Automotive Route Optimization for a Logistics Service Provider Pre-study for Route Planning and Optimization Software Investment Master’s Thesis in the Master’s Programme Quality and Operations Management Supply Chain Management ALDIN AVDIC ZHENGYANG XIANG Department of Technology Management and Economics Division of Service Management and Logistics CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2017 Report No. E 2017:111 MASTER’S THESIS E 2017:111 Automotive Route Optimization for a Logistics Service Provider Pre-study for Route Planning and Optimization Software Investment ALDIN AVDIC ZHENGYANG XIANG Tutor, Chalmers: Gunnar Stefansson Tutor, Company: Patrick Magnusson Department of Technology Management and Economics Division of Service Management and Logistics CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2017 Automotive Route Optimization for a Logistics Service Provider Pre-study for Route Planning and Optimization Software Investment ALDIN AVDIC ZHENGYANG XIANG © ALDIN AVDIC & ZHENGYANG XIANG, 2017. Master’s Thesis E 2017: 111 Department of Technology Management and Economics Division of Service Management and Logistics Chalmers University of Technology SE-412 96 Gothenburg, Sweden Telephone: + 46 (0)31-772 1000 Chalmers Reproservice Gothenburg, Sweden 2017 ACKNOWLEDGE We would first like to thank our thesis supervisor Gunnar Stefansson in the Division of Service Management and Logistics at Chalmers University of Technology and LM at Autolink for your support. Writing the thesis was a valuable and interesting experience to both of us and even though challenges occurred, Gunnar Stefansson and LM steered us in the right direction in both the theoretical and practical knowledge gathering. Furthermore, we would like to give our thanks to the employees from Nordic Car Logistics (NCL) who took their time to participate in our master thesis, as well as the additional organizations involved in this thesis project for supporting us during the writing process and data collection. We would especially like to acknowledge the different interviewees from the NCL Group, customer companies, software providers and others for providing us with the necessary information. Without their support, the master thesis was not able to be performed. Last but not least, we would like to show our profound gratitude to our parents, friends and partner for providing us with the continuous encouragement and support throughout this thesis project. Without their support and help, the completion of this study would not be possible. Thank you. Aldin Avdic and Zhengyang Xiang Gothenburg, July 2017 ABSTRACT Due to the fast development of the global market, the Swedish automotive logistics industry has experienced tough competition and price pressure in recent years. In this case, the end customers including both single customers as well as different car brands or wholesalers are asking for a whole solution provider through the whole value chain. With the background of this, the Swedish biggest vehicle logistic provider with over 90% of market share, Nordic Car Logistic (NCL), is moving forward with the aim to be a 4PL in this industry step by step. In order to doing so, a suggestion on the route and load planning system implementation is made for Autolink, a subsidiary of the NCL-brand which focuses on the vehicle distribution to the end customers all around the Sweden. By combining theoretical findings within route planning, customer relationship management and information and communication technology, it was possible to identify a model based on literatures that contains the potential factors which influence the actual route and load planning in Autolink. Several case studies were done in order to clarify the potential benefits with the use of a route and load planning system within different industries. Moreover, with the chosen interviews of respondents from different organizations including Autolink, customers and hauliers, different demands or requirements from different points of perspective including managers, planners, customers etc. were collected and concentrated into the final evaluation criteria which includes five main groups with 22 sub-features in the software benchmarking model as the last part of this report. Therefore, based on the criteria identified, one software providers were stand out in most of the five categories among all the three softwares and was chosen as the final recommendation of the potential route planning system investigation. Furthermore, four interesting points that is found during the study were discussed in the last chapter including total transportation solution, capacity issues, centralized or decentralized route planning and the future trend which were beyond the scope of this study. These areas were believed as the fields that worth Autolink looking into as the next step after the route planning system implementation toward the road to be a 4PL or a whole solution provider in the vehicles logistics industry. Keywords: route planning system, information and communication technology, customer relationship management, vehicle industry Abbreviations AL: Autolink CB: Car Brand CIC: Centralized Information Control CMS: Compound Management System CRM: Customer Relationship Management CTM: Collaborative Transportation Management CVRP: Capacitated Vehicle Routing Problem DCVs: Destination Coded Vehicles DM: Distribution Manager EDI: Electronic Data Interface ERP: Enterprise Resource Planning ETA: Estimated Time of Arrival FMS: Fleet Management System FTL: Full Truck Load ICT: Information and Communication Technology IoT: Internet of Things IT: Information Technology ITS: Intelligent Transportation Systems IVC: Inter-Vehicular Communication LSP: Logistics Service Provider NCL: Nordic Car Logistics PDI: Pre-delivery Inspection RPS: Route Planning System SCM: Supply Chain Management SKT: Skandia Transport SMT: Scandinavian Motortransport SP: Software Provider SVRP: Stochastic Vehicle Routing Problem TDM: Transportation Demand Planning TM: Transport Manager TMS: Transport Management System TP: Transport Planner TTEA: Trip Time Estimation Agent VDS: Vehicle Distribution System VIN: Vehicle Identification Number VRP: Vehicle Routing Problem VRPTW: Vehicle Routing Problem with Time Windows 3PL: Third-party Logistics 4PL: Fourth-party Logistics Table of Contents 1. Introduction ................................................................................................................................ 1 1.1 Aim ......................................................................................................................................... 3 1.2 Purpose .................................................................................................................................. 3 1.3 Research Questions............................................................................................................... 3 1.4 Limitations ............................................................................................................................ 4 2. Method ......................................................................................................................................... 5 2.1 Research Strategy ................................................................................................................. 5 2.2 Literature review .................................................................................................................. 5 2.3 Empirical Study .................................................................................................................... 5 2.3.1 Choice of interviewees ..................................................................................................... 6 2.3.2 Interviewing technique ..................................................................................................... 6 2.3.3 Observations .................................................................................................................... 6 2.3.5 Benchmarking .................................................................................................................. 7 2.3.6 Validation of data ............................................................................................................. 7 2.3.7 Ethical Considerations ..................................................................................................... 7 2.3.8 Presentation and analysis of data ..................................................................................... 8 3. Theoretical Framework ............................................................................................................. 9 3.1 Route Planning ...................................................................................................................... 9 3.1.1 The Vehicle Routing Problem ......................................................................................... 9 3.1.2 Stochastic VRP (SVRP) ................................................................................................. 10 3.1.3 Route Planning System .................................................................................................. 10 3.1.4 Parameters ...................................................................................................................... 12 3.1.5 Fleet Management .......................................................................................................... 18 3.1.6 Case Study: Route Planning and Optimization .............................................................. 18 3.1.7 Conceptual Model: Route Planning ............................................................................... 24 3.2 Customer Relationship Management (CRM) .................................................................. 25 3.3 Information and Communications Technology (ICT) .................................................... 26 3.3.1 Intelligent Transportation Systems (ITS) ....................................................................... 27 3.3.2 Collaborative Transportation Management (CTM) ....................................................... 28 3.3.3 Route and Load Planning Software ............................................................................... 29 3.3.4 Centralized or Decentralized Software Implementation ................................................ 30 4. Empirical Data Collection ....................................................................................................... 33 4.1 Manager and Internal Personnel Interviews ................................................................... 33 4.1.1 Chief Operating Officer (COO) ..................................................................................... 33 4.1.2 Logistics Manager (LM) ................................................................................................ 34 4.1.3 Marketing Manager (MM) ............................................................................................. 36 4.1.4 Lead Transportation Planner (LTP) ............................................................................... 38 4.1.5 Transportation Planner Autolink (TPA) ........................................................................ 41 4.2 Customer Interviews .......................................................................................................... 45 4.2.1 CB 1: Transportation Manager 1 (TM 1) ....................................................................... 45 4.2.2 CB 2: Transport Manager 2 (TM 2) ............................................................................... 48 4.2.3 Distribution Manager (DM) ........................................................................................... 50 4.3 Haulers ................................................................................................................................. 51 4.3.1 Hauler 1 .......................................................................................................................... 51 4.3.2 Hauler 2 .......................................................................................................................... 