-Why’s analysis originating from the Lean 

principles developed for the Toyota Production System by Toyoda (Liker, 2004) was done. 

This enhanced the initial understanding of the problem. As the project advanced it became 

evident that Foria’s problems had several root causes, which generated a need for a deeper 

analysis. 

2.3.1 QUALITATIVE ANALYSIS METHODS 
A so-called logic tree was created where a “tree” grows from the effect/problem with 

causes to the problem, which aims at finding the root causes to the overwhelming problem 

(Rasiel and Friga, 2002). The method is similar to the principle of an Ishikawa diagram first 

described by Kaoru Ishikawa (1968). The gain from this approach is a deeper understanding 

of the causes and effects of the problem compared to the simpler 5-why’s analysis. To find 

solutions to the identified problems the same tree is used, but instead of asking “why”, one 

asks “how”.  

Allenström and Linger visualization model 

A drawback from a logic tree analysis is that it is not easy to overview from a reader 

perspective. To improve the presentation of the problem analysis for the intended audience, 

results from the logic tree analysis where transferred to the visualization model presented by 

Allenström and Linger (2010). This model is based on the well-known 7M’s used in Ishikawa 

diagrams for production companies but adjusted to fit the specific environment for hauler 

firms.  

The visualization method is presented in Figure 3. It is a matrix with two axes; the 

horizontal axis corresponds to the main processes of a T&D company identified through the 

use of the lean tool Value Stream Mapping (VSM). The vertical axis, motivated by the 7 M’s 

used in Ishikawa diagrams, correspond to the possible categorizations of the problems 

identified (Allenström and Linger, 2010). 

 

 
Shipper Order Entry 

Planning & 
Traffic control 

Transport 
Execution 

Invoicing & 
Registration 

Routines           

Manpower & 
Management           

Equipment           

Environment           
Figure 3 Visualization model (Allenström and Linger, 2010) 



13 

 

2.3.2 HOW WAS THE FINANCIAL ANALYSIS TOOL CREATED AND ANALYZED? 
With the quantitative empirical data it is possible to calculate breaking points between 

the different pricelist on which is the best in different weight spans. The BI-program Qlikview 

was chosen as a method and a project specific interface was created in Qlikview. Before that 

was possible the raw data and pricelist needed to be analyzed and transformed into formats 

possible to load into the program. E.g. possible errors in the raw data were accounted for and 

scenarios were created to adjust for them. When the tool was built in Qlikview and the data 

was loaded the analysis was straightforward to perform in the visual interface of Qlikview. 

2.3.3 HOW WAS THE SIMULATION MODEL CREATED AND ANALYZED? 
The aim of the simulation model is to emulate the work of a transport planner and thus 

empirical data regarding their way of working were the starting point. A process flow chart 

was created and approved. The optimization technique called Greedy Algorithm was used to 

in order to determine the routing of the trucks. The simulation results were then compared and 

analyzed against each other and conclusion of likely real world implications were drawn from 

a haulers point of view and an environmental point of view.  

2.4 VALIDATION 
In order to ensure a high validity, i.e. making sure that we measure the right thing, the 

authors have interviewed people with good knowledge and insight to the operations as well as 

collecting several viewpoints by talking to all involved parties. Continuous contact with Foria 

and feedback on the proposed model has also made sure that the validity has been kept high. 

2.4.1 FINANCIAL ANALYSIS 

Quantitative empirical data as input is considered to have a very high validity, it is 

consists of historical data shipping and sales data, and up-to-date pricelists from their 

information system (IS). Some errors in weights are identified but they are adjusted for in the 

financial analysis, so overall the validity of quantitative analysis should be high. 

Qlikview BI analysis software 

Qlikview is well-known software, it presents the loaded excel data in a visually and easy 

to understand way, it does not alter it. Therefor the use of Qlikview does not affect this 

research validity in any negative way. 

