Analyze material flow before changing the system
Material flow analysis creates a transparent picture of the current state or a planned volume structure. It examines sources and sinks, transport relations, quantities, frequencies, routes, inventory, process times and space. This reveals bottlenecks, unnecessary transport, crossing traffic, missing buffers and conflicts between production and logistics.
Depending on the question, we examine a single area, a production line, a warehouse or the entire plant. The result is not an isolated diagram but a reliable data basis for layout, process and technology decisions.
- Quantitative: quantities, transport intensity, frequencies, utilization, inventory and times
- Spatial: routes, distances, areas, crossings, sources, sinks and staging locations
- Organizational: control rules, priorities, responsibilities, information and order flow
Validate dynamics with material flow simulation
A static analysis reveals volume relationships and structural weaknesses. Material flow simulation adds a dynamic view where temporal dependencies, fluctuating loads or shared resources determine system behavior. A discrete-event model represents processes, resources and control rules over time.
This allows throughput, waiting times, buffers, queues, equipment and vehicle utilization, and robustness under disruption to be compared. Simulation is not an end in itself: model scope and detail follow the concrete decision.
- Capacity and bottleneck scenarios under different order profiles
- Dimensioning buffers, transport equipment, AGV/AMR fleets and operators
- Comparing control rules, shift models and ramp-up scenarios
- De-risking costly layout and automation decisions
Optimize material flow and translate findings into action
Material flow optimization connects findings from analysis and, where required, simulation with concrete changes. It considers not just transport routes but the interaction of layout, batch sizes, inventory, staging, transport technology, control and organization.
We develop options and evaluate them against agreed criteria such as throughput, space demand, investment, staffing, flexibility, process stability and implementation risk. The outcome is a preferred concept with prioritized measures rather than an unconnected list of ideas.
When is analysis sufficient and when is simulation useful?
The method follows the decision, not the other way round. First determine whether a static relationship must be explained, time-dependent behaviour validated or an implementable preferred option selected.
- 01
Understand structure
Volumes, relations, routes and areas are analysed statically.
- 02
Check dynamics
Variability, queues and shared resources trigger the need for simulation.
- 03
Evaluate options
Performance, effort, robustness and feasibility are compared together.
- 04
Implement improvement
The preferred option becomes actions, responsibilities and measurable targets.
Simulation therefore starts with a target metric, system boundary and decision criterion rather than software. If the question can already be answered through volume structure, a from-to matrix and layout, static analysis remains the more economical evidence.
| Question | Suitable approach | Typical evidence |
|---|---|---|
| Which routes and quantities dominate? | Static material flow analysis | From-to matrix, Sankey or spaghetti diagram |
| Where do spatial conflicts occur? | Analysis with layout evaluation | Transport intensity, distance and space comparison |
| How do fluctuations and waiting times interact? | Discrete-event simulation | Throughput, buffer, waiting-time and utilization distributions |
| Which fleet or buffer size is robust? | Calculation plus simulation | Peak-load, disruption and reserve scenarios |
| Which option should be implemented? | Optimization and option evaluation | Weighted comparison, economics and roadmap |
Data required for analysis and simulation
The required data depth depends on the decision. At project start, we therefore define system boundaries, reference periods, data sources and quality requirements. Missing data is documented as assumptions and tested through sensitivities instead of being hidden behind false precision.
| Data area | Typical sources | Use |
|---|---|---|
| Items, volumes and orders | ERP, production planning, WMS, transactions | Material families, volume structure and load profiles |
| Processes and times | Routings, MES, observation, interviews | Sequence, capacity, dependencies and control |
| Inventory and buffers | WMS, counts, system extracts | Coverage, space demand and decoupling |
| Layout and routes | CAD, 3D scans, factory model, site walks | Distances, traffic space and layout options |
| Resources and disruptions | Operating data, shift models, experience | Simulation of utilization, availability and robustness |
KPIs with an explicit measurement rule
Before options are compared, every KPI is defined by unit, system boundary, reference period and data source. Otherwise, two apparently identical values may describe different conditions. A useful KPI set connects volume, time, inventory, space and quality without overloading the project with measurements.
| KPI | Measurement rule | Planning meaning |
|---|---|---|
| Transport intensity | Material quantity or moves per relation and reference period | Prioritises strong relations and critical source-sink pairs |
| Transport effort | Transport quantity × distance in a documented unit | Makes the spatial effect of layout options comparable |
| Throughput and service level | Good output or demand fulfilled on time per period | Tests whether performance and supply meet the target programme |
| Inventory and coverage | Average or maximum inventory related to consumption | Connects working capital, decoupling and space demand |
| Waiting and lead time | Time from a defined start event to a defined end event | Reveals queues, poor synchronisation and control losses |
| Utilisation and robustness | Occupancy plus performance under peak load or disruption | Prevents maximum calculated utilisation from being mistaken for a stable optimum |
KPI selection follows the project's target system. The German glossary article on operational logistics KPIs defined in VDI 4490 explains why metrics should be process-oriented rather than collected as an unrelated dashboard list.
Methods and tools matched to the decision
We combine methods instead of treating one tool as the standard answer. Methods include from-to matrices, Sankey and spaghetti diagrams, value-stream and layout analysis, ABC/XYZ segmentation, process data analysis, CAD and digital factory models, and discrete-event simulation.
Depending on the task, we use tools such as visTABLE, Siemens Plant Simulation, CAD systems and matflow. Digital as-built data can be integrated through 3D scanning and digital factory models.
Results and deliverables
- Agreed system boundary, data basis and documented assumptions
- Material flow map with sources, sinks, volumes, routes and transport intensity
- Bottleneck, cause and space analysis with prioritized action areas
- Where required: a validated simulation model with defined scenarios and KPIs
- Evaluated process, layout and technology options
- Preferred option with an economic framework and implementation roadmap
Project process from question to implementation
- Clarify objectives and boundaries: decision, KPIs, scope and scenarios are defined.
- Capture and validate data: system data, layout, processes and observations form one shared basis.
- Analyze material flow: structures, bottlenecks, waste and causes become transparent.
- Develop and validate options: solutions are calculated, evaluated and simulated where dynamics matter.
- Specify the preferred option: measures, responsibilities, dependencies and next planning steps are defined.
Practical example: from material flow to a better layout
Within a factory planning project, material flow assessment provides the quantitative basis for comparing layout options by performance and robustness as well as space.
Our layout optimization use case for an anonymized compressor manufacturer shows how routings, production orders, movement data and bills of materials support spatial improvements. The learning-factory article Material flow analysis: comparing methods provides a concise methods guide.
Definitions remain documented separately in the glossary: material flow planning, material flow optimization and material flow simulation.