52 4.4 Statistical Data Collection .................................................................................................. 54 4.5 Software Providers ............................................................................................................. 55 4.5.1 Software Provider 1 (SP1) ............................................................................................. 55 4.5.2 Software Provider 2 (SP2) ............................................................................................. 57 4.5.3 Software Provider 3 (SP3) ............................................................................................. 60 5. Analysis ..................................................................................................................................... 63 5.1 Managerial Views ............................................................................................................... 63 5.1.1 Vision ............................................................................................................................. 63 5.1.2 Managerial Requirements .............................................................................................. 63 5.2 Customer Needs .................................................................................................................. 65 5.2.1 Customer and interviewees selection ............................................................................. 65 5.2.2 Demand .......................................................................................................................... 65 5.3 Optimization........................................................................................................................ 67 5.4 Working Flow ..................................................................................................................... 68 5.5 System Integration .............................................................................................................. 69 5.6 Haulers ................................................................................................................................. 71 5.7 Benefits ................................................................................................................................ 72 5.8 Software Benchmarking..................................................................................................... 73 5.8.1 Core Needs ..................................................................................................................... 73 5.8.2 Additional Needs ........................................................................................................... 74 5.8.3 Serviceability ................................................................................................................. 75 5.8.4 Others ............................................................................................................................. 76 5.8.5 Price ............................................................................................................................... 77 5.8.6 Benchmark Analysis ...................................................................................................... 77 6. Result ......................................................................................................................................... 83 7. Discussion .................................................................................................................................. 85 7.1 Total transportation solution ............................................................................................. 85 7.2 Capacity constraint............................................................................................................. 85 7.3 Centralized or decentralized route planning ................................................................... 86 7.4 Leased cars trend ................................................................................................................ 88 8. Conclusion ................................................................................................................................. 89 References ..................................................................................................................................... 90 Appendix A - Questionnaires ...................................................................................................... 95 Appendix B - Route Allocation ................................................................................................. 103 1 1. Introduction The Swedish automotive logistics industry has in recent years been characterized by tough competition and price pressure (Business Sweden, 2016). Meanwhile, customers are showing an increased interest in functional solutions that have more or less forced companies to compete in supply chains (Agrawal et.al, 2015). In order to create successful business, companies now have to come together and create value for customers. According to Autolink (2017), the Swedish automotive logistics industry is a rather underdeveloped one. It is heavily influenced by older business models and ways of working. Therefore, it is not uncommon for the Swedish automotive logistics industry is subjected to several delays, capacity issues and overly costed services. The European Commission (EC) (2011) clearly states the importance of transportation in today's society and economy, highlighting how future transportation should be developed in a more efficient manner. To overcome issues and move towards a more efficient transportation flow, companies are constantly trying to find advantages to improve their flow of goods. Furthermore, the EC (2011) has in recent years concluded that the Swedish transportation market is moving from road to rail, even though road makes up roughly 62% of the transportation made within the country (EC, 2011). On the other hand, the Swedish Transportation Administration state that Swedish railways suffer from capacity problems on several lines. The same phenomena have been experienced by Autolink and its business partners, whereas even the road transportations are suffering from capacity problems and inefficient vehicle transportation flows (Autolink, 2017). The EC (2012) has also predicted an increase in transportation services needed in the future along with the GDP. This is considered to be a hard-managed demand increase according to Autolink (2017) and the Swedish Transportation Administration (2012) since the increase in demand cannot be argued from a socio-economic point of view. As a result, logistics service provider companies are doing everything in their power to continuously find more efficient solutions and ways to overcome potential problems, while conducting business in a rather underdeveloped market. While this is true, Autolink (2017) is adamant that the potential for improvement is huge and that there are plenty undiscovered areas which have not been tapped into. Such trends have in recent time affected the Swedish automotive industry, more specifically the logistics activities of importing vehicles (Skandiatransport, 2016). The continuous increase in GDP has resulted in monthly increases in vehicle purchases. Skandiatransport (SKT), Autolink (AL) and Scandinavian Motortransport (SMT) are companies who provides such logistics services. Everything from unloading the cars, inspection and delivery to car dealerships is covered by their service offerings. Once competing firms has now evolved into a joined venture due to changing market demands and high competitiveness, owned by Nordic Car Logistics (NCL). The joined venture NCL’s organization structure is primarily structured through Scandector and Autolink Group, where both groups have an equal 50% share. As shown in figure 1.1, Scandector consist of SKT and SMT, where SKT offers customized vehicle management and pre-delivery inspections to general agents and dealers of new imports vehicles based on the four ports around the Sweden and SMT provides logistics solutions for inland distribution for general agents and 2 dealers of new and used vehicles in Sweden and Denmark. Autolink, as another part of the jointed venture, have almost the same functions as Scandector but with the focus on the Swedish and Norwegian market. The NCL-brand as a whole is able to offer pre-delivery inspections, storage opportunities, workshop services and transportation services to newly imported cars from all over the world. Figure 1.1 - Company Structure Below in Figure 1.2 is a simplified figure of how the NCL-brand is integrated to the automotive supply chain. Depending on where the producing car brand is located geographically, the supply chain can look differently. The idea is that both SKT, Autolink and SMT can supply the dealerships with the right cars, in the best possible quality within a certain time limit in order to satisfy the end customer. Figure 1.2 - NCL Supply Chain Scope The firms have realized synergistic benefits in cooperating as one brand. As a result, the ventures have identified a series of issues, mainly within their cargo planning and route optimization processes. Currently, cars are not being sent in full truckloads and there are delays during deliveries (SKT, 2016) (LM, 2017). In addition, as LM (2017) states, Autolink and the NCL- brand are also facing potential issues such as; increase in the distribution volume, shortage of truck drivers, increased customer base while also being affected by lead time and capacity requirements. Consequently, this has led to negativity towards the current structure of their system and customer dissatisfaction which increase the workload and reduce the working efficiency at the same time. Instead of outsourcing this function to a third-party logistics (3PL) company, Autolink prefers to integrate the function into their own system. LM concludes the reason of this decision is based on two points of consideration: First, this operation can help the company to gain more freedom and it is easier to control the situation. For example, when facing an increased distribution volume, with a build-in system, Autolink can force the department to delivery instead of asking for the approval from a 3PL provider. While the second reason is due to the better cooperation between the inner company inside of NCL and enable them to work as a whole service provider (one- station service). Constructing schemes and plans for optimized routes has become a task for companies in the field of transportation in order service the customers in the best way (Berhan et.al, 2014). It is 3 commonly referred as a problem, defined as the vehicle routing problem (VRP), due to its complex nature of designing optimized routes for transportation. Such issues have been affecting Autolink whereas they don’t feel in complete control of their route planning of vehicles (LM, 2017). Currently, Autolink is handling over 90% of car imports made to Sweden from their ports in Gothenburg, Halmstad, Malmö and Södertälje. This is executed manually through a spreadsheet that has to be managed continuously in a complex manner. The company has identified that the desired way to work is with a system that is able to automatically compute optimized paths depending on certain characteristic and parameters to simplify the workflow and transportation planning. 1.1 Aim With the issues stated above, the aim of this thesis report is to find a way to improve the current route planning operations of Autolink based on their needs and market demands. 1.2 Purpose The intended purpose is to address the issues Autolink has with their current route planning methods and potentially improve their operations. It is the interest of the company to identify which set of parameters and characteristics that influence route planning and optimization based on market preferences and customer offerings (LM, 2017). Furthermore, the report has the purpose of establishing sufficient information to make a decision regarding a potential IT- software investment that seeks to improve the planning process for Autolink. 