2.4.2 SIMULATION MODEL 
The simulation model simulates a year of shipments, emulates the work of a transport 

planner and measures the distance driven and the number of stops. Empirical data was the 

input for the creation of the algorithm describing how the simulation program works. Traffic 

controllers and an internal process developer scrutinized and confirmed that the algorithm is a 

valid representation of how transport planners work when they plan shipments. 

To make sure that this program works as intended a visual validation of randomly 

selected shipments were made. Simulated shipments were loaded into to a driving 



14 

 

optimization software using Google Maps
1
. The real world distance from Google Maps was 

compared with the simulated distance and the map offered a visual representation that 

provided confirmation that the routes chosen by the simulation model were logical. The error 

between the randomly selected simulation shipments and real world distances from the 

optimization program was also statistically examined and the confidence interval from 

analysis is presented in the results. 

The coding of the program was done through pair programming. This reduces 

programing errors (Cockburn and Williams, 2000) and increases validity. Furthermore, when 

the program was finished it was scrutinized line by line simultaneously of three people to 

make sure that it followed the previously approved algorithm. 

Possible errors and their effects in the simulation model 

The program is a simulation and it is not as flexible as transport planner could be. E.g. 

the strict division into regions in the program would not be enforced as strictly in the real 

world and the transport planners would also take the possibilities of a return shipment into 

consideration when deciding whether to add the final orders or not. However, the effect of 

this is considered small since this is the same for all simulation runs. 

The simulation program is loaded with the historic shipping and sales data. After the 

simulation program was created the authors identified a few possible errors in the smallest 

orders. This means that a truck could be loaded with more orders than possible in the real 

world. However, the analysis is made over an average of a year of simulated shipments; the 

impact of this should not have a noteworthy effect on the conclusions from the analysis. 

2.5 RELIABILITY 
Reliability, i.e. will that the results be repeatable and consistent, is thought to be very 

high. Both the financial analysis and the simulation analysis use the quantitative empirical 

data as input, which is consistent over time for the time period analyzed.   

Reliability of input from the qualitative empirical data and qualitative analysis are not as 

high as the quantitative data since it derives from a subjective appreciation of the operations. 

However, people interviewed have many years of experience and descriptions from different 

actors from all corners have overlapped and matched. The problems and difficulties with 

these shipments have been well known for many by actors involved. Therefor we conclude 

that other researchers would get the same answers and should reach the same results. 

  

                                                 
1 http://gebweb.net/optimap/ 

http://gebweb.net/optimap/


15 

 

3 LITERATURE REVIEW 
This chapter describes and explains the academic literature that supports and function as 

reference in the analysis part of the master thesis. Three main areas will be presented and broken 

down into different subareas: Efficiency in LTL Transportation, DND and Fleet/Vehicle 

Differentiation. 

3.1 LOGISTIC PERFORMANCE  
Logistics has become one of the most important factors in business competitiveness. 

Smooth connections among the supply chain (the essential function of logistics) has become 

the grounding of competitiveness in the global market where innovation and operation 

technology are no longer enough as qualifiers since everyday they are more reachable for the 

different players (Kim, 2010). 

Logistic performance has been defined by the efficiency and effectiveness in the 

execution of the logistic related activities, e.g. T&D (Mentzer and Konrad, 1991). Within the 

recent years an increasing awareness of the importance of customers’ value has enhanced the 

need for excellence and differentiation in the performance of the logistic activities. Thus, 

logistic performance is recently defined as “the degree of efficiency, effectiveness and 

differentiation associated with the accomplishment of the logistic activities” (Smith, 2000, 

Bobbitt, 2004). 

 

Figure 4 Logistics Performance Model (Fugate et al., 2010) 

3.1.1 LOGISTICS EFFICIENCY AND EFFECTIVENESS  
Efficiency is the measure of how the resources are utilized in order to achieve a goal 

and it is expressed as the percentage of the stated normal level of inputs supposed to be 

utilized, compared against the actual level of inputs utilized (Mentzer and Konrad, 1991). 



