Digital twins are becoming an ever-increasingly popular choice to optimise operations within certain sectors of the supply chain. A digital twin is a digital replica of a physical system, facility, or route for example a warehouse’s digital twin is created using data from sensors as well as other sources.
In the context of the supply chain, a digital twin can be a virtual model of a distribution centre, warehouse, or other supply chain assets such as transport routes these models are backed up by the data associated with their real-world counterparts.
The digital twin allows you to visually monitor in real time what events are playing out on the ground and optimise operations or simulate specific conditions using the data associated with the real-world counterpart.
Below we will explore how to set up a digital twin, the benefits of digital twins in the context of various sectors of the supply chain and discuss the five ways in which digital twins can be useful to supply chain management.
The creation process of the digital twin.
Now that we have a better idea of what a digital twin is let’s move on and analyse the steps involved in creating a digital twin in the context of warehousing.
1. Defining your core objectives
The very first step to setting up your digital twin should be to define the goals and objectives of the project. To do this, you will need to identify key performance parameters otherwise known as KPI. These KPIs are essential as the digital twin will use the KPI to monitor and measure events and create models. For example, in the case of a warehouse, the KPI can be labour productivity, inventory levels, order fulfilment time, or other metrics crucial to the specific sector.
2. Data gathering
Once our KPIs have been identified the next step is to gather the necessary data to create the digital twin in the case of the warehouse this data may include location tracking data, RFID tags, and data from various sensors.
3. Building on your digital twin’s model
Possibly one of the most important steps in this process is building the model for the digital twin in the case of a warehouse this includes building a 3D model of the warehouse. The completeness of this model combined with the quality and detail of the real-world associated data you have collected will determine how accurately the digital twin can optimise operations.
While having a large amount of high-quality data is important, it is equally important to ask yourself based on the objectives you have set up, how much data you need, and how detailed the data needs to be.
For example, when optimising transport routes, it is unnecessary to scan every truck that enters and exits your facility. Instead, you can use the GPS tracking data of your vehicles to monitor the time taken for each route. This data can be utilised by the digital twin to make predictions about the best-suited route for achieving your objectives based on the initial goals and associated data.
4. Validating your digital twin.
The model should accurately represent the physical characteristics, behaviour, and interactions of the real-world object or system. This requires defining the model parameters, such as dimensions, material properties, and operating conditions. For example, compare the digital twin’s data against your company’s historical data.
What this means is you should be using the digital twin at this point to run simulations for example simulate demand suddenly increasing and what effects this would have cross-reference the results with data you have from experience of a similar event to validate if the results are within an expected range or not.
This can be incredibly helpful in identifying problems with the data and model and understanding why there may be a major difference in results. This can be used to fix the root cause of the issue before your business takes the digital twin’s predictions seriously.
5. keep your digital twin well-fed.
By this stage, your digital twin should be running, and you will have confirmed the validity of the results it’s producing. Live data should be consistently flowing to the digital twin model at this point so that live results can be monitored, and any problems can be quickly addressed. Now that you have a live overview of your facility or system you can keep track of the situation on the ground and optimise operations with the assistance of your new digital twin.
Benefits to transportation
In the case of transportation, you can use your digital twin to optimise transportation routes as well as visualise these routes, in addition, your digital twin can also be used to simulate disruptions on routes and visually assist in the planning of alternative routes should these simulated situations take place in future.
You can see a real-world example of route optimisation using a digital twin in both Singapore and Shanghai’s digital twin systems where it’s used to optimise the flow of traffic as well as energy consumption.
Warehousing
In the case of the warehousing sector, your digital twin can be used most effectively in several ways. The first is to optimise energy efficiency as your digital twin can inform you if a particular system is using a large volume of electricity or if unnecessary electricity is being used in a certain sector of the warehouse allowing the warehouse to cut down on wasted electricity costs in the future. Preventing supply chain disruptions is another big advantage of a digital twin as discussed, the digital twin can optimise route planning however it is also capable of optimising the flow of movement and goods within the warehouse to insure items get from point A to point B without disruption.
Supply chain management
In the context of supply chain management not only does the digital twin allow various sectors to optimise routes, inventory, product movement, and energy efficiency it also allows its users to run simulations to make predictions on future issues or worst-case scenarios that could cause disruptions to the overall success of the supply chain.
These scenarios are often referred to as stress tests, and with the assistance of the digital twin different sectors of the supply chain can run simulated stress tests in the context of their specific conditions to put a plan in place to mitigate a multitude of situations. This makes digital twins a versatile tool for the various sectors wishing to make data-driven decisions quickly in a more reliable way.
Conclusion
Digital twins provide a wide variety of benefits to the supply chain, the most beneficial of which is the optimisation of the quality of the supply chain’s efficiency. While digital twins have yet to see a high adoption rate moving into the future, they could become a key part of day-to-day operations across many sectors of the overall supply chain network.