Real world problems are characterized by large-scale interdependent parameters as well as variability and uncertainties. This leads to complexity in decision making for both strategic and operational problems. DBTC’s dynamic simulation technologies and competencies, models these problems with accurate depiction of reality and corresponding solutions. They are as diverse and range from vehicle movements, scheduling, capital investment decisions, capacity analysis, plant layouts, and risk models to service center designs.

We use large scale simulation to understand the workings of operational systems and to get insights into their workings under different scenarios. Our modeling and simulation experts use discrete event simulation or continuous simulation depending upon the nature of the problem that needs to be addressed.

Discrete event simulation utilizes a mathematical/logical model of a physical system that portrays state changes at precise points in simulated time. Both the nature of the state change and the time at which the change occurs mandate precise description. We use platforms like ProModel and Automod as our discrete simulation engine. For large scale vehicular and traffic models, we use Corsim.

System dynamics is a computer-aided approach to policy analysis and design. It applies to dynamic problems arising in complex social, managerial, economic, or ecological systems - literally any dynamic system characterized by interdependence, mutual interaction, information feedback, and circular causality. Conceptually, the feedback concept is at the heart of the system dynamics approach. Diagrams of loops of information feedback and circular causality are tools for conceptualizing the structure of a complex system and for communicating model-based insights. DBTC uses platforms like Vensim and Stella for modeling and simulating system behavior.

Risk Analysis a part of every decision we make. We are constantly faced with uncertainty, ambiguity, and variability. And even though we have unprecedented access to information, we can’t accurately predict the future. Monte Carlo simulation (also known as the Monte Carlo Method) lets you see all the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty. DBTC experts use Monte Carlo simulation for cash flow calculations in highly uncertain environments, investment decisions under uncertainty, project risk evaluations across areas like mining and manufacturing.

In conjunction with simulation engines, DBTC uses advanced 3-D visualization engines and animation systems to understand and communicate system behavior to its clients.

Frequently, we have to model physical processes and movements to understand their behavior in real-time. Using fundamental principles from engineering, science, management and economics we are able to model the systems to get better insights and provide prescriptive suggestions on improvements and optimizations. Typical platforms and tools used are Mathematica, Matlab, SAS.