16 

 

Based on this general definition and applied in a logistic perspective, (see Figure 4) Fugate et 

al. (2010) defined efficiency of the logistic function as “the measure of how well the 

resources expended are utilized”. 

In a general management perspective, effectiveness is expressed as the percentage of 

the actual outputs compared to the expected or stated normal outputs. Consequently, being 

100% effective denotes full accomplishing of a particular goal (Mentzer and Konrad, 1991). 

Extending the definition to the area of interest of this thesis, logistic, Mentzer and Konrad 

(1991) defined logistic effectiveness as “the extent to which the logistics function’s goals are 

accomplished”. 

Efficiency and effectiveness on transportation has been assessed by several researches 

within two main different perspectives: (a) Technical / Physical and (b) Strategic / Planning, 

and impacting mainly Environmentally, Economically and the Service level (See Table 4).  

Depending on the scope of the organizations, private companies may focus on a combination 

of them in order to secure profitability and good image while government may be strongly 

focus on reduce pollution (Samuelsson and Tilanus, 1997). 

Table 4 Summary of articles relating to logistic efficiency and effectiveness 

 Perspective Impact 

Author (Researcher) Year Technical / 

Physical 

Strategic / 

Planning 

Environ-

mentally 

Economic-

ally 

Service 

Level 

Samuelsson & 

Tilanus 
1997 X  X X X 

Kim 2010 X   X  

Crainic and Roy 1988  X  X X 

van de Klundert and 

Otten 
2010  X  X X 

Vilkelis 2011  X X   

Aronsson and Brodin 2006  X X X  

Li et al. 2006  X  X X 

Apte and 

Viswanathan 
2002  X  X X 

Wang and Regan 2008  X  X X 

Chapman, Soosay 

and Kandampully 
2003  X  X X 

Harmatuck 1990 X X  X  

 

Samuelsson and Tilanus (1997) described a general framework for measuring the 

physical efficiency of LTL based on four basic transportation dimensions: time, distance, 

speed and transportation, emphasizing the importance on not to overlook possible efficiency 

loss in the physical part of the transportation, often easier to measure, rather than going 



17 

 

straight for route optimization and other types of strategic approaches. In the same way, Kim 

(2010) evaluated various technical efficiency results in order to estimate logistics 

performance of trucks. Both Samuelsson and Tilanus (1997) and Kim (2010), consistently 

agreed on the importance of the identification and measure of physical and technical 

efficiencies in transportation as one important step for profit maximization. 

On the other hand, strategic or planning perspectives can also improve the efficiency of 

transportation. Crainic and Roy (1988) developed a mathematical model for the tactical 

planning of freight transportation. By approaching it as and optimization problem where 

economic efficiency as well as service level were of main interest, their model proposed a 

better operating planning and minimizing cost compared to a manually done one. Moreover, 

strategies aiming to increase capacity utilization of the transport, e.g. consolidation, better IS, 

etc. will result in cost reductions and mitigation of negative environmental impact, i.e. 

congestion and pollution (Vilkelis, 2011, van de Klundert and Otten, 2010, Aronsson and 

Brodin, 2006). 

Li et al. (2006) described and presented an example of a shipping consolidation problem 

(SCP), which main goal is to minimize the total cost (transportation and inventory) while 

satisfying service level constrains. Similarly, cross docking is another distribution strategy 

thoroughly described by Apte and Viswanathan (2000) which also aims to reduce 

transportation cost by efficiently maintain low inventory levels without compromising the 

deliveries. However, cross docking is just one innovative strategy that may be used together 

with other strategies, e.g. postponement, vendor managed inventory, mass customization, 

time-based scheduling and among others, in order to boost logistic and transportation 

efficiencies (Apte and Viswanathan, 2000, Wang and Regan, 2008).  

