Optimization

In today's fast-paced world, the logistics industry is faced with increasingly new challenges. From the efficient use of limited resources to minimizing delivery times, the demands placed on logistics companies are diverse and complex. This is where mathematical optimization comes into play - a powerful tool that helps companies make decisions based on precise data analysis and optimize processes. Optimization is the key to many problems: What is the best order to process? Which orders can be used to optimize capacity? Which route is optimal? How much needs to be ordered and when? How do I minimize the distances in order picking? Which orders can be sensibly combined?

Mathematical optimization and algorithms can be used to calculate solutions to all these problems. However, companies face multiple challenges that make the introduction and implementation more difficult. These include:

Data collection and analysis:

Relevant data must be collected and thoroughly analyzed to gain insights into existing processes.

Modelling:

Developing mathematical models that accurately represent the real-life realities of logistics processes, objectives and restrictions requires experience and the ability to abstract.

Solution development:

Calculating optimized solutions is only one part of a solution, as the results must also reach the processes and be implemented. Technologies such as logistical assistance systems, handhelds and augmented reality are effective interfaces to users and complete the concepts.

Implementation:

An agile approach has proven itself for the implementation of concepts, including the interfaces to corporate IT and software tests. Users must not be left out in the cold. Participatory design, qualitative feedback, user acceptance tests and training ensure that the solutions are also accepted by the employees.

Monitoring and customization:

Software is alive. Every solution requires continuous monitoring and, if necessary, adjustments, be it to add or improve functions for users, increase performance or ensure IT security.

The effort is worth it. By optimizing workflows and using efficient planning tools, employee waiting times can be reduced, resources used efficiently, and waste avoided. This not only increases productivity, but also employee satisfaction, as they can use their working hours effectively.

"An infinite number of solutions, only a few are optimal."

- Benjamin Korth, Head of Department

Humans must not be overburdened when dealing with mass data, algorithms, and exponentially growing solution spaces. The Information Logistics department at Fraunhofer IML takes care of this. Optimization algorithms are designed for customers' specific problems and integrated into their IT infrastructure or used in the form of stand-alone logistics assistance systems.

Analysis

• Potential analysis: Where is there potential for optimization in the company?
• Needs analysis: Determination of specific requirements and goals.
• Problem identification: Precise definition of the optimization problem
• Feasibility study: Assessment of the feasibility and the required effort

Conceptualization

• Model development: Creation of mathematical models to represent the problem
• Strategy development: Elaboration of solution strategies and algorithms
• UX design: Design of efficient and intuitive applications

Implementation

• Software development: Programming customized software solutions
• Integration: Seamless integration of the solution into existing systems
• Optimization: Fine-tuning for maximum efficiency and performance

Roll-out

• Accompanying the introduction of the solution
• Communication: Regular updates and information on the progress of the project to gain support and minimize resistance
• Pilot phase: Start with a pilot phase to test the implementation in a controlled environment and gather feedback.
• Training programs: Develop comprehensive training programs for end users to ensure they can work effectively with the new software.

Optimization projects usually begin with a workshop to analyze the problem and a process analysis. This reveals the potential that can be exploited through optimization. If there is a risk that a solution cannot be realized, a feasibility study can help.

Once the problem has been precisely identified, the conceptualization phase begins. The problem is modelled and described so precisely that it can be solved by algorithms. At this point, the complexity becomes visible so that strategies for the solution can be developed. Exact solutions, heuristics or even artificial intelligence can be building blocks for the solution. A human-centered UX design ensures acceptance by future users.

Implementation is carried out according to agile process models. Regular reviews ensure transparency about progress and user feedback is obtained and taken into account in the further development process.

Support during the roll-out phase is crucial. Resistance is nothing unusual here and concerns must be dispelled through persuasion. Naturally, the results of the optimization are evaluated and potential for improvement is identified.

Together we will solve your optimization problems!