In the supply chain business, when optimizing stock levels, we are mainly interested in reducing the stock as much as possible while avoiding stock-outs in the warehouse, or in other words, the goal is to have the right product in the right place at the right time. An oft-forgotten segment of stock optimization is the price of transportation. Without optimizing transportation, significant amounts of money can be wasted on the transportation of empty containers. In other words, we are spending money on the transportation of – air.

1. DSC Adria 2023
Nino Požar
Data Scientist & Product Owner, BE-terna
+385 91 4007 075
nino.pozar@be-terna.com
https://www.linkedin.com/in/nino-pozar
WHAT ARE WE SHIPPING –
FRESH AIR OR VALUABLE ITEMS?

2. 1. Introduction & overview
2. Transportation optimisation
1. The problem
2. Working example
3. Use case example
3. Conclusions
Agenda

3. Introduction
Challenge
1|DEMAND
FORECASTING
2|OPTIMISE
PRODUCTION
PLANNING
3|SCHEDULE
PURCHASE
ORDERS

4. Introduction
Challenge
2|OPTIMISE
PRODUCTION
PLANNING
4|TRANSFERS
BETWEEN STORES
1|DEMAND
FORECASTING
3|SCHEDULE
PURCHASE
ORDERS

6. ?
?
?
?
?

7. 1.
2.
5|TRANSPORTATION
OPTIMISATION

8. The problem
Bin packaging problem
Knapsack problem
Transportation

9. Transportation optimisation problem
Bin packaging & knapsack problem
Bin packaging problem
• items of different volumes must be packed into a finite number of bins/containers each of a fixed given volume
in a way that minimizes the number of bins used
• minimize the number of trucks (containers)

10. Transportation optimisation problem
Bin packaging & knapsack problem
Bin packaging problem
• items of different volumes must be packed into a finite number of bins/containers each of a fixed given volume
in a way that minimizes the number of bins used
• minimize the number of trucks (containers)

11. 0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
20 14
20 11 19
4
23
10
3
Transportation optimisation problem
Bin packaging & knapsack problem
Knapsack problem
• given a set of items, each with a weight and a value, determine the number of each item to include in a
collection so that the total weight is less than or equal to a given limit and the total value is as large as possible
• fill trucks with the most valuable items
10 20
12
4 17
14
23
3
14
5
17
50
19
20 10
10 11

12. 0
1
2
3
4
5
6
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
8
9
20 14
20 11 19
23
10
3
17
20 14
20 11 19
4
23
10
3
Transportation optimisation problem
Bin packaging & knapsack problem
Knapsack problem
• given a set of items, each with a weight and a value, determine the number of each item to include in a
collection so that the total weight is less than or equal to a given limit and the total value is as large as possible
• fill trucks with the most valuable items
10 20
12
4 17
14
23
3
14
5
17
50
19
20 10
10 11

13. 0
1
2
3
4
5
6
0
1
2
3
4
5
6
7
8
2
3
4
5
6
7
8
9
20 14
20 11 19
23
10
17
17
20 14
20 11 19
23
10
3
17
Transportation optimisation problem
Bin packaging & knapsack problem
Knapsack problem
• given a set of items, each with a weight and a value, determine the number of each item to include in a
collection so that the total weight is less than or equal to a given limit and the total value is as large as possible
• fill trucks with the most valuable items
10 20
12
4 17
14
23
3
14
5
17
50
19
20 10
10 11

14. 0
1
2
3
4
5
6
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
20 14
20 11 19
23
10
17
17
Transportation optimisation problem
Bin packaging & knapsack problem
Knapsack problem
• given a set of items, each with a weight and a value, determine the number of each item to include in a
collection so that the total weight is less than or equal to a given limit and the total value is as large as possible
• fill trucks with the most valuable items
10 20
12
4 17
14
23
3
14
5
17
50
19
20 10
10 11

15. 0
1
2
3
4
5
6
4
5
6
7
8
9
0
1
2
3
4
5
6
Transportation optimisation problem
Bin packaging & knapsack problem
Knapsack problem
• given a set of items, each with a weight and a value, determine the number of each item to include in a
collection so that the total weight is less than or equal to a given limit and the total value is as large as possible
• fill trucks with the most valuable items
23
20 17 14
17
50 20 11 19
10 12
4
3
14
5
10
10