1.3 Research Questions In order to address the issues Autolink has with their current route planning methods and potentially improve their operations, different elements which have impact on their route planning should be taken into account. Some factors can be found in previous studies, but there is still a need to go for another direction to find specific parameters or characteristics which align with Autolinks current logistic situation. The following research questions have been formed to be answered in the end of the thesis report and to follow the aim: • What requirements have an impact on the planning and optimization of routes and cargo for Autolink? To successfully gather the complete set of data, the needs and requirements of customers is also important. Customers also impose needs on Autolink and it is therefore the objective of this thesis report to also gather data from customers to get overview of how the network looks like and operates while at the same time satisfying the customers. The following question will therefore be answered: • In terms of customer requirements, which needs do they impose on the route planning and how does it affect Autolink? As required by the company, the result of this degree project will be a basis for a future 4 investment made in a software. This will not only help them with the current route planning and optimization but also could help Autolink in face of the increasing demand by the customer side like more detailed delivery time windows instead of a time limit etc. Since the software has not being implemented yet, the question below should be solved: • How does a software implementation benefit Autolink to improve their planning process? And which kind of solution is most suitable for Autolink? 1.4 Limitations The thesis report will solely put focus information that is beneficial for a future software investment decision. This means that report will not cover the actual implementation or execution of the software. Furthermore, the report does not aim to find a solution to any theoretical hypothesis or model concerning route planning or optimization. As mentioned previously, the whole chain of the business process of the jointed venture is ranging from the car arrival in ports until the vehicles are delivered to the customers including several steps as shown in Figure 1.3. Figure 1.3 - Scope of this Study Since the report is going to put the main focus on the route planning operations, other areas of business like pre-delivery inspection (PDI) etc. will not be of concern and only the distribution phase will be taken into the scope of this thesis work. Even if the route planning is affected by other elements of the business, they will not be covered in detail. In addition, this means that only truck transportation on Swedish roads will be focused on, whereas other transportation modes like railway and vessels will be disregarded. Besides these, the final benchmarking model is rated by the researchers of this study. Since the study in the company may influence the researchers to rate different software providers based on their personal judgement or wrong rating due to the unbalance available information of each provider, the rating can be seemed as bias and may lead to a defective result. 5 2. Method This section describes the method of how the master thesis team plan to collect and analyse data in order to fulfil the purpose of master thesis as discussed above. Furthermore, the chapter intends to show how the work is going to be executed and which sources of information that will be used 2.1 Research Strategy Bryman & Bell (2015) state the research strategy is very important because it gives writers a general direction to conduct the business research. The qualitative research strategy is chosen to collect and analyse the empirical data both from Autolink and their customers because the fundamental information regarding the current logistic situation will put more focus on the interview of people from different department levels within Autolink. As concluded by Bryman & Bell (2015), qualitative research usually emphasizes on words rather than numbers in the data collection. However, quantitative data collection will also be done through the thesis project. Data such as and load of the different trucks that the company already have, dealers’ locations and needs etc. will be collected in the beginning to support the further research. According to Bryman & Bell (2015), the abductive research approach is more suitable to use in this study. This means the existing theory and empirical data in the master thesis will be used in an interactive process to develop theory (route planning). Since this problem starts with a puzzle, it will involve back-and-forth engagement with the social world as an empirical source for theoretical ideas which aims to find the most appropriate way for Autolink to optimize route planning as well as for their future software investment. 2.2 Literature review The secondary data that is intended to be used for this thesis report will serve as a theoretical framework when analysing the findings. Topics that intend to be covered in the report will be based on customer relationship management (CRM), information technology (IT) and route planning and optimization. Such data is going to be collected through the Summon 2 engine which is implemented on the University of Chalmers library webpage and Google Scholar search engine as both engines are well suited to find literature. Furthermore, additional secondary data will be collected from the firm webpages if deemed relevant to the thesis project. The aim is also to utilize external expertise from professionals if possible. This suggests that the thesis report might include other external sources of information through interviews with experts in the subject fields. 2.3 Empirical Study The thesis report aims to provide primary data from Autolink and their customers in order to conduct a proper analysis. One way to do this is to perform interviews with involved parties. According to Kvale (1996), there are three different ways of performing interviews; structured, semi-structured and unstructured. 6 2.3.1 Choice of interviewees To make sure that there is enough primary data collected to make a sufficient analysis, different stakeholders and employees will be interviewed so that a broad range of information and preferences are gathered. The choice of interviewees will therefore be based on which connection they have to Autolink, where a close connection is most preferable. Which entities to interview, as of now, will be presented below. • Employees and executives in Autolink - In order to get a proper analysis of how the system currently operates and functions it will be most beneficial to interview the people who work in the system on a daily basis. In addition, different departments of Autolink will also be considered, such as; marketing and administration. • Customers - It is important to capture the needs and desires of customers in order to create something that is also connected to their operations and processes. Examples of customers in this case would be; car brands, car dealerships and hauliers. • Partners - Since Autolink is working closely with SKT and SMT input from their end would also be beneficial to determine how work is carried out and which preferences they ask for. While it is also important to take the hauliers into consideration since the trucks and drivers are under their management. • Suppliers - Additional contact will be kept up with the many software providers to determine their service offerings. 2.3.2 Interviewing technique As mentioned in the beginning of the chapter, there are certain ways of conducting interviews according to Kvale (1996). The aim is to utilize a semi structured interview methodology in a way that questions are prepared in beforehand for each interview while giving the interviewee the freedom to expand their answers into relevant topics which might not have been considered at first. It is deemed necessary to have some prepared questions according to Kvale (1996) as conducting fewer interviews with a focus on quality often receive better results. Therefore, a large emphasis is going to be put on asking questions that are relevant for each specific interview. This means that the questions will be based on literature that has been read in beforehand while focusing on gathering information to answer the research questions. The aim is also to conduct an interview where their goal is to create a friendly a non-judgmental communication in order to reach common understanding and allowing the interviewee to answer to its full potential. This suits well with the semi-structured approach of an interview while the interviewee is guiding the interview. 2.3.3 Observations While conducting the empirical data collection the goal is also to successfully observe how Autolink works with their planning and administration of their transportation network. By observing, the aim is to find out how the planners work with dispatching vehicle transports and 7 get an overview of the process. 2.3.4 Case Study Besides the interviews and observation, several case studies will be investigated in order to show the potential influences that may be brought by the implementation of a route and load planning system. The results of the case studies can be seen as a reference in addition to the final recommendation from this study and help Autolink to facilitate the decision-making process. 2.3.5 Benchmarking Additional data is going to be gathered on potential suppliers that can provide a software solution. In addition to holding interviews with the suppliers, a benchmarking comparison will be outlined to analyse the potential benefits and drawbacks of each supplier compared to others. The benchmarking is aimed at making the decision in the end whether the company should invest simpler by providing a straight comparison depending on specific preferences and needs. The benchmarking model is used in this study with the aim to compare the different software providers and support the final software decision-making process. The benchmarking model is a representation of a similar benchmarking method used at Autolink. In this model, different software features are compared between the software providers. Furthermore, the authors of this report will connect the benchmarking model to the theoretical framework used in this thesis report in order to choose which features and functions to compare. To determine the result of the benchmark, a Likert scale from one to five will be used to rate each feature criteria. The rating is made by the researchers of this report with information obtained from the software providers website and proposal documents provided by Autolink. 2.3.6 Validation of data In order to guarantee a high degree of quality and credibility, it is of high importance to validate the data that has been gathered. Validation can be done through checking with the respondents or firms if the data gathered is true and interpreted correctly (Denscombe, 2009). Furthermore, the questions that are going to be asked and sent to participants might be biased towards the theory in the theoretical framework. This type of validation is deemed necessary since there are clear objectives towards gathering data which reflects the nature of the research carried out in the thesis report. 2.3.7 Ethical Considerations Ethical issues are critical to be considered in business and management researches. Bryman & Bell (2015) state four ethical areas: harm to participants, lack of informed consent, invasion of privacy and deception. To deal with these potential issues, researchers shall give a presentation of themselves and introduce the purpose and process including interviews and survey which may occur in the study. The companies and the interviewees whom involved in this study will also be asked if they are willing to show their name in the research and the security of piracy information should be 8 granted. Permission of recording the interviews will also be obtained before the interviews and the participants will be noticed about their rights to refuse answering particular questions, review or modify their answers. Last but not least, researchers will make sure all the contents quoted from others are referenced to avoid plagiarism. 