Fraunhofer Institute for Material Flow and Logistics IMLFor logistics process optimization, Fraunhofer IML combines mathematical optimization with practical decision support systems. Our institute analyzes big data from transportation, warehousing, and production and uses it to formulate precise optimization models. Algorithms calculate robust scenarios for sequencing, routing, order quantities, and resource allocation. Graphical user interfaces present the results in an easy-to-understand format and support the day-to-day decision-making of logistics planners. Agile implementation and continuous optimization ensure the sustainable impact of the models.
Process optimization in logistics
Mathematical optimization methods for data-driven logistics processes
Mathematical optimization makes complex logistics processes manageable and provides answers to key questions: In what order should orders be processed to make the best use of capacity? Which route minimizes travel distances for transportation and order picking? Which order quantities yield the greatest savings?
To optimize their logistics processes, companies must collect, clean, and analyze relevant data. Our institute develops models that reflect real-world processes, objectives, and constraints. Optimization algorithms generate solutions that reduce wait times, improve resource utilization, and minimize waste. Our logistics support systems, handheld devices, and augmented reality applications integrate these solutions into operational processes and support employees directly at their workstations.
Our services for logistics process optimization
Optimized logistics offers a wide range of benefits for companies and employees. Process optimization can lower costs while simultaneously reducing CO₂ emissions.
Feel free to contact us regarding your logistics planning needs in the following areas, and we’ll help you by finding the best solution for your problem.
Logistics Process Optimization Services at a Glance
Our team develops and implements tailor-made concepts for efficient production, logistics, and inventory processes—from strategic planning to operational implementation. Among other things, we offer:
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Test our demonstrator: simulated optimization
This demonstrator places you in a fictional logistics planning scenario in the e-commerce sector. Your task is to select an ideal mix for mixed pallets so that efficient picking processes result when your orders are assembled based on the sales forecast for your items. The warehouse’s performance is then simulated and evaluated using your mix. The optimal warehouse composition determined by our experts is used as a benchmark for the evaluation.
Explore practical optimization solutions in a secure test environment!
Solving industry-specific challengeswith mathematical optimization
Mathematical optimization describes the search for solutions that best fulfill a clearly defined objective within given constraints. In logistics, increasing data volumes, limited resources, and short response times lead to complex decision-making scenarios. Many companies continue to plan their workforce, warehouses, and transportation using spreadsheets or monolithic systems. Our research institute uses mathematical optimization to precisely model these situations. Workshops, process analyses, and feasibility studies clarify which problems can be addressed with algorithms and what effort is required for process optimization. Based on this, Fraunhofer IML develops strategies that enable companies to prepare structured, data-driven planning decisions.
Heuristic process optimization methods
Heuristic optimization complements algorithmic optimization in logistics wherever exact methods reach their limits due to long computation times. Through practical rules – such as selecting the nearest customer or forming sensible clusters – high-quality solutions are generated for route planning, storage location assignment, picking routes, and loading planning. Planners thus receive robust suggestions for optimizing logistics processes without having to exhaustively search the entire solution space. Terms such as “optimization mathematics” and “algorithmic optimization” describe the theoretical framework. Search queries such as “process optimization approach” or “continuous optimization” refer to the entire path from model development to recurring application in support systems.
»There are an infinite number of solutions, but only a few are optimal.«
Dipl.-Inf. Benjamin Korth, Head of the information logistics department at Fraunhofer IML
Logistics planning automation
Planning automation links optimization algorithms with support systems that automatically generate scenarios. For each variant, the system calculates action options and evaluates them using target metrics, forecasts, and risk assessments. Opportunity-risk profiles support goal-oriented planning across departments and the entire company. This provides planners with a structured basis for decision-making without having to prepare each variant manually. Research efforts focus on continuous or event-driven optimization, as well as on digital twins that realistically simulate planning processes and test changes in production and logistics.
Digital planning support
Digital planning support relies on customized optimization solutions built on logistics support systems. Microservices integrate ERP systems, warehouse management, and simulation tools into an optimization framework. This framework processes the data, applies appropriate algorithms, and generates evaluated scenarios for order scheduling, sequence planning, machine utilization, resource and personnel planning, all the way through to production program planning. Visualizations and key metrics make the results transparent for logistics planners. They can identify bottlenecks, respond flexibly to unplanned events, and leverage external planning expertise to increase efficiency and reduce costs.
