IN
DEGREE PROJECT ENVIRONMENTAL ENGINEERING, SECOND CYCLE, 30 CREDITS
STOCKHOLM SWEDEN 2020,
A sustainability assessment in the production of heavy-duty trucks
A case study at Scania: investigating the reduction of environmental impacts through design customization and LCA
LILIANA ISABEL CELEDÓN CRUZ
A sustainability assessment in the production of heavy-duty trucks
A case study at Scania: investigating the reduction of environmental impacts through design customization and LCA
LILIANA ISABEL CELEDÓN CRUZ
Supervisor
MIGUEL MENDONCA REIS BRANDÃO Examiner
MIGUEL MENDONCA REIS BRANDÃO
Supervisor at Scania KARIN MAKOWSKI
Degree Project in Environmental Engineering and Sustainable Infrastructure KTH Royal Institute of Technology
School of Architecture and Built Environment
Department of Sustainable Development, Environmental Science and Engineering SE-100 44 Stockholm, Sweden
TRITA-ABE-MBT-20758
Abstract
The transport sector is currently facing challenges to reduce environmental impacts during the vehicle’s operation due to its reliance on fossil fuels. The introduction of new technologies such as alternative fuels or battery electric vehicles (BEVs) are therefore rapidly growing because they can significantly reduce the vehicle’s tailpipe emissions. There is however the concern that these could transfer environmental burdens to other life cycle phases such as production. Therefore, a development towards sustainable transport will require more than just the development of alternative fuels or EVs, but also a more sustainable production.
Considering that 80% of the product related environmental impacts are determined during the design phase of a product, the significance of product design is studied. Scania offers the opportunity to customize trucks with a high level of detail through customized design, also called S-order design.
Design engineers want to know if their customized solutions have the potential to reduce environmental impacts within the production of a truck. Therefore, the life cycle assessment (LCA) framework is used to know the environmental impacts of a truck designed with S- and A-order design and to compare them in order to determine if there is an environmental performance difference between these two designs.
The results show that the production of a truck with a S-order design has on average 3% lower environmental impacts on all impact categories than when it’s produced with an A-order design. This is due to the S-order design’s great level of flexibility to consider small details of the truck’s functionality. Nevertheless, this design flexibility can lead to multiple configurations for one truck, thus meaning that the results will vary from product to product since the customer decides the specifications of the truck. The main conclusion is that the early implementation of adaptations through S-order design in heavy truck development at Scania can potentially reduce resource consumption and environmental impacts, and aid to sustainable production.
Keywords
Design, customization, heavy-duty vehicles, sustainable production, resource consumption, environmental impacts, life cycle assessment, sustainability.
Sammanfattning
Transportsektorn står för närvarande inför utmaningar för att minska miljöpåverkan under fordonets drift på grund av dess beroende av fossila bränslen. Introduktionen av ny teknik som alternativa bränslen eller elektriska fordon (BEV) växer därför snabbt eftersom de avsevärt kan minska fordonets utsläpp från avgasröret. Det finns emellertid oro för att dessa skulle kunna överföra miljöbelastningar till andra livscykelfaser som exempelvis produktionen. Därför kommer en utveckling mot hållbara transporter att kräva mer än bara utveckling av alternativa bränslen eller eldrift, men också en mer hållbar produktion.
Med tanke på att 80% av de produktrelaterade miljöeffekterna bestäms under en produkts designfas studeras därför produktens design. Scania erbjuder möjligheten att skräddarsy lastbilar med hög detaljnivå genom skräddarsydd design, även kallat S-orderdesign. Designingenjörer vill veta om deras skräddarsydda lösningar har potential att minska miljöpåverkan inom tillverkningen av en lastbil. En livscykelanalys (LCA) används därför för att känna till miljöpåverkan från en lastbil konstruerad med S- och A-orderdesign och för att jämföra dem för att avgöra om det finns en skillnad i miljöprestanda mellan dessa två konstruktioner.
Resultaten visar att tillverkningen av en lastbil med S-orderdesign har i genomsnitt 3% lägre miljöpåverkan på alla kategorier av miljöpåverkan än en med A-orderdesign. Detta beror på S- orderdesignens stora flexibilitet för att ta hänsyn till små detaljer gällande lastbilens funktionalitet.
Dock kan denna konstruktionsflexibilitet leda till flera konfigurationer för en lastbil, vilket innebär att resultaten kommer att variera från produkt till produkt eftersom kunden bestämmer lastbilens specifikationer. Huvudslutsatsen är att det tidiga genomförandet av anpassningar genom S- orderdesign vid utvecklingen av tunga lastbilar hos Scania potentiellt kan minska resursförbrukningen och miljöpåverkan och stöd till hållbar produktion.
Nyckelord
Design, anpassning, tunga fordon, hållbar produktion, resursförbrukning, miljöpåverkan, livscykelbedömning, hållbarhet.
Acknowledgements
I would like to thank Scania for giving me the opportunity to collaborate together throughout this thesis work, especially my supervisor Karin Makowski, who gave me all the time and help necessary for my work. I also want to thank to all my co-workers at RSMU department who assisted me in any way when I needed it.
Likewise, I would like to give thanks to my supervisor at KTH, Miguel Mendonca Reis Brandão, for all the support, guidance and feedback during this thesis work.
Lastly, and most important, I would like to thank my family for their unconditional love and encouragement during all my studies. I would also like to thank my friends for been present and made this thesis work fun. All of you made me achieve this thesis, thank you for everything.
