FLEET MANAGEMENT

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RESEARCH METHODOLOGY

With a wide and a significant part of the objective based not only on an understanding of the topic but also on how surrounding coherent activities and components relate to the topic, an inductive research approach was chosen for this research. Lacking of existing specific theory regarding the research questions, required the formulation of a theory built from research from the bottom up, which is in cohesion with an inductive approach. Furthermore, due to the restriction of time and the broad problem definition the research requires an inductive approach which is suitable for small data sets that cannot be validated quantitatively. A cornerstone in this thesis was to gain an understanding of the operational data from connected vehicles and the current usage of it. Further exploratory purposes could therefore not be considered until that understanding was complete. The resulting formulation was therefore both descriptive and exploratory. A single case study was used as a research strategy since the comprehension process had to be made in real context through observations and interviews.

DATA COLLECTION

Primary qualitative data was collected through semi-structured interviews and observations both internally at Scania and externally with Scania customers. In order to first gain an understanding of the order process, observations at and interviews at customers was an important part of the customer contact. The ambition was to gain a broad insight into road carriers managed their transport operations and what their challenges in achieving improved performance. The ambition had however to be in relation to the limited time available for interviewing customers. Although, the ambition was never not to be able to draw any valid generalisations from the road carrier population. The ambition was rather to give an insight into the operations and challenges in a qualitative manner in order to facilitate the creativity process associated to research question three. The selection of road carriers was based on small to medium sized companies working within distribution as main business. The sample was a version of convenience sample where customers were contacted through recommendations from Scania maintenance market vendors. The sample of face-to-face interviews were also selected based on convenience to reduce the travel time. In total three types of non-standardised interviews were conducted: two face-to-face with Scania customers, six telephone interviews with Scania customers and two Scania maintenance market vendors. The interviews were conducted during three days and after all eight interviews were done, the notes were discussed and summarised.
The face-to-face interviews were conducted semi-structured with transport planners according to the questions in Appendix 1. The questions were sent in advance in order for the interviewee to be able to prepare. The questions were initiated with closed questions and then continued with open questions all in a logical order to lead into the more relevant questions. Together with the interviews, observation at the same companies were made. Specifically, the order process was observed, from incoming order to a planned and ready transport route. Both visits lasted one hour and were conducted with one interviewer asking questions and one taking notes.
The interviews over telephone were conducted in a similar manner for both service market vendors and customers, although referring to different questionnaires, seen in Appendix 1. The questionnaire was not sent out in advance and there was also a variation in the structure of interviews due to different time allowances from the interviewees, where in certain cases only a couple of questions could be asked and in other cases the questions could be asked exhaustively. The interviewees were either transport planners or associated with the management of the company with varying responsibilities within distribution planning. The interviews lasted between ten and thirty minutes. The interviews were conducted with both group members, one asking questions and one taking notes.
The case study on Scania consisted of semi-structured interviews, non-structured interviews and a workshop combined with and secondary data. Eight semi-structured interviews with key project managers, researchers and other relevant personnel from different departments at Scania were held in order to map and understand the current research and project developments. These interviews answered both research question one and two by contributing to insights on how operational data is used within different settings outside the delimitations of the research. They were selected based on snowball sampling with recommendations from previous interviewees or the mentor at Scania. Each interview lasted in average one hour. Furthermore, multiple non-structured interviews were conducted with the Scania mentor and employees in the same department, mostly in informal settings. The secondary data consisted of different reports and technical descriptions of the electrical system on board vehicles and other relevant project information. During the data collection phase, a document was used to gather potential ideas related to the third research question. Everything with a chance of being important for the analysis were documented.
On the 28th of March about 15 Scania employees attended a half-time presentation. After the presentation, the participants were divided into three smaller groups with different competence. Each group had at least one with knowledge about the operational data. The groups got the two questions from Appendix 1 to discuss for 15 minutes. The groups documented the discussions on paper.
The literature review was based on the search of the following search words in order to find relevant theoretical information: Transport Management System, Operations Research, Route Planning, Vehicle Routing Problem, Stochastic Vehicle Routing, Dynamic Vehicle Routing, Lean, Logistics, Visualisation, PDCA; Supply Chain Coordination, Time Utilisation, Fill Rate. The search engines applied was Ebsco and Google scholar and each cited article was reviewed for peer review and number of citations.

