Computer Vision in manufacturing

Computer vision can help improve time-efficiency, accuracy, and reduce the cost in manufacturing. Particularly, computer vision can be used for anomaly detection, packaging and safety inspection, vision-guided robots, labeling and tracking of products, and many more. For example, Kawasaki Heavy Industries is using computer vision to accurately assemble each component of their hydraulic pumps used in heavy machinery. Mining companies use computer vision to closely monitor drilling equipment and identify defects and/or damages.

Predictive Capabilities Skyrocket When AI in Logistics is Implemented

The capabilities of AI are seriously ramping up company efficiencies in the areas of predictive demand and network planning. Having a tool for accurate demand forecasting and capacity planning allows companies to be more proactive. By knowing what to expect, they can decrease the number of total vehicles needed for transport and direct them to the locations where the demand is expected, which leads to significantly lower operational costs.

The tech is using data to its full potential to better anticipate events, avoid risks and create solutions. This allows organizations to then modify how resources are used for maximum benefit – and Artificial Intelligence can do these equations much faster and more accurate than ever before.

For example, DHL analyzes 58 different parameters of internal data to create a machine learning model for air freight. Rather than subjective guesswork, this method allows freight forwarders to predict if the average daily transit time is expected to rise or fall up to a week in advance. Furthermore, this solution can identify other factors which could influence shipment delays like climate and operational variables. Such insights are incredibly valuable in a sector like air freight, where it accounts for only 1 percent of global trade in terms of tonnage but 35 percent in terms of value.

In general, the predictive analytics solutions in logistics and supply chain are on the rise. However, while the technology is available, there is still a scarcity of people who can make sense out of the incomplete and low-quality data, the case commonly presented in the logistics industry.

Only a few largest companies can afford to hire a whole team of data science professionals to develop such a tool in-house, as in the case of UPS. Meanwhile, other players can also benefit from AI predictive capabilities by implementing already available solutions. The most well-known examples are Transmetrics and ClearMetal, which were both mentioned in the latest DHL’s Logistics Trend Radar.

AI analysis can also be used to safeguard against risk. Another good example from DHL is their platform which monitors more than 8 million online and social media posts to identify potential supply chain problems. Through advanced machine learning and natural language processing the system can understand the sentiment of online conversations and identify potential material shortages, access issues and supplier status.