Descripció del projecte
Keeping up with global players in the field of transportation and logistics will become more challenging than ever before. The crisis in 2008/09 forced actors to cut their margins and many have not fully recovered to old strength, yet. In the wide field of logistics, not all modes were affected to the same extend and figures show that trucking and postal/ CEP e.g. even managed to double or triple their return to shareholders from 2004 and 2013. Other areas, such as air and ocean transportation are on a downward trajectory ever since the crisis.
Fighting this negative trend requires bold strategic decisions and an agile organization that drives innovation to support uprising trends in trading and consumption across the globe. Challenges are numerous and the plurality of consumers, customers, trading partners and competitors due to the globalization or the growing concern about environmental consequences are creating complex and dynamic market conditions. And while especially in air and ocean investments in assets remain noticeably high, customers are demanding faster and smarter transportation solutions to the lowest possible price. Additionally, deregulation efforts from the EU are starting to affect the market, increasing the number of competitors per sector.
Carefully balancing investment and service costs stays an ever-green topic in this field, but companies also identified the power of pricing strategies to leverage or to hinder improvements in operations. Prices fulfill variable roles in business, i.e. they are used as an instrument to attract the desired clientele, to increase profitability or to temporarily outperform competitors. At all times pricing needs to satisfy requirements from marketing and operations and defining an optimal pricing strategy underlies multiple constraints that are often contradictory due to different purposes from these business areas. In a sector with rapidly changing demand structures and typically low margins, optimal pricing remains an outstanding problem to be solved.
As an optimization problem in this complex environment it counts to the so-called NP-hard problems in the field of computer science, meaning that current run times of algorithms describing the problem are too high to find an optimum in a timely manner.
Today computer science is using metaheuristics, simulation, a combination of both (simheuristics) and machine learning techniques to find the optimum solution to real-world problems in their full extension. While metaheuristics are characterized by their deterministic approach – and therefore their lack of uncertainty – simulation offer the integration of uncertain events within a certain probability, but only for a small part of a complex environment. It is therefore common combine both techniques whenever possible and to design and apply simheuristics to given NP-hard problems. With machine learning computer science is given an emerging tool to use past and present data for dynamic predictions of future events.
The objective of this project is to integrate one or all these techniques to create a sales structure and pricing strategy that optimally responds to consumer and shareholder needs to all times, requiring fast re-computations, scenario modelling and trustworthy results that lead to an increased profit.