ANALYSIS METHOD

The analysis of this thesis was divided into two phases. The first phase took place during the data collection. All new information was continuously analysed and compared with the existing information. Each interview and observation with customers were discussed amongst the authors and key perceptions were highlighted. The aim of this phase was to do shallow analysis to be able to guide the research in the right direction. The result from this phase was a list of all ideas that was agreed upon to contribute to the research objective.
The second phase of the analysis was done when all data was collected. The phase started with a deep and thorough analysis of the already gathered suggestions. This time with a wider knowledge base and more time allowance for creativity and problem solving. This phase included going through all available electric control unit families that were available to discuss new possible usage areas, see Table 1.

VALIDITY AND RELIABILITY

During the data collection, there has been validity and reliability uncertainties. The three parties; researchers, customers and Scania personnel have all understood the questions differently and also all have agendas and are in different ways bias. Both conducting and asking the type of questions needed to get the right information is interviewer bias due the fact that the answers were desired, even if the questions were to be objectively formulated. The interviewee might have been affected by external factors such as stress or having colleagues listening to what answers are given. Since the case study was based on qualitative interviews, the error margin decreased due to less dependence on the answers since no generalisation was made based on them. The interviewees were selected based on opinions and tips from Scania employees. To find the right interviewees and interview enough managers and customers for this research requires more resources than what was available and is a deficit in the validity of the research. The interviewed customers had answerers that were potentially incomplete because they felt uncomfortable discussing somewhat sensitive company secrets such as work methods or customer related information. The interviewed Scania personnel had different reasons for not giving complete and honest answers. One reason could be not trusting master’s thesis students with research secrets and another being their interest in promoting their own project to give a better picture than the reality or being bias towards their own project. Improvement of the validity was ensured by interviewing a large number of customer in order to understand a broader but not generalizable picture. Furthermore, the validity was also ensured by sending the questions in advance in order for the interviewees to be able to prepare and give answers more closely related to the research. It was also strengthened by conducting a triangulating analysis supported by three different information sources; literature, customer needs and Scania research.
The reliability of any inductive case study is low concerning the repeatability of the method. Especially since the results are influenced by time and a repeated research will have a different outcome in the future. Furthermore, other contacted road carriers’ answers will not likely be similar. The interviewed customers were also not completely objective even when they were aware that they were both anonymous and not recorded. The consequence of making the interviewee anonymous made it difficult to trace the source of information affects the reliability. However, reliability in the aspect of conclusions is relatively good. Because if a similar study is conducted with triangulation from customer, employees and literature in order to gain a basic understanding of the challenges and opportunities and would analyse the operational data similarly, the likelihood of similar conclusions are probable.

