The table given below provides the details of monthly sales (in lakhs of rupees) and the value of products returned by the customers (as a percentage of sales) of an e-commerce company for three product categories for the year 2024. Net sales (in lakhs of rupees) is defined as the difference between sales (in lakhs of rupees) and the value of products returned (in lakhs of rupees).
Among the following four months, for which month the value of the Footwear returned (in lakhs of rupees) was the highest?
Show Hint
- March
- July
- June
- September
The Correct Option is B
Solution and Explanation
The question asks to find the month with the highest value of returned Footwear. The value of returned products is calculated by multiplying the sales of that month by the percentage of products returned.
Step 2: Key Formula or Approach:
Value of Returned Footwear = (Sales of Footwear) \( \times \) (Percentage of Footwear Returned)
Step 3: Detailed Explanation:
We need to calculate the value of returned Footwear for each of the four months listed in the options.
March: Sales = 121 lakhs, Return % = 7%. Value returned = \( 121 \times 0.07 = 8.47 \) lakhs.
July: Sales = 119 lakhs, Return % = 9%. Value returned = \( 119 \times 0.09 = 10.71 \) lakhs.
June: Sales = 111 lakhs, Return % = 5%. Value returned = \( 111 \times 0.05 = 5.55 \) lakhs.
September: Sales = 118 lakhs, Return % = 9%. Value returned = \( 118 \times 0.09 = 10.62 \) lakhs.
Comparing the calculated values: March: 8.47 lakhs July: 10.71 lakhs June: 5.55 lakhs September: 10.62 lakhs The highest value is 10.71 lakhs, which occurred in July.
Step 4: Final Answer:
The value of the Footwear returned was the highest in July.
By what percentage the net sales for June increased as compared to May in the Footwear category?
Show Hint
- 7.21 percent
- 18.97 percent
- 62.58 percent
- 60.87 percent
The Correct Option is C
Solution and Explanation
The question asks for the percentage increase in net sales from May to June for the Footwear category. First, we need to calculate the net sales for both months. Net sales is defined as Sales minus the Value of products returned.
Step 2: Key Formula or Approach:
1. Calculate Net Sales = Sales \( \times \) (1 - Return Percentage). 2. Calculate Percentage Increase = \( \frac{\text{Net Sales in June} - \text{Net Sales in May}}{\text{Net Sales in May}} \times 100% \).
Step 3: Detailed Explanation:
Calculate Net Sales for May (Footwear):
Sales in May = 69 lakhs
Return Percentage in May = 6%
Value Returned in May = \( 69 \times 0.06 = 4.14 \) lakhs
Net Sales in May = \( 69 - 4.14 = 64.86 \) lakhs
Calculate Net Sales for June (Footwear):
Sales in June = 111 lakhs
Return Percentage in June = 5%
Value Returned in June = \( 111 \times 0.05 = 5.55 \) lakhs
Net Sales in June = \( 111 - 5.55 = 105.45 \) lakhs
Calculate Percentage Increase:
Increase in Net Sales = \( 105.45 - 64.86 = 40.59 \) lakhs
Percentage Increase = \( \frac{40.59}{64.86} \times 100% \)
Percentage Increase \( \approx 0.62583 \times 100% \approx 62.58% \)
Step 4: Final Answer:
The net sales for June increased by approximately 62.58 percent as compared to May.
Which month had highest percentage decline in monthly sales as compared to previous month for the Apparel category?
Show Hint
- October
- June
- March
- December
The Correct Option is A
Solution and Explanation
We need to calculate the percentage decline in sales for the Apparel category for each of the given months compared to its preceding month and identify the month with the highest percentage decline.
Step 2: Key Formula or Approach:
Percentage Decline = \( \frac{\text{Sales in Previous Month} - \text{Sales in Current Month}}{\text{Sales in Previous Month}} \times 100% \). We only need to perform this calculation for months that showed a decline.
Step 3: Detailed Explanation:
Let's calculate the percentage decline for each of the months listed in the options.
October: Sales in October = 222 lakhs. Sales in September (previous month) = 288 lakhs. Decline = \( 288 - 222 = 66 \) lakhs. Percentage Decline = \( \frac{66}{288} \times 100% \approx 22.92% \).
June: Sales in June = 230 lakhs. Sales in May (previous month) = 249 lakhs. Decline = \( 249 - 230 = 19 \) lakhs. Percentage Decline = \( \frac{19}{249} \times 100% \approx 7.63% \).
March: Sales in March = 236 lakhs. Sales in February (previous month) = 279 lakhs. Decline = \( 279 - 236 = 43 \) lakhs. Percentage Decline = \( \frac{43}{279} \times 100% \approx 15.41% \).