Table of contents
Abstract ... iii
Sammanfattning ... iv
Acknowledgements ... v
Table of contents ... vi
List of figures ... viii
List of tables ... viii
Abbreviations ... ix
1 Introduction ... 1
1.1 Aim and research questions ... 3
1.2 Structure of the thesis ... 3
2 Background information ... 4
2.1 Previous studies in the field ... 4
2.2 S-order design at Scania ... 6
3 Methodology ... 9
3.1 Life cycle assessment (LCA) framework ... 9
3.1.1 Goal and scope ... 10
3.1.2 Life cycle inventory (LCI) ... 10
3.1.3 Life cycle impact assessment (LCIA) ... 11
3.1.4 Interpretation of results ... 11
4 Tracing sustainability at Scania ... 12
4.1 Sustainable transport ... 12
4.2 Environmental footprint ... 12
4.3 Health and safety ... 13
4.4 Diversity and inclusion ... 13
4.5 Key impact categories ... 13
5 Life cycle assessment of trucks: Comparing the production of a chassis with either S-order or A- order design ... 14
5.1 Goal and scope ... 14
5.1.1 Goal ... 14
5.1.2 Product and Function description ... 14
5.1.3 Functional unit (FU) ... 15
5.1.4 System boundaries ... 15
Geographic boundaries ... 15
Temporal boundaries ... 15
Allocation procedures ... 15
Exclusions (Cut-offs) ... 16
5.1.5 Assumptions ... 16
5.1.6 Limitations ... 17
5.1.9 Company confidentiality ... 19
5.2 Life cycle inventory ... 19
5.2.1 Data collection ... 20
5.2.2 Material composition ... 20
5.2.3 Chassis assembly ... 22
5.2.4 Transport processes ... 23
5.3 Life cycle impact assessment and interpretation ... 24
5.3.1 Results ... 24
5.3.2 Sensitivity analysis ... 30
6 Discussion ... 33
6.1 Uncertainties ... 34
6.2 Further research suggestions ... 35
7 Conclusions ... 36
Reference list ... 37
Appendices ... 41
Appendix I: Material composition datasets ... 41
Appendix II: Impact assessment ... 52
Appendix III: Networks of chosen impact categories ... 54
List of figures
Figure 1: S-order design process at Scania (Scania 2019b) ... 6
Figure 2: Adapted length of rear overhang (Scania 2020c) ... 7
Figure 3: Hole pattern in chassis frame for bodywork (Scania 2020a) ... 8
Figure 4: Hidden tank on a chassis frame (Scania 2017) ... 8
Figure 5: Life cycle assessment framework (International Standards Organization 2006) ... 9
Figure 6: Different LCA stages ... 10
Figure 7: Scania’s three pillar approach to sustainable transport (Scania n.d.) ... 12
Figure 8: An S-order design truck with a body, together form a rear loader waste collector truck (Scania 2019a) ... 14
Figure 9: General flowchart of a waste collector truck at Scania with geographical boundaries ... 15
Figure 10: Detailed flowchart of a chassis with either S- or A-order design for a waste collector truck ... 19
Figure 11: Material composition of a chassis truck with either S-order design (left) or A-order design (right) (Scania 2020b) ... 21
Figure 12: Total environmental impacts from the production of a truck with an S-order design, characterized results ... 24
Figure 13: Total environmental impacts from the production of a truck with an A-order design, characterized results ... 26
Figure 14: Comparison of the total environmental impacts of the production of a truck with either S- or A- order design, characterized results ... 27
Figure 15: Comparison of S- and A-order design on the impact category “Global warming” showing the contribution from the production ... 28
Figure 16: Comparison of S- and A-order design on the impact category “Mineral resource scarcity” showing the contribution from the production ... 29
Figure 17: Comparison of S- and A-order design on the impact category “Fossil resource scarcity” showing the contribution from the production ... 30
Figure 18: Comparison of the S-order and A-order designs where the type of steel in the chassis component is changed ... 31
Figure 19: Comparison of the S-order and A-order designs where the type of aluminum in the chassis component is changed ... 31
Figure 20: Comparison of the S-order and A-order designs where the percentage of recycled steel in the chassis component is changed ... 32
List of tables Table 1: Modular design in a Scania heavy truck (Scania 2020b) ... 20
Table 2: Energy consumption of the main components production and final assembly of a chassis with either S-order or A-order design ... 22
Table 3: SimaPro dataset for the transport during manufacturing phase of an A-order design chassis ... 23
Table 4: SimaPro dataset for the transport during manufacturing phase of an S-order design chassis ... 23
Abbreviations
GDP Gross development product LCV Light commercial vehicles MCV Medium commercial vehicles HCV Heavy commercial vehicles
CO2 Carbon dioxide
GHG Greenhouse gas
BEV Battery electric vehicles
ICEV Internal combustion engine vehicle SDG Sustainable development goals S-order Special order design
A-order Standard order design LCA Life cycle assessment LCI Life cycle inventory WTW Well-to-wheels BOM Bill of materials
PDM Product data management
EPD Environmental product declaration
Hp Horsepower
MWh Megawatt per hour
GLO Global
RoW Rest of the world
RER Europe
SE Sweden
Eq Equivalents
1 Introduction
In 2019, the global vehicle production reached slightly over 90 million units (International Organization of Motor Vehicle Manufacturers, 2019). Of these, about 20 million were manufactured in Europe of which 86% were passenger cars, 11% light commercial vehicle (LCV), and 3% medium and heavy commercial vehicle (MHCV) (European Automobile Manufacturers Association, 2019c).
The vehicle industry provides direct and indirect jobs to 13.8 million Europeans, representing 6.1%
of the total EU employment (European Automobile Manufacturers Association, 2019b). The industry also generated a revenue over 7% of GDP, and 3.5 million manufacturing jobs (European Commission, n.d.; European Automobile Manufacturers Association, 2019b). For those reasons, it makes it one of the strongest economic sectors on the continent.
