Intrepid Design Studio's Strategy Analysis - Example

Intrepid Design Studio analysis: Name: Instructor: Course: Date: Introduction: This report shows the optimal decision that Intrepid Design Studio should make in order to achieve desired goals, a number of solutions are highlighted and they include the optimal production of its products, which are multisport jackets, cycling jackets and soft shell jackets. The other solution is the best sites to advertise its products with reference to age groups, the decision are made with reference to descriptive and histograms of the frequencies. The other solution entailed analyzing the relationship between unique visits and purchases. This way the company would be able to make decision on whether to target unique visitors in order to achieve higher sales. Finally, the probability theory was used to analyze the frequencies in relation to age, gender and type of shopper. Analysis: 1. Optimal production: The first step was to determine the optimal production level in order to maximize profits, the conditions provided are that only 100 jackets can be produced and only 365 labor hours are available, profits for each type of jacket were as follows: Multisport jacket -$30 Cycling jackets - $9 Soft shell jackets-$15 Resource consumed by each jacket type is as follows: Multisport jacket -6.5 labor hours Cycling jackets – 4.2 labor hours Soft shell jackets-2.25 labor hours Using the solver solution tool in excels the optimal production level were as follow: Multisport jacket s= 32units Cycling jackets = 0 units Soft shell jackets = 67 units Optimal point profit = $1,994 This means that the optimal level of production will yield a profit of $1,994, Decline in soft shell jackets profits: A change in the profits for the soft shell jackets from 15 to $8 will yield the following optimal results: Multisport jacket =56 units Cycling jackets = 0 units Soft shell jackets = 0 units Maximum profit = 1684 When the profits for the product decline, the firm’s optimal production level will be the production of 56 units of multisport jacket only, which will yield a profit of $1,684? Increase in cycling jackets profits: A change in the profits for the soft shell jackets from $9 to $15 will yield the following optimal results: Multisport jacket =0 units Cycling jackets = 71 units Soft shell jackets = 28 units Maximum profit = 2217 The optimal solution will require the production of 71 cycling jackets and 28 soft shell jackets that will yield a profit of $2, 21 Shadow prices: Referring to the original solution, for the number of products, the shadow price is 7.05, this means that the firm will be willing to pay 7.05 costs to increase the production level, however a cost greater than this value will not add value to the firm. For the number of hours, the shadow price is 3.52, this means that the fir m will be willing to increase labor hours of the cost of labor is less or equal to 3.52, a price greater than this value will not add value to the firm. Increasing production hours: If the production hour’s increase from 320 to 260, there will be an increase in profits, the optimal solution will be as follows: Multisport jacket =0 units C-cycling jackets = 92 units S-soft shell jackets = 7 units The above production levels will yield a profit of $2,423 2. Advertisement and age group: Results indicate that there are certain websites that are more appropriate for advertising, the histograms generated indicate that there are websites that are above others in terms of visits, when targeting age groups 25 to 44 customers the best sites to advertise include live.com ebay.com.au ninemsn.com.au yahoo.com youtube.com facebook.com When targeting the age group 45 to 64 customers, the best websites to advertise include: qantas.com.au blogspot.com ebay.com.au ninemsn.com.au live.com yahoo.com youtube.com facebook.com Therefore for advertising purpose the company should target face book, you tube, yahoo, nine msn, eBay and live.com. 3. Unique visits and purchases Regression analysis was used to analyze the relationship between unique visits and purchases, results show that there is a positive relationship between the two variables. The estimated mode shows that an increase in unique visits by one unit will increase online orders by 0.075; also, if unique visits are zero, then the online order value is 7.95. For the r-squared value, which is 0.838, this value means that 83.8% changes in online orders is explained by unique, visits variable. This means that the company should aim at increase the number of unique visits in order to increase purchases. An example shows that if the targeted unique visits are 80,000, the expected online orders will be equal to 6007.95. 