LITTERATURE REVIEW

LEAN

Lean is a philosophy for a long-term organisation toward a, for the customer, value creating process by continuously eliminating waste and inefficiencies in the process (Sayer & Bruce, 2007). In order to force a new position from business as separate entities in a supply chain and traditional batch production toward the value chain as a whole and process focus Womack and Jones (1996) suggest a Lean thinking framework of five steps: specify value, identify the value stream, create flow, use pull and work toward perfection. They argue that all these steps will shape the organisation and thinking towards creating value for the customers by eliminating wasteful activities.
A fundamental starting point according to Womack and Jones (1996) for Lean is to define value accurately. The opposite of value creation is waste or muda in Japanese which in Lean philosophy means any activity that uses resources but does not create any value for the customer (Womack & Jones, 1996). However what value is differing from context to context and can therefore only be defined and meaningfully expressed as a customer’s need for a product at a specific price and time (Womack & Jones, 1996). Therefore, the authors argue that Lean thinking must start by defining value in terms of specific customer needs. Although, when asking customers what they want, they often answer with variants of what they are getting today or return to simpler formulations such as lower cost, faster delivery and higher quality (Womack & Jones, 1996). Instead Womack and Jones (1996) argue that in order to reach a better definition, value should be analysed by challenging traditional means of working and advanced definitions. Furthermore, customers often just look at their own needs, instead of thinking of the whole value chain were value often is created.
When value is defined Womack and Jones (1996) suggests streamlining the organisation towards only working with what creates value. More precisely identifying the activities that are value adding, necessary but non value adding and non-value adding. Where non-value adding activities are neither necessary nor value adding and in theory should be removed first from the process. All the remaining activities should then be managed to achieve flow, which often require a change in mind-set, an example of such a change being from make-to-batch to make-to-order (Womack & Jones, 1996). Another often common required change is to not consider the current tools for achieving customer value since these might be designed for economy of scale and cannot deliver flow without unnecessary waiting time (Womack & Jones, 1996).
Lastly Lean thinking is about seeing the benefits in cooperation and transparency in working towards perfection. According to Womack and Jones (1996) much of the muda created in business is results of protectionist reasoning and an atomistic thinking. By being more transparent and sharing information the authors mean that waste between companies can be identified and removed and the profit shared between the companies and the customer. Aside from finding intercorporate waste Lean thinking also facilitates visualisation of processes internally in organisations. Bicheno and Holweg (2009) highlight examples of waste related to logistical services: overproduction (not used transport capacity), waiting, movement, inventory, defects, time, variation, information duplication and unclear communication.
Lindskog, Vallhagen, Berglund and Johansson (2016) found through visualisation that risks and problems associated with the planning process could be avoided. When they in their process aimed to define a Lean production environment different areas were covered in order to eliminate waste in the flow of material, handling of material at nodes and infrastructure for maintenance. The result of visualisation as a tool was a large confidence in the planning process and timesaving in planning and execution (Lindskog et al., 2016). Visibility of performance mandatory for any Lean implementation (Bicheno & Holweg, 2009). A method for visualisation of a logical flow is Value Stream Mapping (VSM), a Lean methodology to efficiently satisfy customer demand with short lead times (Lindskog et al., 2016). Lindskog et al. (2016) also point to the 5W2H method as a method to complement VSM when visualising a process. 5W2H are the questions; why, when, who, where, what, how and how much posed in order to comprehend the purpose of an activity.
Continuous improvements and risk management is an important part of Lean implementation (Lindskog et al., 2016; Womack & Jones, 1996). Lindskog et al. (2016) point out the LAMDA model as an important tool in achieving continuous improvements and risk management, however; Schmidt, Elezi, Tommelein and Lindemann (2014) lifts the Plan, Do, Check, Act (PDCA) cycle as a foundation for continuous improvement. According to Bicheno and Holweg there are important considerations in each step of PDCA. Plan is to understand the customer and their requirements, define a detailed time plan and set common goals for the achievement, check relates back to the defined goals in plan and is important to carry out frequently with discipline. Lastly act is the part of improvement action toward fulfilling the goals completely. All together the PDCA method for improvements embraces the philosophy of kaizen (Bicheno & Holweg, 2009).

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PERFORMANCE MEASURING

According to Bicheno and Holweg (2009) measurement is waste in Lean, it should be limited and minimized. General qualities of measurements should be indicators of problem, contribute to the improvement loop by surfacing problems and focus on improvement. Measures accodring to the authors should not be motivational but rather informational in order to assist in improvement. Furthermore there are four neccesary types of measurements in Lean: lead time, customer satisfaction, schedule attainment and turnover rate of inventory (Bicheno & Holweg, 2009). Durak and Akdoğan (2016) compiled performance measures used by studies of logistic companies into: Delivery time, quality, consistency, productivity, sales costs, production time, delivery security, service quality, flexibility, market share, customer loyalty, activity, efficiency and conformance to standards. Two important measures to further consider in performance measuring in supply chains are vehicle time utilisation and vehicle fill rate (Samuelsson, 2017).

