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.
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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Warehouse Management
Mathematical Optimization can create more efficient paths for storage and retrieval of items, reducing travel time within the warehouse. For example, it might could allocate inventory in the warehouse in a manner that minimizes the distance covered during picking operations.
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Transportation Management
Mathematical Optimization can identify the most efficient routes and schedules to minimize fuel consumption and delivery times. For instance, given various factors such as vehicle capacities, delivery windows, and road conditions, optimization algorithms can formulate the most economical routes for a fleet of delivery vehicles.
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Order Management
Optimization can sequence the processing of orders in a manner that maximizes efficiency and customer satisfaction. For example, it can prioritize orders based on an assortment of promised delivery time, customer priority, order value or any combination of other attributes.
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Inventory Management
Optimization can determine the most efficient network inventory levels to minimize holding and shortage costs while still meeting service level objectives. For example, based on demand forecasts and lead times, it can determine the optimal safety stock and order quantities that minimize total inventory costs while meeting service goals.
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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.