Diagramming for Supply Chain Management

Visualize and optimize supply chains—with graphs!

Using network flows, Sankey diagrams, and gantt charts, yFiles can visually model all flows in a supply chain. This works for lean production, just in time and just in sequence production where the goal is minimizing time, labor, and materials.

Network flow algorithms optimize resource movement in supply chains by visualizing capacities and identifying bottlenecks using node-based hubs.

Network flow algorithms apply to directed networks in which edges have certain capacities and a flow moves from source to sink nodes. (Source code demo)

Network flow algorithms can help ensure that resources flow from one location to another within a network as efficiently as possible. This visualization through time and space in the navigating graph (see the network flow demo) shows the capacity of each path, which can be indicated by its thickness in the graph. The nodes, which represent hubs, can help pinpoint potential bottlenecks. Depending on the case, the manipulation of the nodes can also be used to manage and control the supply chain.

Sankey diagrams visualize flow with edge thickness proportional to quantity, aiding lean production by ensuring timely resource availability and pinpointing complex production issues.

Sankey diagrams are used for visualizing flow information in which the thickness of the edges is proportional The data consists of tasks which define the horizontal lanes and activities, each of which is assigned to a task. (Source code demo)

Sankey diagrams are used for visualizing flow information, with the thickness of the edges proportional to the flow quantity. Here, ingoing and outgoing data points are visualized over time, facilitating the identification of problems in the flow. For example, in a lean production environment, managers can use Sankey diagrams to ensure that parts and resources are available precisely when and where they’re needed. The more complex the production flow gets, the more helpful visualization becomes in pinpointing potential problems.

Gantt charts visualize activities with start and end dates, highlighting dependencies crucial for supply chain management. They link input/output data to timestamps and geodata, ensuring quality and compliance in production processes.

The Gantt Chart demo shows how to create a Gantt component from JSON data. This demo uses Luxon to parse, manipulate and calculate dates. (Source code demo) In short, yFiles offers a range of powerful visualization techniques tailored for supply chain management. Through features like network flows, you can model the movement of resources within your supply chain network, optimizing paths and minimizing bottlenecks. Sankey diagrams provide a clear visualization of flow information, allowing for the identification of inefficiencies and areas for improvement. Additionally, Gantt charts offer a comprehensive view of activities with start and end dates, enabling effective scheduling and coordination of tasks across the supply chain.

Gantt charts can visualize an activity that has a start and end date, and optionally a lead time and follow-up time. Also, an activity can depend on other activities, which is very helpful in representing SCM dependencies, and how to resolve them. Gantt charts and the Sankey diagrams can link input/output volume to timestamps and geodata in the production process. This can assist in ensuring quality and compliance for a complete bill of materials (BOM), where a comprehensive inventory of materials and goods used in production is required.
In short, yFiles offers a range of powerful visualization techniques tailored for supply chain management. Through features like network flows, you can model the movement of resources within your supply chain network, optimizing paths and minimizing bottlenecks. Sankey diagrams provide a clear visualization of flow information, allowing for the identification of inefficiencies and areas for improvement. Additionally, Gantt charts offer a comprehensive view of activities with start and end dates, enabling effective scheduling and coordination of tasks across the supply chain.

As supply chains grow in complexity, simulation becomes an increasingly valuable tool. By simulating various network and flow scenarios, teams can test and compare alternatives prior to investing in production facilities. Equally, individual variables or nodes in an existing network can be isolated in a simulation to determine the effects on the rest of the supply chain. The process mining and interactive maps features can be used to simulate upstream and downstream supply chain process issues. For example, when filtering for one node, the heat map in the process mining tool shows which elements are nearing capacity as a result. Interactive maps can also visualize relations in space as well as time – for example, when aggregating geodata about the location or origin of products.

The Interactive Map demo integrates a GraphComponent with Leaflet, using geo-coordinates to place nodes accurately on the map.

The Interactive Map demo shows how to integrate a GraphComponent with Leaflet. The GraphComponent is included in a custom Leaflet Layer. The placement of the nodes uses geo-coordinates to get the correct locations on the map. (Source code demo)

The demo illustrates animated process mining visualization, depicting steps in a processing pipeline and entity movement through it.

This Process Mining Visualization demo shows how to create an animated visualization of a process flow. | The diagram shows the various steps in a processing pipeline and how entities move through the pipeline. (Source code demo)

Monitoring the supply chain network allows for real-time visibility and the ability to identify weaknesses and optimize flow dynamics, enhancing overall business outcomes.