SUPPLY CHAIN

Looking at a single company improving and streamlining their operations toward only creating customer value is not optimal, Bicheno and Holweg (2009) claim that large dysfunction can accumulate if each company and actor in the supply chain focus on their own improvement. Such a focus can give rise to increasing variations of interpreted demand and accumulation of uneven production and wasteful inventories (Bicheno & Holweg, 2009). Preventing such a supply chain behaviour according to Lean can be achieved through shared incentive systems and defining the shared supply chain strategy (Bicheno & Holweg, 2009). Digitalisation also facilitates implementation of Lean since it enables immediate distribution of demand data along the supply chain and smart factories can also produce faster and customized products (Netland, 2015).
According to Bicheno and Holweg (2009) there are two main strategies for a supply chain, either efficient or responsive. They have furthermore summarised three threats to a Lean supply chain: Inventory and delay, uncertainty, number of actors. These are according to Bicheno and Holweg (2009) managed with correct information or they cited Michael Hammer: “Inventory is a substitute for information” which is interpreted as with bad or no information and uncertainties inventories are used instead. Information of actual sales and forecasted demand allows for better alignment of resources and future strategic capacity planning (Bicheno & Holweg, 2009). In multimodal transportation, which is the case of almost any supply chain, Jarašūnienė, Batarlienė & Vaičiūtė (2016) mean that the large flow of information and diverse parameters between transports require efficient communication systems to manage a material flow. Other means of tackling ineffective supply chains are according to Bicheno and Holweg (2009) by making varying and infrequent deliveries as frequent and regular as possible similar to the “milk-round” strategy. With a frequent and set route and time slots for delivery reduces amplifications and variations and fosters a steady and efficient flow with reduced lead times (Bicheno & Holweg, 2009). Bicheno and Holweg (2009) also emphasize the opportunities with a very frequent service interval with small batches in a well-planned manner to ensure a high fill rate in the vehicles. Well planned operations are important to reduce waste and create customer value, but is a complicated procedure not easily accomplished by manual computing.

OPERATIONS RESEARCH

Operations research is a branch of applied mathematics used to analyse, describe and find different means of action in technical or economical decision problems (Lundgren, Rönnqvist, & Värbrand, 2003). It is according to Gass and Assad (2005) a scientific tool for management decisions regarding their operations in a quantitative manner. The science was first utilised during the second world war, thus the name Research on (military) Operations (Lundgren et al., 2003). This analytical tool is also named operational analysis and is used to find the optimal solution to quantitatively expressible problems. More specifically operations research can make a decision based on a problem proposition with a defined target and limitations (Lundgren et al., 2003). Transport and logistics is a common area for operations research. It enables planning of travel routes and resources such as trailers, vehicles and personnel. This research is important for supply chain actors in order to increase the efficiency of distribution of goods (FFI, 2015). It has a higher potential for efficiency increase than for example improved fill rate would have for distribution carriers (FFI, 2015).
Vehicle routing problem (VRP) is the scientific title of the operations research segment that involves defining a target of minimizing the overall transportation costs whilst reaching all target destinations (Lundgren et al., 2003). The model then evaluates all different routes based on the cost incurred at each option. It originates from the traveling salesmen problem (TSP) which is a demanding operations research problem. It belongs to the most difficult class of mathematical modelling problems which are non-conclusive in polynomial time (Kloster & Hasle, 2007). Kloster and Hasle (2007) therefore claims that there is no algorithm that can solve a VRP to optimality. There is however possible to solve the VRP problem exactly below the threshold of 50-100 orders using different heuristic models (Kloster & Hasle, 2007). Within the limit, however; VRP has proved very effective in reducing waste in supply chains, applying VRP modelling will reduce costs of transport by 5-30% (Kloster & Hasle, 2007).
VRP is however only the simplest model for supply chain application, research is focusing on enhanced compatibility to reality through vehicle capacity constraint (CVRP), driver working restrictions (DVRP) and order time windows (VRPTW) (Kloster & Hasle, 2007). Further additions can be how to load vehicles for optimal unloading a category of operations research called bin packing problem (Lundgren et al., 2003). CDVRP is a problem with constrained load for each vehicle and constrained time or distance for each vehicle or driver, but does not consider time windows for deliveries. VRP however require much and well-prepared information before usability. In the above example numbers of drivers and vehicles, exact order information with time-of-delivery, location and dimensions and weight of goods is required for an optimal calculation. It also requires a careful formulation and calculation time. Dudas et al. (2015) speculate in that when order data is missing it could be possible to analyse quantitative historical data in order to detect the operational flow of road carriers. This would enable route optimisaiton in hindsight and identification of improvement areas such as logistical bottlenecks, vehicle deviations and benchmarking (Dudas et al., 2015). When defined, however; modelling within the same definition is possible to perform within minutes depending on the size of the dataset. Operations research enables a scientific perspective of transports in supply chains and is an important tool for efficient transportation system management (Lundgren et al., 2003).

