Indian manufacturing company wants to launch a new business unit with focus on global trade and logistics in USA, Canada and Australia. Looking for insights around potential commodities, trade amount, year, quantity of that commodity and countries.

1. Global Trade Analysis
(Using Tableau Public)
Submitted By Alok Tayal

2. Problem Statement
 Indian manufacturing company wants to launch a new business unit with focus on
global trade and logistics in USA, Canada and Australia
 Import and Export data available for 12 major categories and the commodities
under them along with year (from 1988 to 2016), Trade amount (USD) & quantity.
 Looking for insights around potential commodities, trade amount, year, quantity of
that commodity and countries.
 The visualization should be innovative and interactive dashboards which allows
company to deep dive into any parameters.
 Clean up the commodity data for missing values, required computed fields and
incorrect values.

3. Assumptions & Understanding
 As the company is focusing on global trade and logistics in USA, Canada and
Australia, following criteria should be important to select the potential commodities
 Higher Trade Value for import and export
 Spread of trade across multiple countries
 Positive trend of trade value in the last few years
 Lower Weight to Revenue ratio is preferable from logistics side as company will
get higher revenue for less weight and can accommodate more commodities

4. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Lower Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

5. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

6. Data insights for cleaning (1 / 2)
1. trade_usd column has values up to ~4 Billion USD. For better visualization, convert this column in Million USD.
2. There are 4 different type of units (Wt. in Kg, Vol in liters, No of items & No qty) present for various commodities. To analyse them uniformly,
convert all them into one unit measurement. Point a, b, c & d are the efforts to make all the records consolidated into weight_kg column.
a. Total observations are 59090 out of which 53962 observations are having quantity_name as “Weight in kilograms” . In this case quantity
and weight_kg column has same values. Therefore no action required.
b. For quantity_name as “Volume in litres”, 480 records exist. In this case, ratio of weight_kg and quantity is approximately ~1. This is due to
different viscosity of the fluid. In this case, there are 31 records, where weight_kg is 0. This clearly indicates that weight column has missing
values. Copy the quantity column values into weight_kg column.
country_or_areayear comm_codecommodity flow trade_usd trade_musd weight_kg quantity_name quantity category
Australia 2016 10111 Horses, live pure-bred breeding Export 128577553 128.577553 900450 Number of items 1882 01_live_animals
Australia 2016 10119 Horses, live except pure-bred breeding Re-Import 4928989 4.928989 47240 Number of items 104 01_live_animals
Australia 2016 10119 Horses, live except pure-bred breeding Export 11812782 11.812782 153587 Number of items 276 01_live_animals
Australia 2016 10119 Horses, live except pure-bred breeding Import 90430302 90.430302 1082493 Number of items 2073 01_live_animals
Australia 2016 10120 Asses, mules and hinnies, live Export 58473 0.058473 5805 Number of items 416 01_live_animals
country_or_areayear comm_codecommodity flow trade_musd weight_kg weight_kg/quantityquantity_name quantity category
Australia 1998 40310 Yogurt Re-Export 0.000373 183 0.968253968 Volume in litres 189 04_dairy_products_eggs_honey_edible_animal_product_nes
Australia 1994 40130 Milk and cream not concentrated nor sweetened < 6% faExport 7.384149 12686062 0.990098981 Volume in litres 12812923 04_dairy_products_eggs_honey_edible_animal_product_nes
Australia 1993 40110 Milk not concentrated nor sweetened < 1% fatImport 0.009479 5859 1.029882229 Volume in litres 5689 04_dairy_products_eggs_honey_edible_animal_product_nes
Australia 1992 40110 Milk not concentrated nor sweetened < 1% fatImport 0.00012 27 1.038461538 Volume in litres 26 04_dairy_products_eggs_honey_edible_animal_product_nes
Australia 1992 40130 Milk and cream not concentrated nor sweetened < 6% faImport 0.000041 24 1 Volume in litres 24 04_dairy_products_eggs_honey_edible_animal_product_nes
Canada 1992 40120 Milk not concentrated nor sweetened 1-6% fatExport 0.262212 351198 1.010101068 Volume in litres 347686 04_dairy_products_eggs_honey_edible_animal_product_nes
Australia 1988 81210 Cherries provisionally preserved Import 1.428488 0 0 Volume in litres 998500 08_edible_fruit_nuts_peel_of_citrus_fruit_melons

