Operations, Logistics, and Supply Chain Management
Managing operations, logistics, and supply chains is essential to the success of many businesses and organizations. These areas can be streamlined to enhance the decision making process and improve efficiency by applying advanced analytical techniques such as mathematical modeling and simulation, and statistical analysis and optimization. To conduct these analyses, accurate models of the operations or supply chains, a framework supporting the handling of data, and the integration of mathematical techniques need to be developed.
Leveraging industry background and lessons learned from hundreds of customer engagements, MathWorks Consulting Services works with you to model your systems, integrate mathematical techniques, and manage changes in operational or supply chain configurations.
Determining the appropriate level of fidelity to model the supply chain or operation in support of business decisions
MathWorks Consultants have experience in modeling systems to answer logistic and operational questions. We help you determine the correct modeling techniques and paradigms—from statistical and neural network techniques, to continuous and discrete time, to discrete event based simulation modeling paradigms—along with providing the appropriate level of detail required to answer your business or organizational questions. MathWorks Consultants work with you to build models of your systems and libraries of components, and train you how to create your own.
Integrating into a single environment big data from numerous sources, mathematical analysis, optimization, modeling and simulation
Harnessing skills gained from industry and designing applications ranging from optimizing inventory levels to predicting mine output, plus a deep knowledge of MATLAB and Simulink, MathWorks Consultants teach you efficient techniques to implement a framework for conducting operational and logistical studies. We coach you on the best method for importing data, processing data including performing statistical analysis, sensitivity analysis, and Monte Carlo or optimization studies, while automating these steps. We guide you in the integration of the broad range of techniques required to perform operational and logistical studies.
Building internal capability to support business or organizational questions
MathWorks Consultants empower you to answer your own business or organizational questions. We provide support to quickly ramp up and build capability within your team through knowledge transfer, coaching sessions, and hands-on model and library development. Your team can now quickly and efficiently respond to changes in requirements or operational or supply chain configuration.
MathWorks Consulting Services works with you to:
- Optimize your operations and logistics by integrating tools to support information-driven decision making
- Determine the appropriate predictive modeling and simulation techniques, and apply this to your operational configuration
- Automate the testing and comparison of a broad range of operational and supply chain configurations
- Transfer knowledge to build in-house competency through customized, project-based coaching sessions
Predictive Modeling for Mines
Branko Dijkstra is a principal technical consultant specializing in Model-Based Design workflows for process industry optimization. Prior to joining MathWorks, Branko was an engineering manager for the development of automotive climate control and electric vehicle thermal management systems. Before that, he worked in the microlithography industry. Branko received his M.E. based on his work modeling a batch crystallization plant. He received his Ph.D. in control engineering (microlithography) from Delft University of Technology, the Netherlands based on his thesis Iterative Learning Control Applied to a Wafer Stage.
Related Conference Papers and Technical Materials
- IEEE 2000: Input Design for Optimal Discrete Time Point-to-Point Motion of an Industrial XY-Positioning Table (Conference paper; IEEE sign in required)
- ACC 2002: Extrapolation of Optimal Lifted System ILC Solution, with Application to a Waferstage (Conference paper; IEEE sign in required)
- IEEE 2002: Noise Suppression in Buffer-State Iterative Learning Control, Applied to a High Precision Wafer Stage (Conference paper; IEEE sign in required)
- IEEE 2002: Convergence Design Considerations of Low Order Q-ILC for Closed Loop Systems, Implemented on a High Precision Wafer Stage (Conference paper; sign in required)
- IEEE 2003: Exploiting Iterative Learning Control for Input Shaping, with Application to a Wafer Stage (Conference paper; IEEE sign in required)