Computational Intelligence for the Manhattan Active Platform
Continuous algorithmic monitoring of engines, models, and other systemic behavior.
Learned Operational Enhancements
Autonomous online sensing, learning, and reacting with optimal responses to variances in operational dynamics.
A probabilistic and analytical mechanism used to evaluate and make optimal choices based on available information, experimentation, and insights from prior decision analysis.
A systemic, quantitative, and often visual approach to making both tactical and strategic business decisions under conditions of uncertainty.
Applied Mathematical Methods
Uses scientific mathematical methods that optimally extract knowledge and develop insights from data to help make informed decisions and build predictive models.
Provides descriptive, predictive, and prescriptive analytical capabilities to characterize data, forecast future events, make recommendations, and determine which choices should be made.
Sophisticated Mathematical Models
Frames analytical tasks and complex business problems with sophisticated and rigorous mathematical models.
Leverages combinatorial and continuous optimization, heuristics, and metaheuristics.
Omnichannel Commerce Intelligence
See how machine learning and algorithms contribute to our applied intelligence for Omnichannel Commerce solutions.
Supply Chain Planning Intelligence
Below are examples of applied intelligence across Manhattan inventory solutions like allocation, demand forecasting, replenishment, and planning.
Supply Chain Execution Intelligence
Below are examples of applied intelligence across Manhattan supply chain execution solutions like warehouse management, labor management, transportation management, and more.
Everything you wanted to know about computational intelligence.
Data science is a field that focuses on using data to gain insights and make informed decisions. It involves using a variety of techniques and tools to collect, analyze, and interpret data from various sources. Data scientists use statistical analysis, machine learning, and visualization techniques to extract meaning from data and communicate their findings to stakeholders.
Data science can help improve efficiency and effectiveness in the supply chain by allowing companies to use data and advanced analytical techniques to make informed, data-driven decisions.
Decision science is a field that focuses on the use of mathematical and statistical methods to understand and optimize decision-making processes. It involves developing models and tools to help individuals and organizations make better decisions based on data and analysis. Decision science often involves the use of operations research, economics, and psychology to understand how people and organizations make decisions, and to identify ways to improve those decision-making processes.
Decision science systems help improve efficiency and effectiveness in the supply chain by allowing companies to make informed, data-driven decisions that optimize resources and minimize risks.
Adaptive systems adjust their behavior or characteristics in response to changes in their environment or input. These systems are designed to adapt to new situations and changing conditions, allowing them to continue to function effectively even when faced with unexpected or unfamiliar circumstances.
Adaptive systems can help improve efficiency and effectiveness in the supply chain by allowing companies to respond quickly and effectively to changes in demand, resource availability, and other factors.
Optimization systems are systems that are designed to find the best or most efficient solution to a problem by maximizing or minimizing some objective function. These systems use a variety of techniques, such as mathematical programming and machine learning, to search for the optimal solution to a problem within a set of constraints.
Optimization systems can help improve efficiency and effectiveness in the supply chain by allowing companies to find the best solution to a wide range of optimization problems, such as demand forecasting, resource allocation, and transportation routing.
Artificial intelligence refers to the ability of a computer or machine to perform tasks that normally require human intelligence, such as understanding language, recognizing patterns, and making decisions. AI systems can be designed to mimic various aspects of human intelligence, such as learning, problem-solving, and decision-making.
Machine learning is a subset of artificial intelligence that involves the use of algorithms to automatically learn and improve from experience without being explicitly programmed. Machine learning algorithms are trained on a dataset, and they use this training data to make predictions or decisions. As they are exposed to more data, they can improve their performance over time.
Everything Works Better With Manhattan Solutions
Manhattan offers a complete breadth of solutions that when unified, provides total coverage for your supply chain commerce needs.
Control demand, supply, labor, and automation across your entire network with Manhattan Active® Warehouse Management.
Manage every carrier, rate, route, and load with Manhattan Active® Transportation Management.
Manhattan Active® Order Management provides stores with visibility into available inventory held anywhere, supporting endless aisle selling.
Technology partners that help Manhattan deliver the only cloud native, evergreen, and extensible supply chain commerce platform.