The transport sector was responsible for nearly 30% of the EU’s total CO2 emissions of which 72%
came from road transportation in 2016. The distribution of emissions by transport mode resulted as 61% cars, 12% LCV, 26% HCV and 1% motorcycles (European Parliament, 2019). This is because more than 90% of the total energy consumption and greenhouse gas (GHG) emissions of a vehicle occurs during its use phase according to previous studies performed (McAuley, 2003; Mayyas et al., 2012).
This it is strongly linked to the sector’s reliance on available fossil fuels, and long vehicle lifetime, where the average age for passenger cars, LCV and HCV is about 11 years (European Automobile Manufacturers Association, 2019a).
Since most environmental impacts of vehicles are generated during their use phase, it is therefore reasonable that legislators and vehicle manufacturers are strongly focused on reducing tailpipe emissions through the development of new technologies. Some of these are alternative fuels such as biodiesel, biogas, hydrogen, ethanol, electric batteries or hybrid technology using both conventional combustion engines and electric motors (International Organization of Motor Vehicle Manufacturers, n.d.). However, diesel and petrol still remain as the most widely used fuels in Europe (European Automobile Manufacturers Association, 2019d).
The introduction of new technologies such as battery electric vehicles (BEVs) is rapidly growing because they can significantly reduce the environmental impacts of vehicles during their use phase.
However, these could transfer environmental burdens to other life cycle phases such as raw material extraction and production (Geyer, 2016; Gradin et al., 2018; Dolganova et al., 2020). A study revealed that the CO2 emissions from the production of a passenger car EV were 60% higher than the levels of an internal combustion engine vehicle (ICEV). This is mainly because the production of BEV components is energy-intensive, electric motors and battery systems require specific rear earth elements and other precious metals (Qiao et al., 2017; Cerdas et al., 2018). These effects could be similar for vehicles using alternative fuels, or lightweight materials to increase fuel economy (Geyer, 2016).
Even though that these new vehicle technologies address one of many crucial problems our society is currently facing such as decreasing environmental impacts from transport, and yet they still forget to address the reduction of resource consumption (Gomes da Silva & Gouveia, 2020). This is an important concern because the development of these new technologies will increase resource consumption and put more pressure on natural resources. Therefore, a development towards a sustainable transport will require more than just the development of alternative fuels or EV, but also a more sustainable production. Considering that the vehicle industry boosts the economy, but still faces environmental challenges, it can be identified as a key system to lead the way towards more sustainable practices through both products and production processes.
According to the UN Environment Programme, at a global level one of the most difficult challenges is to combine environmental sustainability with economic growth and well-being by mitigating environmental deterioration from economic growth and doing more with less (United Nations Environment Programme, n.d.). For that reason, the Agenda 2030 for Sustainable Development, which consists of 17 Sustainable Development Goals (SDGs), includes SDG 12 called “Sustainable consumption and production patterns”. Its main aim is to stop resource depletion and to make a transition towards a greener and more socially inclusive global economy (United Nations General Assembly, 2015). Taking into account that the vehicle industry enables global supply chains, which puts a lot of pressure on natural resources, it is important to investigate different ways of manufacturing vehicles in other to reduce their environmental footprint.
In the field of vehicle manufacturing, Scania is a world leading provider of transport solutions, including trucks and buses for heavy transport applications combined with product vehicle services (Scania, n.d.). It offers to its customers product variety, and the opportunity to customize them with a high level of detail due to its modular system. The model is based on grouping simple and small product components into more complex subassemblies, and then combing these subassemblies to create a whole product (Ma & Kremer, 2016). Scania’s aim is to drive the shift towards a sustainable transport system, creating a world of mobility that is better for business, society and the environment.
This sustainable way of thinking is reflected in the whole vehicle life cycle from first development phases through production as well as the on-road usage of the final vehicle.
Scania has a broad spectrum of customized trucks called “Standard order (A-order) design” directly to fit the most common customer needs, however even this wide product range has its limitations.
Therefore, it offers a deeper customization of trucks called “Special order (S-order) design”. This product segment understands the customer demands, finds the smartest solution for the customer, and develops truck orders with customers’ specific adaptations which could be a change of design or add a functionality that is not offered in A-order options. Possible solutions range from alternatives fuels to specialized applications such as mining, fire trucks, waste management, etc. Most of these trucks require a specific structure on their chassis, which is only possible to define through S-order design, in order to fit the body onto it.
The modular system has shown to increase manufacturing efficiency and to enable product customization according to the needs of an individual customer (Berman, 2002; Piller & Kumar, 2006; Ma & Kremer, 2016). It has also shown to reduce manufacturing costs when details or adaptations are considered at early stages of product development (Dahlberg, 2011). 80% of the product related environmental impacts are determined during the design phase of a product (European Commission, 2018). Therefore, design engineers at Scania involved in the product development process want to know if their customized solutions have the potential to reduce environmental impacts within the production of a truck throughout the use of S-order design. This can be investigated by using the life cycle assessment (LCA) method, and therefore it is adopted in the product development process of a special truck at Scania. The LCA method measures potential environmental impacts of a product’s life cycle from cradle to grave (Curran, 2015). The use of sustainability tools like LCA aid the industry to identify underperforming aspects of their products and create more sustainable products (Stoycheva et al., 2018).
1.1 Aim and research questions
The aim of this study is to analyze the production of a customized truck at Scania, also known as S- order design, in order to determine its potential to reduce environmental impacts and contribute to sustainability. To achieve the aim, an LCA of two chassis trucks will be made, one manufactured with S-order design and the other with A-order design, in other words with and without adaptations respectively. Furthermore, they will be compared from an environmental perspective to define which design is more environmentally friendly.
The following research questions are formulated in order to meet the aim:
1. Is there an environmental performance difference between the production phases of a chassis with S- and A- order design?
