Part 1

The Fast Fashion Group (FFG) is a fast fashion retailer company located in Spain and is one of the largest retailers in the country (their overall sales has grown by about 50% in five years). FFG sells clothes and accessories. The customer is at the heart of its business model, which strives to link customer demand to manufacturing, and manufacturing to distribution. Michelle Taylor, the supply chain manager of the company, is concerned about fast moving products and high volatility. Since the number of SKUs has grown dramatically in the last year, she believes that the SKUs should be segmented and has asked you to run an ABC analysis on a sample of 60 SKUs (all of them are jeans).

In the linked file here, you will find the information you need regarding the unit cost, unit sales price, weekly sales volume, and weekly sales standard deviation for each SKU. Each SKU is represented by its ID, which is an alphanumeric code (e.g.: J46598). All currencies are in Euros.

Which SKU has the most volatile demand (highest coefficient of variation)? The coefficient of variation is defined as . Just enter the SKU_ID.

J47511 Explanation The coefficient of variation is defined as Cv=/ where is the weekly sales standard deviation for each SKU and is the average weekly sales for each SKU. You have to calculate the CV for each SKU independently and then compare them. Following this procedure, Product J47511has the highest coefficient of variation at 0.986. The coefficient of variation is used to measure the volatility or dispersion of a distribution with respect to the mean of the population.

Part 2

When Michelle Taylor saw the high volatility of this item, she asked you to analyze the volatility of the sample of 60 SKUs in terms of Coefficient of Variation (CV). (Note: When calculating the CV of each item, please do not round it.)

What is the percentage of SKUs that have a CV greater than or equal to 0.54?

Just enter a value and do not include the % sign. For example, if you think the value is 35.54%, just enter 35.5 (round to nearest 1 decimal places)

Part 3

In addition to the volatility, Michelle Taylor also wants you to analyze the fast moving items. Michelle believes that fast moving, highly volatile SKUs should be managed separately, with more time spent on improving forecasting capabilities. She has decided that the criteria for fast moving is to be selling 38 or more units per week, and the criteria for highly volatile is to have a CV greater than or equal to 0.54. (Note: When calculating the CV of each item, please do not round it.)

What percentage of the total profit is contributed by fast moving and highly volatile SKUs (both criteria satisfied)?

Just enter a value and do not include the % sign. For example, if you think the value is 35.54%, just enter 35.5 (round to nearest 1 decimal place)

Part 4

Let's run now the ABC analysis based on profitability.

What percentage of the total profit is contributed by the top 15 SKUs of the 60, as ranked by profit?

Just enter the value and do not include the % sign. For example, if you think the value is 35.54%, just enter 35.5 (round to nearest 1 decimal place)

Part 5

What percentage of the total profit is contributed by the bottom 25 SKUs of the 60, ranked by profit?

Just enter the value and do not include the % sign. For example, if you think the value is 35.54%, just enter 35.5 (round to nearest 1 decimal place)

Part 6

Demand uncertainty, particularly in the fast fashion industry, is a significant concern. For this reason, Michelle Taylor has decided to take a closer look to the distribution of the demand of several items.

She has requested you to analyze the demand of one of the company's basic jeans, the "regular fit straight blue jeans" (SKU J34052X). The weekly demand for this item is found to be normally distributed, with a mean of 53 units and a standard deviation of 18 units. (Note that this SKU is not in the data set.)

If you stocked 65 units of the "regular fit straight blue jeans" (SKU J34052X) for week 1, what is the probability that the demand during week 1 exceeds the stocking level?

Enter the probability as a number between 0 and 1 with 4 decimal digits (e.g. for 12.34%, enter 0.1234 as your answer).

Part 7

How many "regular fit straight blue jeans" (SKU J34052X) do you need to have in stock to reduce the probability that the demand exceeds the stocking level to 14.0%?

Enter your answer as an integer.

Part 8

A few regular customers have asked FFG for SKU J80132Z, which is the "ultra stretch blue jeans" model. Michelle Taylor has agreed to stock it, even though sales for this SKU have been slow. After analyzing past data, you have found that a Poisson distribution with a mean of 2.6 closely fits the weekly sales pattern for this SKU. The cost of the SKU J80132Z is $18.25 and the unit sales price is $39.99.

