Article originally published on medium.
A digital twin is a digital replica of a physical object or system.
A Supply Chain digital twin is a computer model that represents various components and processes involved in the supply chain such as warehouses, transportation networks, and production facilities.
In this article, we will try to take a step back and build a Supply Chain Digital Twin that will represent your complete end-to-end operations from production to store delivery.
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What is a Supply Chain Digital Twin?
A goal-oriented network
A supply chain is a goal-oriented network of processes and stock points used to deliver goods and services to customers.
To create a digital twin of a supply chain using Python, you would first need to define the various components and processes that make up the supply chain.
Data & parameters
Next, you would need to collect data on the various components and processes of the supply chain, such as
- locations and capacities of warehouses
- routes and capacities of transportation networks
- production rates of production facilities
- customer and store demand
This data could be stored in databases or other data storage systems or directly connected to your warehouse management systems and ERP.
Once you have the data on the supply chain components and processes, you can use Python to create algorithms and simulations that replicate the behavior of the supply chain.
This could involve using optimization algorithms to
- determine the most efficient routes (output) to deliver stores using replenishment orders (input) coming from the store model
- improve the picking processes (output) in your warehouse to prepare stores replenishment orders (input)
- schedule your production (output) based on demand forecasts built with the stores’ sales historical data (input)
Examples of building blocks
Overall, creating a supply chain digital twin with Python would involve a combination of data collection and analysis, algorithm development, and simulation modelling.
It would likely require a deep understanding of supply chain management and experience with Python programming.
Let us take three examples of elementary blocks built with Python.
How to simulate warehouse operations?
We define a Warehouse class that has attributes for the warehouse’s location, capacity, and inventory.
The add_inventory and remove_inventory methods can be used to add and remove items from the warehouse’s inventory, respectively.
This is a simple example, you can add additional attributes and methods by considering processes productivity, warehouse costs structure, workforce management or picking processes to improve the model.
How to simulate road transportation?
We define a Truck class that has attributes for the truck’s location, capacity, and load.
The move_to method can be used to move the truck to a new location, and the load_cargo and unload_cargo methods can be used to load and unload cargo, respectively.
How to simulate store inventory management?
We define a Store class that has attributes for the store’s location and inventory.
The place_order method can be used to place an order for a given item and quantity.
- If the store has enough of the item in inventory, the order will be fulfilled and the inventory will be updated accordingly.
- Otherwise, an error message will be printed.
Connect the blocks
Now that you have built your elementary independent blocks you need to connect them
- Your store is sending replenishment orders to the warehouse management system using the ERP
- Your warehouse prepares the orders, packs the items and put them on pallets
- Pallets are loaded in a truck
- The truck delivers the pallets to the store
And add external parameters such as customer demand or raw materials supply constraints.
Supply Chain Analytics
Now that you have the replica of your supply chain, you can play with the parameters and use data to perform
Descriptive Analytics: monitor your processes with dashboards and visuals
- Road Transportation Network Visualization, Samir Saci
- Deploy Logistics Operational Dashboards using DataPane, Samir Saci
Diagnostic Analytics: automate root cause analysis processes
- Logistic Performance Management Using Data Analytics, Samir Saci
- Lead Times Variability and Supply Chain Resilience, Samir Saci
Predictive and Prescriptive Analytics: add forecasting and optimization models into your digital twin to improve ordering rules or planning
- Machine Learning for Retail Sales Forecasting — Features Engineering, Samir Saci
- How To: Machine Learning-Driven Demand Forecasting, Nicolas Vandeput
- Production Fixed Horizon Planning with Python, Samir Saci
- Optimize Warehouse Value Added Services with Python, Samir Saci
- Improve Warehouse Productivity using Pathfinding Algorithm with Python, Samir Saci
- Machine Learning for Store Delivery Scheduling, Samir Saci
For more details,
Your digital twin can be seen as a core model in which you can add models that solve specific issues.