Deloitte’s 2024 Manufacturing Industry Outlook found that the majority of manufacturers placed automation and digital transformation as their strategic priority into 2026. Labor shortages continue to plague the manufacturing industry. Not only is there a shortage of skilled labor, but the average age of existing workers continues to edge toward retirement. As a result, manufacturers, regardless of size, view their workforce as an underlying factor in all industry challenges.
Improved Quality Inspection
According to McKinsey’s 2024 Global Manufacturing Survey, manufacturers using artificial intelligence (AI) to detect defects saw a substantial improvement over manual inspection. Categories such as weld inspection and assembly verification showed consistent evaluation as opposed to human inspections, where fatigue can create inconsistency.
Successful AI inspections need data for accurate training. Automating data capture during in-process measurements bypasses manual data entry that is time-consuming and prone to error. Data collection throughout production reduces the number of defective products reaching the end user. Industrial PoE infrastructure supports the bandwidth and power requirements needed for advanced inspection technologies while simplifying deployment throughout the facility.
Increased Uptime
The Industrial Internet of Things (IIoT) has positioned the manufacturing industry to achieve a long-term goal of increased uptime through predictive maintenance. Vibration and temperature sensors can send data continuously to centralized platforms for predictive maintenance algorithms to analyze. Power consumption can be monitored to ensure optimum performance.
Although monitoring systems can generate alerts, operators must establish the right workflows for maintenance responses. For example, should an off-hours alert be logged or sent to a designated employee? Can an AI-based solution automatically schedule preventative maintenance based on data collection? Defining an AI-based alert-to-action workflow can reduce unexpected downtime.
When integrated through a robust PoE infrastructure, these connected systems help maintenance teams respond proactively and reduce costly disruptions.
Automated Reporting and Documentation
Most manufacturers still rely, in part, on manual data entry or paper records for reporting, which can cause delays and increase the risk of errors. Data often moves between multiple systems before reaching management teams. Operators complete end-of-shift forms and supervisors manually enter production numbers. Data analysts export the data to other tools for reporting.
By connecting equipment, sensors, and reporting platforms through a unified PoE-based network, manufacturers can automate data collection, improve reporting accuracy, and support regulatory documentation requirements.
Growing Integration Debt
Although manufacturers have been investing in automation tools for years, most deployments have been task-specific, meaning the tools do not communicate with other systems. Equipment performance data is not shared with maintenance scheduling software. Production counts are inaccessible to warehouses. Inspection data must be manually entered for reporting. This lack of connectivity is known as a manufacturer’s integration debt — the cost a company pays because it failed to integrate systems as they were deployed.
For example, environmental sensors are placed on the factory floor to monitor operating conditions. Adjustments in humidity or temperature may be needed to reduce potential equipment damage. However, that information is not shared with the predictive maintenance model. As a result, the AI-based solution lacks a crucial datapoint for its analysis. By maintaining siloed solutions, manufacturers cannot realize the full potential of automation.
This integration debt limits visibility and prevents organizations from fully leveraging operational data. PoE manufacturing and automation infrastructure helps bridge legacy and modern systems to create a more connected manufacturing environment.
