Methodology of Decision Support Systems
In mathematical programming we use abstract models to represent real systems, such as production or distribution systems. Cutting-edge mathematical optimization algorithms process these models to optimize one or multiple criteria of the represented system.
An Overview
Input
Client production systems provide the relevant raw data. In most cases, input data require cleaning, formatting and validating, which can be automated within a software system. Almost any data source can be used to 'feed' a planning model. Because a planning model defines a structure independent from concrete data, it can process an array of different input data scenarios.
Examples:
- Time-dependent demands of a product
- Production coefficients
- Cost coefficients
- Setup times
- Transportation capacities
Model
We formulate a mathematical model. A system of equations encodes the business process with all relevant activities and resources. Smart algorithms optimize one or more objectives while adhering to the mathematical constraints of the modelled system. Mathematical equations encode the logic of the system. Scientific algorithms compute the optimal decisions for a given input.
Examples (click for details):
Decision
The model output often directly correspond to managerial or operational decisions. The model structure is reusable, which means we can compute optimal or near-optimal decisions for any valid input. Decisions can be visualized in a dashboard to make them easy to interpret and actionable.
Examples:
- Profit maximal selection of manufactured products
- Cost minimal location assignment of products in the warehouse
- Cost minimal routing of goods in transportation networks
- Balanced crew schedule
Your Benefit
Optimal solution
Applying a scientific decision support approach allows you to make optimal or near-optimal decisions even in complicated scenarios. In many cases, the exercise of rigorously modelling a planning situation already reveals valuable information about the structure of the business process.
Increase efficiency
An effective process successfully generates a desired outcome. An efficient process generates the desired outcome with the lowest effort possible. Using a mathematical programming approach can bring your business processes to maximal efficiency.
Fast decision making
A compute-based decision-making process is automated and fast. Complex tasks that might require weeks of manual planning can be computed within seconds on modern hardware.
Cost-cutting
Reduce cost by automating the decision making process. Save managerial resources while maintaining excellent planning quality.
Interested?
This list is a peek into the world of planning problems that can be tackled with mathematical programming. Our team is looking forward to assist your business with our deep knowledge and experience in the field of Operational Research.