Optimization in Supply Chain Commerce
Frame complex scenarios with sophisticated and rigorous mathematical models. Leverage real-world data and advanced algorithms. Ensure the best possible solve for your business problems with Manhattan’s Optimization in Supply Chain Commerce.
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Applied Mathematics and Operations Research
Like artificial intelligence and machine learning, optimization is often a misapplied and misused term. Fundamentally, optimization transforms an operation or outcome to make it as effective or functional as possible. Essentially, we are looking for the best solution based on specified criteria.
In the supply chain and commerce disciplines, the foundation for truly optimized performance is found within the 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’s 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 defines 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 look at the objective of constructing multiple elevators in a building, we could use Mathematical Optimization to solve for the least amount of travel possible for each elevator to take while still solving the objective of visiting each occupant's desired floor destination.
In 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—necessitates the utilization of sophisticated technologies like Mathematical Optimization. Its derived advanced mathematical basis, principals, and constructs—combined with advanced computational algorithmic implementations—ensures it can 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
Warehouse Management
Mathematical Optimization can create more efficient paths for storage and item retrieval, reducing travel time within the warehouse. A good example is it can allocate inventory in the warehouse to minimize the distance covered during picking operations.
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.
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.
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.
Key Takeaways
- Mathematical optimization helps find the best possible solution from a complex set of available alternatives.
- Optimization plays a critical role in supply chain commerce activities including inventory management, distribution, logistics, and more.
- Manhattan has been leading the industry with mathematical optimization capabilities in supply chain commerce solutions for more than three decades.
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