challenges
Turn uncertainty into operational advantage
Modern value chains are incredibly complex, making it difficult to achieve truly optimal planning without advanced support. Modeling and optimizing end-to-end operations in real-world environments comes with deep technical challenges. Here are three common complexity hotspots:
How do I model complex, custom value chains?
Every value chain is unique. Off-the-shelf tools often can't handle specific constraints or business rules, forcing companies to oversimplify or rely on inefficient manual fixes. Capturing real operational complexity requires custom, adaptable optimization models.
We work closely with your team to capture real operational complexity through custom, adaptable optimization models, ensuring decisions truly reflect your business reality. We can leverage Composor, our open-source optimization framework, to accelerate modeling even the most intricate environments.
We design scalable, AI-driven optimization models that combine state-of-the-art solvers with smart problem decomposition and heuristics. When needed, Composor allows for easy comparison between different solver backbones, which guarantees your solution is performing optimally.
Delays, demand spikes, supplier issues, … Uncertainty is the norm in value chains. While forecasts help, turning them into robust, real-time plans is a major challenge. Handling unpredictability well is key to resilient operations.
Our approach integrates advanced forecasting models with adaptive planning techniques that respond dynamically to changing conditions. And if you want a seamless way to connect forecasting to optimization, Composor can plug into your existing data ecosystem for real-time, uncertainty-aware planning.
our approach
We get to know the key stakeholders and their specific needs, map out the current operational flow to spot inefficiencies or gaps, and identify the most effective AI solution to address the core challenges and opportunities.
We collect the necessary data to model a simplified operational environment. This involves subsequently implementing and testing the required model components.
We evaluate the model with operational managers to assess operational performance and understand practical strengths and weaknesses. This allows for identifying high-value, low-effort components that can be implemented in a next modeling phase.
We scale the model for real-world use, build reliable infrastructure to support it, ensure the approach remains cost-effective, and integrate the solution smoothly into the existing framework for maximum efficiency and impact.
AI planning solutions
AI planning use cases
Why partner with Superlinear for your planning and scheduling needs?
Superlinear delivers high-impact planning and scheduling solutions, built for your industry, aligned with your goals, and ready for real-world deployment.
Each value chain is different. We design modular solutions that fit your operational workflow and environment.
Thanks to our in-house developed accelerator package Composor, we are able to re-use frequently occurring value chain components and develop solutions quickly.
We build our solutions together with the end-user and focus on scalable and reliable solutions, allowing for maximum adoption rate.
Whether you are in manufacturing & production or logistics & service providing, our planning & scheduling systems are optimizing value chains across different industries.
From use case definition to deployment and continuous improvement, we support your entire journey while empowering your teams for long-term success.
SUCCESS STORIES
Proven planning and scheduling projects in action
Need a boost in your planning projects?
Check out our free accelerator.
Accelerator
Composor is an in-house Python package developed by Superlinear that aims to resolve the above challenges by offering off-the-shelf building blocks to build and solve custom value chain management problems in a scalable way. It also provides an interface to forecasting frameworks.
Modular
FAQs about planning and scheduling
Planning insights
ARTICLE
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AI in logistics and supply chain: Optimizing processes and driving impact. Explore how AI optimizes demand forecasting, production planning, and inventory management, leading to more responsive and resilient supply chains.
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Learn how to implement efficient operations research (OR) solutions using OR-Tools. This guide offers practical tips, covering key strategies like optimizing variables and constraints, trying different solvers, and adding symmetry-breaking constraints.
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Contact Us
Discuss with our expert about your planning and scheduling needs.
Joris Roels
Solution Architect