4. Type of shoppers, generation and gender: The following chart summarizes the type of shopper and gender: Results show that there are more store loyal male customers than females, also there are female channel adopted than males, also there are more male functional shoppers than females while traditional multichannel shoppers are relatively equal among the gender groups. This means that the firm should focus more on male products when advertising online. For generation type, results are summarized in the chart below: The above rtesults shopw that for online shping the majority of online shppers are likely to be generation x and y, baby boomers are liekyl to be store loyal. Conclusion: The company should increase production hours as indicated by the solution where the production hours are increased from 320 to 360 hours, however, according to the shadow prices values, the production should only be increased if the cost of production is equal or less than $7.05, also if labor costs are equal or less than $3.52. when the production hours are increased then the firm will only be required to produce the 92 units of cycling jackets and 7 units of soft shell jackets. For advertising purposes, the firm should only advertise in a number of websites that are outliers above the interquartile range, evidence show that the firm should concentrate advertising with face book, you tube, yahoo, nine msn, eBay and live.com. Average values also indicate that the age group 45 to 65 has an average of 147 compared to 127 for age group 24 to 44; this means that the age group more likely to be targeted using online advertising is the age group 45 to 65. Regression analysis show that unique visits are likely to increase online orders, if the unique visits are 80,000, the expected online orders will be equal to 6007.95. This means that the company should increase the production level from 100 to approximately 6000 in order to achieve the desired profit levels. Results are that there are more store loyal male customers than females, also there are female channel adopted than males, this means that the firm should focus more on male products when advertising online. The above rtesults shopw that for online shping the majority of online shoppers are likely to be generation x and y, baby boomers are liekyl to be store loyal. Appendix: Question 1: a. Decision variables are: M-multisport jacket C-cycling jackets S-soft shell jackets Objective: The objective is to maximize profits Maximize Profit = 30M+9C+15S Constraints: M≥0 C ≥0 S≥0 M + C + S ≤ 100 Resources: 6.5 M + 4.2 C + 2.25 S ≤ 365 b. SOLUTION: Target Cell (Max) Cell Name Original Value Final Value $B$10 MAXIMISE M 976.9230769 1994.117647 Adjustable Cells Cell Name Original Value Final Value $B$3 NUMBER M 0 32.94117647 $C$3 NUMBER C 87.17948718 0 $D$3 NUMBER S 12.82051282 67.05882353 Constraints Cell Name Cell Value Formula Status Slack $C$20 M + C+S C 100 $C$20=0 Binding 0 $D$3 NUMBER S 67.05882353 $D$3>=0 Not Binding 67.05882353 Optimal solution: M-multisport jacket = 32 C-cycling jackets = 0 S-soft shell jackets = 67 Optimal point profit = 1994 C. if profits for soft declined from 15 to 8: Cell Name Original Value Final Value $B$10 MAXIMISE M 1524.705882 1684.615385 Cell Name Original Value Final Value $B$3 NUMBER M 32.94117647 56.15384615 $C$3 NUMBER C 0 0 $D$3 NUMBER S 67.05882353 0 Cell Name Cell Value Formula Status Slack $C$20 M + C+S C 56.15384615 $C$20=0 Binding 0 $D$3 NUMBER S 0 $D$3>=0 Binding 0 The optimal solution will be: M-multisport jacket =56 C-cycling jackets = 0 S-soft shell jackets = 0 Maximum profit = 1684 D. if profit of C increase from nine to 25, the optimal solution is as follows: Cell Name Original Value Final Value $B$10 MAXIMISE M 1994.117647 2217.948718 Cell Name Original Value Final Value $B$3 NUMBER M 32.94117647 0 $C$3 NUMBER C 0 71.79487179 $D$3 NUMBER S 67.05882353 28.20512821 Cell Name Cell Value Formula Status Slack $C$20 M + C+S C 100 $C$20=0 Not Binding 71.79487179 $D$3 NUMBER S 28.20512821 $D$3>=0 Not Binding 28.20512821 The optimal solution will be: M-multisport jacket =0 C-cycling jackets = 71 S-soft shell jackets = 28 Maximum profit = 2217 E. shadow prices and feasibility: The shadow price is equivalent to the multiplier in solution b, Constraints     Final Lagrange Cell Name Value Multiplier $C$20 M + C+S C 100 7.058823529 $D$22 6.5 M + 4.2 C + 2.25 S ≤ 395 S 365 3.529411765 For the number of products, the shadow price is 7.05, this means that the firm will be willing to