Our client Kingspan, a global leader in high-performance building insulation and district heating solutions, plans to construct a greenfield manufacturing site in Europe. While detailed plans covered most aspects of the project, the layout had not been stress-tested for the high volume of freight traffic expected to move through the site.
Story
With more than 500 truck movements forecast per day and strong seasonality, time-critical customer delivery requirements and an unpredictable customs and logistics environment, is there a danger of the investment becoming gridlocked?
Our team of data scientists and software developers built a detailed Python model that simulated each step in the process flow of individual inbound and outbound trucks for a full year of operation: modeling production, downtime and inventory. By visiting existing Kingspan sites we defined and refined 195 variable input parameters for 25 realistic scenarios. We used Power BI to analyze and present the 67 million records of generated event data, identifying bottlenecks.
The simulation captured the quantity of each resource utilized and the length of the queue, allowing us to identify bottlenecks and the optimum balance for each process step.
Stress testing the system with scenarios that pushed limits to quantify impacts and find potential gridlock events.
Conclusion
By including an industrial architect and logistics experts in our project team, we were able to propose and validate practical mitigation actions and best practices to prevent negative or catastrophic outcomes. As an output of our detailed process map, we specified the features and requirements of a digital solution to manage the operational flow which we can support Kingspan to source or develop. Our report has become a key input to decision-making for Kingspan Logistics.
“The Pacellico model stress-tested the site design and the recommendations provided will improve the operating model”
Project Director, Kingspan