Chapman et al. (2003) discussed how innovation on logistics firms can help to re-design 

their structures and enhance relationships through information sharing and coordination, 

resulting on overall efficiency improvement, flexibility for upcoming market changes and 

increase customer service. Furthermore, Harmatuck (1990) described the United States (US) 

carriers’ strategies after the 1980 US Deregulation
2
. A large number of commodity carriers 

made strategic and operational strategies in order to cope with the competition. Terminal 

Expansions, Equipment, Discount pricing, Service Levels and Labor agreements were the 

most important and resulting on profit increase and efficiency on operations, however service 

quality did not increased as the others.  

Another interesting approach to improve transportation technical efficiency, i.e. increase 

fill rate without diminishing the service level, is a model called “Foliated transportation 

network” (see Figure 6). Presented by Persson and Lumsden (2006) and furthermore 

explained by Kalantari and Sternberg (2009), this model combines the advantages of the 

direct distribution strategy and the hub-and-spoke distribution strategy. 

                                                 
2 The 1980 US Deregulation, known as the Motor Carrier Act of 1980, opened free pricing and routes to be served by 

the truckers resulting in a significant growth of the competition and number of independent firms.  



18 

 

3.2 DISTRIBUTION AND TRANSPORTATION NETWORK DESIGN 
Distribution and transportation network design decisions concerning storage, location, 

markets, etc. will guide towards the determination of a proper supply chain structure (Chopra 

and Meindl, 2007). In the pursuit of this proper and profitable network structure, 

organizations are devoted to the optimization of their T&D networks through the use different 

strategies and consequently minimize inventory and reduce transportation costs (Li et al., 

2006). 

3.2.1 DISTRIBUTION NETWORK DESIGN (DND) 
As constantly customer needs change in any business, organizations must periodically 

evaluate their current distribution network and adapt it to match business requirements. In 

doing so, the most important goal is to find the balance between cost and service level (Tiede 

and Kay, 2005). Although most companies design their distribution networks based on cost 

and speed, these processes also have an influence on other factors, including carbon 

emissions. Optimizing a network design can reduce both costs and carbon emissions 

significantly (Vilkelis, 2011). 

There are two key decisions regarding DND: Whether deliver to the customer location or 

picked up from a predefined site and second, if the flow includes an intermediary or 

intermediate location. Based on the choices for the two decisions, six distinct DND´s are 

proposed as shown in Table 5 (Chopra, 2003, Chopra and Meindl, 2007). 

Table 5 Proposed Distribution Networks Designs (Chopra, 2003) 

Distribution Network Design 

Manufacturer storage with direct shipping 

Manufacturer storage with direct shipping and merge in transit 

Distributor storage with carrier delivery 

Distributor storage with last mile delivery 

Manufacturer/distributor storage with costumer pick-up 

Retail storage with customer pick-up 

 

There are two main criteria in order to select the most suitable DND: meeting the 

customer needs and the cost of meeting those needs. Then, the performance of the distribution 

network will depend on the satisfaction of the customer needs, directly impacting the 

revenues, and the supply chain costs of the network (Chopra, 2003). 



19 

 

Sharma, Moon and Bae (2008), adapted Chopra`s framework in order to outline the most 

important criteria and sub-criteria towards the design of an optimal distribution network (see 

Figure 5), where is necessary to prioritize the metrics both related to costs and customer 

service. For the cost factors, Chopra (2003) distinct: inventories, transportation, facilities and 

handling and information. Accordingly, in order to fulfill the customer needs, the factors to 

consider are: response time, product variety, product availability, etc. 

 

Figure 5. Designing  the Optimal Distribution Network (Sharma et al., 2008) 

Crainic and Roy (1988) classified in three groups the problems and policies required 

when designing a transportation network: strategic (long-term), tactical (medium-term) and 

operational (short-term). The strategic group entails for large investments and the decisions 

connected with this level of planning are connected to physical network design, location, 

resource acquisition and service policy definition. The tactical planning, which is not as 

dynamic as the previous group looks after the performance of the whole system and the 

decisions are sensitive only to wide variations. The selection of routes, traffic distribution and 

service network design are examples of decisions regarding this group. The operational 

planning, characterized by a dynamic environment, consisting on decisions as: scheduling, 

maintenance, terminals and routes daily operations, allocating resources, etc. 



