The Technology Behind Modeling at Scale

Ganesh Ramakrishna
Mar 14, 2025
9 min read

TABLE OF CONTENT

• Understanding the Fundamentals
• The Challenge of MIP Problems
• The Role of Software in Network Optimization
• Beyond Network Optimization
• The Critical Data Management Challenge
• The Risks of Poor Data Management
• The Lyric Advantage
• Looking to the Future

Modeling supply chains at scale is a major technological challenge - one that surpasses traditional optimization methods. As supply chain complexity continues to rise, so does the difficulty of finding the best solution. Did you know that a redesign selecting the 45 best locations from 50 options results in 2.1 million possible combinations to sift through? When we expand that to more like 90 locations out of 100, that skyrockets the combinations to 17 trillion! At this scale, conventional approaches simply can't keep up.

Early network design tools were constrained by desktop computing. Cloud-based solutions improved accessibility, but most still rely on traditional modeling techniques. Lyric is breaking this mold, enabling enterprises to move from incremental improvements to full-scale network reimagination. Our platform is built to handle massive datasets, integrate multiple optimization layers, and orchestrate end-to-end decision-making—turning supply chain modeling into a strategic, AI-driven advantage.

"From desktop models to AI-driven intelligence—supply chain modeling has evolved, but many solutions still rely on conventional techniques."

In this blog, we’ll explore how network modeling technology has evolved, the challenges of modeling at scale, and how Lyric’s approach is redefining what’s possible.

Understanding the Fundamentals

The core of supply chain modeling is the "network model" - an abstraction capturing all network structure nuances: suppliers, plants, production lines, warehouses, customers, and associated costs (production, warehousing, transportation). These models excel at abstracting network complexity and identifying the right network configuration for the business.

Technically speaking, network models are linear programs (LPs) or mixed-integer programs (MIPs). Understanding this mathematical foundation helps reveal the inner workings of the technology itself. At a basic level, LPs optimize business goals like cost reduction while respecting constraints around capacity, demand, and transport limitations. MIPs add complexity by introducing whole number decisions - like facility count or binary choices around opening locations.

The Challenge of MIP Problems

What makes MIPs particularly difficult is that they are classified as NP-hard problems. As the problem grows, say, by adding more facilities or decision variables, the number of possible solutions grows exponentially. This exponential growth in complexity is what makes MIPs one of the hardest algorithm categories to solve. It’s also why having the right solver is so important. In the early days, solvers like CPLEX were used, but today, Gurobi is the industry standard, known for its speed and ability to handle the enormous complexity of modern supply chains, especially in solving NP-hard MIP problems.

"Network models help businesses design efficient supply chains by mapping suppliers, plants, warehouses, and transportation costs. The challenge is making them adaptable and scalable."

The Role of Software in Network Optimization

Network modeling software performs three key functions:

1. Constructs complex mathematical models
2. Interfaces with solvers to process solution spaces
3. Presents results through intuitive visualizations and reports

The software also enables scenario analysis through "what-if" modeling capabilities, helping teams evaluate multiple potential network configurations.

"Network models help design efficient supply chains by mapping suppliers, plants, warehouses, and transportation costs. The challenge is making them adaptable and scalable."

Beyond Network Optimization

While network optimization is critical, it’s far from the only area requiring modelers’ attention. A comprehensive solution must address several additional optimization challenges:

• Transportation Optimization: Ensuring efficient final mile delivery requires specialized optimization. Direct Store Delivery focuses on getting products to retail locations on time at minimal cost.
• Inventory Strategy Optimization: Managing inventory levels is a balancing act between minimizing stockouts and reducing holding costs.
• Policy Simulation: Simulating the impact of different policies, including changes in transportation routes, sourcing strategies, and inventory policies.

Each area requires deep expertise and specialized algorithms. A holistic supply chain optimization strategy must consider all these interconnected pieces.

The Critical Data Management Challenge

Running a large-scale supply chain modeling program isn't just about optimization algorithms and technology; it’s equally important to have a robust system to manage and organize the necessary data. Companies need to build and maintain a comprehensive data catalog.

Essential supply chain data points include:

• Production/Sourcing Capacity Data: Information about the maximum output a facility/supplier can produce/supply.
• Warehouse Cost and Capacity Data: Data on how much inventory a warehouse can hold and the associated operational costs.

The Risks of Poor Data Management

Not having this critical data organized can lead to serious challenges in supply chain modeling:

• Slow Execution: Without a structured data catalog, teams spend too much time searching for, cleaning, and verifying data.
• Inaccurate Results: Poor or outdated data leads to flawed models.
• Unsuccessful Programs: Supply chain modeling programs often fail due to inadequate data management.

The Lyric Advantage

Over the past three years, the Lyric team has built what we believe is the solution to the challenges in executing a successful supply chain modeling discipline at scale. The heart of this innovation is a completely new architectural layer between the model and the application, called the 'sequence.'

The Sequence Layer: Game-Changing Modeling Tech

Lyric Studio’s sequence layer empowers modeling teams to create comprehensive, algorithmic workflows that cover the entire journey—from raw data to model-ready data. This flexibility allows users to build complex algorithmic workflows tailored to their unique needs.

"Modeling at scale requires a structured approach to data processing, workflow automation, and cross-team collaboration."

Key Advantages of Lyric's Upgraded Architecture:

1. Scalable Data Processing for Massive Datasets
2. Collaborative Data Products for Validation
3. Daisy Chain Models for Complex Use Cases
4. Distributed Framework with Specialized Compute Capabilities
5. Tailored Applications for Stakeholders

Looking to the Future

The convergence of next-generation modeling platforms and advanced computing is opening new frontiers in supply chain modeling. Innovations are set to transform the industry, including advanced modeling capabilities, accelerated compute, and integrated solutions.

As supply chains become more complex, traditional modeling methods struggle to keep pace. Lyric’s approach enables handling massive datasets, connecting different optimization layers, and building flexible workflows without coding. This reimagining of supply chain modeling makes it possible to tackle larger problems and make smarter decisions faster.