2.3.8 Presentation and analysis of data The report aims to clearly present the data and empirical findings in a way that is satisfactory and simple for the intended readers to interpret. In a structured way, the data is going to presented in matrices that are based on the theoretical framework. In such way, the reader can easily find a connection to the theoretical framework and the empirical findings. The theoretical framework will further be used to analyse the data that has been retrieved while allowing flexibility in terms of company demands and requirements. 9 3. Theoretical Framework In the following section of the report, the theoretical framework and secondary data is going to be presented. As mentioned in the previous chapters, the focus will be held on route planning, CRM and ICT in order to give proper theoretical backbone to the subject of this report. 3.1 Route Planning Route planning is quickly becoming an increasing subject for logistics driven companies in order to maximize their transportation performance and profitability (Wang. X., Kopfer. H, 2013). Logistics service providers (LSP) are becoming more aware of methods that concern taking shipments from point A to B in the most efficient way depending on certain constraints derived from the demands of the markets, customers and other influences (Berhan E. et. al., 2014). Consequently, route planning is set to achieve certain goals - which is to find routes or set of routes that aim to fulfill the demands of customers in the fastest and most cost-efficient way possible. However, with the amount of constraints and requirements that are imposed onto a route planning system (RPS), such as; time, distance, capacity, road conditions and others, it becomes increasingly complex to manage the systems. Denos C. et. al. (1997) argues that drivers and planners plan and select their routes depending on both unknown and known data. The more data that is known, the better decision can be made when planning optimal routes. In addition, Selamat. A et. al. (2013) discusses how such data can be obtained and coordinated by different agents in order to compute a real estimation on the actual travel time. 3.1.1 The Vehicle Routing Problem The complexity of route planning is referred to as a problem called Vehicle Routing Problem (VRP) (Kumar. S. N., Panneerselvam. R, 2012; Berhan. E et. al., 2014). Being connected to route planning, the VRP is defined by designing optimal routes for vehicles that aim to fulfill the demands of certain customers under the influence of constraints, as mentioned before. Such designs are constructed in a way where the solutions consist of certain routes that both start and end at a given destination, whereas each customer in the design is visited once by one vehicle without the demand exceeding the capacity of the vehicle while the total cost for the trip is minimized (Berhan. E et. al, 2014). To properly display the problem, it could be better described as a network consisting of nodes connected by lines. In route planning scenarios, the nodes are customer destinations and depots, while the lines represent the roads between the nodes (Berhan. E et. al, 2014). Each road is affected by certain parameters and constraints that are either known or unknown, which will be described later in the chapter. From a central unit or depot, vehicles with certain limitations are dispatched on the roads to fulfill the customer demands at each customer destination (Berhan. E et. al, 2014). As mentioned before, the objective becomes to gather all the roads into a optimal route, which considers certain elements and constraints, for the vehicles to complete at the lowest cost possible. The Figure 3.1 below shows a simple visualization of how the network first looks like and then how a possible solution might look like. 10 Figure 3.1 - Visualization of the vehicle routing problem 3.1.2 Stochastic VRP (SVRP) As mentioned before, much of the complexity that arises in VRP and RPS are the parameters that are unknown or random and too complex to manage (Denos. C et. al., 1997). When such data and parameters become random to handle, they are stochastic in nature. The idea and desired outcome is to manage the random data to the point that SVRP can be optimized (Berhan. E et. al., 2014). When a high portion of the data is random it becomes increasingly difficult to create systems that fulfil all constraints (NEO, 2013). According to Networking and Emerging Optimization (NEO, 2013) there are three common SVRP mentioned, such as; • Stochastic customers - where customer locations are random • Stochastic demand - where customer demand is random • Stochastic time - where both service times and delivery times are random. In the end, the objective of solving SVRP becomes to minimize the fleet time or travel time necessary to supply each customer according to their needs and requirements in the best way possible (NEO, 2013). 3.1.3 Route Planning System To effectively manage the different constraints, parameters and data that affect the network, route planning systems (RPS) are implemented. Such systems make use of data and information to determine optimal routes and later communicate the decision to the vehicles (Denos. C et. al., 1997). The aim with RPS’s is to determine optimal vehicles routes based on the data fed into the system. Consequently, such systems need to be able to handle certain actions and procedures in order to work properly. 3.1.3.1 Order Handling According to EECA Business (2017), advanced RPS should be able to handle the input from customer orders. This is necessary in order to generate the optimal plans for all customers and deliveries. Orders contain information such as; customer destinations, goods volume, type of goods and delivery dates (delivery times) which is fundamental for RPS to function properly 11 (EECA, 2017). 3.1.3.2 The Optimization Procedure The following procedure can be considered to be the core of the RPS as shown in Figure 3.2. The figure below depicts the logic explained by Selmat. A et. al. (2012) in the article Route planning model of multi-agent system for a supply chain management. Figure 3.2 - Simplistic layout of a route planning system As the figure shows, the system consists of several agents responsible for reporting their input to the system based on data that has been received. In this case, the agents can be replaced with databases that are connected to the RPS to collect and store data. As mentioned before, there are several constraints that affect route planning, which is being shown in Figure 3.2 with incoming data (time, distance, traffic, weather, trip plans, road) to decide the optimal route. The data is stored and gathered to a Trip Time Estimation Agent (TTEA) which is the decision-making body in the system who uses the RPS algorithm to compute the optimal route and a trip plan. As mentioned previously, when data points and parameters are stochastic in nature, it becomes increasingly hard to handle them (NEO, 2013). Either the system plans a route with some constraints unfulfilled, or it has corrective actions implemented making it possible to iterate the trip, route or plan depending on updates or new data EECA (2017). In addition, the system also takes top management decisions into considerations. This means that the RPS can be strategically aligned depending on the policies and strategies developed by company managers (Selmat. A et. al., 2012). 3.1.3.3 Route Dispatch and Communication When a trip plan has been established by the responsible personnel and system algorithm, it needs to be communicated and forwarded to the drivers (Selmat. A et. al., 2012). This is done so that the driver receives information about which route and which cargo to transport to the customer destination. Figure 3.2 shows how a trip plan is transferred to the drivers and trucks. This communication takes place telemetrically by having the vehicle and driver linked to the RPS (Lee. J et. al., 2007). According to Lee. J et. al. (2007), the main reason why telematics are implemented is to be able to monitor the vehicles and drivers while on road. The telematics technology combines navigation capabilities and geographical information with radio 12 communication so that the planners and drivers are able to communicate together. This is particularly effective since planners are able to handle real-time data and be up to date with vehicle speed, traffic conditions, accidents and more (Lee. J et. al., 2007). In turn, the driver is able to receive updated information on the trip on the vehicle device that is connected to the RPS and its servers. Figure 3.3 shows how the resources and technology are connected into one system to enable telematics communication. Figure 3.3 - Telematics connection 3.1.3.4 Reporting and Invoicing Since route planning systems are designed towards optimally calculating the best route for any given vehicle, it is important to manage how successful the operations are. With the amount of data that is available in a RPS, it is greatly beneficial to be able to form relevant key performance indicators of the data. According to Coredination (2017), different work report templates is a beneficial feature for a RPS in order for the drivers, planners and managers to quickly track the performance of the transportation operations. Reports on distance, working time, travel time, load factors and more are all relevant performance measures in determining how well logistics service provider is performing. With the handling of orders and route planning in RPS, invoicing can be integrated into the system to implement an effective billing procedure, according to Coredination (2017). The invoices are able to show the real costs for each order, depending on the trip plan that has been generated by the software. This means that each customer can be billed in a simple way, corresponding to their demand (Coredination, 2017). The idea is to have a price structure or model integrated with the invoicing function of the RPS which in the end results into an effective invoicing process. 3.1.4 Parameters Since certain parameters inherit a certain randomness, it becomes important to properly manage and coordinate them in order to make controlled decisions (Selmat. A et. al., 2012). In the 13 following part of the report, the different parameters will be outlined and explained to show what kind of data RPSs could consists of. Even though some parameters have been mentioned before, they will now get a more detailed description. 