20 

 

3.2.2 TRANSPORTATION NETWORK DESIGN (TND) 
Regarding TND the decisions are mainly affected by the tradeoff between the service 

level (responsiveness) and the inventory and transportation costs. Different options proposed 

by Chopra and Meindl (2007) are shown in Table 6. 

Table 6 Proposed Transportation Networks Designs (Chopra and Meindl, 2007) 

Transportation Network Design 
Direct Shipping 

Milk-runs 

Central DC with storage 

Central DC with cross-docking 

Milk-runs via DC 

Tailored network 

 

Direct shipment transportation consists on the delivery of goods from one supplier to one 

buyer location, eliminating intermediaries and reducing complexity and coordination. Direct 

shipments are suitable if economy of scale can be found and if the demand is high enough, 

that optimal lot size is close to the size of a full truck. The effectiveness of direct shipping 

deteriorates as the economic lot sizes decrease (Gallego and Simchi-Levi, 1990, Persson and 

Lumsden, 2006, Chopra and Meindl, 2007). 

Milk Runs or Peddling is a distribution strategy where one truckload is delivered to more 

than one customer, i.e. two or more drop points. The use of milk runs enables consolidation of 

multiple deliveries, which may result in better utilization of a truck (Chopra and Meindl, 

2007). 

Burns et al. (1985) presented the use of delivery regions and sub-regions for the analysis 

of the milk run transportation network. This region division of the different customers enables 

the definition of the truckloads and outlines the geographical area for the milk run routing. 

For TND involving the use of a central distribution center, organizations can increase the 

service level with bigger product assortment and quick responsiveness but the inventory cost 

will be higher. An alternative of this is the use of cross docking strategies, however it requires 

more coordination and synchronization through the use of information technologies (Chopra 

and Meindl, 2007). In the same way, Distribution Centers allow the use of milk runs, which 

will depend on the size and consolidations possibilities of the goods. 

According to Persson and Lumsden (2006), transportation companies today are hardly 

operating with a fix distribution strategy as pure direct shipments or completely hub-and-

spoke networks. Thus, a tailored network design facilitates an appropriate combination of the 

previous described transportation network designs (Chopra and Meindl, 2007). Foliated 

transportation network, shown in Figure 6, is an example of a tailored network design. 



21 

 

 

Figure 6 Foliated transportation network (Persson and Lumsden, 2006) 

3.3 DISTRIBUTION AND TRANSPORTATION OPTIMIZATION 
Optimization describes the process of finding an optimal solution among a large number 

of possibilities. Optimization problems involve decisions characterized by the three 

components: (1) resource constraints, e.g. time, money, etc. (2) variables, e.g. distance, cost, 

etc. and (3) objectives e.g. minimize cost. 

Optimization problems directly related to T&D include the Traveler Sales Problems 

(TSP) and greedy algorithms, which are further described below. 

3.3.1 TRAVELER SALES PROBLEM (TSP) 

The TSP is a well-known optimization problem whose objective is to find the minimum 

total distance travelled by a salesman, from an origin location to a defined number of different 

cities, returning to the origin and visiting each city only once time (Gutin and Punnen, 2002). 

One of the first persons to study similar problems to TSP problems was Leonhard Euler 

back in 1759, however it is believed that the first person on reported in a mathematical 

formulation a comparable TSP problem was Karl Menger with his “messenger problem” 

(Gutin and Punnen, 2002, Klanšek, 2011). 

