Light-Guided Picking (PTL): A Study on Improving Rapid Picking and Replenishment Efficiency in On-Line Warehouses in Manufacturing Facilities

I. Introduction

With the deepening implementation of Industry 4.0 and smart manufacturing, manufacturing companies“ production models have shifted from traditional large-scale mass production to personalized customization and flexible, small-batch production, resulting in a significant increase in the frequency, variety, and complexity of material flows within the workshop. As temporary material storage areas located immediately adjacent to production workstations, line-side warehouses perform core functions such as the storage, picking, replenishment, and turnover of raw materials, components, and auxiliary materials; they serve as the ”logistics capillaries” that ensure continuous, uninterrupted operation of production lines. Compared to central warehouses, shop-floor side-line warehouses are characterized by a high density of material SKUs, strict replenishment timelines, limited operational space, and tight synchronization with production rhythms. As such, they place extremely high demands on picking accuracy, timely replenishment, and operational efficiency.

Currently, most small and medium-sized manufacturing enterprises in China still rely on traditional manual operations for their line-side warehouses, where workers use paper documents and electronic ledgers to manually verify materials, locate storage bins, and tally inventory. This model relies heavily on employee experience and has numerous drawbacks: First, the picking process is cumbersome; manually locating items and verifying documents takes too much time, resulting in a very high proportion of unnecessary movement and unproductive work; Second, the picking error rate is high, with frequent instances of incorrect, missed, or excess picks, directly leading to production downtime, material waste, and an increase in defective product rates; Third, replenishment lacks foresight and is often reactive; the replenishment process is triggered only after materials are completely depleted, which can easily lead to production line shutdowns due to material shortages; Fourth, inventory data updates are delayed, and discrepancies between book and actual inventory are widespread, making it impossible to meet the dynamic material allocation needs of flexible production.

Pick-to-Light (PTL) SystemAs a new-generation visual intelligent picking technology, it relies on an integrated hardware-software architecture to eliminate the traditional manual paperwork-based workflow. Through light guidance, digital visual counting, and intelligent system control, it standardizes, streamlines, and enhances the accuracy of material picking and replenishment operations, perfectly meeting the high-frequency, small-batch, and high-precision operational requirements of on-line warehouses in production facilities. Currently, PTL (Light-Guided Picking) technology is widely used in on-line warehouse logistics scenarios across industries such as automotive parts, electronics manufacturing, and machining, and has become one of the core technologies for enterprises to achieve lean logistics and intelligent upgrades in their production facilities. Based on this, this article conducts an in-depth study of the application logic of PTL light-guided picking technology in shop floor side-line warehouses, targets specific optimizations for picking and replenishment workflows, and quantifies the value of efficiency gains, thereby providing technical support and practical guidance for manufacturing enterprises to reduce costs and improve efficiency.

II. Analysis of Pain Points in the Traditional Picking and Restocking Model for On-Line Warehouses in Manufacturing Facilities

The core operational objective of a shop floor line-side warehouse is to accurately and quickly supply materials to workstations, ensuring production continuity while maintaining reasonable control over line-side inventory to prevent both material backlogs and shortages. Traditional manual operations are constrained by technology, processes, and management models, making them unable to adapt to the pace of modern production. Specific pain points are concentrated in four key areas: picking, replenishment, inventory management, and workforce management.

2.1 Low picking efficiency and a high proportion of non-value-added activities

Traditional line-side warehouse picking follows a fully manual process of “receive order—locate items—verify—pick—recheck,” in which workers must hold a production bill of materials and search for corresponding items one by one among densely packed line-side storage locations, manually verifying the item models, specifications, and quantities. Throughout this process, locating storage bins and verifying against documents account for more than 70% of the total operation time, while the actual time spent on effective picking is extremely low. Industry data shows that under the traditional manual model, the average time spent locating a single bin is 3–5 minutes, and the average number of rows picked per worker per day is only about 350. Furthermore, as the number of SKUs increases and bin layouts become more dense, the difficulty of locating items rises significantly, and picking efficiency continues to decline. At the same time, manual picking lacks optimal route planning; it is common for workers to move around haphazardly and make repeated trips back and forth, resulting in long distances of ineffective movement, which further exacerbates efficiency losses.

2.2 High order-picking error rate and significant production risks

The accuracy of manual order picking relies entirely on employees’ sense of responsibility and operational experience. Under long-term, high-intensity work conditions, visual fatigue and verification errors are highly likely to occur, leading to issues such as mispicks, missed picks, and overpicks. This is particularly true in the electronics manufacturing and precision components industries, where material models are highly similar and specifications are finely differentiated, making manual identification difficult and resulting in persistently high error rates. When incorrect materials enter the production line, it not only leads to product rework and scrap—increasing production costs—but also causes production process delays and order delivery delays, resulting in direct economic losses and damage to the company’s reputation. Under the traditional model, the lack of real-time verification mechanisms means errors are often not detected until the production and assembly stages, resulting in delayed corrections and a further expansion of the scope of potential risks.