2. Will the use of S-order instead of A-order design in the chassis align better with the sustainability goals set by Scania?
3. Are there any possible trade-offs or risks correlated with the use of S-order design?
1.2 Structure of the thesis
Chapter 2: Background information presents previous LCA studies done in the vehicle industry, mainly focused on trucks. This also defines what S-order is at Scania in order to know its process, functionalities, and relevance in the production of a truck.
Chapter 3: Methodology describes the selected methodology for this study, which is mainly the life cycle assessment framework (LCA).
Chapter 4: Tracing sustainability at Scania determines Scania’s sustainability goals, these will be the basis for the selection of the key impact categories, which will be used to analyze the results of the LCA.
Chapter 5: Life cycle assessment of trucks presents the comparison of the production of a chassis truck with S- and A-order design. SimaPro software is used to modelled both chassis trucks, the results are presented and analyzed.
Chapter 6: Discussion presents the discussion derived from the results of the LCA. It includes the answer to the research questions, addresses the source of uncertainties in the results, and recommends further research.
Chapter 7: Conclusions presents the general conclusions of the performed LCA study.
2 Background information
In this section the background information of previous LCAs conducted in the vehicle industry will be presented. The study will focus on heavy-duty vehicles, but due to limited studies performed on that type of vehicle, the studies will also include medium, light-duty vehicles and passenger vehicles.
The background information will also define and describe what S-order is at Scania.
2.1 Previous studies in the field
In order to learn generic information about the life cycles of heavy-duty trucks, previous studies have been analyzed. The following studies discussed below were used to gain knowledge on how LCA’s for trucks are conducted, and to access useful or relevant data.
Romare and Hanrap (2017) conducted an LCA from a cradle-to-grave perspective of a distribution truck for urban applications, with either diesel or otto engine running on different fossil and bio- based fuels. The impact assessment was done with both CO2-eq emissions and environmental damage cost by using the Environmental Priority Strategy methodology (EPS). They wanted to explore impacts on different perspectives with respect to sustainability evaluation. Their results confirmed previous studies, where agriculturally based biofuels show approximately 50% CO2-eq emissions reduction compared to fossil alternatives and waste based fuels give approximately 80% reduction.
In the EPS assessment, the production of the vehicle itself distinguished due to the use of rare platinum group metals in the after-treatment catalysts, and proper recycling schemes were also emphasized. In the use phase there are differences in EPS scores for different fuels mainly due to the variations in CO2 emissions and fossil resource use implying that the fuels with the highest impact in CO2-eq category also has a higher contribution form the use phase (Romare & Hanarp, 2017).
Yang et al. (2018) developed a cradle-to-grave analysis to assess the greenhouse gas (GHG) emissions and the total cost of ownership (TCO) of light-duty and medium-duty diesel trucks, plug-in electric trucks and battery-swap electric trucks. Their results presented that the average GHG emissions of light-duty electric trucks is 69% lower than that of light-duty diesel trucks, while that of medium-duty electric trucks is 9.8% higher than that of medium-duty diesel trucks. The plug-in electric trucks are 37.8% lower and battery-swap electric trucks are 21% higher than that of light-duty diesel trucks, whereas for medium-duty plug-in electric trucks are 6.7% lower and battery-swap electric trucks are 18.9% higher than that of medium-duty diesel trucks. Their conclusion was that light-duty plug-in electric trucks demonstrated the largest GHG emissions reduction and cost savings (Yang et al., 2018).
Sen et al. (2017) developed an LCA study from a cradle-to-use perspective to explore differences between conventional and alternative fuel technologies used in heavy-duty trucks (HDTs). They distinguished them with respect to their LC emissions, costs, and externalities. They claimed that the differences found represent an important opportunity to develop a more holistic overall analysis of future HDTs. Their results showed that battery electric (BE) HDTs outperform all other types of trucks overall, despite their incremental costs and electricity generation-related emissions. They also found a slight difference in the life-cycle costs (LCCs); 33% more GHGs with compressed natural gas (CNG) than conventional HDTs. Their study concluded that CNG trucks yield no improvements in either HDTs life-cycle environmental impacts or LCC compared to their conventional counterparts.
Further, they also concluded that the LC performance of a BE truck would significantly improve when electricity is generated from renewable energy sources (Sen et al., 2017).
Rupp et al. (2018) conducted a cradle-to-use study to analyze and compare hybrid and conventional heavy-duty trucks in long-haul operation. They assumed the same vehicle glider; only differing parts
of both drivetrains were considered to calculate environmental burdens of the production. They modelled the use phase by backward simulation in MATLAB/Simulink to represent a driving cycle.
Their results presented that the hybrid vehicle released 4.34 g CO2-eq/t km fewer emissions compared to the diesel truck. Their break-even analysis resulted that the larger emissions generated during production were compensated after a distance of nearly 16,000 km or about 1.5 months in operation (Rupp et al., 2018).
Qiao et al. (2017) carried out an LCA from a cradle-to-gate perspective to investigate and compare the total CO2 emissions from the production phase of both and ICEV in China. Their main aim was to identify the ability of EV to save energy and reduce GHG emissions. Their results revealed that the CO2 emissions from the production of an EV were 60% higher than the level of an ICEV. They determined the lithium batteries and additional components such as the traction motor and electric controller were the main contributors. mainly due to the lithium ion batteries and other components.
Lastly, they concluded that there is a large reduction potential of CO2 emissions from EV in China as manufacture techniques of batteries are growing and material recycling is developing (Qiao et al., 2017).