Optimization software had increase the reach of the TSP within the last years, e.g. it is 

possible to compare TSP studies before the 80’ with no more than 500 nodes to recent studies 

with almost one million of nodes or visits (Klanšek, 2011). Moreover, TSP optimization 

models have been used in numerous fields as manufacturing, logistics, and operational 

research among others (Klanšek, 2011, Gutin and Punnen, 2002). 

  



22 

 

3.3.2 GREEDY ALGORITHM 
A greedy algorithm is the kind of algorithm that makes a choice base on the best 

available option at that precisely moment, called “local optimal” and, once the choice is 

made; it never goes back on previous decisions. Every step is constructed towards the overall 

solution of the problem, called “global optimal” (Curtis, 2003). 

Curtis (2003) described the four greedy principles: Best global, better global, best local 

and better local (see Figure 7). While all greedy algorithms compile with the best global 

principle, the compliance with the other three principles defines how strong the algorithm in 

the pursuit for the optimal solution is. Following this, Curtis (2003) classified the greedy 

algorithms as shown in Table 7. 

Table 7 Greedy Algorithms Classification (Curtis, 2003) 

Greedy Algorithm 

Classification 

Strength Description 

1. Best-global only 
Minimum Greedy 

Algorithm 

Best local choice can ultimately lead to a better 

solution 

2. Better-Global and Best-Global 

only 
Stronger than 1 

Better local choice can ultimately lead to a better 

solution 

3. Best-Local and Best-Global only Stronger than 2 
Repeatedly best local choice always results in a 

partial solution that is best so far. 

4. Best-Local, Better-Global and 

Best-Global only 
Stronger than 3 

Repeatedly better local choice always results in a 

partial solution that is best so far. 

5. Better-Local, Best-Local, Better-

Global and Best-Global 
The Strongest 

Better partial solution can lead to one that is still 

better after the next construction step 

 

The most emblematic and well-known greedy algorithms deal with minimum spanning 

tree theories, e.g. Dijkstra’s minimum spanning tree, Prim’s minimum spanning tree, 

Kruskal’s minimum spanning tree, etc. (Chang et al., 2008, Wu et al., 2004). These three 

examples are situated on the “best-local” classification because by choosing the least weight 

of paths at every step (local optimal), this will secure the best solution until completing the 

Figure 7 The four greedy principles with implications 

(Curtis, 2003) 



23 

 

solution to the problem. 

3.4 ENVIRONMENTAL IMPLICATION OF TRANSPORTATION  
Thomas and Harrison (2004) explained that the major environmental impacts from the 

transport sector are: human health implications, dilution of the ozone layer, GHG effect, 

hyper fertilization, acidification and destruction of landscapes that creates barriers. Table 8 

presents the links between main emissions from the freight transport and environmental 

impacts. 

Table 8. Link between emissions from transport and environmental impact (Thomas and Harrison, 2004) 

Environmental 
impacts 

CO NOx HC PM SOx CO2 

Human health 
Nerves 

and 
Heart 

Lungs and 
breathing by 

forming ground 
level ozone 

Nerves and 
breathing, 
and may 

cause cancers 

Important 
effects on 

life 
expectancy 

Lungs and 
breathing 

X Dilution ozone 
layer 

X 

Through N2O only 

X 
X 

X 
Acidification 

Damages forests 
and fish through 

acid rain 

Damages forests 
and fish through 

acid rain 

Greenhouse 
effect 

Through N20 Through CH4 

X 

Increase of the 
global 

temperature and 
sea levels 

Hyper 
fertilization 

Leads to a lack of 
oxygen and dead 

sea bed as the 
number of algae 

increases 
X X 

 

In order to develop a strategy concerning environmental performance targets and, at the 

same time securing the company’s long term economy success, it is necessary to identify the 

specific environmental impact on the transportation system and thus propose viable 

alternatives leading to a low environmental impact (Aronsson and Brodin, 2006). 