16. Working example
Ana
CFO
Bob
Purchaser
Dan
Warehouse mng
…and his colleagues
Transportation

17. A
A A
B
A
C
C
B
C
A A
A
C
C
C
C
1. First (current) order
2. Next order
Order priority
1. A
2. B
3. C
Classification priority
Ana
CFO
Dan
Warehouse mng
Bob
Purchaser
Round
SKUs!
A A
A
A
C
C
B
C
A
A
A
C
C
C

18. A
A A
B
A
C
C
B
C
A A
A
C
C
C
C
1. ROUNDING
C
2. Next order
Order priority
1. A
2. B
3. C
Classification priority
1. First (current) order
Ana
CFO
Bob
Purchaser
Dan
Warehouse mng
A A
A
A
C
C
B
C
A
A
A
C
C
C
Priority
SKUs!
Round
SKUs!

19. C
2. FILL THE TRUCKS
A
A A
B
A
C
C
B
A A
A
A A
A
A
C
C
B
C
A
A
A
C
C
C
C
C
C
C
C
C
C
C
C
C
A
A
A
A
A
1. First (current) order
2. Next order
Order priority
1. A
2. B
3. C
Classification priority
Ana
CFO
Dan
Warehouse mng
Bob
Purchaser
Fill 2nd
truck!
Group
SKUs!
Priority
SKUs!
Add
truck!

20. A
A
A
A
A
C
3. REARRANGE THE TRUCKS
A
A
A
A
A
C C
A
A
A
A
A
A
A
C
C
C
B
C
B
A
A
A
C
C
Ana
CFO
Dan
Warehouse mng
Bob
Purchaser
Group
SKUs!
Round
SKUs!
C C
C
C

21. A
A
A
B
A
C
C
B
A A
3. REARRANGE THE TRUCKS – ROUNDING
A
A
C
Ana
CFO
Dan
Warehouse mng
Bob
Purchaser
Round
SKUs!
Priority
SKUs!
C
C C
C
C

22. A | OOS
A
A
B
A
C
C
B
A A
C
A
C | OOS
C | ALERT
C
C
3. REARRANGE THE TRUCKS
C
A
A
LET'S
GO!
I’M
READY
Ana
CFO
Dan
Warehouse mng
Bob
Purchaser
Priority
SKUs!

23. Bob
Purchaser
How can AI help Bob?
Use case

24. Bob
Purchaser
• Bob is presented with results – BI application
Presented
with
results
Change & confirm
recomm.
Reoptimise
cargo
Check new
results
Confirm
results

25. Bob
Purchaser
• Bob is presented with results – BI application
Presented
with
results
Change & confirm
recomm.
Reoptimise
cargo
Check new
results
Confirm
results

26. Bob
Purchaser
• Bob is presented with results – BI application
Presented
with
results
Change & confirm
recomm.
Reoptimise
cargo
Check new
results
Confirm
results

27. Bob
Purchaser
• Bob is presented with results – BI application
Presented
with
results
Change & confirm
recomm.
Reoptimise
cargo
Check new
results
Confirm
results

28. Bob
Purchaser
• Bob is presented with results – BI application
Presented
with
results
Change & confirm
recomm.
Reoptimise
cargo
Check new
results
Confirm
results

29. Bob
Purchaser
• Bob is presented with results – BI application
Presented
with
results
Change & confirm
recomm.
Reoptimise
cargo
Check new
results
Confirm
results

30. Conclusions?
Possibility of controlling incoming orders
• Date of expected order arrivals
• Date of stock availability
Service level is increased
• Less delays in production
• Less delays in delivery
Advantage in managing warehouse
• Less jams in warehouses
Verification of orders through dashboard
• Ability to modify & recalculate
• Direct integration with ERP
Automation as the ordering procedure is
transferred into algorithms of ML platform
• Increases productivity
• Decreases manual work
• Quicker learning curve for new
purchasers (replacements)
Sustainability effect
• Decrease in carbon footprint Financial benefits
• Less money spent on transportation

31. Thank you!
Nino Požar
+385 91 4007 075
nino.pozar@be-terna.com
https://www.linkedin.com/in/nino-pozar