The ability for a supply chain to deliver quality products on time to their customers while keeping costs low is a constant challenge – and one that is directly impacted by the company’s network of suppliers, customers, distribution centers, and production facilities. In order to achieve these objectives, supply chain professionals must evaluate and optimize their network design. One way that supply chain leaders can do this is by incorporating AI augmentation in network design.  

How AI Can Optimize Network Design 

The decisions that influence network design configuration have long-lasting impacts, whether it be the contractual obligations, the start-up costs to launch a new production line, or the investment needed to integrate a new supplier, facility, or distribution center into the organization’s technical landscape. Due to the impact and costs associated with network design, these decisions benefit from combining human thought and intuition with advanced models capable of considering numerous variables, such as cost, shipping lanes, transit times, sustainability impacts, and more.  

These models are especially beneficial if they include the company’s managed network, and even more so if they can take into consideration the broader network and its impacts, such as the network of suppliers and customers. Given this complicated landscape and the vast amount of data to consider, advanced analytics and Artificial Intelligence (AI) are great tools to leverage in these decisions, augmenting the team’s ability to consider these complex data points. 

AI and Network Design 

The complex, interconnected landscape of network design makes it a prime candidate for leveraging AI tools. For example, to minimize costs in a complex scenario, such as choosing between multiple contract manufacturers, various pricing and transportation scenarios could be integrated into the model to assess the cost and timing implications of working with each partner. There may be a contract manufacturing partner who is more expensive to work with but has a closer proximity to suppliers and distribution centers. As a result, timing and shipping costs may be significantly reduced, ultimately making the more expensive partner the optimal decision.  

A well-designed analytics model would provide valuable insights for minimizing costs while also considering other variables such as timing or inventory management. When these additional service considerations are layered in, the decision could be much better informed and optimized for your specific goals and use cases.  

Getting Started with AI Augmentation for Network Design 

To get started with AI augmentation for network design, we recommend taking a phased approach, as outlined below: 

Crawl 

As with most data analytics topics, having a solid data foundation on which to build is key. Investing in strengthening your data management and data pipelines is a great place to start as you embark on your AI journey, or even as a step independent of AI. Investing in your data analytics foundation adds value immediately and enables analytic growth in the future, while focusing on data optimization can yield notable benefits. 

Perhaps you have an upcoming decision about your network design, such as choosing a new contract manufacturer or deciding whether to expand a current distribution center or to find a new additional distribution center altogether. This ‘one-time’ use case could be a great chance to test out an AI model and learn how it could augment your analytic assessment of the options. 

Walk 

A more developed phase could be to integrate an AI model into your supply chain data landscape, so that it could be utilized to assess optimal network designs for more frequent impacts, such as a cost change from a supplier, increased costs on a shipping lane, or an anticipated increase in orders to a specific region. The model could predict the potential impacts of these more frequent situations and augment your team’s analytic toolset, as they work to optimize the network design over time. 

Run 

Another more involved approach could be to fully integrate an AI model into the ongoing management of your network to view and assess impacts in real time, as your supplier and customer networks evolve. This data analytic infrastructure could certainly help you optimize and evolve your network design over time. 

Moving Forward with AI Augmentation in Network Design 

Utilizing AI to augment your network design capabilities could add real benefits. To start, evaluate your current data analytics landscape and your overarching goals around network design. From that foundation, you can then build out your use case and ensure that you are pursuing the optimal approach for your organization. 

Our team of supply chain and advanced analytics experts has experience with guiding teams on their AI augmentation and network design journey. Reach out to us today to connect with our network design and advanced analytics consulting experts.