Hallberg et al. (2013) performed a well-to-wheel (WTW) analysis to gather and compile available environmental data for vehicle fuels in Sweden, and further for use it in environmental assessment, such as LCA. The fuels studied were ethanol with about 94% denaturized (ED95), fossil diesel, rape seed methyl ester (RME), hydrotreated vegetable oil (HVO), biogas and natural gas. Their results showed that for most biofuels, the total well-to-wheel impacts was much lower than for the corresponding fossil fuels. Their results showed that the impact from ED95 and RME was less than 40% of the impact from diesel. The HVO from rape-seed oil was around 35% less compared to diesel.
Biogases showed an impact between 10-30% of the impact form natural gas. They identified one biofuel with a similar range of impact as fossil fuel which was HVO from palm oil. In their conclusions they remarked that their choice of methodology can have a substantial influence on the LCI results (Hallberg et al., 2013).
Börjesson et al. (2016) made a comparative WTW assessment to analyze and describe the energy, greenhouse gas (GHG) and cost performance of existing and potential new methane-based vehicles.
The included technologies were light-duty and heavy-duty vehicles using spark ignited (SI) otto engines and dual-fuel (DF) diesel engines. The general conclusions of their study were that the use of renewable methane vehicle fuels systems leads to significant WTW GHG benefits in comparison with fossil-based vehicle fuel systems. However, the WTW systems based on gas showed a slightly lower energy efficiency and slightly higher costs than gasoline- and diesel-fueled vehicles based on their current market price at that time (Börjesson et al., 2016).
The previous studies are quite different, especially on a system boundaries perspective, although they include an analysis of environmental impacts of trucks. It is noticed that all have different research questions and include diverse phases which give various conclusions regarding critical processes in the life cycle. A repeated conclusion between the previous studies is that the use phase of ICEVs results with the largest GHG emissions due to a high energy consumption, whereas for BEV is the production phase. These studies have been helpful with information and data regarding environmental effects;
however none of the papers investigated if there is an environmental performance difference between the production phases of two different designs to manufacture a heavy-duty truck. The results of this study can contribute to this field and be used by Scania for decision making.
2.2 S-order design at Scania
A truck is a motor vehicle designed to carry goods or to perform a special function such as a waste collector. Trucks can be distinguished as either tractor or rigid. A truck tractor consist of two or more separate frames connected by special connectors, while a rigid truck has all its axles attached to a single frame (Encyclopedia Britannica, 2020). From the manufacturer perspective, a tractor is ready to use once it leaves the factory since a trailer is just attached to it, whereas a rigid truck needs to undergo bodywork in order to be ready for usage. Therefore, Scania counts with a department focused on customized truck development called “Special order (S-order) design”.
S-order design handles truck orders with customers’ specific adaptations that require a change of design or add a functionality that is not offered in A-order options. This type of order builds on A- order options, however it offers the flexibility to adapt them according to the customer needs which could be from a new configuration to alternative fuels or electrification. Approximately 10-12% of the produced vehicle volume is customized with S-order (Scania, 2019b).
The S-order design process is divided into three sub-processes: request, order and production as presented in Figure 1. Every S-order starts with a request in a product request system (PRS). That is often made by a pre-salesperson at a distributor or by a Scania internal salesperson. The request contains the specifications of an A-order vehicle with detailed description of the preferred adaptations. The request is simultaneously attended by different cross functional departments. The S-order planner coordinates and distributes all requests to concerned functions. It also collects individual answers from different functions and summarizes them into a final answer. If all functions accept the request, it is approved for ordering. The S-order planning is composed by four functions, internal sales, design, purchasing, and logistics. Designers are responsible to give a technical answer to a request including lead time confirmation and development time. At the same time, the purchasing department, who is responsible of supplying parts and materials for S-order solutions, updates the request with prices and lead time since it has contact with suppliers. The logistics department investigates if a solution is possible to build since components production sites also have to give an answer to a request. These include cab production in Oskarshamn, frame production in Lulea, and engine and axle assembly in Sodertalje (Scania, 2019b).
Figure 1: S-order design process at Scania (Scania, 2019b)
When the request is approved for ordering, designers develop the complete structure of the chassis with its components according to the requested adaptations. Their work is registered as a 3D model, drawings, and assembly guidelines since these are input to production preparation for the final assembly. Finally, the production phase coordinates the component supply from other Scania production facilities, material supply, and schedule for assembly. Each sub-process has a dedicated person responsible for handling every request. Depending on the characteristics of a request, it is not necessary for a request to pass all functions every time because it is common to compare new requests with previously built solutions (Scania, 2019b).
Customer Distributor Product request (PRS)
• Planning
• Internal sales
• Design
• Purchasing
• Logistics
Product solution (Order)
• Design
• Structure development
Production
• Logistics
• Material supply
• Vehicle assembly
Distributor Customer
Scania internal
The production of an S-order truck consists of two important parts: the chassis design and manufacture, and the body manufacture and assembly. When the S-order chassis truck is assembled, the chassis leaves Scania’s main assembly line and heads to an internal or external bodybuilder workshop. A bodybuilder is a company that is specialized in the bodywork or equipment assembly of trucks with a certain type of transport need, for example, tipper truck, waste collector truck, and among others. Even though that this is out Scania’s scope, this is still part of the vehicle manufacture process. With an S-order chassis, the bodywork stage can be a short because its structure has the required adaptations which allows a smoother installation of the body and additional components, considering that it is possible to build the body meanwhile the S-order truck is being produced.
However, the bodybuilder workload depends on how customized and optimized the truck was ordered.
The specific adaptations vary, and these depend on the customers’ needs, the application and main functionality of the vehicle. The shortening of the rear overhang, hole pattern in the chassis frame, change the size or location of elements, removal of parts, changes on the inside or outside the cab, different configurations, adjusting the power take-off (PTO), change axel distance, body adaptation brackets, chassis reinforcement, software development for a specific functionality, the addition of cable harness for bodywork functions, and among others are some adaptations handled by designers.