What is the probability of selling 0 units of SKU J80132Z ("ultra stretch blue jeans") in a week?

Enter the probability as a fraction, e.g. as a number between 0 and 1 with 4 decimal digits (e.g. for 12.34%, enter 0.1234 as your answer).

Part 9

After seeing the previous result, Michelle Taylor decided to stock only 6 units of SKU J80132Z every week.

What is the probability that your demand for SKU J80132Z ("ultra stretch blue jeans") exceeds the stock level set by Michelle of 6 units?

Enter the probability as a fraction, e.g. as a number between 0 and 1 with 4 decimal digits (e.g. for 12.34%, enter 0.1234 as your answer).

Part 10

What is the probability that your demand for SKU J80132Z ("ultra stretch blue jeans") will be no less than 1 unit and no more than 6 units?

Enter the probability as a fraction, e.g. as a number between 0 and 1 with 4 decimal digits (e.g. for 12.34%, enter 0.1234 as your answer).

Data on SKUs 36 J16144 20.86 32.14 21 SKU 2.1 Cost Price Sales Dev 37 164845 5.63 17.85 40.6 J47511 23.86 32.3 45.91 57.6 55.2 38 J60546 22.52 35.24 194912 23.78 24.5 10.3 55.4 67 37.8 39 199347 12.51 27.48 54.3 128213 23.04 27.6 33.33 14.5 1.7 40 18948 13.41 154714 26.71 74 5.87 62.2 40.42 59.4 18 125949 6.12 121415 20.02 42.3 20.8 13.82 35.07 7.9 5.5 12 J41950 5.05 28.34 J10116 17.11 17.6 43.73 15.2 37.8 4.9 120651 7.62 42 180517 6.4 19.97 4.6 48.99 9.2 7.4 14 130852 23.73 59.03 33.9 188718 22.88 16.3 61.58 32.2 26.5 45 J84153 20.04 42.17 197719 22.01 27.3 4.7 46.38 57.8 18 183054 26.57 56.33 5.3 182320 21.21 3.3 55.03 23.5 15.9 47 J74555 16.59 50.29 66.3 188721 29.6 13.16 35.18 77.1 14.8 J31256 17.73 57.31 48.6 172422 43.3 12.58 27.07 4.4 3.1 19057 16.49 54.97 147823 20.65 7.8 1.2 32.53 60.9 23.1 50 J75658 18.51 50.15 34.4 178724 32 25.48 41.91 2.4 1.8 175459 29.67 66.1 163025 8.45 3.7 2 47.9 47.7 5.9 52 133860 22.82 48.4 J81226 26.7 16.99 45.87 40.2 39.5 18.8 53 170861 12.54 51 10.3 8 149127 5.74 31.93 15.4 10.6 154162 12.14 33.36 20 14928 68.6 20.54 19.5 59.79 33.1 25.3 35 123063 27.78 60.5 187829 53.3 24.05 47.4 39.32 83.7 26.3 56 149164 21.52 47.45 120930 37.7 22.47 11.1 35.35 67.7 43.5 57 140965 27.59 49.25 199931 80.7 18.7 26.75 40.7 72.6 50.2 153866 21.95 159632 15.64 32.24 56.2 19 41.28 48.9 45.6 59 J31867 11.21 43.52 196733 7.1 84.7 14.4 39.22 25.5 18.4 50 16668 9.52 16634 20.11 10.38 77.6 30.7 24.14 26.6 9.8 51 J87469 21.19 34.83 174235 31.7 29.15 61.47 28.9 53.2 35 52 165070 15.03 172936 36.31 24.78 79.4 44.88 52.2 53.7 33.1 53 163637 11.82 36.76 4.6 2.2 124438 54 12.86 51.03 21.2 Data on SKUs 8.6 160439 15.64 35.41 28.7 23.3 Threshold Threshold Threshold Threshold 147240 24.27 59.58 14.8 6.5 65 for Q2 for Q3 for Q4 for Q5 153041 16.5 52.2 64.4 25.4 0.54 0.39441 U.3/103 38 15 25 34 181742 11.54 21.77 16.4 28.8 0.62069 77.62169 15 122343 7.03 19.84 22.1 9.5 0.429864 0.122388