pay 7.05 costs to increase the production level, however a cost greater than this value will not add value to the firm. For the number of hours, the shadow price is 3.52, this means that the fir m will be willing to increase labor hours of the cost of labor is less or equal to 3.52, a price greater than this value will not add value to the firm. F. Cell Name Original Value Final Value $B$10 MAXIMISE M 2217.948718 2423.076923 Cell Name Original Value Final Value $B$3 NUMBER M 0 0 $C$3 NUMBER C 71.79487179 92.30769231 $D$3 NUMBER S 28.20512821 7.692307692 Cell Name Cell Value Formula Status Slack $C$20 M + C+S C 100 $C$20=0 Not Binding 92.30769231 $D$3 NUMBER S 7.692307692 $D$3>=0 Not Binding 7.692307692 The optimal solution will be: M-multisport jacket =0 C-cycling jackets = 92 S-soft shell jackets = 7 Maximum profit = 2423 Question 2: a. Histogram: Age group 25 -44 Descriptive statistics: Age group 25 -44 Mean 127.56 Standard Error 8.381130826 Median 92 Mode 57 Standard Deviation 102.64747 Sample Variance 10536.50309 Kurtosis 7.205014688 Skewness 2.431740206 Range 572 Minimum 48 Maximum 620 Sum 19134 Count 150 Largest(1) 620 Smallest(1) 48 Confidence Level (95.0%) 16.56122468 Age group 45-64 Descriptive statistics: Age group 45-64 Mean 146.2933333 Standard Error 9.789779388 Median 110 Mode 110 Standard Deviation 119.899821 Sample Variance 14375.96707 Kurtosis 8.297718001 Skewness 2.569962687 Range 698 Minimum 52 Maximum 750 Sum 21944 Count 150 Largest(1) 750 Smallest(1) 52 Confidence Level (95.0%) 19.34473275 B. SHAPE: Both the 25-44 age group and 45 – 65 age group histograms are positively skewed, this means that more values are on the lower side of the scale. Outliers using the 1.5 X IQR: 25 – 44 age group: q1 58 q2 92 q3 167.5 q4 620 IQR= q3-q1= 109.5 1.5 X IQR=164.25 Above the third quartile, the value is 331.75 Outliers include: live.com 430 ebay.com.au 470 ninemsn.com.au 470 yahoo.com 520 youtube.com 570 facebook.com 620 45 – 64 age group: Above the third quartile, the value is 347.625 Outliers include: qantas.com.au 350 blogspot.com 380 ebay.com.au 470 ninemsn.com.au 510 live.com 520 yahoo.com 630 youtube.com 690 facebook.com 750 C. dispersion: Age group 25 -44 Mean 127.56 Median 92 Mode 57 Standard Deviation 102.64747 Sample Variance 10536.50309 Minimum 48 Maximum 620 Age group 45-64 Mean 146.2933333 Median 110 Mode 110 Standard Deviation 119.899821 Sample Variance 14375.96707 Minimum 52 Maximum 750 Question 3: a. Scatter diagram: b. Trend line: Online orders = 7.95 + 0.075 unique visits R squared = 0.838 The regression model states that an increase in unique visits by one unit will increase online orders by 0.075; also, if unique visits are zero, then the online order value is 7.95. For the r-squared value, which is 0.838, this value means that 83.8% changes in online orders is explained by unique, visits variable. c. Predicting: Online orders = 7.95 + 0.075 unique visits Given unique visits = 80,000 Online orders = 7.95 + 0.075 (80,000) Online orders predicted = 6007.95 Question 4: a. Stacked chart b. Probability: total pr Store loyal 170 34.00% Channel adopter 140 28.00% Traditional multichannel shopper 105 21.00% Functional shopper 85 17.00% Probability that a respondent is a Channel adopter is 0.28 or 28% c. respondent is a channel adopter, if: y X Baby Boomer total Store loyal 20 35 115 170 Channel adopter 64 61 15 140 Traditional multichannel shopper 18 47 40 105 Functional shopper 10 12 63 85 total 112 155 233 500 Joint probability: joint probability y X Baby Boomer Store loyal 0.04 0.07 0.23 0.34 Channel adopter 0.128 0.122 0.03 0.28 Traditional multichannel shopper 0.036 0.094 0.08 0.21 Functional shopper 0.02 0.024 0.126 0.17 0.224 0.31 0.466 1 (i) He or she is Generation Y. =0.128/0.28 = 0.4571 (ii) He or she is Generation X. =0.122/0.28 =0.43571 (iii) He or she is a Baby Boomer. =0.03/0.28 =0.1071 d. traditional multichannel shopper Probability that a respondent is a traditional multichannel shopper is 0.21 Respondent is a traditional multichannel shopper, if: (i) He or she is Generation Y. =0.036/0.21 = 0.1714 (ii) He or she is Generation X. =0.094/0.21 =0.44762 (iii) He or she is a Baby Boomer. =0.08/0.21 =0.380952 e. channel adopter, if: Joint probability: male female Store loyal 0.21 0.13 0.34 Channel adopter 0.076 0.204 0.28 Traditional multichannel shopper 0.088 0.122 0.21 Functional shopper 0.142 0.028 0.17 0.516 0.484 1 (i) They are male. =0.076/0.28 =0.3 (ii) They are female. =0.204/0.28 =0.72857 f. traditional multichannel shopper i. They are male. =0.088/0.21 =0.4190 ii. They are female. =0.122/0.21 =0.5810 Read More