Optimizing outbound supply chain design and processes to reduce transportation costs
HomeCasesOptimizing outbound supply chain design and processes to reduce transportation costs
Reference Case
Client profile
Client: Curium Industry: Pharmaceutical industry Process: Logistic optimization
Curiumis a global provider of medical products. They specialize in the production and supply of radioactive tracers used in nuclear medicine, which are used for diagnosis and treatment for a variety of diseases. Curium has a global logistics network that allows them to efficiently produce and distribute their products to healthcare professionals and patients around the world.Theirlogistics capabilities are a key factor in their ability to provide high-quality products and services to their customers.
Customer challenge and project approach
The logistics department of Curium is dealing with rising transportation costs due to increasing fuel prices and labor shortages in the logistical sector. However, lowering costs by optimizing resource utilization and network routing is complex due to the dependency on many service providers. In addition, the goal is not only to reduce costs, but to do so while maintaining current service-levels for the customers.
Together with Curium, we have taken a data-driven approach in analyzing and optimizing their outbound supply chain network. Our team modeled a Python-based digital twin of the transportation network, which allowed us to thoroughly analyze their supply chain and identify opportunities for improvement. We used this model to optimize their routing, improve vehicle utilization and reduce overall transportation costs. The digital twin allows for evaluating what-if scenarios throughout the entire supply chain, by accessing the impact on:
Customers
Transportation costs
Vehicle utilization
CO2 emissions
Current setup
New setup
The image illustrates an example of a network redesign scenario in which linehaul vehicles are consolidated to reduce costs and use resources more efficiently.Our digital twin has evaluated the new design based on costs and capacity restrictions. Results show a 33% reduction in transportation costs and a 21% decrease in CO2 emissions for this specific section of Curium‘s network.
Bright Cape conducted a comprehensive analysis using company-wide data, providing valuable insights into our logistical processes. As a result, we achieved significant cost reductions and implemented structural improvements. The collaboration has been very pleasant, positive and human-centered.
Added value
Curium was able to optimize theirtransportation network, reduce costs, and improve operational efficiency with the implementation of our data science solution. Cost savings realized during the first project phase is20%.The client continues to use our services for monitoring network performance and making data-driven decisions.
20% cost reduction
Across the transport network
Insight in most important cost drivers
Through network cost analysis
Supply chain digital twin
For data-driven decision making
Bright Cape has enabled us to make supply chain decisions while considering the full complexity of our network. I am more than happy with their positioning and continuous progress steps.
Unlocking the potential of logistical data
Data science and data analytics have a vast potential allowing businesses to optimize their supply chain operations, reduce costs, and improve customer satisfaction. With the growth of big data and the increasing importance of data-driven decision making, companies can use network digital twins and dashboarding tools to analyzevast amounts of supply chain data.
At Bright Cape, we believe in leveraging data science and analytics to help businesses to accelerate. Are you looking to optimize your operations? Contact us, and discover how our data-driven solutions can help you achieve your business goals.
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