7. Data insights for cleaning (2 / 2)
c. For quantity_name as “Number of items”, 3124 records exists. In this case, 1234 records don’t have weight (either 0 or blank). Since
very limited information available about the dependency of weight on trade_usd and quantity, delete these 1234 records.
d. For quantity_name as “No Quantity”, 1524 records exists. In this case, 1207 records don’t have weight (either 0 or blank). Since very
limited information available about the dependency of weight on trade_usd, delete these 1207 records.
3. After cleaning up all the above, there are 28 records which has weight = 0. Remove these records. Total records remaining = 56621
4. Remove quantity_name and quantity column as they no longer required.
5. Insert a new column as “wt/trade” and calculate the formula as “weight / trade_musd”

8. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

9. Country wise Important categories based upon trade value
This chart is developed to visualize
country wise important categories
based upon the higher trade value.
Based upon these charts, following
categories are important for
individual countries
 Australia – Category 2, 3, 4 & 10
 Canada – Category 2,3,7,8,10,12
 USA – Category 1, 6 & 10

10. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

11. Positive trend of Categories by trade value and their spread
This chart provides 2 insights
1. Positive trend of trade value in the last few years
2. Positive spread of trade across multiple countries
Based upon this chart, following categories seem important and demonstrate positive trend over a period of time on trade value with
2+ countries.
 Primary Categories – 2, 10
 Secondary Categories – 1, 3, 5, 6, 12

12. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

13. Import / Export Insights
Looking at the following visualization, it is clear that majority of the trade belongs to Export. Only "Bovine animals,
live, except pure-bred breeding" & "Cut flowers and flower buds for bouquets etc" has significant Import for USA .

14. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

15. Potential commodities under the top categories
 By restricting the total trade value to >= 15,000 MUSD, the trend starts emerging with 14 top commodities spread
across 5 main categories.

16. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

17. Weight and Trade Ratio Insights
 This indicates that top 6 commodities have very less weight/trade ratio compare to others. Therefore if
weight is a parameter to consider against trade value, one should look at first 6 commodities as
potential ones to do the business.

18. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

19. Conclusions & Recommendations (1/3)
 From the analysis, by keeping the trade value as one of the significant parameter, the trend has
emerged with 14 top potential commodities spread across 5 main categories.
 Only "Bovine animals, live, except pure-bred breeding" & "Cut flowers and flower buds for bouquets etc"
has significant Import and that is for only USA.

20. Conclusions & Recommendations (2/3)
 From the trade value perspective and positive trend, 6 potential commodities are recommended
(Marked in Red Brown Box)
 From Weight / Trade value ratio, top 6 potential commodities are recommended (Marked in Blue color)
 From Import/Export perspective, Category 1 and Category 6 is preferred. (Marked in Green Color)

21. Conclusions & Recommendations (3/3)
 By looking at the complete analysis, top 9 potential commodities are identified among 430+
commodities. These commodities fulfil 1 or more assumptions considered in the entire study. These top 9
commodities are marked with Green Arrow ( ) in the previous screen & listed here
Sr. Category Comm Code Commodity
1 01_live_animals 10290 Bovine animals, live, except pure-bred breeding
2 02_meat_and_edible_meat_offal 20120 Bovine cuts bone in, fresh or chilled
3 20220 Bovine cuts bone in, frozen
4 20319 Swine cuts, fresh or chilled, nes
5 20329 Swine cuts, frozen nes
6 06_live_trees_plants_bulbs_roots_cut_flowers_etc 60310 Cut flowers and flower buds for bouquets, etc., fresh
7 10_cereals 100590 Maize except seed corn
8 100190 Wheat except durum wheat, and meslin
9 12_oil_seed_oleagic_fruits_grain_seed_fruit_etc_ne 120500 Rape or colza seeds

22. Approach
 Clean up the commodity data for missing values, required computed fields and incorrect values.
 Visualization
 Country wise Important categories based upon trade value
 Positive trend of Categories by trade value in the last few years
 Spread of trade across multiple countries
 Import / Export Insights
 Identify potential important commodities under the top categories
 Weight to Revenue ratio insights for more profitable categories
 Conclusions & Recommendations

23. Thank you