DecisionMaking Tools Bartu Kaleagasi Katie Olney Decisionmaking O

Decision-Making Tools Bartu Kaleagasi Katie Olney

Decision-making O There is an element of risk associated with every business decision O Main aim is to quantify as much of the risk as possible, in order to make a rational decision O This can reduce risk or ensure that unbeneficial decisions are avoided

Scientific Decision-making Based on facts and evidence, scientific decision-making does not include subjectivity or emotions Benefits: O Based on factual evidence, easier to communicate to staff and less risk O Rational and logical O Simpler to justify

Scientific Decision-making Based on facts and evidence, scientific decision-making does not include subjectivity or emotions Costs: O Expensive and time consuming O Does not take into account possible factors such as irrationality, emotions or morals

Intuitive Decision-making Based on perceptions, beliefs, instincts and gut feelings. Usually favored by entrepreneurs or experienced managers. Benefits: O Takes into account unquantifiable factors O Requires little money and time = quicker decision O Considers certain issues such as social impacts

Intuitive Decision-making Based on perceptions, beliefs, instincts and gut feelings. Usually favored by entrepreneurs or experienced managers. Costs: O Not backed up by concrete facts or evidence O More complicated to communicate to staff, harder to justify O Higher possible risk

Decision Trees O Quantitative decision- making tool O Calculates probable value for different options a business may face O Helps minimize risk by doing so

Decision Trees Step 1: Decision O A business decision’s options are represented by a square node O This can go into 2 or more branches

Decision Trees Step 2: Chance O A circle node represents different chances for each given decision O This can also have 2 or more outcomes in itself

Decision Trees Step 3: Value O The value of either decision is calculated O This is done by multiplying the value of each outcome by its probability and adding them up

Decision Trees Example: Here, the decision to lease can result in 20% probability of receiving no money, and 80% probability of 150, 000 dollars. Multiplied by their coefficients, the outcomes of this decision add up to 120, 000$.

Decision Trees Example: The decision to own business can yield a 50% chance of 200, 000$ and a 50% chance of 160, 000$. Multiplied by the coefficients of 0. 5 and added up, the value of this decision becomes 180, 000$.

Decision Trees What can be concluded from this, is that the rational scientific decision would be to own the business. This is due to the fact that the total probable value of that decision (180 k$) is more than that of the alternative (120 k$).

Decision Trees Advantages of decision trees O Clear and logical layout O Considers risks of decision-making O Provides the costs of each decisions alternative O Tangible insight to the problem

Decision Trees Disadvantages of decision trees O Probabilities are only estimates, and therefore can be wrong/inaccurate O The entire tool is based on quantifiable factors, and therefore any other issues such as social and moral are ignored