BALANCING AI AND HUMAN IN LOGISTICS OPERATIONS

Article by: Asst. Prof. Suwan Juntiwasarakij, Ph.D., Senior Editor

Consumer increasingly drives innovation from the heart of the supply network, rather than being on the receiving end of a supply chain. Traditional linear models of many consumer goods supply chains have been challenged. These traditional models are fundamentally changing as businesses are shifting towards consumer-led, data-driven, highly complex supply networks. Consumption trend shifts demand mass production customization, more accurate supply-chain planning and synchronization, and faster multichannel retail. This will enable innovation, ensure optimum service levels and deliver growth at low cost.

Undoubtedly, advancements in Artificial Intelligence (AI) and Robotics Automation have the potential to overcome these challenges and revolutionize supply chains. However, this could discount the important of human workforce since machines themselves, for now, are not able to handle certain incidents where solutions to the incidents involve a variety of sticky, tacit sets of knowledge residing in skill experts. Therefore, technologies and humans continue to co-exist to achieve optimal performance in logistics industry.

VISUAL INSPECTION IN LOGISTICS ASSETS

Advancements in computer vision are allowing us to see and understand the world in new ways, and logistics operations are no exception. AI-Powered Visual Inspection is another high-potential area for AI in the logistics operational environment. IBM Watson is using its cognitive visual recognition capabilities to do maintenance of physical assets with AI-driven visual inspection. In industrial sectors like logistics, damage and wear to operational assets over time are simply inherent. Using a camera bridge to photograph cargo train wagons, IBM Watson was recently able to successfully identify damage, classify the damage type, and determine the appropriate corrective action to repair these assets.

To harvest the power of AI-driven visual inspection, it begins with cameras installed along train tracks to gather images of train wagons as they drove by. The images were then automatically uploaded to an IBM Watson image store where AI image classifiers identified damaged wagon components. The AI classifiers were trained on where to look for wagon components in a given image and how to successfully recognize wagon parts and then classify them into seven damage types. As more data was gathered and processed, Watson’s visual recognition capabilities improved to an accuracy rate of over 90% in just a short period of time. The anomalies and damages discovered by Watson were sent to a workplace dashboard managed by maintenance teams. This model and process can loosely be applied to other types of logistics asset including but not limited to aircraft, vehicles, and ocean vessels.

AI-DRIVEN COMPUTER VISION IN INVENTORY MANAGEMENT

Not only logistics giants, but also retailers can benefit from AI advancement. Recently, French startup Qopius is developing computer vision-based AI to measure shelf performance, track products, and improve retail store execution. Using deep learning and fine-grained image recognition, Qopius is able to extract characteristics of items such as brand, labels, logos, price tags, as well as shelf condition such as out of stock, share of shelf, and on-shelf availability. In warehouse inventory management, similar use of computer vision AI offers potential for real-time inventory management at the individual piece and SKU level.

Furthermore, TwentyBN, a startup based in Canada, is working on deep learning AI that is able to decipher complex human behavior in video streams. Previous applications of its technology include autonomous detection from video feeds alone of things like an elderly person falling, aggressive behavior on public transport, and shoplifting in stores. Considering that many warehouses today are equipped with surveillance cameras for safety purposes, this type of AI technology can be used to optimize performance (by detecting, for example, successful pick and pack tasks) and increase operational safety (for example, with instant alerting of accidents involving workers).

TAKE-HOME MESSAGE

The is clear that the future of AI in logistics is bright and powerful. As supply chain leaders continue their digital transformation journey, AI will become a bigger and inherent part of day-to-day business, accelerating the path towards a proactive, predictive, automated, and personalized future for logistics. Ultimately, AI will place a premium on human intuition, interaction, and connection allowing people to contribute to more meaningful work.