A few of them are briefly described below.
Adapted length of rear overhang
The length of the rear overhang refers to the distance between the centerline of the foremost driving rear axle and the rear edge of the frame side member (Scania, 2020c). The rear overhang length can be ordered in lengths increments of 10 mm between the limit values 750 mm and 5,200 mm, as shown in Figure 2. A shorter rear overhang enables a more compact vehicle.
Figure 2: Adapted length of rear overhang (Scania, 2020c)
Hole pattern in the chassis frame for bodywork
The hole pattern is optimally designed according to the application of the vehicle because it is used to attach the bodywork in the truck’s chassis frame. The holes are positioned 50 mm center-to-center and 60 mm form the upper edge of the frame side member as seen in Figure 3 (Scania, 2020a).
Figure 3: Hole pattern in chassis frame for bodywork (Scania, 2020a)
Changing the size and location of elements
Elements on the frame can be arranged in a convenient location and position according to the body of the vehicle. For example, tanks can be changed to a smaller volume, and they can also be hidden under the chassis frame to make space for other components on the chassis frame as displayed in Figure 4.
Figure 4: Hidden tank on a chassis frame (Scania, 2017)
3 Methodology
In order to meet the aim and objectives of this study, a life cycle assessment (LCA) framework is therefore followed. This framework provides quantitative results of environmental impacts that correspond to the different phases of a product and makes it possible to identify the most contributing processes to environmental impacts. The performed LCA is presented in chapter 5.
3.1 Life cycle assessment (LCA) framework
Life cycle assessment (LCA) is an analytical tool that quantifies the environmental impacts related to a product or service during its complete life cycle. This environmental approach provides a comprehensive view of the environmental aspects of products or processes throughout its life cycle (Curran, 2015). It involves quantifying energy and resource consumption as well as emissions, from all life cycle stages including raw material extraction, processing, transport, manufacture, use and disposal.
The LCA framework is composed by the following four phases (International Standards Organization, 2006):
1) Goal and scope definition, 2) Life cycle inventory (LCI),
3) Life cycle impact assessment (LCIA), and 4) Interpretation.
As seen in Figure 5, it enables an iterative process where the interpretation provides feedback to the rest of the phases. Additionally, the LCA approach aids to identify opportunities to improve the environmental performance of products at various points of their life cycle, inform decision-makers in industry, government or any organizations about their product design or redesign, select key indicators of environmental performance, including measurement techniques, or make an environmental claim for marketing (International Standards Organization, 2006).
Figure 5: Life cycle assessment framework (International Standards Organization, 2006) Interpretation
Goaland scope
Inventory Impact
assessment
3.1.1 Goal and scope
A well-defined goal and scope is crucial in order to understand the life cycle assessment and its results.
The goal describes the main purpose to conduct a life cycle assessment study, specifies the intended application and mentions the target audience (Curran, 2015). Another important part is to choose the appropriate LCA method which depends on the purpose of the study. There are two main types of methods for LCA, these are attributional and consequential. Attributional LCA describes the relevant environmental physical flows to and from a life cycle and its subsystems, whereas consequential LCA describes the consequences of change in environmental physical flows due to alternative decisions (Finnveden et al., 2009).
The scope defines the product being studied, the functional unit, and the system boundaries including geographical and temporal boundaries, allocations procedures, exclusions, assumptions, limitations, impact assessment methodology, interpretation approach to be used and data requirements (Curran, 2015). In essence this part also sets the life cycle stages included in the study as presented in Figure 6. Moreover, the most important parameters in the footprint have to be evaluated through a sensitivity analysis in order to assess the quality of the results in relation to changes on those parameters.
Figure 6: Different LCA stages
3.1.2 Life cycle inventory (LCI)
The aim of this phase is to compile, quantify and calculate inputs and outputs for a product throughout its life cycle (Curran, 2015). The extent of the inputs and outputs come in different types, for example some possible inputs are natural resources, energy, materials, whereas for outputs are materials, waste and emissions. The LCI phase is based on unit process which is the smallest process considered in the LCI analysis when input, and output data are quantified. During this phase a unit process is commonly considered as a “black box” because one unit process turns into a collection of inputs and outputs (International Standards Organization, 2006; Curran, 2015). Alternatively, this phase can be carried out with an LCA software, this enables to model and connect a high quantity of processes at a unit process level. When modeling it is important to separate the foreground and background systems. The first system includes the processes that are under control of the decision maker, and those that are being investigated. Meanwhile, the second system includes the data outside immediate control of the decision maker, and it is not a focal point of the investigation (Finnveden et al., 2009; Curran, 2015).
3.1.3 Life cycle impact assessment (LCIA)
The purpose of this phase is to provide additional information to help evaluate the results from the LCI in order to understand better their environmental significance (Grant, 2009). The methodology of an LCIA consist of both mandatory and optional steps. According to the ISO standard, the selection of impact categories, classification and characterization are mandatory steps (International Standards Organization, 2006). Classification assigns the emissions from the inventory to the selected impact categories according to the substances’ ability to contribute to different environmental problems. Characterization allows adding the contributions from all emissions and resource extractions within each impact category, translating the inventory data into a profile of environmental impact scores (Ibid.).
The optional steps of the LCA are normalization and weighting. The former one calculates the results of each category in relation to a reference value, and the latter one weighs the impact categories against each other (International Standards Organization, 2006). Neither of these are applied in this LCA, only the mandatory steps are included.
3.1.4 Interpretation of results
The interpretation is the final phase of the LCA study, and its main intention is to analyze the results of the inventory and impact assessment in connection to the defined goal and scope in order to formulate conclusions, recommendations and present the findings in a transparent manner (Finnveden et al., 2009). Other several elements should be also included such as the identification of significant issues, the contribution of each life cycle stage, evaluation through a sensitivity analysis or consistency and limitations.