3.1.4.1 Travel Time Travel time is the parameter that states how long it takes the transportation vehicle to perform a certain route under certain conditions when it comes to route planning. According to Selamat. A et. al. (2013), the above definition is referred to as travel time. Depending on the conditions and constraints, there is going to be a specific time estimation of the travel time when planning optimal routes (Selamat. A et. al., 2013). Consequently, LSPs are then able to make much more reliable time estimations of their vehicle transports, which is the goal. Selmat. A et. al. (2013) present how certain conditions affect the estimation of time in route planning. They specifically mention a couple of parameters that are managed by the RPS, such as; • Road conditions In terms of safety, road conditions are, used to determine the safety factor of a road (Selamat et. al., 2012). In turn, such factors apply to overall safety of the driver and the goods while simultaneously putting a effect on the travel time. Selamat et. al. (2013) goes on to describe that road engineering determines road defects and triggers that may cause errors (accidents). In turn, such errors usually cause a deviation in RPS and need to be accounted for in beforehand so that such roads can be avoided. Road classification data is available in road databases as an input from engineers, where roads are rated depending on their condition. Additional data can be gathered from police, hospital and construction reports (Selamat. A et. al., 2012). The Table 3.1 below displays an example of how such classification can be made. Table 3.1 - Road condition classification • Traffic conditions The conditions of the traffic flow determine how the traffic behaves under a given time period or road length. Speed limitations, intersections and other traffic elements have a prominent role in determining the traffic conditions (Selmat. A et. al., 2012). The capacity and certain time periods of roads determine the level of congestion on the roads. High traffic congestion means that transportation vehicles will have a more difficult time traveling to their destinations, which affects the estimation of travel time. • Weather Weather has an effect on the road condition and speed of transportation vehicles (Selmat. A et. al., 2012). Even though the effect is slight according to Selmat. A et. al. (2012), it still has an 14 impact on long distance trips. Weather that is considered disruptive (rain, snow, wind) cause inaccurate time estimates, which is why it needs to be accounted for. • Unloading and loading Included in the estimated travel time, loading and unloading times specify the time it takes for the truck drivers to load their cargo at a terminal and in the end unload at a customer destination to fulfil the need (Kumar S. et. al., 2012). • Distance Distance is one of the most common parameters to determine when planning routes. As mentioned in previous chapters, VRP takes different destinations into consideration such as; central dispatch depot, customer depot or destination and end destination (Berhan. E et. al., 2014). This means that customer destinations and other potential end destination have to be known in terms of addresses, in relation to the starting destination. The more accurate the information is, the better the distance estimation will be. The distance it takes to a destination also specifies the time it takes to any given destination. 3.1.4.2 Capacity In route planning scenarios, the customer demand on a given route cannot exceed the vehicle load capacity (Berhan. E et. al., 2014). This implies that the dimensions of a transportation vehicle play a role in how much can be loaded onto it. Both the weight of the load as well as the volume affects the capacity restrictions on transportation vehicles. Companies generally want to maximize their load factor by as much as possible as long as it does not overflow the available capacity (Santén. V, 2016). According to Santén. V (2016), the load factor is something that should be regarded as a key factor when improving the efficiency. To determine the load factor, the current required capacity (customer demand) needs to be compared to the available capacity (truck/trailer load capacity), as presented by the formula: 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝐹𝐹𝐿𝐿𝐹𝐹𝐹𝐹𝐿𝐿𝐹𝐹 = 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑅𝑅𝐶𝐶𝐶𝐶 𝐴𝐴𝐴𝐴𝐶𝐶𝑅𝑅𝐴𝐴𝐶𝐶𝑏𝑏𝐴𝐴𝑅𝑅 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑅𝑅𝐶𝐶𝐶𝐶 (Santén. V, 2016) This means that the capacity of the trucks needs to be known and which type of vehicle combination is being used when optimizing route and load plans. In theory, by achieving a higher load factor, companies are able to reduce the number of trips and the cost as a result (Santén. V, 2016) 3.1.4.3 Customer Input Customer demands are increasingly becoming important to manage in transportation systems, and LSPs will need to become more responsive towards demands. Regan. A et. al.(2000) describes how demand planning has to be connected to Transportation Planning and calls it Transportation Demand Planning (TDM). By connecting demand with transportation, the customer aspects are connected to the firm's ability to deliver valuable services and products. In route and load planning scenarios, there are customer aspects that need to be taken into consideration. In order to determine how much should be transported to each customer destination, the customer demand needs to be known (Selamat. A et. al., 2012). This should be fed into the RPS in the form of order schedules and plans. In turn, these order schedules should easily be able to display the quantity that needs to be delivered to each customer destination, type 15 of cargo (dimensions), delivery dates and customer verification (Chandra. P, 1994). Orders are generally connected to a certain lead time in which the delivery has to be made. In certain route planning scenarios, there might even be situations where additional cargo needs to be picked up along the road, which also needs to be accounted for. This suggest that planners need to account for the demand at each customer depot, to make sure that all quantities are considered for in order to plan optimal routes (Chandra. P, 1994). Even in such scenarios, the customer demand cannot exceed the capacity of the transportation vehicle (Berhan. E et. al. 2014). The prediction of customer demand is becoming increasingly important for transportation companies in order to handle potential variations to accommodate capacity while keeping the customer satisfied (Gopalakrishna, D. et. al., 2012). Time windows restrictions are also being specified by customers stating that cargo should be delivered within specific time window (Berhan. E et. al., 2014). In turn, such demand has to be accounted for when estimating the travel time so that the cargo arrives within the time window. Additional specified demands are put on cargo that need urgent delivery to the end destination, a so-called cargo prioritization. Prioritized cargo units need to be accounted for when planning optimal routes since it decides which cargo needs to be delivered first on a route. 3.1.4.4 Vehicle and driver In order to carry out a route transport, transportation vehicles need to be available in the system (Selamat A et. al., 2012). According to Selmat A. et. al. (2012), trip confirmations are sent to the RPS system in order to make sure that the vehicle is ready to load the cargo and start a route. Consequently, this also means that the right combination of vehicle and trailer needs to be used in order to allocate the cargo capacity. • Vehicle Condition According to Selamat et. al. (2012), RSPs should be able to recognize the conditions of the transportation vehicles based on rating systems that outline defects, damages, deteriorations, emissions and others. This is implemented in order to further check if a certain transportation vehicle is available to use for a certain route trip and whether a repair is needed. Table 3.2 shows how such rating system can look like. Table 3.2 - Vehicle condition rating system • Fuel efficiency and emissions Fuel efficiency has become a relevant subject towards defining how a company is performing. This is why companies are increasingly taking control of their fuel costs when investing in new equipment and resources, according to Highway and Public Works (HPW) (2016). While a large portion of the fuel consumption is decided by the type of vehicle being driven, careful planning 16 has also proven to improve the environmental performance (HPW, 2016). In route planning, this means being able to avoid routes with many turns, heavy congestion, hills and many other factors. Being able to identify with routes are flat, with the least turns and fastest could greatly improve the environmental performance as well as result in additional cost savings according to HPW (2016). By being able to monitor the fuel consumption and economic driving from a planner and driver perspective, companies can gain more control of the environmental performance. Parameters such as idle time, braking and accelerations can be monitored in real time to plan the route more optimally. • Driving patterns Different driving patterns affect how vehicles perform on the road. According to Winter. S (2002) certain driving patterns affect the shortest path algorithm. In his article Modelling Costs of Turns in Route Planning, turn costs are looked into. By doing as little turns as possible in an efficient manner you avoid deviations in the planned route. A route with minimal deviations is considered to be optimal (Winter. S, 2002). Turns make the vehicle slow down and brake, which adds to the fuel cost. Additional waiting time ensures as well since turns do not represent straight roads due to its turn radius. • Driver regulations According to Swedish working laws, there are restrictions on how many hours a driver is able to operate the vehicle (Transportstyrelsen, 2015). In turn, this puts a maximized limitation on the availability of drivers in the system. The restrictions are expressed in working hours per day, week, month and year and is implemented to prevent the driver from fatigue, minimize accidents and promote a good relationship between drivers and LPS’s. The information obtained from Transportstyrelsen (2015) puts emphasis on the driving hours and break time of drivers and how it is regulated in multiple ways (Transportstyrelsen, 2015). The following driving and rest regulations are set by Transportstyrelsen; • Driving Hours 1. The daily driving limit is limited to 9 effective hours, with the exceptions that driving hours can be extended to 10 hours twice a week. 2. The maximum driving limit per week is 56 hours and 95 hours for two weeks that follow each other. • Breaks 1. When the driver has had a driving period of 4.5 hours, there is a mandatory 45-minute break as shown in Figure 3.4. The break can be replaced with a daily or weekly rest. The break can also be split into one 15-minute break and one 30-minute break within the same driving period, as shown in Figure 3.5 and Figure 3.6. 2. When the break is over, another 4.5 hour driving period starts. Figure 3.4 - Example 1 break time and driving hours 17 Figure 3.5 - Example 2 break time and driving hours Figure 3.6 - Example 3 break time and driving hours • Daily Rest 1. The daily rest implies that drivers should have a minimum of 11 hours rest after a 9-hour working period. Alternatively, the daily rest could also be split into two resting periods which amass into at least 12 hours. The further means that the first period needs to be at least 3 hours and the second at least 9 hours long. 2. A driver should, at the latest, have another daily rest approved within 24 hours of the back of a daily rest. 3. Daily rests can also be reduced to 9 hours, but are limited to 3 reduced daily rests per week. • Weekly Rest 1. The definition of a week made by Trafikverket is Monday 00.00 to Sunday 24.00. The weekly rest is either defined as normal or reduced. 2. A normal weekly rest should at least be 45 hours long, while a reduced weekly rest cannot fall under 24 hours. 3. At the latest, after six 24-hour periods at the end of a weekly rest the drivers are able to begin a new weekly rest. However, if the weekly rest begins in one calendar week and carries onto the next, it needs be rearranged so that it is counted for one calendar week. 4. At least two normal weekly breaks, or one normal and one, should be taken in a period where two weeks follow each other. 5. There is nothing that hinders the driver to take several weekly rests under a calendar week. 3.1.4.5 Cost In the case of route transportation and optimization, cost is often referred to the extent which resources are used (Litman. T, 2009). Furthermore, Litman. T (2009) exemplifies how costs can be incurred depending on different aspects. In the article Transportation Cost and Benefit Analysis Techniques, Estimates and Implications it is explained how transportation cost can be affected by internal (user), external (others) and societal parameters. The notion is that transportation costs are affected by many different parameters. This further implies that parameters such as; time, distance, traffic conditions, fuel consumption and capacity utilization (mentioned in the chapters above) do not only affect the reliability and quality of transportation services, but also the cost of transportation. Since route planning is more or less stochastic in its nature, the total transportation cost is going to be heavily influenced by parameters in route planning scenarios. Besides, the price structure of different contracts the company have will also influence the total cost in an indirect way. Ultimately, the cost decides if a route is feasibly executable, with the end goal of minimizing the costs as much as possible (Berhan. E et. al., 18 2014). 