3.4.1 APPROACHES TO REDUCE EMISSIONS 
There are different solutions or approaches to reduce transportation emissions and it is 

possible to classify them in three different categories: There are technological solutions such 

as alternatives fuels, greener engines or catalytic converters. There are also logistics solutions 

such as better vehicle utilization, route optimization or improving of route planning 

(Lumsden, 2006a). The last category concerns social or behavioral solutions for example 



24 

 

better planning of the way of driving by reducing speed or braking patterns.  

The International Energy Agency (1999) affirm that these solutions can reduce the 

environmental impacts on transport but none of these can stand alone as the ultimate solution. 

Holden and Høyer (2005) go even further, stating that these changes won’t be enough as road 

transport increase and therefore there is a need to change the means of transportation. 

3.4.2 ENVIRONMENTAL PERFORMANCE IN TRANSPORTATION 
The Network for Transport and Environment (NTM) group developed a set of documents 

for transportation where it is provided the tools, instructions and pre-defined data in order to 

calculate and evaluate the environmental performance of a transport activities (NTM-Road, 

2008). Even though NTM-Roads provided default data selected in order to be representative 

of a normal transport performed in European countries today, it is recommended to use 

situation specific data when available for more close to reality results. 

Vehicle types and Characteristics 

NTM-Road (2008) described ten different vehicles for road cargo transportation (see 

Table 8). From the smallest Light Cargo Vehicle (LCV) to the biggest Heavy Duty Vehicle 

(HDV), the descriptions and characteristics match all normal ranges of cargo trucks. 

Fuel Consumption 

When specific data is not available, NTM-Road (2008), suggest the use of fuel 

consumption figures contained in Table 45 (Appendix E). The fuel consumption data, given 

in liters per kilometer (l/km), is subject to different variants: the type of truck, the cargo 

capacity utilization (CCU) of the truck, the type of engine (Euro I – Euro V) and the type of 

road (motorway, rural or urban). 

Vehicles Emissions 

NTM-Road (2008) provide a compendium of tables where is possible to find pre-defined 

values for pollutant emissions in the road transport. These emissions are: HC, CO, NOx, PM, 

CO2, CH4 and SOx. Similarly than the fuel consumption data, the values are depending on 

the type of truck, the type of engine, the CCU, and the type of road. 

Calculation Strategy 

In order to calculate the environmental impact of a given transportation activity, NTM-

Road (2008) defined the following strategy steps: First select the appropriate vehicle from 

Table 9. Secondly, set and calculate the fuel consumption from Table 45 (Appendix E) and 

with the parameters explained before. The next step is to select the appropriate emissions 

based on the tables and restrictions described before. And finally, find the distance for the 

transportation activity. 



25 

 

Table 9. Vehicle concepts/types and cargo capacity (NTM-Road, 2008) 

 
 

 

  



26 

 

 
4 EMPIRICAL FINDINGS 

In this chapter the empirical findings are presented. This includes the roles, responsibilities and 

activities of the involved actors. Furthermore, the nature of the goods that is being transported as well 

as the properties of the vehicles being used for these transports today. 

4.1 OPERATIONS OF INTEREST FOR THIS PROJECT  
Figure 8 below describes how the studied shipments are performed from a historical 

perspective as well as how they performed as of December 2011. 

 

4.1.1 HISTORICALLY 
Historically Lantmännen was the one who planned these shipments, and the already 

constructed and formed LTL-shipment plans were sent to Foria who booked a truck and 

driver. Foria then planned for loading at the warehouse in Västerås, and then charged 

Lantmännen based on the amount of kilometer driven, number of stops, waiting time etc. This 

meant that there were no incentives for Foria and their drivers to be effective and provide any 

efficiency improvements since they were reimbursed based on the amount of resources that 

were used to perform the transports. Interviews and field studies have shown that 

inefficiencies of these shipments were well known among the involved actors.  

4.1.2 THE NEW CONTRACT – A SHIFT IN RESPONSIBILITIES 
The inefficiencies were over time raised as an internal problem at Lantmännen. 

