By Sursha Wu
Recommended Reading: What is HOP? Key Points to Consider Before Implementing HOP
Introduction
In the wave of smart manufacturing, many enterprises have begun investing in AI applications.
According to a 2023 market report, the United States has invested $290 billion in AI over the past five years. In terms of boosting profits and reducing costs, manufacturing ranked second among industries benefiting from AI adoption.
Source: IngestAI, The Global AI Investment Landscape: Trends, Players, and Opportunities

The Rise of AI Stocks and Market Performance
Source: IngestAI, The Global AI Investment Landscape: Trends, Players, and Opportunities
PowerArena HOP (Human Operation Platform) is a smart manufacturing AI solution designed specifically for the manufacturing industry.
Using AI vision , it builds automated production line management systems to address pain points that arise in labor-intensive production environments.
Learn more: What issues can HOP solve on the production line?
HOP has been successfully implemented in many case studies. Each deployment has resulted in many benefits for the production sites.
Read about its success in the semiconductor industry

Semiconductor factory
See its success in the electronics manufacturing industry

EMS Factory
Discover its application in the electric vehicle industry

EV Factory
This blog shows the five key steps to implement HOP, from decision-making to deployment, highlighting PowerArena’s role in ensuring successful adoption.
Key Considerations Before Implementing HOP
Implementing HOP requires careful planning and preparation.
Read the 6 key assessment points before adopting HOP
Identify Problems to Solve
Start by analyzing the production line to identify bottlenecks or challenges in efficiency, quality, or transparency. Clear objectives will guide how HOP can optimize operations.
Establish Standardized Operating Procedures (SOPs)
The core of HOP lies in tracking and recording anomalies, which requires standardized SOPs. We recommend factories establish clear operational processes to provide a framework for AI analysis.
Prepare Digital Infrastructure
To support HOP’s operation, enterprises need a basic level of digital infrastructure or a willingness to invest in it. This includes setting up cameras, network configurations, and servers.
The specifications will depend on the production line’s needs to ensure smooth image transmission and data processing.
The HOP Implementation Workflow
Unlike installing software on a smartphone, implementing HOP on a production line requires close collaboration and iterative adjustments to maximize value.
Step 1: Reassess Production Pain Points
We’ve observed that factories often misalign their initial needs with the core issues truly impacting performance.
PowerArena’s expert team works closely with clients to refine their focus, analyze production conditions, and uncover the real pain points.
For example, a client initially sought HOP’s AI vision to detect whether an indicator light was on, aiming to confirm the execution of a specific process. However, after thorough discussions, we discovered the real issue was ensuring the process itself was accurately carried out—not merely checking the light.
With advanced AI technology and extensive experience in factory operations, PowerArena takes a consultative approach. Instead of hastily executing client requests, we collaborate to identify the root problems and develop tailored HOP deployment strategies that deliver meaningful results.
Talk with our consultant. We are more willing to help!
Step 2: Train AI Models for Production Scenarios
AI model must fully understand the environment and operations of the production line.
The collected video data should cover all possible actions, operators’ behavior, and characteristics of the objects. This ensures the AI has sufficient context to “see” the production environment comprehensively.
HOP includes a fundamental model pre-trained to recognize basic elements such as hands, people, vehicles, and printed circuit boards (PCBs). If your production process falls within these capabilities, training time can be substantially reduced.
Learn more about HOP fundamental model and use case.
Step 3: Support Standardization of Production
After data collection, PowerArena assists clients in refining and defining operational standards to ensure consistent processes.
Often, production managers are unaware of the exact realities on the line, leading to ambiguous standards.
For instance, an SOP might specify using the right hand to hold a tool and the left hand for a component. However, video analysis might reveal operators using their dominant hand regardless of instructions.
In collaboration with production managers, we assess whether such deviations affect efficiency or quality. If they do, detection systems are configured; otherwise, the SOP itself may need to be optimized.
This phase ensures actions are standardized and SOPs are meaningful, reducing unnecessary detection variables. It also enhances management’s understanding of operator behaviors and highlights potential SOP improvements.
Step 4: Deployment
Once AI training and process standardization are complete, HOP can be adopted in the production line.
For processes already covered by the fundamental model, deployment takes just 2–3 weeks.
During the initial operation, HOP often uncovers previously hidden production issues. At this stage, certain production steps may need further optimization to eliminate ineffective variables.
PowerArena provides ongoing professional support to help clients extract the most valuable insights from their production lines.
Step 5: Fine-Tune and Maintain the AI Model
PowerArena’s role doesn’t end with successful deployment.
Regular adjustments and maintenance are critical to keeping the AI model effective amidst changes in the production line.
Data Drift
Over time, the AI model’s accuracy may fluctuate due to data drift, caused by changes in production conditions like lighting, equipment relocation, or tool wear.
Unlike humans, who adapt to changes, AI requires updated data to relearn these variations.
To counteract this, PowerArena incorporates mechanisms to minimize the impact of data drift:
- Confidence Monitoring: Continuously track model confidence levels to detect anomalies early.
- Model Adjustments: Adapt the model to new data distributions to ensure accuracy.
- Dynamic Adaptation: HOP’s AI model is designed to handle common changes such as lighting, rotation, and translation, significantly reducing errors from environmental factors.
PowerArena’s Role in HOP Implementation
PowerArena acts as both an AI vision expert and a smart manufacturing consultant.
We combine cutting-edge AI technology with years of domain know-how to manufacturing. As a result, we can offer deep insights into factory management and production operations.
Our value extends beyond technology—we help clients pinpoint core production issues and provide practical solutions to improve processes. We believe that only the combination of technology and real-world application can create benefits for production lines.
Immediate Benefits of HOP for Production Lines
HOP revolutionizes the traditional reliance on limited sample data for assessing production line performance.
By collecting and analyzing comprehensive production data, managers can now see the entire production picture rather than relying on fragmented information.
The implementation process also prompts managers to reexamine workflows, notice issues and gain deeper insights for the production lines.
With HOP, managers can monitor the real-time performance of each workstation. Whether for overarching strategies or detailed optimizations, clear and reliable data supports their decisions. This enables quick problem identification, enhances production capacity, improves product quality, and facilitates the development of precise production strategies, significantly boosting overall efficiency and management effectiveness.
Continue Reading: More benefits HOP brings to your production line