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Decision-Making Process and Management Science

Dual Problem in Decision-Making

A dual problem arises when different decisions are made based on diverse information variables. The main problem facing management teams is how to select optimal information variables. For example, take a firm engaged in production and marketing. Assume that expenditure on these two activities must be selected for a certain period. Let x stand for money assigned to production and let ax represent the output.

On the same note, let y represent money set aside for marketing and let yb represent the demand created. Assuming that the products are perishable, the actual amount of goods sold will thus be less than the two quantities (ax and yb). If the units are selected such that the price of the product is 1, then the resultant profit from these expenditures (x, y) will be min (ax, yb)-(a+b). Knowledge of dual problem helps decision-makers to select optimal variables at minimum costs.

The Steps in the Decisions-Making Process

There are five steps involved in the decision-making process; the first step is to identify and understand the problem. The second step is to identify alternatives by carrying out sufficient research. Third, you need to assess your alternatives using either SWOT analysis or Decision Matrix to rank your alternatives in the order of their merits. The fourth step in the decision-making process is to select the best alternative from the list. The fifth and final step is to implement your decision to achieve your desired objective. You need to make a follow-up to ensure that your decisions are implemented according to the plan.

Management Science Improving Decision-Making Abilities

The cost of making a wrong decision is very high. However, management science can help decision-makers make optimal decisions for their organizations. For example, data envelopment analysis (DEA) is an important tool that can be used to evaluate and boost production and service operations in a business organization. It has been used in benchmarking and performance assessment in the banking industry, schools, production plants, and hospitals.

Sensitivity Analysis in Operations Research/Management Science

The sensitive analysis is the investigation of changes and errors in assumptions and parameter values of any model. A sensitivity analysis is a useful tool in OR/MS since it enables decision-makers to understand the status of a solution. It can also be sued for: model development; increased understanding of the model; and development of proposals for decision-makers.