Negotiations started which led to a new contract with Foria, which will reduce Lantmännen’s 

Figure 8 Value stream mapping of current operations 



27 

 

costs for these shipments. The new contract has a new reimbursement structure where Foria is 

reimbursed a fixed amount per order based on the distance from Västerås and its transport 

determining weight. The contract also includes a shift in responsibilities and ownership for 

some work processes and activities. From December 2011 Foria is also responsible for 

constructing and planning the LTL-shipments from the available orders, which means they 

now have a bigger possibility to impact the way these transports are performed. 

Furthermore, the new contract means that Foria now have incentives to plan and 

perform these transports as effective and efficient as possible. And to quote the chairman of 

Foria:  

“We have to do this in a better way since we won’t get any economy in doing it the old 

way anymore. The price we have negotiated with Lantmännen for this new contract won’t 

cover our expenses for this to be run as it always has. “– Håkan Larsson
3
 

The underlying reason for this thesis from Foria’s perspective is to gain knowledge of 

possible efficiency improvements. This through analyzing alternative distribution models in 

order for them to achieve  profit in this new contract so the new contract terms becomes a 

win-win situation for both Lantmännen and Foria.  

4.2 THE INVOLVED ACTORS 
There are currently four different actors involved in these operations. The transport 

planners at Foria’s office in Nyköping, the local haulage contractors performing the physical 

transports, Lantmännen AB Division Lantbruk
4
, who is the supplier of cargo, and the end 

customer, the farmers purchasing agriculture supplies. The following subchapters will 

describe them closer. 

4.2.1 TRANSPORT PLANNERS AT FORIA 

In Foria’s office in Nyköping sits six transport planners responsible for building and 

planning shipments from the incoming orders from Lantmännen. This is done manually with 

the support of IS showing available orders. Figure 28 in Appendix B displays a print screen 

from the order view from that IS.  

Stefan Palmgren at Foria management team compares the job as a transport planner at 

Foria to the work of an air traffic controller
5
. The job requires a high multitasking skill since 

the planning and optimization work is done manually. The job does not require a university 

degree but the work profile generally requires a vocational education to become a qualified 

transport planner.  

  

                                                 
3 Interview/meeting with Foria and Lantmännen management in Nyköping, Sweden, September 22nd 2011 
4 Lantbruk means agriculture in Swedish 
5 Interview/meeting with Foria and Lantmännen management in Nyköping, Sweden, September 22nd

 
2011 



28 

 

How the transport planners perform their work 

The main order information the transport planner use when planning these shipments 

besides the address where the cargo is heading is: 

 The method of delivery (“Leveranssätt” in Figure 28) 

o Some farmers and some cargo require a different unloading method, i.e. crane, 

forklift or the farmer might want to unload him or herself. 

 First available day when the cargo is available for loading at Västerås  

(“1:a dagen då godset finns tillgängligt” in Figure 28) 

o Some orders are available a long time before the last required departure date; this 

means that they can be planned optimally. 

 Last day of delivery (“Sista dag för leverans in Figure 28) 

o A delivery run for these shipments takes between 1-3 days depending on the 

number of stops and how widespread the drop-off points are. This means that the 

transport planner manually needs to estimate when the cargo will be delivered 

based on other orders on the same truck for the order not to be late. They have a 

service level requirement of 98% within latest delivery time and currently they are 

between 96-97%
6
. 

 Amount/Weight (“Mängd” in Figure 28) 

o The weight and size of the order is of course vital for the transport planner to 

know. The cargo is mainly weight dependent but the transport planner needs to 

manually adjust for some orders that will be limiting when it comes to their size. 

 

With this information the transport planner tries to fill trucks so that orders going to the 

same region will be loaded together to minimize driving distance as well to try to achieve a 

high fill rate. Besides this they also take into account where the truck will finish so it is 

possible to find a return shipment to minimize empty driving. 

4.2.2 FORI