a)
very high | high | moderate | low | very low | Minimum payoff | Maximum payoff | Sum of all uncertainities | Minimax regret criterion | |||
S1 | S2 | S3 | S4 | S5 | |||||||
Sell shop and leave industry | D1 | -1000 | -750 | 0 | 1500 | 2000 | -1000 | 2000 | 1750 | 2500 | |
Sell shop and open cart | D2 | -500 | 500 | 1000 | 1250 | 1500 | -500 | 1500 | 3750 | 1750 | |
Status quo | D3 | 800 | 700 | 500 | 250 | 0 | 0 | 800 | 2250 | 1750 | |
Expand hours | D4 | 1500 | 1400 | 1000 | 0 | -500 | -500 | 1500 | 3400 | 1500 | |
Expand hours and retail area | D5 | 1750 | 1500 | 1000 | 0 | -750 | -750 | 1750 | 3500 | 1750 | |
Relocate to university Avenue | D6 | 2500 | 1750 | 1250 | -1000 | -2000 | -2000 | 2500 | 2500 | 2500 | |
Marcia's optimun Decision alternative | |||||||||||
Maxmin Decision | Status quo | ||||||||||
Maximax criterion | Status quo | ||||||||||
Principle of insufficient Reason | Expand hours and Retail area | ||||||||||
Minimax Regret criterion | Relocate to university Avenue | ||||||||||
b)
very high | high | moderate | low | very low | Expected value | ||
S1 | S2 | S3 | S4 | S5 | |||
Sell shop and leave industry | D1 | -1000 | -750 | 0 | 1500 | 2000 | 412.5 |
Sell shop and open cart | D2 | -500 | 500 | 1000 | 1250 | 1500 | 750 |
Status quo | D3 | 800 | 700 | 500 | 250 | 0 | 435 |
Expand hours | D4 | 1500 | 1400 | 1000 | 0 | -500 | 625 |
Expand hours and retail area | D5 | 1750 | 1500 | 1000 | 0 | -750 | 637.5 |
Relocate to university Avenue | D6 | 2500 | 1750 | 1250 | -1000 | -2000 | 362.5 |
PROBABILITY | 0.2 | 0.25 | 0.1 | 0.2 | 0.25 | ||
Optimum decision using expected value approach | Sell shop and open Cart | ||||||
.
3. | very high | high | moderate | low | very low | Expected value | |
S1 | S2 | S3 | S4 | S5 | |||
Sell shop aand leave industry | D1 | -1000 | -750 | 0 | 1500 | 2000 | 412.5 |
Sell shop and open cart | D2 | -500 | 500 | 1000 | 1250 | 1500 | 750 |
Status quo | D3 | 800 | 700 | 500 | 250 | 0 | 435 |
Expand hours | D4 | 1500 | 1400 | 1000 | 0 | -500 | 625 |
Expand hours and retail area | D5 | 1750 | 1500 | 1000 | 0 | -750 | 637.5 |
Relocate to university Avenue | D6 | 2500 | 1750 | 1250 | -1000 | -2000 | 362.5 |
PROBABILITY | 0.2 | 0.25 | 0.1 | 0.2 | 0.25 | ||
Max figure | 2500 | 1750 | 1250 | 1500 | 2000 | ||
Calculation of EVPI( expected Value of perfect information) | |||||||
Firstly, calculate ERPI(Expected return with perfect information) i.e (max value)x(probability) | 1862.5 | ||||||
EVPI =ERPI-EREV (calculated in b) | |||||||
EVPI = | 1112.5 |
4.
very high | high | moderate | low | very low | Expected value | ||
S1 | S2 | S3 | S4 | S5 | |||
Sell shop and leave industry | D1 | -1000 | -750 | 0 | 1500 | 2000 | -75 |
Sell shop and open cart | D2 | -500 | 500 | 1000 | 1250 | 1500 | 412.5 |
Status quo | D3 | 800 | 700 | 500 | 250 | 0 | 517.5 |
Expand hours | D4 | 1500 | 1400 | 1000 | 0 | -500 | 880 |
Expand hours and retail area | D5 | 1750 | 1500 | 1000 | 0 | -750 | 975 |
Relocate to university Avenue | D6 | 2500 | 1750 | 1250 | -1000 | -2000 | 1037.5 |
PROBABILITY | 0.3 | 0.2 | 0.15 | 0.25 | 0 | ||
Max figure | 2500 | 1750 | 1250 | 1500 | 2000 | ||
Calculation of EVPI( expected Value of perfect information) | |||||||
Firstly, calculate ERPI(Expected return with perfect information) i.e (max value)x(probability) | 1662.5 | ||||||
EVPI =ERPI-EREV (calculated in b) | |||||||
EVPI = | 625 |
Marcia owns a coffee shop on Main Street. She is contemplating the future of her current...
SYNOPSIS The product manager for coffee development at Kraft Canada must decide whether to introduce the company's new line of single-serve coffee pods or to await results from the product's launch in the United States. Key strategic decisions include choosing the target market to focus on and determining the value proposition to emphasize. Important questions are also raised in regard to how the new product should be branded, the flavors to offer, whether Kraft should use traditional distribution channels or...