3.1.5 Fleet Management Nowadays, with the increasing number of outsourcing logistics, logistics services provider (LSP) has become an important element in the market who offers a wide range of services to customers. As a LSP, transport can be seen as one of the most important service which require the LSP to have an efficient fleet management to establish a high level of customer service (Vivaldini, M. et. al., 2012). Fleet Management, is a term of management of a wide range of transportation tools including cars, vans, trucks, trailers and other way of transportation like aviation, ships as well as rail cars (Wikipedia, 2017). It is function of oversee, coordinates and facilitates various transport and transport related activities, not limited in planning, supervision and control of fleet operations based on constraints, but also including operational activities like vehicle financing, maintenance, driver management, speed management, fuel management and health and safety management etc. (Billhardt, H. et. al., 2014; Wikipedia, 2017). With the use of fleet manage system, the companies can reduce and minimize the overall costs by a fully utilization of resources such as vehicles, fuel, spare parts etc., and reduce risk associated with vehicle investment while increase quality of service to the customers. (Billhardt, H. et. al., 2014; Logistics Operational Guide, 2017; Wikipedia, 2017) As conclude by Logistics Operational Guide (LOG), the fleet management can be divided into several aspects range from identifying needs, acquisition process, insurance for the vehicles, vehicle leasing (internal & external), vehicle management including fleet management systems and vehicle maintenance and upkeep to vehicle life-cycle management and health, safety & security which contains comply with legislature and security requirements as well as driver management. Due to the rapid development on the technology in sensors, communication and networking technologies as well as geographic information system, Hu, Y. C. et. al. (2015) states the Fleet Management Systems (FMS) has evolved into complete enterprise management tools and there is a trend of turning this into planning tools. Currently, the FMS is going toward the direction of real-time or dynamic management focusing on current fleet locations and prediction of planned tasks. And Billhardt, H. et. al. (2014) have taken the autonomous (self-driving) vehicles into consideration and conclude a possible structure called smart cyber fleet management system which include cyber vehicles drivers with cyber interfaces. 3.1.6 Case Study: Route Planning and Optimization In order to outline potential benefits of implementing a RPS, different case studies on route optimization are going to presented. The case studies aim to show benefits in performance, reduction of waste and minimization of costs. In this section, three case studies were conducted not only to find out how a software implementation beneficial to the logistics provider but also aims to help Autolink benchmarking the companies which have already install the related software and find out the areas for further improvement. 19 3.1.6.1 Fast-moving Consumer Goods The first case is regarding how a four years of continuous utilisation system influence a fast- moving consumer goods distributor (Hereinafter referred to as A) in Croatia by replacing the manual routing process. A is a big fast-moving consumer goods distributor which has an average of 1400 orders per day to handle with over 6000 active delivery locations during the last two years. As stated by A, there is about 1400 orders generated from 700 delivery locations per day on the average which means it is possible there are more than one orders from the same location. Besides the huge number of orders, there are also constraints on the customer side which can be classified into the follow session: • Time window delivery • Fleet sizes and types • Working time limitation • Multiple and partly overlapped geographical regions, depots and satellites • Unpredictable service time • Designated vehicle delivery for specific orders Optimization Process During the four year of everyday software implementation, many different scenarios have had to be considered to face different requirements and constraints. The relaxation of constraints and acceptance of infeasible solutions are used a lot during the optimization process not only aims to reduce the cost but also to fulfil all the constraints and eliminate penalties due to delay etc. The four years’ experience make A figure out it is more suitable to put dispatchers in the central position beside the system where one can choose the most suitable optimization solver. The manual and automatic routing should work as a complementary to each other. It is quite clear that it is impossible for one solver to suit every situation, the dispatchers’ experience can be valuable to handle special requirement or something hard to predict in the model. Only the dispatcher can do changes in working time or relaxation of some other minor constraints when the benefit it brings can outweigh the additional cost or lose. The final optimization process including manual modification takes around half an hour on the average and is done once a day during the afternoon. Figure 3.7 below shows the whole optimization solution. The automatic optimization system has two solvers called default solver and fixed solver. While the default solver can be divided into two phases with the use of different algorithm, the first phase attempt to construct a feasible solution that uses a minimal number of vehicles and the second phase aims to reach a minimal total time of the routes. On the contrast, the fixed solver is used for peak days. As stated above, the default solver aims to generate a feasible solution but the fixed solver will enable a maximum vehicles usage even relax some constraints if a feasible solution cannot be constructed. However, this solution is not encouraged by the algorithm itself which means it can only be approved by the dispatcher. During the system implementation, the geographical factor is also improved to further reduce the cost. A has closed four small remote depots and replace them by satellite locations (which refers 20 to locations where goods are transferred from large vehicles to smaller outbound vehicles that are used for delivery in the surrounding territory) with no inventory holding facilities at all. Figure 3.7 - Optimized RPS solution Benefits In this case, a delivery optimization system is implemented in a distributer of fast-moving consumer goods. The problem is modelled as a mix fleet multi-depot VRPTW with multi- commodity. With over four years implementation, the computer-aided optimization system works well with dispatchers’ work. Several significant benefits were gained through this process which can be concluded as follow: • Reduce the overall distribution costs, total time and fuel consumption. • More efficiency vehicle fleet. • Close down several depots to serve a leaner process. • Handle more customer requests without invest in the vehicle fleet. • Reduced number of dispatchers. At the second year of implementation, due to economic recession in Croatia, the business model has changed since customers trend to order smaller amounts of goods but more frequently. In face of this scenario, the dispatcher works with the system to enable a cluster delivery. Through the simulation, the new delivery method can save up to 26% in distance, 15% in overall cost and a significant drop on working hour by 78%. While here the the computer aided optimization system is not only a system that saves dispatchers working load but also has served as a decision- support system for the company management. As the mentioned in the case study “The engineering skill to mix old routines with new tools was very important too, because the 21 interactive use of manual and automatic routing arises like a winning combination in practice.” With this in mind, the following strategic benefits which could come from the computer-aided system can be summarized as follow: • Enable the dispatcher to start thinking differently and use software tools instead of their own experience. • Have a reliable decision-support system with quantitative information. • More adaptable drivers who can work in different regions since the knowledge of geographic location become less important. • A better organization of transport which gives clear picture to the management. 3.1.6.2 Bergendahls Food AB The second case focuses on a wholesale and retail trade company called Bergendahls Food AB. It is one of the subsidiaries of the fifth largest trading group of Sweden, Bergendahls & Son AB which acts within three business units including food, fashion and home furnishing. The customer base for Bergendahls Food AB consists of 30 large supermarkets and 200 smaller convenience stores while the case study focuses on one third of the customers which is served by their own fleets. With the large investment in the company which leads to a strong company growth in market share. Comparing with the growth on market share, the organization is not complied with the spend of the growing complexity in the route planning with a manually route planning based on experience. Optimization process With the simulation and analysis through route optimization tool Route Planner, the potential of improvements of the current transport plan were primarily analysed. The framework of the optimization is shown below in Figure 3.8 and can be concluded as four steps: Step 1: Input data First of all, all the data regarding vehicle, customer information, orders, constraints etc. is putting into the Route Planner as the preparation for the current situation simulation. Step 2: Current situation simulation The first simulation aims to identify the basic setting of the program. All the information are exactly the same as they were expected in reality and the result is served as the control group. Step 3: Optimization of current situation From the first simulation, it is found the orders are easily be split up into two which increase the mileage when two trucks need to visit the same location on the same day. In this step, this is optimized by brought these split orders into one. Step 4: Optimization for the future scenarios The final step can be divided into two future scenarios which is “New time windows” and “Deliveries six days/week”. The scenario “New time windows” was simulated with a larger time windows with 7 AM to 10 AM and 9 AM to 2 PM for large supermarkets and convenience stores 22 respectively. While the second scenario “Deliveries six days/week” aims to shift the demand from weekdays to weekend to make the flow of goods more levelled and the costs of additional deliveries were compared to the saving in the weekdays due to the shorter distances. Figure 3.8 - Optimization Steps Findings and Benefits Four simulations were conducted to each scenario listed above. One critical problem was found in the first simulation that the actual number of distance is 325 kilometres higher than the simulation according to the information inputted to the software. The key reasons is concluded in the case study as the fact that the truck drivers will leave their trailer to make the routes more smooth and flexible and sometimes the difference on route can reach up to 80 percent. With this in mind, if Bergendahls Food AB can eliminate 50 percent of the additional distance, they could save up to 590 000 SEK per year with 5 percent reduction on emission which equal to 49 tons CO2 and 0,4 tons NOx. By using the route optimization software, the split orders and external transports can be eliminated by doing the second simulation with 5000 SEK and 3000 SEK per day respectively. Based on the current