4 Tracing sustainability at Scania
This chapter presents the identified sustainability goals set by Scania. This will aid to identify key impact categories that mirrors Scania’s sustainability goals. Scania’s sustainability strategic plan includes several areas, and few are described such as sustainable transport approach, environmental footprint, health and safety, and diversity and inclusion.
4.1 Sustainable transport
Scania’s approach to sustainable transport lays on three pillars that aim to optimize the transport system levels: energy efficiency, renewable fuels and electrification, and smart and safe transport, as presented in Figure 7. Each pillar is described as follows (Scania, n.d.):
• Energy efficiency: Scania has the aim to provide the most efficient technology in its products combined with services through powertrain performance improvement, vehicle optimization and fuel consumption.
• Renewable fuels and electrification: Scania has the objective provide variety of engines for renewable fuels and electrification technologies for vehicles and infrastructure in order to phase out the use of fossil fuels.
• Smart and safe transport: Scania has the goal to create more efficient logistical flows through digitalization, connectivity, and autonomous vehicles.
Figure 7: Scania’s three pillar approach to sustainable transport (Scania, n.d.)
Scania believes that when these three pillar are combined, they can aid to make their transport systems cleaner, safer and more efficient. Scania considers that partnerships with society, policy makers, operators, and customers, are crucial for reshaping the ecosystem of transport and logistics.
4.2 Environmental footprint
Scania is determined on reducing the environmental impact; therefore they are strongly focused on using resources as efficiently as possible in their operations and production processes. Their efforts are built on the preventive principle and the life cycle perspective aiming to contribute to a circular economy. The strategic plan includes energy efficiency, reduction of greenhouse gas (GHG) emissions, and increased of recycling of waste. Each one is briefly described as follows (Scania, n.d.):
Energy efficiency
Improving powertrain performance Vehicle optimization
Fuel consumption
Renewable fuels and electrification
Ethanol, liquified or compressed biogas, biodiesel - HVO/FAME
Hybrid and Battery electric vehicles (BEV)
Fuel cell vehicles
Smart and safe transport
Digitalization
Connected vehicles Autonomous vehicles
(AV)
• Efficient energy use: Scania’s priority is continuous improvement on energy efficiency;
therefore they have decided on a target to reduce the usage of energy within the production.
• Greenhouse gas (GHG) emissions: CO2 is the most emitted gas by Scania’s operations derived from direct and indirect energy use. Their ambition is to continuously reduce GHG emissions in both industrial and commercial operations and increase the energy share from fossil-free sources.
• Waste: For Scania, it is important to have a good way of handling waste; therefore they have set the target to increase reuse and recycling within production. Material waste reduction within production is also considered crucial since this is in the scope of their core value
“elimination of waste”.
4.3 Health and safety
Scania’s goal is to preserve and promote safety, health and well-being at work for its employees.
Scania also strives to achieve a sustainable workplace and a good working environment from both psychosocial and physical aspects and commits to comply with any demands in accordance with legal and other applicable requirements issued by national authorities and by Scania self-appointed targets within the area (Scania, n.d.).
4.4 Diversity and inclusion
Diversity and inclusion at Scania is about continuously developing their corporate culture, forming their strategy in the area by using the collective intelligence of the Scania organization. In order to have a diverse organization in terms of gender, age, background and experience, Scania implemented
“Skill Capture program”. This is a designed program to broaden the scope of diversity, to address all the ways that their employees are diverse; gender and cultural diversity as well as personality and experience as well as looking at how they can improve their inclusiveness. In 2019, Scania increased the number of countries participating in measuring the index from 5 to 43, and Scania also recorded a shift where women take the lead in equal opportunities to become a manager (Scania, n.d.).
4.5 Key impact categories
From Scania’s sustainability goals, three key impact categories are selected because are considered to reflect on the impacts that are important to Scania. These impact categories will be analyzed in detail after the rest of the impact categories are briefly analyzed. The key impact categories selected based on Scania’s sustainability goals are listed and motived as follows:
• Global warming: Scania has a goal to reduce GHG emissions from their operations, therefore this impact category, global warming is relevant to analyze and choose as a key impact category.
• Mineral resource scarcity: Scania works to use resources as efficiently as possible in their operations and production processes since they require a significant number of materials derived from precious minerals, therefore this impact category, mineral resource scarcity, is crucial to examine and select as a key impact category.
• Fossil resource scarcity: Scania’s goal to reduce GHG emissions are linked to this impact category, since part of Scania’s strategy is to decrease the use of fossil fuel resources in their operations and production processes, therefore this impact category, fossil resource scarcity,
5 Life cycle assessment of trucks: Comparing the production of a chassis with either S-order or A-order design
5.1 Goal and scope
5.1.1 Goal
Scania has two ways to design chassis, namely, special (S-order) and standard (A-order). An S-order chassis is built with detailed preparations while an A-order truck is built with a limited level of customization. Since the design of a truck influences on its whole life cycle, it is important to Scania to determine if there is a difference in GHG emissions between the production phases of these two designs.
The goal of this study is to compare the environmental performance associated with the production phases of two chassis designs, S-order and A-order. The chassis to be compared are both of the brand Scania and made of the same materials. This will aid to determine how big the GHG emissions difference is between the production phases of the two designs, and to identify the most contributing processes of the two options regarding environmental impacts.
The results will be used to inform Scania about the difference in environmental performance of the two design alternatives through the production and different impact categories. This could aid designers, manufacturers, and consumers in decision-making for a more sustainable production. At the same time to emphasize that small decisions and details specified at the beginning of the product development can result as better products, sustainable practices and achieve sustainable goals.
Furthermore, a sensitivity analysis will be conducted to evaluate the robustness of the study.