The Network Monitoring Demo shows a basic network monitoring tool. You can watch the traffic flowing through the network and even influence the network by deactivating nodes. (Source code demo)

Analysis of the entire supply chain and network is perhaps the most impactful element of SCM. By breaking down the chain into phases and stages, from the acquisition of raw materials all the way through to delivery of a product to customers, it’s possible to find inefficiencies and improve the overall business outcomes. In the network monitoring graph, you can watch traffic flowing through the network and even influence the network by deactivating nodes. This ability to influence the data flow and see what happens when a node fails, is helpful in identifying points of weakness and failure. It’s also possible to connect a graph with real-time processes to manage SCM in real time, not just to perform analysis.

Neighborhood view graphs help analyze root causes and dependencies in supply chains, aiding troubleshooting and efficiency prediction.

The Neighborhood View Demo shows the neighborhood of the selected node alongside the graph. There are different neighborhood types like predecessors, successors, and folders. (Source code demo)

The neighborhood view graph can be used to show root cause analysis (predecessors) and dependency analysis (successors) of various phases and actors in a supply chain. This feature can be used for troubleshooting, e.g., product recall (based on units such as a production batch) or understanding and even predicting areas of inefficiency.

Critical path analysis in project and supply chain management identifies essential tasks and durations for success. yFiles' demo visualizes complex processes and critical paths effectively.

The Critical Path Analysis (CPA) Demo shows how to perform critical path analysis in project management with yFiles. (Source code demo)

Critical path analysis is used in project and supply chain management to identify the individual tasks – and the time required for those tasks – that must be completed if a project or production process is to be successful. The critical path analysis demo shows how yFiles allows you to visualize all of the components and relations in a complex process and to map respective critical paths.

Supply chain optimization involves enhancing the efficiency and effectiveness of all activities across the supply chain, from procurement and production to transportation and delivery. It's essential for reducing costs, avoiding delays, increasing competitiveness, and swiftly responding to market changes. This is achieved by analyzing supply chain data, visualizing complex networks, and using smart tools for better decision-making, helping companies identify bottlenecks, streamline operations, and adapt to disruptions.

Here are three examples of how supply chain optimization can be put into practice:

The Fraud Detection Demo visualizes and optimizes supply chain management, identifying suppliers and potential fraudsters with a main graph display.

Fraud affects many companies worldwide causing economic loss and liability issues. This demo shows how yFiles for HTML can be used for detecting fraud cases in time-dependent data. (Source code demo)

The Fraud Detection Demo has three powerful components that can be applied to visualizing and optimizing SCM. The main visualization graph allows you to see related data points as nodes and rings which could be suppliers in a procurement process. (In the demo, the rings designate fraudsters in a financial services scenario.) Second, there is a timeline component, which shows when an event or incident was created or occurred. The final view combines the details of each ring with its timeline. You can show or hide specific time periods using the sliding time window. This selective data visualization is useful for analyzing details in a process that has a time component. For example, in an SCM scenario, you can visualize dependencies among the flow of parts and the sequence of parts suppliers in a production chain over time.

The Large Graph Aggregation Demo visualizes and filters supply chain data using interactive mapping for detailed analysis.

The Large Graph Aggregation Demo shows how to use the smart NodeAggregation algorithm for drill down exploration of a large graph. (Source code demo)

In the Large Graph Aggregation Demo, it’s possible to take huge amounts of data from complex supply chains and aggregate the data, e.g., by source country of products, or by continent or city. Thus, you can visualize on a map (using the interactive map tool) and zoom down to visualize specific nodes of a supply chain. Depending on the variables you are interested in analyzing, you can filter by narrow criteria to understand your supply chain data better. You can also modify the parameters of various processes to represent objects in detail and find what’s important to the process, such as pinpointing a machine capacity issue or a lack of raw materials.

The interactive Aggregation Demo illustrates graph analysis by aggregating nodes, making it easier to focus on specific areas of complex supply chain networks.

The Interactive Aggregation Demo shows how to analyze a graph by aggregating groups of nodes. (Source code demo)

The Interactive Aggregation Demo shows how to analyze a graph by aggregating nodes. As supply chains are often huge networks with related and unrelated nodes, with the mechanics of aggregation in this type of visualization (expand and collapse) it is easier to focus on individual areas of the supply chain. Areas that are not so relevant at the moment of observation can be "hidden" without disappearing completely.