Optimization in Supply Chain Commerce

Optimization frames complex business problems with sophisticated & rigorous mathematical models with real-world data and solves uses advanced technologies and algorithms to solve for the best possible, or most ‘optimal’ outcome.

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Optimization in Supply Chain and Commerce Systems

Engineer. Analyze. Optimize.

Applied Mathematics and Operations Research

Like artificial intelligence and machine learning, optimization is often misapplied and misused as a term. Fundamentally, optimization strives to make an operation or outcome as effective or functional as possible. Essentially, we are looking for the best solution based upon specified criteria.

In the supply chain and commerce disciplines, the foundation for truly optimized performance is found within the rigorous scientific fields of Applied Mathematics and Operations Research. Operations Research (OR), is an interdisciplinary field of applied mathematics and science that uses advanced analytical methods to make better decisions. It is all about finding optimal solutions to complex real-world decision-making problems to enhance the operations and efficiency of an organization.

Mathematical Optimization

Mathematical Optimization is a branch of Applied Mathematics that seeks to find the best possible solution from a set of available alternatives. It usually involves maximizing or minimizing a function (which is our ‘objective’ for the problem or task) by systematically searching and choosing values for variables, attributes or factors within an allowable set that define that objective. The function to be optimized is often referred to as the objective function, and the set of permissible values is called the constraints. For example, if we had a bank of elevators in a building, we might use Mathematical Optimization to optimize for our objective, which is the least amount of travel possible and still visit each person’s floor.

Within the realm of supply chain commerce, Mathematical Optimization plays a critical role across various aspects, including inventory management, distribution, logistics, fulfillment and more.  As the complexity, scale, and speed of modern commerce and supply chains increase, so does the need for Mathematical Optimization. It better informs decision support and intelligently automates prescriptive decisions amidst a vast and highly dynamic multitude of operational variables and constraints. The result is increased efficiency, resiliency, agility and cost savings across omnichannel commerce, supply chain planning and supply chain execution operations.

Optimization and Manhattan

The growing expectations of advanced operational capabilities and elevated efficiencies coupled with the complexity, scale and speed of modern commerce and supply chains necessitate the use of sophisticated technologies like Mathematical Optimization. Its rigorously derived advanced mathematical basis, principals, and constructs along with advanced computational algorithmic implementations ensure it can truly find the best solutions for complex supply chain commerce problems in an automated, efficient, and reliable manner.

Manhattan infuses advanced computational intelligence, powered in part by Mathematical Optimization, into its supply chain commerce solutions to help you drive efficiency, resilience, and competitive advantages.

Examples of Supply Chain Commerce Optimization

Optimization has been a part of commerce, distribution and logistics since Manhattan started more than three decades ago and over the years it’s usage has expanded and matured. Today, Optimization is an integral component of computational intelligence in supply chain commerce.

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Let’s make supply chain commerce smarter

Learn how Manhattan’s commitment to advanced computational intelligence technologies like Data Science with Machine Learning, Decision Science, Adaptive Systems and Optimization to drive efficiency, resilience, and competitive advantage for supply chain commerce organizations.

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