Big Data

The scope and diversity of data in logistics and supply chain management is growing exponentially, bringing established systems and methods of data processing to their limits. The digitization of business processes and technical developments including the Internet of things, cyber-physical systems and autonomous software agents in transportation and in-house traffic are driving growing data streams onward.

This opens up new possibilities for increasing efficiency and effectiveness using data-driven planning and management processes. In order for supply chain processes to benefit from available data, 4flow research evaluates systems and procedures for the analysis, cleansing and structuring of data, as well as new optimization algorithms and simulation methods.

Risk management

Everyday risks and unexpected events are often underestimated in supply chains. Supply chains are often not sufficiently prepared structurally for disruptions to operations. At the same time, the threat of natural catastrophes and unexpected events is increasing, making the analysis of risks and their aftereffects an important issue in research today.

The research we do at 4flow in the area of risk management aims to increase the robustness of supply networks in the face of fluctuations and disruptions. By developing enhanced planning and management processes along with new methods and tools for analysis and optimization, networks can be optimally prepared for exceptional situations.

Optimization algorithms

Due to their complexity, the optimization of supply networks today cannot be performed without the help if IT. The demands on supply chain design comprise more than just minimizing costs at a certain service level. The fluctuation of key factors is just as important as much as environmental sustainability.

4flow research aims to develop innovative new planning algorithms for strategic as well as tactical transportation planning. Complex network structures, material flow and transportation rates are described in mathematical models and optimized using precise heuristic methods. The aim is to get practical solutions for everyday situations that connect minimal costs with maximum performance and sustainability with the most amount of flexibility.

The results of the research will further increase the performance ability of our standard software for supply chain planning 4flow vista®. 4flow research also collaborates with leading experts in mathematics and information technology for mathematic modeling and developing new planning algorithms.

Green logistics

Sustainability has become an important topic for companies and supply chain professionals. The standards for calculating and evaluating sustainable processes, however, are not all encompassing – universal statements about costs and benefits can hardly be made.

The supply chain research we conduct at 4flow in the field of green logistics aims to measure the effects that supply chain activities have on the environment, thereby making them more able to be analyzed rationally. At the same time, strategies and measures for improving sustainability are tested. The long-term objective is to solidly integrate ecological sustainability into the design and management of supply chains and transportation networks and also to develop new methods of optimizing.

4flow Supply Chain Management Study 2013: Costs and Benefits of Green Logistics

Optimized supply chain processes can help reduce carbon emissions and increase the ecological sustainability of business processes. The 4flow Supply Chain Management Study 2013 contains the results of 11 supply chain measures for reducing emissions. Costs and benefits of supply chain measures were investigated with quantitative methods, based on case studies.

4flow Supply Chain Management Study 2010: The Influence of Oil Price on Distribution Networks in Manufacturing and Commerce

The 4flow Supply Chain Management Study 2010 quantifies the effect that the price of oil on transportation costs and the effect the price has on supply chain costs in different types of networks. In model calculations for manufacturing and commerce, the study gives recommendations for cost-optimized structures depending on the development of oil prices. Realistic models were also created and optimized. Opportunities to reduce CO2 emissions by restructuring networks were analyzed. The results show that the ideal distribution structure only slightly depends on the price of oil.

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