Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
The total lifecycle of a vehicle contains many phases, from production to sales to first customer to second customer and so on until the end of life. Each one of these phases includes different activities in different business areas and under different conditions. This means that the customers´ needs will vary depending on which lifecycle phase the vehicle is in and the offered services have to be adapted to this. Therefore it is important for truck developing companies to know when a transition, from one lifecycle phase to another has occurred.
This study is based on a case study provided by Scania, a company that develops trucks and busses. Delimitations were that the study would focus on connected long-haulage trucks that are in Europe under their first life cycle phase, that the developed services would be described on a conceptual level and not cover any economic aspects. With this in mind, the following research questions were created:
RQ1) What defines a transition phase?
RQ2) How can the long-haulage trucks’ usage pattern be used to identify a transition phase?
RQ3) Which data is needed to identify a transition phase?
RQ4) Based on the results of RQ2 and RQ3, how could the transition alert service be designed?
RQ5) Which applications could the transitions alert service be used for?
The study included a literature study covering product lifecycle theory, servicification, second-hand market, big data, telematics, intelligent vehicles and statistic hypothesis testing. Further, two truck drivers were observed in order to get better understanding of the transportation business and the truck driving activities. Two qualitative interview studies were made with hauliers, service salesmen, truck salesmen and distributors from Czech Republic, Denmark, Italy, Poland, Spain and Sweden.
The results of the empirical studies were analysed and RQ1 could be answered. Transition phase is the period between two different vehicle owners and/or two different ways of utilizing the truck. The analysis also gave a good picture of how the trucks are used during their life and in the transition phases, which gave an idea about usage patterns that could answer RQ2. The answer was formulated as something named phase-DNA, composed by six parameters that should change during a transition phase: Geography, Route, Driver, Traffic Condition, Assignments and Services.
Through a group brainstorming with experts in connected services, ideas of which data that could be used to describe each one of the parameters in the phase-DNA were found. These were sorted and evaluated until at least one data type for each parameter was set. The specific data types were chosen because they reflected their parameter well and because they were data that were accessible in order to conduct tests and validations. The final set of data types consisted of: Route Shape, Amount of Stops, Run Time, Idle Time, Distance Driven, Coasting, Driver ID, Average Speed, Fuel Consumption and Workshop History Data. This set of data types was used for the formulation of a hypothesis, that said that after a transition phase at least some of these data types should change. This was also the point where RQ3 was answered. II
The hypothesis was analysed using an exploratory analysis by plotting all the data types over time and observing if a change could be seen close to the change of ownership. The result showed that Amount of Stops and Driver ID were the most indicative data types, these two were further analysed with a statistical hypothesis test and a visualisation method. The results were used to develop an algorithm that is able to give an indication if a transition phase has occurred. The algorithm searches for changes in the six data types: Driver ID, Amount of Stops, Run Time, Distance Driven, Idle Time and Route Shape.
The results from the empirical studies were used to define requirements for the development of a service based on the information of phase transition called transition alert service (TAS), which is the answer to RQ4. Furthermore possible stakeholders that could be interested in the transition phase information were investigated together with an examination of their needs. TAS fulfils the five main needs identified from the stakeholders: ease start and cancellation of services, avoid unnecessary telecom expenses, avoid that information goes to the wrong customer, find new customers and customize services. In order to solve this, an algorithm detecting a transition phase was developed; it was done by searching for changes in the six data types: Driver ID, Amount of Stops, Run Time, Distance Driven, Idle Time and Route Shape.
Moreover if the TAS information is combined with other information it could be used for creating new services. Through different idea generation workshops a large number of new ideas and concepts were generated, which became the answer to RQ5. In total eleven applications for the transition alert service were developed: nine connected to change in ownership and two connected to change in utilization. Additionally, one support service named "Vehicle History" that is based on collected historical TAS was created.
Further, one total solution named "No Worries Second-Hand" was created that includes five of the developed services. This total solution offers the customer the perfectly suitable second-hand truck without having to spend time searching for it. It also consists of a contract saying that if the customer signs a R&M contract, the dealer will buy back the vehicle and offer a new used vehicle when the old one gets too old or used. TAS makes this total solution possible by giving the dealer access to information about the truck and through this predict phase transitions.
In conclusion, the developed services and especially the combination of them into a total solution would, according to the authors, favour the transition from a product focused company to a total solution provider, and extend the knowledge about the second-hand market.
2015. , 281 p.