Engineering Innovation in
ENG4PHARMA
Our team of experts created a simulation model to simulate and analyze supply chain network behavior to cope with demand.
Approach & Solution
Approach
The customer is a global pharma company and one of the top 20 in the world by revenue. They developed a vaccine to prevent dengue, which is currently undergoing clinical trials and, if approved, they must be ready for large-scale production and distribution.
The customer's current capability in long range supply capacity forecasting is built around a classical spreadsheet approach, which takes into consideration more than 300 assumption inputs across a 10+ year time frame. Such analytical model doesn’t allow for granular analysis.
Our team created a simulation model to simulate the supply chain structure and provide an analysis tool that allows the company to make crucial network capacity related decisions with a higher level of confidence.
The customer's current capability in long range supply capacity forecasting is built around a classical spreadsheet approach, which takes into consideration more than 300 assumption inputs across a 10+ year time frame. Such analytical model doesn’t allow for granular analysis.
Our team created a simulation model to simulate the supply chain structure and provide an analysis tool that allows the company to make crucial network capacity related decisions with a higher level of confidence.
Solution
We are designing and implementing a supply chain simulation model combining both agent-based and discrete event modeling approaches to maximize flexibility and provide a scenario analysis tool.
Every supply chain production step or inventory is modeled as a building block, instantiable multiple times to handle with capacity expansion.
The overall supply chain network is modeled by joining the scenario building blocks in a modular approach. In this way, the user easily defines production and inventory parameters for the building blocks, together with the forecasted market demand over the simulated time.
Every supply chain production step or inventory is modeled as a building block, instantiable multiple times to handle with capacity expansion.
The overall supply chain network is modeled by joining the scenario building blocks in a modular approach. In this way, the user easily defines production and inventory parameters for the building blocks, together with the forecasted market demand over the simulated time.
Results
Decision making support
Higher Supply Chain visibility (network capacity utilization, inventory levels)
Flexibility of the model, which can be extended in the future
Enabling Technologies
Digital Twin
Project Team
Industries eXcellence
Industry & Services