situation, the cost saving can up to 530 400 SEK on average per year and the same, 5 percent on the emission reduction. When comes to the future scenario, a more significant benefit stands out base on the simulation on the Route Planner. With a wider time-windows, more amount of solutions are possible to be evaluated. The distance can be reduced on average by 1910 km per week which results in a cost reduction up to 1,4 million SEK per year and 9 percent on the environmental saving. On the contrast, the six days per week delivery enable a more even distribution of the orders and help transportation planning. Even it does not provide any economic benefits but the smoother 23 planning and shorter routes benefit the customers by removing the supply peaks, less lead time and lower inventory costs. To sum up, with the implement of the route planning software, the benefits can be achieved according this case study as listed below: • Cost reduction • Minimized the environmental impacts • Avoiding complex planning solution by planners’ manually work • Better distribution and customer service • Positive effect on the stores and final customers 3.1.6.3 Further improvement opportunity The final case can be seen as an add-on beside the previous two cases which shows how the freight cost can be reduced in today's market. Thomas A. Moore concludes four steps in the past for the cost reduction evolution which starts from negotiation through streamlining, enhanced communication and into route optimization. Each phase has been summarized as one sentences: • Negotiate price: All the big opportunities to negotiate lower costs are gone; • Streamline operations: All the big opportunities to streamline are gone; • Communicate with carriers: Partnerships and communication, while worthwhile, won’t generate big savings; • Optimize routing: The low hanging fruit is gone. The next step With the above information in mind, the author starts thinking about the next step for further cost reduction. The answer is “Shipping full trucks” which is quite obvious to everyone. The reason for this answer is due to several aspects: • The trucks should not only be loaded at maximum weight but also supposed to be loaded at the maximum space level. • Shipments going out on longer trailers planned than the actual need. • How to balance the customer order which only reach the minimum quantity for free freight. • Different goods on the same origin and destination but each being “full” by a different constraint like weigh, cube or floor position, i.e. the displays could put on the top of the bricks and the transportation cost for it is almost free. Optimization process and benefits Stand on the basis of traditional way for route and load planning by ERP and route planning system to reach the “low-hanging fruits”, the optimization of “Truly filling shipment” need consideration on not only axle, weights, carrier-lane capacity or how the position loads in the truck, but only on the optimal mix for allocating different products. With the use of sophisticated vehicle load builder, the optimal mix can be reach to enable 7% improvement in load size by the research from P&G. The outcome of this sophisticated vehicle 24 load builder system should be a set of detailed load plans down to “Which case pick cases must go on which pallet – and how they stack to minimize the potential for damage.”. One example in this case is shown in Figure 3.9. Two “full” truck with different goods inside can be optimized by the system to reach the optimal mix and enable 37% improvement on the total weight delivery with the same workload for drivers as well as the truck usage. Figure 3.9 - Truly Filled Load Planning In the end, Moore T. A. states with the truly filling truck, the opportunity to plan better loading solution can save the freight cost up to 4% to 10% lower than the traditional way with the route and load planning by analysis through sophisticated tools. 3.1.7 Conceptual Model: Route Planning In order to summarize the information obtained from the chapters above, Figure 3.10 has been constructed by the authors of this report to show a simple visualization of RPS solution. The conceptual model displayed below is similar to the logic used in Figure 3.2 which is theorized by Selmat. A et.al (2012). In simplistic terms, Figure 3.10 shows how sets of data parameters affect the planning and the decision making. The authors of this report chose to set up data categories in which specific data is included. For instance, the Customer Input category contains specific data such as volumes, type of cargo and lead times obtained from the customer agreements. The inclusion of data categories and specific data is explained mainly from the theory used in this thesis report. Papers from Berhan. E et.al (2014), Kumar S. et.al (2012), Selmat. A et. al (2012), Winter. S (2002) and Litman. T (2009) were used to obtain which data is necessary in a RPS and how the cost is affected accordingly. Information obtained from EECA Business (2017) was used to show the relation between order handling, RPS and the inclusion of data obtained from the customers. In chapter 3.1.3.4 it is described how Invoicing and Reporting works in a planning environment by Coredination (2017). Lee. J et.al (2007) is used as theoretical background to describe the telematic capabilities necessary between the drivers, trucks and the transportation planners. Once all the data is computed by the planners and the algorithm, a final decision is made and trip plan is sent to the drivers. 25 Figure 3.10 - Visualization of RPS Solution. 3.2 Customer Relationship Management (CRM) Over the past few years, Customer Relationship Management (CRM) have got a lot of attentions by senior level managers as well as academicians, and it has been considered as one of the most important key factors which influences the success of the organization (Bhat and Darzi, 2016). As defined by Mohan and Deshmukh (2013) “CRM is the combination of business strategies and processes with technologies to acquire and analyse the customers for gaining and retaining the customers’ confidence on organisation and its offerings, while keeping in mind cost, quality and profitability of its offer.” Many research papers have investigated the influence of the CRM implementation. For example, Bhat and Darzi (2016) state the positive effect of CRM on customer loyalty as well as the competitive advantages with the use of structural equation modelling; Mithas S. et. al. (2005) concluded that CRM applications are positively associated with improved customer knowledge and higher customer satisfaction. While from the game theory point of view, Ren, Z.J. et. al. (2010) also point out that the long-term supply chain relationship leads to a more efficient, truth- sharing outcome compare to one time relationship among supply chain parties that without forecast sharing and supply chain. Considering the focal company in this thesis report, the customers of Autolink are all the dealers and some individual customers around the Sweden while the suppliers of Autolink are different oversea car brands which want to import car into Sweden. Autolink serves as an intermediator 26 between supply and demand which shows a urgent demand to integrate the CRM into the daily supply chain management (SCM). Mohan and Deshmukh (2013) state the link and differences between SCM and CRM, while SCM seeks to optimize the supply side with the focus on production and execution, CRM seeks to optimize demand which is revenue focused and aim to satisfy customers need. They state the integration of SCM and CRM which named as SC^2M-R is the best way for organizations who have a demand to match both supply and demand sides so as to reap the advantages of the synergy and its associated multiplier effects. Ku, E. C. et. al. (2016) also conclude with the combination of suppliers and customers view, the company can integrate customers’ opinions and queries for the planning and development of new products and redefine their business models. However, when CRM is linked to route planning, the traditional vehicle routing problem (VRP) only focuses on developing the routes planning for vehicles in order to minimize the total cost, distance or lead time travelled by the fleets under a set of constraints. The focus has only shifted towards customer requirements in recent years (Groër, C. et. al., 2009). As mentioned above, the customer integration in the SCM is quite beneficial for company to reach a better performance. Through the data collection based on 200 plants in different countries by Danese, P. and Romano, P. (2013), it is found the customer integration alone is not enough to ensure the cost reductions of the supply chain, the reason is due to the fast supply network with a good network design is served as a moderator between the customer integration and efficiency relationship. Also with the use of CRM system, the focal company can assess the locations of customers in a visualized way and reduce the travel time and costs through the optimum route. 3.3 Information and Communications Technology (ICT) As concluded by Roche, E. M. (2016), Information and Communications Technology (ICT) is going through a rapid progress since Turing (1936) defined that all the calculation can be represented in a binary way and started the computer age. Over the past 80 years, ICT has walked through several steps from a single calculate machine to a internet-based communication platform and currently with the emphasis on big data as well as Internet of Things (IoT). Based on the recent advance of ICT, it becomes possible to come up with innovative new business and operational paradigms within transportation area in order to make transportation more efficient and environmentally friendly (Hernández, S. et. al., 2011). By studying over 100 service firms and their customers, Polo Peña, A. I. et. al. (2014) claim that ICT capabilities have a positive impact on value co-creation between service firms and customers, as does value co- creation will influence customers perceived value and loyalty as a next step. According to Chinomona, R. (2013), with the focus on Small and Medium Enterprises (SMEs), the ICT serves as a facilitator of suppliers’ collaborative communication, network governance and consequential relationship longevity with their clients in a significant way. When comes to the transportation improvement, Wagner, S. M. (2008) conclude the transportation industry is a traditional area which operates in an extremely competitive environment with endless seeking of lower cost and higher service level. Due to this reason, the competitiveness of logistic service providers is increasingly depending on the technology innovation. Also, since the supply chain are becoming much more complex and globalized, the ICT systems are adapted by more and more transport and logistic service companies in recent years, not only to enhance the collaboration with business partners but also aiming to coordinate 27 and plan operations, as well as supporting the decision-making process (Hosie et al., 2012). According to the literature study by Evangelista, P. and Sweeney, E. (2014), the implication of ICT in the field of freight transportation has many potential benefits including reduce cost, improve customer satisfaction and thereby leverage the competitive advantages (Hong et al., 2010; Wallenburg and Lukassen, 2011; Forslund, 2012). For example, in the current case of this thesis report, the lead time of deliver cars from ports to dealers by road distribution could differ a lot depend on the many factors including weather, traffic congestion, possible accidents on the roads etc. But with the implementation of different ICT like real-time road situation forecast or traffic alert, majority of these potential issues could be prevented in order to avoid possible delay or a better route replanning. In the next session, several widely used ICT in the transport and logistic field will be introduced: 3.3.1 Intelligent Transportation Systems (ITS) Intelligent Transportation Systems (ITS), which is considered as a part of Internet of things. Many scholars have made