December: Sales in December = 221 lakhs. Sales in November (previous month) = 228 lakhs. Decline = \( 228 - 221 = 7 \) lakhs. Percentage Decline = \( \frac{7}{228} \times 100% \approx 3.07% \).
Comparing the percentage declines: October (\( \approx 22.92% \)), June (\( \approx 7.63% \)), March (\( \approx 15.41% \)), and December (\( \approx 3.07% \)). The highest percentage decline occurred in October.
Step 4: Final Answer:
October had the highest percentage decline in monthly sales as compared to the previous month for the Apparel category.
For which categories the value of the products returned (as a percentage of sales) increased for three consecutive months?
Show Hint
- Only Footwear
- Both Apparel and Footwear
- Only Apparel
- Only Electronics
The Correct Option is A
Solution and Explanation
We need to examine the "Value of products returned (as a percentage of sales)" columns for each of the three categories and find if there is any instance where the percentage increases for three months in a row.
Step 2: Key Formula or Approach:
Scan each category's return percentage data month by month, looking for a sequence of the form \( P_{month\ N}<P_{month\ N+1}<P_{month\ N+2} \).
Step 3: Detailed Explanation:
Let's check each category:
Apparel Return %: 13, 16, 20, 16, 12, 19, 17, 16, 10, 15, 14, 18. The sequence increases from Jan to March (13% to 16% to 20%), which is a two-month increase, not three. No other sequence shows three consecutive increases.
Footwear Return %: 7, 9, 7, 8, 6, 5, 9, 6, 9, 8, 9, 10. Let's check the sequence from October to December: October: 8% November: 9% (Increase from October) December: 10% (Increase from November) This is an increase for three consecutive months (from the value in September to October is a decrease, but the question asks for the value *increasing* for three months. So, we look at the values themselves: 8<9<10. This trend occurs over the period Oct-Nov-Dec).
Electronics Return %: 2, 3, 2, 1, 4, 3, 4, 2, 5, 2, 4, 1. There is no sequence of three consecutive increases in this category. For example, 1% → 4% (Apr-May) is followed by 4% → 3% (May-Jun).
Only the Footwear category shows an increase in the return percentage for three consecutive months (October, November, December).
Step 4: Final Answer:
The value of products returned (as a percentage of sales) increased for three consecutive months for Only Footwear.
Among the following four months, for which month the contribution of the Apparel category in the total monthly sales was the highest?
Show Hint
- January
- April
- August
- December
The Correct Option is B
Solution and Explanation
The question asks for the month where Apparel sales constituted the largest percentage of the total sales for that month. We need to calculate this percentage for each of the four given months.
Step 2: Key Formula or Approach:
Contribution % = \( \frac{\text{Apparel Sales}}{\text{Total Sales (Apparel + Footwear + Electronics)}} \times 100% \).
Step 3: Detailed Explanation:
We need to calculate the total sales for each specified month and then find the contribution of Apparel sales.
January: Apparel Sales = 262. Total Sales = 262 (Apparel) + 104 (Footwear) + 289 (Electronics) = 655 lakhs. Contribution = \( \frac{262}{655} \times 100% \approx 40.00% \).
April: Apparel Sales = 258. Total Sales = 258 (Apparel) + 58 (Footwear) + 325 (Electronics) = 641 lakhs. Contribution = \( \frac{258}{641} \times 100% \approx 40.25% \).
August: Apparel Sales = 252. Total Sales = 252 (Apparel) + 60 (Footwear) + 336 (Electronics) = 648 lakhs. Contribution = \( \frac{252}{648} \times 100% \approx 38.89% \).
December: Apparel Sales = 221. Total Sales = 221 (Apparel) + 86 (Footwear) + 268 (Electronics) = 575 lakhs. Contribution = \( \frac{221}{575} \times 100% \approx 38.43% \).
Comparing the contribution percentages: January: \( \approx 40.00% \) April: \( \approx 40.25% \) August: \( \approx 38.89% \) December: \( \approx 38.43% \) The highest contribution was in April.
Step 4: Final Answer:
The contribution of the Apparel category to the total monthly sales was the highest in April.
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Lucia Rahilly (Global Editorial Director, The McKinsey Podcast): Today we’re talking about the next big arenas of competition, about the industries that will matter most in the global business landscape, which you describe as arenas of competition. What do we mean when we use this term?
Chris Bradley (Director, McKinsey Global Institute): If I go back and look at the top ten companies in 2005, they were in traditional industries such as oil and gas, retail, industrials, and pharmaceuticals. The average company was worth about $250 billion. If I advance the clock forward to 2020, nine in ten of those companies have been replaced, and by companies that are eight times bigger than the old guards.
And this new batch of companies comes from these new arenas or competitive sectors. In fact, they’re so different that we have a nickname for them. If you’re a fan of Harry Potter, it’s wizards versus muggles.
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