LOGISTICS
irregularities in objects, whilst helping decision makers manage predictive maintenance procedures.
ENHANCING PRODUCTIVITY IN SUPPLY CHAIN AND LOGISTICS
As a result, Computer Vision is expected to play a key role in the digital transformation of logistics and supply chain management. According to recent Panasonic Connect Europe research involving 300 transformation and AI/ Computer Vision decision-makers, Computer Vision technology is expected to drive productivity increases of 42 percent on average over the next three years.
Logistics and supply chain respondents expect to see productivity increases of a similar level (41 percent). The research also found that supply chain and logistics companies are mainly utilising Computer Vision technology for quality control testing, or inspection purposes (42 percent) and production line monitoring (28
percent).
Computer Vision is also being used for volume measurement and pallet control. This is achieved through instantly measuring goods’ orientation and available space in a pallet or other containers, and calculating the available capacity before loading. This optimises accuracy.
However, when asked about the main technological barriers to deploying Computer Vision in supply chain and logistics, respondents cited maintaining Computer Vision knowledge within the business (42 percent), a lack of third-party/outsourced support to implement the technology (32 percent) and high implementation costs (30 percent). Whilst these are challenges that are affecting most industries surveyed in our report, knowledge retention is a particularly pertinent pain point for supply chain and logistics companies, especially with labour shortages.
AUTOMATING MANUAL TASKS TO ALLEVIATE LABOUR SHORTAGES Computer Vision technology can help eliminate time-consuming manual work, alleviating some of the pain felt from these shortages. Real time projection mapping is another example where Computer Vision assists in the automated tracking and sorting of objects in warehouses. Furthermore, using intelligent vision analysis, companies can automatically evaluate the quality of materials and workmanship.
Another application is yard management systems which are designed to improve supply chain operations by enhancing visibility, operations within warehouse yards, providing real time tracking and management of trailers and containers. Other use cases include reading analogue dials and displays, without workers physically going to the device and reading it.
AI ENABLING BUSINESS TRANSFORMATION
As many of these industries look to digitally transform their operations, the use of Generative AI (GenAI) technologies will also play a key role in achieving their goals. When we asked respondents how important the use of GenAI technologies is in their overall business transformation journey, 74 percent of logistics and supply chain respondents said that it was important.
Ultimately, due to Computer Vision’s speed, objectivity, continuity, accuracy and scalability, it will quickly surpass human capabilities in eliminating resource-sapping tasks. This makes the application and implementation of such technologies an imperative in today’s digitised world.
Panasonic Connect Europe
www.connect.panasonic.com
FHS-DEC24-JAN25-PG40+
41_Panasonic.indd 41
16/01/2025 18:21
DON‘T DO IT YOUR- SELF. DO IT FULLY AUTONOMOUS.
Intelligent, autonomous and with the functionality of a scrubber dryer, the KIRA B 50 takes on floor cleaning for medium toedium to large areas efficiently and with consistent cleaning r
mous and with a scrubber
0 t
tly and with results.
Available to lease, hir
kaercher.com/uk/pr
hire or buy. rofessional
sional
SECURE A FREE DOCKING
STATION WORTH £2,500!
FACTORY&HANDLINGSOLUTIONS | DECEMBER/JANUARY 2024/25 41
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70