Decision support and automation in logistics
In the research field of decision automation, Fraunhofer IML digitizes decision-making processes in production and logistics. Based on formal objective systems and clear priorities, an intelligent system evaluates courses of action and, if desired, makes decisions without human intervention. Multi-agent systems negotiate capacities or logistics services, while cyber-physical systems implement decisions immediately. Sensors and target-actual comparisons monitor the impact of the measures. The goal is structured, partially or fully automated decision-making that increases responsiveness, accelerates negotiation processes, and improves the quality of results in global supply chains.
Get started with logistics process optimization now!
Would you like to optimize your logistics processes using data-driven methods? The Fraunhofer Institute for Material Flow and Logistics IML offers comprehensive information and personalized consulting services. Take advantage of direct contact with our teams of experts to discuss your requirements, data situation, and suitable optimization approaches.
FAQ on process optimization in logistics
What is process optimization in logistics?
Process optimization in logistics describes the systematic improvement of material and information flows in transportation, warehousing, and production. Data from operational systems is collected, cleaned, and converted into precise models. Mathematical optimization methods use this data to calculate reliable scenarios, such as for sequencing, routing, inventory levels, or resource allocation.
At Fraunhofer IML, we combine this data-driven, algorithmic planning with practical support systems. Graphical interfaces, handheld devices, and augmented reality bring optimization results directly into employees’ daily work. This reduces wait times and planning efforts, while capacity utilization, service levels, and sustainability measurably increase.
What are the 7 methods for process optimization?
Based on our research and project experience, we primarily use the following seven methods:
- Workshops, process analyses, and feasibility studies – Collaboratively determining which logistical issues can be effectively modeled and improved using algorithms.
- Mathematical Optimization (Algorithmic Optimization) – Exact optimization models that take into account objectives and constraints, e.g., in route planning, production scheduling, or workforce planning.
- Heuristic optimization methods – Practical rules and approximation methods used when exact methods reach computational limits (e.g., clustering, “nearest customer”).
- Simulation and Digital Twins – Modeling of warehouse and production systems to test optimization scenarios risk-free and evaluate their impact.
- Planning Automation – Automatic generation and evaluation of scenarios, including opportunity-risk profiles and key metric comparisons.
- Digital Planning Support and Assistance Systems – Microservices, handheld devices, and AR solutions that integrate optimization results directly into operational planning and execution.
- Decision support and automation – Intelligent systems and multi-agent approaches that evaluate course of action options and – if desired – make decisions semi- or fully automatically.
What are the 4 phases of logistics?
ypically, four phases or areas of logistics are distinguished:
- Procurement logistics: Planning and managing the flow of goods from suppliers to the company, e.g., through optimized order quantities, delivery frequencies, and delivery concepts.
- Production logistics: Supplying production with materials, interim storage, and intra-company transport. Optimization models support, among other things, sequence planning, machine scheduling, and material flow.
- Distribution logistics: Storage, order picking, and delivery of finished products to customers. Mathematical optimization helps here with route planning, storage location optimization, and service level control, among other things.
- Disposal and Reverse Logistics: Take-back, recycling, or disposal of packaging, returns, and scrap parts. Data-driven planning increases transparency and efficiency in reverse and circular processes.
In all four phases, logistics process optimization ensures better capacity utilization, lower costs, and greater responsiveness.
How can warehouse processes be optimized?
Warehouse processes can be optimized particularly effectively when data analysis, mathematical models, and support systems work together. The starting point is always the collection and cleansing of relevant warehouse data – such as inventory levels, order structures, routes, and processing times. Building on this, optimization models are developed that map out warehouse layouts, storage location assignments, picking strategies, and replenishment processes.
Typical areas of focus include:
- Warehouse layout and storage location optimization using mathematical and heuristic methods to shorten routes and account for access frequencies.
- Optimization of picking processes (e.g., route planning within the warehouse, creation of mixed pallets, set and batch formation) based on order and forecast data.
- Inventory and replenishment planning through optimized reorder points, lot sizes, and safety stock levels.
- Digital support systems such as warehouse management systems, handheld devices, or AR applications that support employees during putaway, picking, and inventory counting.
- Simulation and continuous monitoring to evaluate the impact of measures and continuously refine warehouse processes based on data.
This results in warehouse logistics that make optimal use of capacity, reduce process errors, and can respond flexibly to changes in demand.