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Logistics Use Cases: Asset Focus

Maintenance Applications

Machine Learning in Predictive Maintenance

Computer vision technology can be used for predictive maintenance by helping to consistently and accurately monitor logistics assets and alert maintenance teams so that they can intervene before any issues arise. By analyzing data from various types of equipment, AI can also predict when critical assets will require maintenance. This allows managers to schedule repairs and upkeep to prolong asset life and prevent failure.

Providing visibility into asset health, the Delaware-based computer vision startup Clarified offers solutions that capture AI-based risk predictions. These not only give early warning of potential problems and reduce unscheduled repairs, but also help to delay capital expenditure, minimize human inspections, and ensure business continuity. The company claims that implementing AI-based predictive maintenance can reduce maintenance costs by 25% and downtime by 35%, and users can achieve 10x return on the initial technology investment.

Using AI in Maintenance to Identify Defects 

Before widespread deployment of computer vision, detecting defects was a labor-intensive, manual operation. It required round-the-clock employee availability and was also prone to human error. 

Today, probable asset flaws, mistakes, anomalies, and problems can be automatically identified when computer vision is used for predictive maintenance. An effective complement to warehouse Gemba Walks, this technology provides valuable additional data when managers walk through the premises gathering observational and interactional information. It can also identify the cost of asset damage and repair, streamlining maintenance processes by providing the asset management system with this data.

Ivisys, a pioneering startup, has created an innovative defect identification solution named Pallet AI, which can be used for predictive maintenance. This technology is specifically designed for enhancing the quality inspection process of pallets, effectively identifying defects, and concurrently improving productivity and employee safety. By employing a sophisticated neural network, the system utilizes a network of cameras to not only identify cracks and holes but also to detect mold and discoloration using advanced pattern recognition techniques. Remarkably, this AI-based predictive maintenance system can inspect a range of 250 to 450 pallets per hour.

Challenges of Implementation

Challenge 1: Computer vision systems cannot capture everything regarding predictive maintenance. Asset performance could be impacted by qualitative factors – such as how a worker interacts with a device – and this may require human observation.

Challenge 2: Typical warehouse computational power may not be sufficient to cater for the complex requirements of AI algorithm analysis. New IT investments may be needed to get the most out of machine learning in predictive maintenance.

  • What is the role of machine learning in predictive maintenance?

    AI is used in predictive maintenance to streamline processes, reduce human error, and forecast when critical assets will require maintenance. It can analyze data from various equipment types to predict when maintenance is needed, allowing for proactive scheduling and cost savings.

    Why is predictive maintenance important?

    Predictive maintenance helps in scheduling repairs, extending asset life, and preventing failures, ultimately reducing maintenance costs and downtime, which could prove costly for a business.

    How does computer vision technology contribute to predictive maintenance?

    Computer vision technology is used to consistently and accurately monitor logistics assets, identify defects, and alert maintenance teams before issues arise. AI-based solutions offer early warnings of potential problems, reduce unscheduled repairs, minimize human inspections, and ultimately help extend asset life while preventing failures. All this contributes towards business continuity because it ensures less downtime or omits it completely.

    How can AI be used to identify defects in predictive maintenance?

    AI, particularly computer vision, can automatically identify probable asset flaws, anomalies, and problems, improving defect detection and streamlining maintenance processes.

    What are some challenges when using machine learning for predictive maintenance?

    One challenge that arises when using computer vision in predictive maintenance is that the AI is unable to capture qualitative factors related to asset performance, which means that human interaction will be required anyway. Another is that adequate computational power and IT investments are required for complex AI algorithm analysis, so typical warehouse computational power may not be sufficient and new IT investments may need to be made.

    How much is the projected cost of unscheduled maintenance expected to rise by 2035?

    The projected cost of unscheduled aircraft maintenance is expected to rise globally from $6.57 billion in 2017 to approximately $13.13 billion by 2035, representing a nearly 50% increase. This underlines the importance of predictive maintenance techniques, including machine learning, in mitigating such costs.

    Are there any startups that specialize in AI-based predictive maintenance solutions?

    Yes, startups like Clarifai and Ivisys are pioneering AI-based predictive maintenance solutions. Clarifai offers AI-driven risk predictions that reduce unscheduled repairs, lower maintenance costs, and minimize human inspections. Ivisys has developed Pallet AI, a technology for identifying defects in pallets, enhancing quality inspection processes, and improving productivity and employee safety.

Asset Management Applications

AI in Asset Management: Utilization & Capacity Assessment

When planning capacity to optimize asset utilization, computer vision can be implemented in asset management to deliver quicker insights than the human eye and those of human experience.

This AI-based technology can assess the overall space inside trucks and containers to calculate available volume prior to loading – information that helps determine the optimal arrangement of items to maximize loads and minimize wasted space. Measurements can be taken throughout the loading process, enabling real-time data-driven decision making that saves time, improves efficiency, increases sustainability, and reduces cost.

