Key challenges
The client manages a high-velocity dynamic supply chain, where the speed at which products move through the network directly impacts shelf life and waste reduction.
Numerous constraints and a continually shifting product portfolio, made it challenging for the client to efficiently optimize the flow paths within the network.
TMX was engaged to assist in three key areas:
Optimization of network flow paths: Our client required a solution to optimize the flow paths within their dynamic supply chain, reducing product travel time and minimizing shelf life and product waste.
Supplier-DC connectivity: They needed assistance in identifying the most efficient connections between suppliers and distribution centers to enhance overall supply chain performance.
Store servicing: The client sought a method to determine the most effective allocation of stores to each distribution center, ensuring efficient product delivery and waste reduction.
Our approach
- Simulation model development: TMX commenced with the development and validation of a simulation model, faithfully replicating client's physical supply chain. This enabled a comprehensive visual representation and swift evaluation of potential supply chain adjustments. Subsequently, scenario tests were conducted to enhance supply chain performance.
- Scenario testing: TMX conducted a series of scenario tests to explore different network configurations and supply chain management strategies.
We examined the impact of automation on both service levels and operational costs.
Utilized the data and insights gained from these scenarios to inform decision-making and enhance the supply chain's performance.
The results
The simulation tool provided substantial advantages to our client:
- Comprehensive visualization: The simulation allowed for a detailed view of the entire network flow, providing the ability to quickly assess the potential impacts of supply chain modifications before they were implemented in the real world.
- Efficiency in scenario testing: Unlike the labor-intensive models used in the past, this tool offered a significant advantage by enabling the rapid simulation of scenarios and the prompt assessment of their outcomes. This efficiency was a notable improvement over historical method.