definitions on this topic, although ITS may refer to all modes of transport, EU Directive 2010/40/EU (7 July 2010) defined ITS as “Systems in which information and communication technologies are applied in the field of road transport, including infrastructure, vehicles and users, and in traffic management and mobility management, as well as for interfaces with other modes of transport.” While Zapata Cortes, J. A. et. al. (2013) argue a brief definition based on the summary of previous research, they define ITS as the set of multiple applications aimed to improve transport systems, for both passengers and cargo. Two broad groups can be categorized in ITS which named by Zapata Cortes, J. A. et. al. (2013) as: ITS located in vehicles (so called “smart vehicles” which equipped with communication technology) and ITS located in infrastructure or in the transportation mode (like traffic guidance system, dynamic signals etc.). Different way of classification can also be found by another author, Zhang, J. et. al. (2011) have distinguished the data-driven ITS as Vision-Driven ITS, Multisource-Driven ITS, Learning-Driven ITS and Visualization-Driven ITS. While Mirzabeiki, V. (2013) states 9 different widely identified freight ITS named traffic control and monitoring systems, WIM systems, delivery space booking systems, vehicle location and condition monitoring systems, route planning systems, driving behaviour monitoring and control systems, crash prevention systems, freight location monitoring systems and freight status monitoring systems. Based on these categories, different technologies are developed which focus on the single category or on the interaction between two categories. Wikipedia (2017) has taken four main technological applied areas of ITS implementation according to the United States Department of Transportation: Basic management systems (Navigation system, traffic signal management system etc.); Monitor applications (Security CCTV systems etc.); Advanced applications (Parking guidance and information systems and Weather information system etc.) and Predictive techniques based on big data analysis. These ITS technologies are widely used all over the world and it is believed that the ITS implication can not only support a more efficient and safer traffic management, but also enable information exchange between drivers to better control traffic congestion as well as managing cargo fleets and vehicles planning (Zapata Cortes, J. A. et. al.,2013). Weber, K. M.et al. (2014) illustrate four case studies with innovative use of different ITS in Austria and Norway ranging 28 from real-time traffic information service, displays and management system to electronic ticket system and parking system. Joseph, A. D (2006) summarize 10 ongoing ITS projects in different countries all over the world with the focus on different aspect of transportation issues. besides the road transportation, Zapata Cortes, J. A. et. al. (2013) also state the implementation of ITS in other transport mode like air, maritime as well as railway. 3.3.2 Collaborative Transportation Management (CTM) According to the Collaborative Transportation Management White Paper (2004), Collaborative Transportation Management (CTM) is defined as a holistic process that brings together supply chain trading partners and service providers in order to drive inefficiencies out of the transport planning and execution process. With the fast growing of urbanization and industrialization, more and more roads and highways are built in recent years. The demand of passengers and freight transportation are growing rapidly, while among all the modes of transportation, road can be considered as the most important mode. From the survey by Fraunhofer (2014), goods transported by road made up the lion's share of the survey, with 78 percent of the total tonnage (14.5 billion tons) transported compare to other ways of transportation like sea, rail and pipeline etc. Kimms, A. and Kopfer, H. (2016) state a high-performance transportation is one of the key-factors of the success logistic- network. Especially for small and medium size companies, the horizontal collaboration of transportation parties is considered as a promising support to reduce the operational cost. Audy, J. F. et. al. (2012) also conclude the logistics collaboration is one means of reducing the logistics activities cost, increasing service level, gaining market share, enhancing capacities, and reducing the negative impacts of the bullwhip effect. When it comes to the more general parts of CTM, LM Carroll (2013) conclude 5 points of benefits of implementing CTM which are “Reduced transportation costs”, “Increased asset utilization”, “Improved service levels”, “Increased revenue & end-customer satisfaction” and “Increased visibility” based on some quantitative data. While Sutherland, J. L. (2006) states 9 enablers which could facilitate the CTM implementation which named: Common interest, Openness, Recognition who and what is important, Clear expectations, Leadership, Cooperation (not punishment), Trust, Benefit sharing and Advanced information technology. Besides these factors, it is also crucial for companies to continue managing the CTM since it involves many parties in the supply chain and the interaction between different parties are quite complex. Audy, J. F. et. al. (2012) point four key questions for entities that involved in CTM to discuss from the very beginning, the four questions are shown below: determining who will be responsible for what, who will own the leadership, how benefits will be shared and which type of information will be needed. As claimed in CTM White Paper (2004), it is extremely hard for a single party in supply chain to solve supply chain problems and this is why more and more organizations have considered the collaboration among supply chain as an essential element of company strategy. To sum up, CTM is not only a partner strategy between carrier and shipper but it is also a new kind of business model which includes the carriers as a part of the supply chain for information sharing and collaboration while the traditional supply chain management only focus on the retailers and suppliers (Chan, F. T. and Zhang, T., 2011). Many researchers have done a plenty of work on the objectives and benefits of implementing the CTM in the company. Chan, F. T. and Zhang, T. 29 (2011) conclude the transportation service occupy a large part of the order lead time which refer to the time from a order placement till the ultimate delivery to the end customers. Take this as a basis, it is quite easy to understand why the CTM White Paper (2004) define the objective of CTM as improving the operating performance of all parties involved in the relationship by eliminating inefficiencies in the transportation component of the supply chain through collaboration. With the use of Multi-agent technology, Li, J. and Chan, F. T. (2012) found that the CTM is an efficient mechanism to handle demand disruption in the supply chain, and the model they built also illustrate the performance of a CTM supply chain is better than a supply chain without CTM by enhancing the cooperation among companies as well as improving the competitive ability of companies. Özener, O. Ö. (2014) identify road transportation as the highest petroleum consuming sector in the world which contributes to the total greenhouse gas emissions in the world. The mechanism based on CTM Özener built enable a logistic network with multiple customers served by a single carrier in order to minimize the transportation cost, allocate the emission and resulting cost among the customers in a fair manner. With the use of CTM, the real-time route planning becomes possible for dynamic road congestions, Riad, A. M. et. al. (2012) state a so-called On- line and Real- time Dynamic Route System (ORDRS) to minimize service delivery and travel time during rush hours downtown. While Hawas, Y. E. and El-Sayed, H. (2015) define an inter- vehicular communication (IVC)-based algorithm for real-time route guidance in urban traffic networks by enable the communication between vehicles in the network to enhance the network productivity. 3.3.3 Route and Load Planning Software As mentioned in the chapters above, the route planning system aims to help operating the fleet more efficiently, reducing costs and improving profitability and competitiveness. William Salter (2015) concludes a good route planning system can drastically optimize the transportation planning schedule. Ten key capabilities for a good route and load planning software are also identified by William Salter ranging from the basic functions like route and load optimization etc. to some soft aspects like “support clients” and “backup planning” while also considers the software development as well as statistical summary. EECA Business (2015) states, besides a digital map of the road network, the road planning system software also help the companies to hold information in the following aspects including customer locations, delivery and collection windows, quantities and types of goods to be delivered or collected, vehicle availability and capacities and driver shift patterns. Customers’ orders can also be input into the system in order to optimize the route. Six common functions or modules of route and load planning software are summarized by Daniel Harris (2017) as “Dispatch & scheduling”, “GPS tracking”, “Route planning and optimization”, “Rates and quotes management”, “Load optimization” and “Fleet maintenance”. Besides the information about the system, it is important to have a basic understanding about how the softwares operate before software implementation. Since the information regarding how the route and load planning system actually work is rare in literature, the flowchart in Figure 3.11 from Dhara S. et. al. (2016), which shows the flow of solving the Capacitated Vehicle Routing Problem (CVRP), is used as a basis to illustrate the flow of how the route and load planning system work in practice. As state by Dhara S. et. al. (2016), the flow chart is different between single and multiple depot CVRP, considering the current terminal setup of Autolink, the method 30 of multi depot CVRP is chosen in this thesis since the transportation of the company starts from 4 ports around the Sweden. Since the initial flow is only used for the CVRP based on the Clarke and Wright savings algorithm, in this case, the criteria will be much more complex, so the calculation will have based on the different software setup of objectives, algorithms as well as the working mechanism of the software providers. The authors identify the following graph as the working flow of the route and load planning software. Figure 3.11 - Flowchart for Software Route planning 3.3.4 Centralized or Decentralized Software Implementation There are two ways to implement the route and load planning system which is centralized or distributed system structure. But as Craig Borowski (2016) states “centralized” and “decentralized” are two ends of a spectrum, and most organizations are somewhere in the middle since both methods have some obvious advantage and disadvantages with the same objective of improving the operation performance. 31 3.3.4.1 Centralized route planning Portatour (2015), which is one of the route planning software providers on the market, concludes that a centralized route planning is done by the in-house team in the company headquarters or in the same place of the company. This way makes the route planning decision based on the communication with the field staff or field sales representatives first and assigns the planning to all the branches or person. In software implementation, this means all the information from the fields are collected and show in the system in the decision centre, while the center can make the decision based on all the shared information and assign the optimized plan to all the parts of the company. The advantage of doing a centralized route planning is quite obvious, many researchers or organizations has identified the following points: First, the centralized route planning can ease the management process and may reduce cost for