Material flow graph visualisation

A graph visualisation draws a production system as a network. Typically:

• nodes stand for machines, workplaces, stores or production areas
• edges stand for the material movements between them
• line weight stands for the quantity moved or how often it moves
• colour stands for different materials, products or processes

What comes out of it is a compact picture of the whole material flow network. Instead of seeing only where the machines stand, you see how strongly the areas are actually tied to one another.

A factory layout shows the spatial arrangement of machines and workplaces. What it usually leaves out is how intensively those areas deal with each other.

Two machines may stand far apart. If several hundred journeys run between them every day, that is where the potential lies, and a floor plan shows the relation only faintly. A graph makes it plain at once:

• which areas are tied most strongly to one another
• where the large quantities move
• which nodes hold a central place in the system
• where material is carried back and forth needlessly
• which areas depend on which

It adds to layout planning the one perspective the floor plan cannot supply.

Large production systems hold hundreds of machines, storage places and movements. As a table, that turns unreadable quickly.

A graph lowers the complexity by showing relations instead of rows. Planners see at a glance which areas are strongly connected. That helps above all in networks with:

• many product variants
• differing production routes
• numerous machines
• intermediate stores
• several material supply areas

The picture makes patterns easier to spot.

A second benefit is that it exposes the important junctions. An area through which many flows run holds a central place in the system. Such areas can:

• turn into bottlenecks
• carry a heavy transport load
• matter especially for layout planning
• affect the whole plant when they fail

Network analysis can also score those nodes numerically, through degree, centrality or density. That way the material flow analysis is supported not only visually but quantitatively.

Transport as a rule adds no value of its own to production. It takes:

• time
• people
• vehicles
• traffic area
• energy

The awkward cases are return journeys and material moved repeatedly between areas that lie far apart. A graph brings those relations out clearly. Where very many journeys run between two areas, it is worth asking whether they belong closer together.

Material flow analysis and space planning together are stronger than either alone. The graph shows which areas are functionally tied; the layout shows where they physically lie. Put side by side, they answer:

• which machines should stand closer together?
• which areas cause the longest journeys?
• where does traffic cross?
• which areas should be considered as one?
• where could the floor be reorganised?

The graph thereby gives layout decisions a footing in data.

One of the more rewarding uses is spotting clusters: groups of machines or areas that mostly exchange material among themselves.

If five machines mainly trade with one another, it may be worth bringing them together on the floor. Such clusters hint at possible:

• manufacturing islands
• production cells
• machine clusters
• assembly areas

Graph-based analysis therefore also serves the design of a new layout.

Graphs suit the assessment of alternatives just as well. Layouts can be laid side by side and examined for:

• how the transport relations change
• whether routes get shorter
• whether particular areas carry more
• whether new bottlenecks appear
• how robust the system is against change

For a rebuild or an extension in particular, variants can be judged more objectively this way.

Modern production systems produce a great deal of data. Material movements can be read out of, among others:

• ERP systems
• MES systems
• warehouse management systems
• tugger train control
• transport management systems
• sensors

That data can be turned into a material flow network. The graph then prepares it so that planners and decision makers reach conclusions faster. It thus sits between production data and factory planning.

A considerable benefit is that decisions need rest less on personal judgement. Instead of working from experience alone, real material flow data can be evaluated.

A machine is then not moved because its position seems awkward, but because the data shows particularly intense flows towards another area. That traceability improves the quality of planning decisions markedly.

The main points at a glance:

• a fast overview of complex material flow networks
• strong transport relations easy to spot
• central machines and areas identified
• needless transport made visible
• support for space and layout planning
• possible production clusters revealed
• layout variants easier to compare
• decisions founded on data
• complex relations easy to communicate

In large and complex production systems in particular, the gain is considerable.

Material flows are networks of machines, workplaces, storage areas and movements. A graph makes those relations visible and helps a complex production structure be understood faster.

It shows not only where material is carried, but which areas are most strongly tied and where the potential lies. Combined with layout planning, material flow data and further figures, it becomes a foundation for a more efficient production system.