The data looks like the following:
Item Customer Year 01 - Jan 02 - Feb 03 - Mar
BA - Item 01 Country 10 2019 15 10 10
BA - Item 02 Country 3 2019
BA - Item 02 Country 6 2019 60
BA - Item 02 Country 9 2019
BA - Item 02 Country 10 2019 35 20 20
BA - Item 02 Country 11 2019
BA - Item 02 Country 12 2019
BA - Item 03 Country 10 2019 10 10 10
BO - Item 2 Country 10 2019 175 200 200
BO - Item 4 Country 10 2019 50 50 50
BO - Item 6 Country 10 2019 50 50 50
BO - Item 7 Country 1 2019
BO - Item 7 Country 2 2019
BO - Item 7 Country 3 2019
BO - Item 7 Country 9 2019
BO - Item 7 Country 10 2019 250 300 350
BO - Item 8 Country 1 2019
BO - Item 8 Country 2 2019
BO - Item 8 Country 3 2019
BO - Item 8 Country 9 2019
BO - Item 8 Country 10 2019 200 250 300
BO - Item 8 Country 14 2019 50
BO - Item 9 Country 1 2019
BO - Item 9 Country 9 2019
BO - Item 9 Country 10 2019 100 125 150
BO - Item 10 Country 10 2019 120 150 200
BO - Item 11 Country 1 2019
BO - Item 11 Country 2 2019
BO - Item 11 Country 10 2019 800 1500 1500
BO - Item 13 Country 10 2019 50 50 50
BO - Item 13 Country 14 2019
BO - Item 14 Country 3 2019
BO - Item 14 Country 9 2019
BO - Item 17 Country 10 2019 4 4 4
BO - Item 18 Country 10 2019 30 30 30
BO - Item 20 Country 3 2019
BO - Item 20 Country 9 2019
BO - Item 20 Country 10 2019 50 50 50
BO - Item 20 Country 14 2019
BO - Item 20 Country 15 2019
BO - Item 21 Country 10 2019 2 1 2
BO - Item 22 Country 3 2019
BO - Item 22 Country 9 2019
BO - Item 22 Country 10 2019 45 75 80
BO - Item 23 Country 2 2019
BO - Item 23 Country 3 2019
BO - Item 23 Country 9 2019
BO - Item 23 Country 10 2019 40 25 35
BO - Item 24 Country 10 2019 1 1 1
BO - Item 25 Country 10 2019 25 25 45
BO - Item 26 Country 10 2019 25 25 40
BO - Item 27 Country 2 2019
BO - Item 27 Country 10 2019 75 90 100