5.1.2 Product and Function description
The product system under the scope of this LCA study is a rear loader waste collector truck with an engine of 280 hp, a 6x2*4 configuration with gas Euro 6, and a gross weight of 26,000 kg. This kind of truck is composed by two essential parts, a chassis and a body as seen in Figure 8. The chassis can be constructed at Scania throughout both S-order and A-order designs, and its body is designed and manufactured by internal or external bodybuilder companies. Since the production of the chassis is Scania’s scope, therefore the chassis structure is examined. The primary function of a chassis is to carry the body and load and transport them.
Figure 8: An S-order design truck with a body, together form a rear loader waste collector truck (Scania, 2019a)
5.1.3 Functional unit (FU)
The functional unit for this study is a rear loader waste collector truck with a 11 years lifespan able to operate about 20,000 km per year.
5.1.4 System boundaries
This study is focused on a cradle-to-gate perspective, which includes the processes of raw material extraction, material transformation, component production and chassis assembly and transport. The flowchart of the product system is delimited by a grey line as presented in Figure 9, which includes all processes, inputs and outputs.
Figure 9: General flowchart of a waste collector truck at Scania with geographical boundaries
Geographic boundaries
The life cycle phases of a truck occur in several geographic locations since it is composed by thousands of elements made of different materials, for example different types of metals, rare earth minerals, rubber, glass, and more. Therefore the origin of the raw materials is assumed to come from different locations around the globe. For the manufacturing phase, its geographical boundary is also global, however the production of the main components of a truck such as frame, axles, engine, gearbox, cabin, and chassis assembly occurs in Sweden. The manufacturing and assembly of the body could take place within Sweden or Europe.
Temporal boundaries
This study is made in 2020 based on available inventory data from 2019, since the chassis for the analyzed rear loader waste collector truck was manufactured in that year. However, data availability varied in different circumstances, therefore older data have been used. Considering the assumed lifespan of a truck, as a rear loader waste collector, it is estimated that it will operate in a time frame of 12 years between 2020 to 2032. The applicability of this study will be limited to a few years from now because technology and manufacturing methods are continuously improved.
Allocation procedures
Allocation occurs when a process has multiple inputs and/or outputs which are not considered by the functional unit of the product system under study, thus the sub-processes involved to handle byproducts must be managed. The Ecoinvent v3.5 database uses Allocation at the point of substitution (APOS), this model adopts an attributional approach to multiple inputs and/or outputs processes and applies system expansion in order to include all treatment processes required of any byproduct (Wernet et al., 2016).
Exclusions (Cut-offs)
The following are excluded from this study:
• Manufacturing of main components are included; however it focuses on the material production and critical primary production processes such casting. Other processes such as machining, injection molding, welding, surface treatment, painting, etc. are excluded due to limited data availability.
• All sort of packaging used from raw materials to the end product is excluded.
• The vehicle assembly processes and facilities are excluded.
• The storage of elements and components
• The production, manufacturing, maintenance and repair of equipment in all Scania facilities.
• The waste management of Scania’s production facilities.
• The transport processes from suppliers to Scania’s production facilities and Scania’s internal logistics has not been taken into account in detail.
• Use phase is excluded because the main focus of this study is on the manufacturing phase, however it is also due to limited data.
• Disposal phase is excluded due to limited data availability; however its impact contribution is significantly small in the complete lifecycle of a truck that could be disregarded (Romare &
Hanarp, 2017).
5.1.5 Assumptions
In this study the following assumptions are considered:
• Functional unit
o Both S- and A-order chassis perform an equivalent function after being adapted as a waste collector truck.
o The lifespan of both waste collector trucks is 12 years or 240,000 km mileage.
• Geographical boundaries
o The raw material extraction, and processing phases take place globally o The manufacture phase mainly occurs globally, however the main components
production and the chassis assembly happen in Sweden.
• Manufacture phase
o A material composition of a rear loader waste collector truck with a 6x2*4 configuration and an engine of 280 hp with gas Euro 6
o The extraction and processing of raw materials are obtained from commercial databases such as Ecoinvent v3.5 and analyzed through SimaPro software. The inputs and outputs of all databases are left as default.
o One type of steel and aluminum alloy was used to represent all kinds of alloys found in the vehicle
o S-order design chassis considers only three adaptations, these are described in the LCI section.
o A-order design chassis does not consider any adaptation.
• Energy
o The total energy consumption per produced vehicle is 7.1 MWh. This includes all processes in all industrial facilitie in Sweden (Scania, 2019c).
o Swedish energy mix is chosen to model the energy consumption per produced vehicle at Scania. According to Scania’s annual report (2019), the energy supplied in all Scania component production sites located in Sweden is 95% renewable energy (Scania, 2019c).
• Transport: Google maps was used to estimate transportation distances. When more than one alternative route was available in Google maps, the shortest route was used.
o Since the frame, engine and gearbox are manufactured in Sodertalje, therefore they travel 0 km
o The cabs travel about 300 km from Oskarshamn to Sodertalje (Google, 2020b) o The axles travel about 935 km from Lulea to Sodertalje (Google, 2020a)
5.1.6 Limitations
The following are limitations of this study:
• Limited access to specific data regarding the production of a truck due to lack of data or confidentiality reasons.
• Due to the high level of complexity in a truck’s structure a detailed analysis of all material flows is very challenging. This can contribute to generalized assumptions and uncertainties in the results.
• Data availability of the truck manufacture varied in the production phase; data collection for the bodybuilder stage was not possible due to health and travel restrictions, and work prioritization in companies due to Covid-19 effects. This contributes to uncertainties in the results.
• Lack of data regarding the percentage of recycled material used in a truck. This can contribute to generalized assumptions and uncertainties in the results.