In the warehouse, computer vision can be used to analyze the dimensions and orientation of pallets and roller cages. This data helps ensure these assets are positioned for optimal load distribution and peak efficiency.

Danish startup Sentispec uses computer vision to manage assets, as it tracks every point of contact with stock in and out of the warehouse. Instead of allowing partially filled trailers and containers to leave the premises, Sentispec Inspector helps record the densities and fill rates of every load, so the planning office can optimize fill rates. 

Using Computer Vision for Asset Counting and Localization

A familiar occurrence inside the warehouse is pallets, cages, trolleys, and other assets going missing. It costs time and money to find and return or replace them. Computer vision AI can be used to count and locate assets, assessing their status in real time to provide visibility and improve efficiency even in warehouse dark zones, where the network signal is weak and tracking sensors may lack connectivity

For object counting, deep-learning algorithms detect and classify objects in an image or video stream, identifying and analyzing image focus points and repeating this process to count all instances of a specific object. Assets can be identified by type (roller cage, rack, forklift) or by a unique identification code linked either to a single asset or to multiple assets within the camera’s same field of view.

A multi-target tracking system using the ‘handshake method’ is effective for localization. As an asset leaves one camera’s field of view, it reappears in the view of another. A backend algorithm analyses this input to estimate and trace the asset’s path throughout the warehouse. The computer vision platform from startup Kibsi uses existing camera networks to track assets in this way and monitor activity within a warehouse. Assets can be georeferenced on a virtual map and warehouse operators can locate assets with accuracy to within an inch. 

The Role of AI in Fleet Management

Assets outside the warehouse can be monitored 24/7 by an integrated system combining computer vision with surveillance. To restrict yard access to registered vehicles only, cameras can identify each truck and log its entry time, exit time, and number of daily trips. The system can also measure asset usage patterns, including idle time, and use this data to help optimize fleet operations.

With its real-time monitoring solution, ThinkIQ claims to eliminate the need for a guard to log trucks into and out of a facility. This AI fleet management system is trained to work in all types of lighting and atmospheric conditions, delivering actionable insights to improve fleet management.

AI is not only useful in asset management, but machine learning can also be used in asset maintenance in asset maintenance since it facilitates predictive maintenance by streamlining processes and scheduling repairs that prolong asset life.

Challenges of Implementation

Challenge 1: A camera’s field of view can be obstructed. For  example, if items are temporarily stacked just inside the truck, this stack may block visibility further in the back.

Challenge 2: Huge data throughput is required for computer vision asset tracking.

Challenge 3: Technical glitches and breakdowns in the AI system would likely cause significant difficulties with asset management, especially in a busy warehouse.

  • How does AI contribute to asset management?

    AI is implemented in the asset management industry to significantly enhance efficiency. It enables rapid calculations, analyses, and data recording, surpassing human capabilities. Machine learning, a subset of AI, allows for faster decision-making processes, ultimately leading to more streamlined asset management operations.

    How does AI optimize how assets are used?

    Computer vision, a type of AI technology, revolutionizes the asset loading process. It quickly assesses the available volume inside trucks and containers, far quicker than human inspection. By continuously monitoring the loading process in real-time, the AI ensures that the contents are optimally arranged to maximize space utilization, reduce waste, and enhance efficiency.

    How does computer vision technology improve warehouse efficiency?

    Computer vision technology analyzes the dimension and orientation of warehouse assets like pallets and roller cages, ensuring optimal load distribution and peak efficiency.

    What is an example of AI being used for digital asset management?

    Sentispec, a Danish startup, uses AI to track stock in and out of the warehouse, recording load densities and fill rates to optimize trailer and container fill rates.

    This example showcases how AI can be applied to digital asset management to maximize resource utilization.

    How does AI aid with counting and locating assets in a warehouse?

    AI plays a pivotal role in asset counting and localization within warehouses. AI uses deep learning algorithms to detect and classify objects in images or video streams, reducing the chance of them going missing or being stolen. This allows the assets, such as pallets, cages, and trolleys to be accurately identified and counted.

    How does AI enhance fleet management?

    AI offers significant benefits in fleet management by continuously monitoring assets located outside the warehouse. An integrated system combining computer vision and surveillance cameras can identify registered vehicles, log entry and exit times, and track the number of daily trips. AI can also analyze asset usage patterns, including idle time, in order to optimize fleet operations and reduce operational costs.

    What are some challenges associated with implementing AI in asset management?

    Implementing AI in asset management does come with challenges. For instance, a camera’s field of view can be obstructed, hindering its ability to accurately track assets or items stacked temporarily inside a truck might block visibility. Additionally, AI-powered asset tracking systems require significant data throughput, which can strain network resources. Technical glitches or breakdowns in the AI system could also pose problems, particularly in busy warehouse environments.

The projected cost of unscheduled aircraft maintenance is expected to rise globally from $6.57 billion in 2017 to approximately $13.13 billion by 2035.