Peak Technologies has launched a cutting-edge solution called Peak Analytics, which caters to the high-volume logistics industry. Tony Rivers, CEO and President of Peak Technologies, highlighted the advantage of the modular design of Peak Analytics, taking into account the scanning data captured in package tunnels that could provide powerful tools for retail, shipping, and logistics operations.
Those seeking further insight into the solution can witness the live demonstrations by Peak Technologies at ProMat 2023 in Booth S459. The implementation of Peak Analytics is set to provide actionable intelligence to address critical business challenges such as identifying non-compliant packages, reducing returns and chargebacks, real-time package monitoring and maximizing equipment efficiency.
Peak Analytics integrates seamlessly into supply chain systems and is vendor agnostic, coupled with a no-code AI approach that offers accurate solutions for every stakeholder in the logistics sector. Sensing devices placed on the edge at warehouses provide real-time updates for each package’s condition, enabling companies to tackle potential problematic inventory early on.
The solution feature called Tunnel Insights is the core component of Peak Analytics, capturing images, dimensions, and barcode data in real-time scans to generate both the TunnelView and equipment-specific dashboards. The Facility Insights lets users network between the entire facility or enterprise-level packages, generating customizable AI reports for efficient supply chain planning.
Learning Insights is an advanced solution that uses AI to deploy, create, and run models to identify problem packages, allowing logistics operations to classify them efficiently. The module is designed to detect reasons for non-reads such as torn bars, obscured labels, and poorly printed labels or improperly focused cameras. In conclusion, Peak Analytics provides a comprehensive solution that enables companies to streamline their supply chain processes, delivering visible benefits in real-time.