Creating Supply Chain Resilience with Mathematical Optimization

Navigating the Waves of Supply Chain Disruptions with Advanced AI Technologies

In an increasingly interconnected global economy, supply chain disruptions have become a common occurrence. These disruptions can be triggered by a variety of factors, ranging from geopolitical events and natural disasters to sudden fluctuations in demand.

Fortunately, businesses today have an array of advanced technologies at their disposal, which can help them combat and overcome these supply chain disruptions. One such technology is mathematical optimization, a potent weapon that has long been established as the go-to technology for supply chain planning and operations.

Mathematical optimization empowers businesses to deal with supply chain disruption by facilitating two types of decision-making:

1. Reactive: Planners and key stakeholders can sense disruptions in real-time and respond to them rapidly and effectively by identifying root causes and dynamically reallocating resources, thereby reducing time to recovery.
2. Proactive: Planners and key stakeholders can analyze supply chain risks and anticipate potential disruptions.

But how exactly does mathematical optimization drive optimal reactive and proactive decision-making? Let’s delve into the top three reasons why this advanced technology is such a potent tool in handling supply chain disruption.

1. A Model of Your Real-World Supply Chain

Every mathematical optimization application is essentially made up of two elements: a mathematical optimization solver—an algorithm-based problem-solving engine—and a mathematical optimization model—a representation or digital twin of your real-world operating environment, with all its complexity and challenges.

When a disruption occurs, mathematical optimization applications enable you to achieve:

• Visibility: Instantly identify the sources of the disruption, such as capacity bottlenecks and sudden fluctuations in supply and demand.
• Flexibility: Modify your model to reflect current operating conditions across your supply chain.
• Agility: Dynamically and automatically reoptimize plans and schedules and determine the best course of action to resolve the disruption as quickly and effectively as possible.

2. Leveraging Up-to-Date Data

Data is the fuel that makes AI technologies run. Unlike machine learning, which relies on historical data, mathematical optimization leverages the latest available data to deliver real-time prescriptive analytics across your supply chain network. This enables continuous intelligence and optimal decision-making, even in the face of the most massive disruptions.

3. Conducting Robust Scenario Analysis

An important part of handling supply chain disruption is assessing risk and proactively planning and preparing for the future. Mathematical optimization empowers your planners and key stakeholders to do this more quickly and easily. With mathematical optimization’s robust scenario analysis capability, you can explore various what-if scenarios, uncover hidden risks, and unlock opportunities to mitigate risk and drive improved supply chain resilience.

Building a Stronger and More Resilient Supply Chain

As businesses continue to navigate the ever-changing business landscape, mathematical optimization will continue to be an essential tool for supply chain leaders. By harnessing the power of this advanced technology, businesses can not only manage and mitigate supply chain disruption but also drive greater supply chain efficiency and profitability, building a stronger and more resilient supply chain for the future.