Exploring Future Pricing Strategies for Electric Heavy-Duty Road Freight Services

The road freight industry is undergoing a transition with the increase in demand for electric trucks and increased digitalisation. The pricing strategies in this industry are still underdeveloped and need reformation. This thesis project aims to: 1. Investigate the implications of various pricing strategies for heavy-duty road freight services in a digitalised, electric-only urban environment, and 2. Provide insights into the development of effective pricing strategies that balance profitability and risk while accounting for the challenges of a future environment with new technologies, cost structures, electrification, and digitalisation. A methodology that incorporates Multi-Objective Robust Optimisation (MORO) and scenario analysis to identify robust pricing policy alternatives that can withstand different stochastic realisations of both deep uncertainties and well-characterised uncertainties was used. The methodology uses EMA (Exploratory Modeling and Analysis) and EMA Workbench as computational modeling tools to analyse complex systems. The methodology section outlines the research design used to achieve the research objectives. A conceptual XLRM model of the system, with relevant pricing levers and uncertainties, was developed through a literature review and expert opinions from the case company that was collaborated with, which was then translated into a computational model using EMA Workbench. Exploratory research using scenario analysis and feature scoring was conducted to assess risks and benefits associated with each pricing strategy, and sensitivity analysis was used to identify parameters with the greatest impact on outcomes of interest. The results of the study show that the methodology incorporating MORO and scenario analysis can be used to explore pricing strategies in systems of deep uncertainty. 12 optimal pricing policies were suggested and sensitivity analysis was used to identify features with the greatest impact on outcomes of interest. The study provides insights into potential risks and benefits associated with different pricing strategies in a transportation system characterised by deep uncertainty. The study concludes that there is no one-size-fits-all pricing policy, there are best performing policies depending on a company’s goals and uncertainties. The 12 optimal pricing policies were divided between dynamic pricing policies, which are pricing each customer individually, flat per km pricing policies, which are setting a fixed price per km for all customers, and flat per month pricing policies, which are setting the same price for each customer. Two of the dynamic pricing policies were found as top-performers, while the only selected flat per month approach seems to be suitable for maintaining predictability of profits and cash flows along with maximising market share and capacity utilisation rate, rather than maximising total profit. Computational models like the MORO approach can be used to explore pricing strategies in deep uncertainty, but decision makers should be cautious of the assumptions and parameters of the model. Future research should explore alternative methodologies and consider behavioral mechanisms in pricing strategies. Overall, this report provides valuable insights into decision making on pricing strategies for heavy-duty electric road freight under deep uncertainty, i.e., in which sort of scenarios different pricing strategies performs optimally and when certain pricing strategies should be avoided

Beräkning av fukttillstånd : enkel metod för praktiker

Med metoden kan fuktflödet genom, och relativa fuktighetens fördelning i en konstruktion beräknas. Metoden kan tillämpas på konstruktioner som består av upp till tre materialskikt. Materialdata redovisas för betong, bruk, makadam, dräneringsgrus, lättbetong, tegel, trä, spånskiva och golvmattor