Menu
Menu
Digital twins and simulation have emerged as powerful tools that significantly improve supply chain operations. They enable supply chain managers to monitor and predict real-time status, test various strategies in a low-stakes environment, and optimize their operations. This article will explore the importance of digital twins and simulation in supply chain management and how they can create a competitive advantage.
Before we discuss the topic, it is important to define a digital twin and how it differs from simulation. According to ChatGPT, a Digital Twin is a virtual replica of a physical entity that mirrors its real-time status, working condition, or position. It enables real-time monitoring, diagnostics, and prognostics. It’s like having a living model of the operation or network that you can explore in-depth without interfering with operations.
Simulation is more strategic or tactical. It is a model of a real system upon which experiments can be conducted to understand its behavior or evaluate various operating approaches. It doesn’t require a physical counterpart or real-time data to function.
Their similarity lies in their purpose: both are used to understand, predict, and optimize systems to achieve better outcomes. However, while simulations are typically used for analysis and testing in the design phase, digital twins serve throughout the entire lifecycle of their physical counterparts. They complement each other, with simulations often feeding into the creation and operation of digital twins. Simulation in Supply Chain Management predates Digital Twins predominantly because sensors and computing power have only recently become economically viable.
There are many applications of simulation in supply chain management.
The optimal location for warehouses and plants is a significant cost and service driver in supply chain management. Simulation has been used to determine the best location for these facilities, considering transportation costs, labor costs, proximity to customers, and access to raw materials. By simulating different scenarios, such as fuel-cost changes or volume growth, supply chain managers can evaluate the impact of other variables on the location decision and make informed decisions that will optimize their supply chain operations and mitigate risk.
A supply chain simulation can show the behavior of a logistics network over time. Each successive period is dependent on the previous one. The logical rules of a supply chain are represented in a simulation model and then executed over time, making the simulation dynamic. This helps supply chain managers understand how the logistics network will behave in different scenarios and conditions. They can use this information to optimize operations and prepare for possible eventualities.
Supply chain simulations can help design a robust network to handle demand fluctuations. For example, Monte Carlo simulations can be used to simulate various demand or fuel cost scenarios.
Using simulation to provide an objective function, mathematical programming can be overlaid to determine the best options, such as which warehouses to open or close. Additionally, the new field of reinforcement learning in artificial intelligence can use simulation as a guide for determining how to come up with the best solution. This is just an extension of testing many scenarios in an automated way.
Supply chain simulations can also be used as an educational tool. For example, the Supply Chain Game is an online supply network simulator where students are divided into teams and compete against each other in assignments.
Digital twins are slowly taking hold. Forty years ago, companies started to model industrial processes. On a computer screen, plant staff could see a schematic of a large and complex operation showing all the pieces of equipment and their operational status, as well as the real-time work-in-process inventories waiting to be processed. It was and continues to be, an excellent means of bottleneck identification. Since its early days, this visibility has expanded to other parts of the supply chain.
Digital twins can be used to map transportation routes and optimize logistics. This is an extension of what we have on our phones today—Waze constantly updates the fastest way home based on traffic bottlenecks. Waste Management, the large trash hauler and recycler, has recently implemented this technology. They call it “Waze on Steroids” and are recognizing significant productivity improvements.
Some believe that digital twins can help with planning and forecasting. The concept is that monitoring the environment or sales can trigger action. For example:
Some warehouse orchestration programs, like AutoScheduler, can adjust shipment times or reallocate inventory by linking expected truck arrival time data with what is happening in the warehouse. The expected truck arrival times can be gleaned from companies like FourKites or Project44. The same is true in manufacturing, where the failure of a second or third-tier supplier can be mitigated. In both these cases, the reaction is generally not real-time but updated on a regular but short cadence – for example, every 30 minutes.
The line is, “Digital twins with optimization can be used to rapidly scale capacity, increase resilience, and drive more efficient operations.” The reality is again that this needs to run on a short-cycle-time cadence, not real-time. And that makes sense because events are generally discrete. Applying optimization requires a steady state or a defined probability distribution.
Consider supply planning. Replenishment plans have traditionally been created without concern for cost or operational constraints. In general, they are quite volatile. Here is a shameless plug: LevelLoad software, an optimizing digital twin, has solved this problem. To manage the volatility mentioned above and work inside real-world constraints, LevelLoad gathers a substantial lift of data and simultaneously optimizes all the flows in the network. The word simultaneously is important here. Adjusting volume on any lane in a distribution network generally impacts other lanes. For example, if a receiving warehouse fills up, the manufacturing site that supplies that location must push the volume to another warehouse to manage its own space constraint.
Optimizing digital twins comes with its own set of challenges. In addition to complexity, these include:
They can significantly improve supply chain operations. They enable supply chain managers to monitor and predict real-time status, test various strategies in a low-stakes environment, and optimize their operations. While simulation is used for analysis and testing in the design phase, digital twins serve throughout the entire lifecycle of their physical counterparts.
To achieve the full potential of digital twins and simulation in supply chain management, supply chain managers must take a comprehensive approach to implementing and optimizing digital twins or switch to simulation—both with or without optimization. This approach should involve strategic planning, skilled personnel, and robust data management practices. By adopting digital twins and simulation, companies can create a competitive advantage in the industry and improve their operations.