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What is a supply chain digital twin?
Digital twins offer more than other simulation models
- Ideas to transform raw data into actionable supply chain twins?
Enterprises have begun to create digital twins of different aspects of supply chains for simulation purposes. Multiple approaches to supply chain twins show greater value in identifying supply chain bottlenecks, meeting sustainability, and improving efficiency.
What is a supply chain digital twin?
A supply chain digital twin is a detailed simulation model of the actual supply chain using real-time data and snapshots to forecast supply chain dynamics. It helps analysts understand a supply chain’s behavior, predict abnormal situations, and develop an action plan.
The supply chain digital twin is used to:
- Discover bottlenecks,
- Understand supply chain behavior and dynamics,
- Monitor risk and testing contingencies,
- Test supply chain development and design changes,
- Plan transportation,
- Analyze cost to serve and cash to serve, and
- Forecast and test operations over a period of time.
Digital twins can be used to create digital copies of product lines, warehouse inventory, manufacturing systems, and other business processes. Creating digital twins allows you to analyze the supply chain by extracting data to predict demand and supply. Ultimately, this allows your business to streamline its operations.
Orderhive offers integrated enterprise resource planning (ERP) solutions for databases. Digital copies mirror supply chain touchpoints and help streamline business operations by pinpointing exact processes.
Implementing digital twin technology with ongoing supply chain operations and touchpoints helps companies pivot to manage or avert hiccups. However, businesses face various challenges in transforming raw supply chain data into digital twins. Twins were challenging to implement as supply chain segments were separated and data were siloed – meaning that data or information can’t or isn’t shared between departments. Thus, there is a need for predictive analytics tools and technology to capture and process data and drive business insights important to the success of digital twins.
Automated supply chain management tools and cloud-based systems have made digital twins more useful in predicting trends, minimizing quality faults, managing warehouse inventory, and integrating seamless flow of data.
We can expect to see digital twins evolve alongside artificial intelligence (AI) enabled modeling and internet of technology (IoT). For instance, sensors and IoT devices located throughout the supply chain expedite the use of data to make predictions regarding supply chain trends, and AI makes the system more powerful.
With the advancement of AI-enabled models, manufacturers can utilize data insights and create digital twin technology to streamline operations, cut down on waste, and predict inventory.
Digital twins offer more than other simulation models
Digital twins are superior to other simulation models.
- A supply chain digital twin is a detailed model that allows users to analyze supply chain interactions on any scale. Digital twins enable functions like financial predictions, SKU flows, scenario testing, and demand variability identification.
- Digital twin technology uses live information feeds like incoming shipment schedules, inventory levels, and vehicle locations to assess the supply chain’s current state for updated forecasts, e.g., quantifying the bullwhip effect from lost inventory.
- They provide configurable notifications, alerts, or alarms to alert you to problem situations. For example, service levels falling below set thresholds.
- Digital twin technology allows users to define custom actions based on preset triggers. For example, when stock falls below a certain threshold, a predefined trigger automates the reorder process
- The technology allows you to develop action plans to address abnormal situations and test those plans to ensure efficacy.
- A digital supply chain twin is part of something bigger — the simulation model integrates databases and business intelligence tools within the surrounding IT environment.
Read more: What is supplier relationship management?
Ideas to transform raw data into actionable supply chain twins?
1. Begin with digital threads
It’s essential to include the digital thread while planning a digital twin. It must work in concert with decision-making and practical analysis within the supply chain.
The digital twin represents the configuration of all assets, including manufacturing and supplier facilities, warehouses, trucks, ships, and planes. Digital twins link to digital thread data including location status, inventory status, and asset conditions.
Developing the backbone for a digital thread helps organizations weave together connections, relationships, and decision trees. Using digital threads creates a complete view that enables users to gain a full understanding of specific supply chains, and their status and actions to keep them operationally efficient.
2. Move from tables to graphs
Most enterprise apps capture data and put it into tables. The links or relationships between objects represented by the data are disclosed upon execution of a query to join the data. This can be costly. As the query grows in complexity and scope, the overhead makes queries across any reasonably sized digital twin too slow to be useful in an operational context — taking hours or even days.
Businesses like luxury vehicle manufacturer Jaguar Land Rover, discovered a way to get around this by building the digital twin using a graph database.
When Jaguar Land Rover tried to build a model for its manufacturing supply chain using structured query language (SQL), testing showed that it took three weeks to run one query to view the supply chain for one car model over six months. When they built the model in TigerGraph, the same query took 45 minutes. Further refinements decreased that to mere seconds.
A graph database approach helped them visualize relationships between business areas that previously existed in silos and identify critical paths, trace processes, and components in greater detail than ever before. This allowed the company to explore business scenarios in a sandbox, i.e., a safe environment.
3. Keep pace with data drift
One challenge for digital twins is data drift. Teams must ensure the data collected for the digital twin accurately and consistently represents the actual conditions of the physical twin.
Having the best quality data is vital to realize the full value of a digital twin. It is slowly getting better as teams move toward streaming analytics, but the practice isn’t yet prevalent. Additionally, it’s imperative to understand the data being collected. Without proper behavioral understanding, the interpretations run the risk of being off-base, leading to poor decision-making.
Companies must build competency to understand how data drift can occur across the supply chain and develop countermeasures to minimize the impact across each aspect of the supply chain, such as pricing and route management.
4. Bridge data silos
Digital systems used for supply chain management including warehouse management systems (WMS) and ERP systems were not developed to connect or share information. This is further compounded by lack of data standardization. Data is siloed across the supply chain.
Newer companies are working to solve this problem in two ways: aggregating existing data or preparing new data sources.
For example, data is aggregated from antiquated systems to make them operational. Then, unique data sources are built to generate accurate, real-time data. The more and more accurate your real-time data is, the better your digital twin is.
5. Improving 3D capture
Supply chain twins focus on modeling relationships between distributors and suppliers. Improved 3D models representing processes, products, and facilities are needed to improve interpretation.
Efforts in photogrammetry attempt to tackle the issue using automation. However, the technology must evolve before using it in complex supply chain applications.
6. Include subject-matter experts
Integrating appropriate systems to ensure a robust digital twin configuration takes a concerted effort. Subject-matter experts are needed to facilitate the configuration of a digital twin. Such experts understand how real-world processes integrate into the flow among ERP, supplier, and third-party logistics systems through point-of-sale systems.
7. Leverage the cloud
Cloud providers are beginning to provide a staging ground to consolidate supply chain data across business apps and partners. For instance, Google Supply Chain Twin and Pulse assembles a digital representation of your supply chain to create end-to-end visibility including triggered event management, analytics, and collaboration among teams. Google reports a 95% reduction in analytics processing time since implementing their supply chain twin.
Until now, large companies exchanged data only based on legacy technologies like EDI. A cloud-based approach improves data sharing across partners. It permits contextual data based on risk, weather, and customer sentiment for deeper insights into operations.
Supply chain leaders are also beginning to take advantage of Microsoft’s digital twin integrations. Microsoft Azure also offers tools that make it easy to combine real-time sensory data using an IoT Hub to visualize supply chain elements with an IoT Central.
Blue Yonder’s SaaS solutions for the supply chain are built on the Microsoft Azure Cloud, and growing rapidly across the globe. The company asserts that supply chain planning in the cloud is the new normal in the supply chain software industry.
Linking an ecosystem of data providers requires time and effort to implement. However, once it’s established, the automated linkages keep operating successfully without excessive human effort. Trends in this vein are likely to continue.
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