Mathematical models are developed in every area from atomic energy to bio technology. Models are generally designed to understand the real world phenomena mathematically/algorithmically. A model is a prototype of reality or an abstraction. Real world contains a lot of assumptions and axioms, despite this we can fit the real world process into a mathematical model.
Every process, business or company can be considered to contain a list of inputs, transformations / systems (working) and finally the output. Of course all of these operate in a set of external and internal conditions (called environment). This real world phenomena can possibly be represented in mathematical form in terms of flowchart & numbers.
Pictorial and mathematical representation will help in optimization and alignment to the business KPIs. Coming to KPIs, these are set of metrics which measure performance of the business. These are performance metrics that are tracked, measured & monitored on regular basis.
Mathematical models contains an additional loop (feedback loop) in normal flow chart, gets formed from output to system as a feedback of performance. This feedback contains information on what to improve and other intelligence that is fed back into the system. The feedback can be however not necessarily limited to effectiveness and efficiency of the system. First is the ability of the system to meet the desired output/ objectives. Second is the relationship between input and output, measure of how good is the relationship.
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Users must understand that all mathematical models will only help support a decision or help solve a problem. At the time of decision making a knowledge worker generally has one or more alternatives to solve the problem. A list of feasible solutions will need to be chalked out from all possible solutions. The best solution or optimum solution to be derived from the list of feasible solutions again based on criteria such as profitability, cost, response etc.