FLEET MANAGEMENT

Fleet management encompasses technology and processes related to a vehicle-based system. Combining data logging, satellite positioning, communication, vehicle-, maintenance-, driver- and transport management with a IT system creates a fleet management system (Fagerberg, 2016). A fleet management system is therefore a management tool used to acquire control over dispersed fleets and enables road carriers to systematically manage risks of fleet reliability and controlling the cost of such reliability (Galletti, Lee, & Kozman, 2010). Galletti et al. (2010) concludes that the risk of fleet ownership causes many businesses that require heavy vehicles for their business to outsource it. The authors mean that fleet management is the management type responsible for attending to these risks and require risky business. There are more than 4,5 million fleet management systems installed in Europe alone with a forecasted strong increase (Berger, 2016). Fleet management performance is according to Galletti et al. (2010) cost effectiveness and customer satisfaction, which is mainly achieved through reliability in vehicle management.
However, without performance measurements and benchmarking with other businesses the authors mean that the operations will affect cost effectiveness, safety, reliability, service level negatively. Moreover, that there is no ground for continuous improvements if there is no such comparison between businesses. A further outcome of benchmarking is the visibility of poor performance areas which can be improved. The authors also claim that fleet managers lack the standardised methods to achieve optimal fleet decisions. With benchmarking, strategic management decisions can be facilitated (Korpela & Tuominen, 1996). Benchmarking allows for knowledge of where the fleet performance is in relation to customer demands and identifies areas of improvement to continuously monitor the fleet (Galletti et al., 2010). Competitive benchmarking allows for road carriers to compare themselves with other companies in the same specific industry that share the same customer base and is thus the most beneficial (Galletti et al., 2010).
Galletti et al. (2010) suggest a detailed framework for benchmarking fleet management performance. In short, they first propose categorising the cost of fleet operations such as fleet investment and maintenance, drivers and transport planners and then determining what cost is associated with each category. Afterwards the focus of the benchmarking should be established where examples according to the authors can be management, personnel or fleet operations. When current business type and benchmark focus is decided, benchmarking can be initiated and used for continuous improvements.

Table of contents :

1 INTRODUCTION
1.1 PROBLEM DESCRIPTION
1.2 RESEARCH PURPOSE
2 RESEARCH METHODOLOGY
2.1 DATA COLLECTION
2.2 ANALYSIS METHOD
2.3 VALIDITY AND RELIABILITY
3 LITTERATURE REVIEW
3.1 LEAN
3.2 OPERATIONS RESEARCH
3.3 FLEET MANAGEMENT
4 EMPIRICAL FINDINGS
4.1 SCANIA
4.2 EXTERNAL DEVELOPMENTS
5 ANALYSIS
5.1 PLANNING
5.2 EXECUTION
5.3 EVALUATING
5.4 FURTHER CONSIDERATIONS
6 CONCLUSIONS
7 RECOMMENDATIONS
8 DISCUSSION
8.1 THEORETICAL CONTRIBUTION
8.2 FUTURE RESEARCH
9 REFERENCES
10 APPENDIX

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