One of the most cumbersome items to buy is a bed mattress to sleep on. It is difficult to know how the product will perform before purchase and the large size makes mattresses difficult to transport. Several firms have begun to explore ways to disrupt this industry but arguably there is a long way to go before a truly viable business model will emerge. Two references are provided concerning this topic. You should consult other sources as needed to answer this question. Be sure to attribute all sources with footnotes and citations. Answer all questions in your own words as much as possible.
a) Describe the value chain in detail for the home bed mattress industry. Be sure to cover every primary and support activity.
b) Identify the 3 most important capabilities needed to succeed in this industry. Are any of them a core competence? Consider using value chain activities and resource identification in your answer
c) Conduct a 5 Forces industry analysis. Is the industry attractive?
d) Use a SWOT matching method to identify potential strategic actions.
e) Would you pursue a cost leadership or differentiation strategy? Justify your answer based upon external and internal environment factors and your SWOT matching results.
a) Which of unsupervised or supervised machine learning is best suited to assessing causation? Explain your choice.
b) Your analytics team presents you with two sets of results that have improved the organization’s ability to predict customer defections. The first method uses deep learning and has a precision of 85%. The second method uses decision trees and has a precision of 70%. The previous approach had a precision of 40%.
i) Make a case for using the results of the deep learning method.
ii) Make a case for using the decision tree method.
In your answers, consider aspects of customer lifetime value and managerial decision making.
c) An analytics team used two different models to predict the likelihood of an outcome. The results from two different analysts are below:
Don’s Analysis
Actual | |||
Positive | Negative | ||
Positive | 220 | 100 | |
Negative | 30 | 650 |
Katie’s Analysis
Actual | |||
Positive | Negative | ||
Predicted | Positive | 170 | 10 |
Negative | 80 | 740 |
i) Use the Confusion Matrix and Index Calculation tables below to calculate the model performance measures.
Confusion Matrix | Actual | ||
Positive | Negative | ||
Predicted | Positive | TP | FP |
Negative | FN | TN |
Formula | Don Calculation | Katie Calculation | |||
Accuracy (completed as an example) | (TP + TN) / (TP + TN + FP + FN) | (220 + 650) / (220 + 650 + 100 + 30) | 0.87 | (170 + 740) / (170 + 740 + 10 + 80) | 0.91 |
Precision | TP / (TP + FP) | ||||
Error rate | (FP + FN) / (TP + TN + FP + FN) | ||||
Recall | TP / (TP + FN) | ||||
Specificity | TN / (TN + FP) | ||||
False positive rate | FP / (TN + FP) | ||||
F-score | 2* ((Precision*Recall) / (Precision + Recall)) |
ii) Describe a medical or business context where you would prefer to use Don’s model. Why do you prefer Don’s model?
iii) Describe a medical or business context where you would prefer to use Katie’s model. Why do you prefer Katie’s model?
Ian is an intern with the team who claims he made a breakthrough with a model that outperforms both Don’s and Katie’s. The confusion matrix for his model is below:
Ian’s Analysis
Actual | |||
Positive | Negative | ||
Predicted | Positive |