2.3 Lag in the Replenishment Mechanism and Inadequate Alignment Between Supply and Demand

Traditional on-line warehouse replenishment is primarily reactive, relying on workstation operators to manually report material shortages. Logistics personnel then receive the notification and proceed to the on-line warehouse to pick and restock items, resulting in a slow response time and a lengthy process. At the same time, manual methods cannot accurately predict material consumption rates, which can easily lead to two extreme problems: First, untimely replenishment—when materials at workstations run out, causing production lines to shut down and resulting in lost production capacity; Second, overstocking, where large quantities of materials accumulate at workstations and in the line-side warehouse, occupying production space, increasing the risk of material backlogs, rust, and wastage, and raising inventory management costs. Furthermore, in scenarios involving simultaneous consumption across multiple workstations and materials, manual replenishment often results in混乱 priorities and fails to match the production line’s dynamic material demands.

2.4 Distorted Inventory Data and a Lack of Standardized Management

Traditional inventory tracking for line-side warehouses relies on manual ledger entries; data is updated manually after picking, restocking, and material consumption. This leads to issues such as untimely updates, recording errors, and omissions, which over time result in discrepancies between book and actual inventory. Managers are unable to monitor material inventory, consumption progress, and remaining stock in line-side warehouses in real time; as a result, production scheduling and material allocation must rely solely on experience, lacking data-driven support. Additionally, there are no standardized records of operational workflows, making it impossible to trace pickers, restocking times, or material flow. When quality issues or material losses occur, it is difficult to pinpoint responsibility accurately, which hinders the standardization and normalization of workshop logistics management.

2.5 Labor costs are relatively high, and there is a strong reliance on personnel

Traditional operations rely heavily on skilled workers, and new employees require extensive training to become familiar with storage location layouts, material specifications, and operational procedures, resulting in high labor training costs and a long onboarding period. At the same time, manual operations have inherent efficiency limits. During peak production seasons or when order volumes increase, the only way to ensure the smooth flow of materials is to add more staff, leading to continuously rising labor costs. Furthermore, high staff turnover can cause fluctuations in operational efficiency, which is detrimental to the stable operation of the workshop’s logistics system.

III. Core Principles and Architectural Advantages of the Light-Gated Sorting PTL System

PTL with Light-Guided SortingThis is a visual, intelligent operations system designed for warehousing and line-side storage scenarios. Leveraging the Internet of Things (IoT), sensor technology, and intelligent algorithms to build an intelligent operation system featuring “system-based control, light guidance, precise operations, and real-time traceability.” It completely revolutionizes the traditional manual document-based operation model and is perfectly suited to the high-frequency, precise, and efficient operational demands of in-line warehouses on the production floor.

3.1 Core Operating Principles of the PTL Light-Guided Picking System

The core logic of the PTL system is “task assignment—light guidance—fixed-quantity picking—real-time confirmation—data synchronization.” The system integrates with the company’s ERP, WMS, and MES production management systems to automatically retrieve data such as production orders, material BOMs, and workstation material requirements, and automatically generates picking and replenishment tasks for the line-side warehouse and dispatches them to the corresponding storage locations. Each bin in the line-side warehouse is equipped with a smart electronic label and an indicator light. Once the system dispatches a task, the light at the corresponding material bin automatically illuminates, and the electronic label simultaneously displays information such as the required quantity, specifications, and the corresponding workstation. Operators do not need to check documents or search for storage locations; they simply follow the light guidance to pick or replenish the required quantity of materials. Upon completion, they confirm the task by touching the electronic label or scanning a QR code. The system automatically synchronizes the operation data and updates inventory information, enabling fully visual and intelligent operations throughout the process. Additionally, the system uses different colored lights to distinguish between task types, allowing for quick differentiation between tasks such as picking, restocking, inventory counting, and returns, thereby enhancing operational clarity and efficiency.

3.2 Overall Architecture of the PTL Light-Guided Picking System

The PTL Light-Guided Picking System features an integrated hardware-and-software architecture primarily divided into three layers, which operate in coordination to ensure efficient and stable operations. The first is the data layer, which interfaces with the enterprise’s MES, WMS, and ERP systems to enable the interconnection of production, material, and inventory data, thereby providing data support for task generation, data updates, and demand forecasting; Second is the control layer, which consists of the PTL backend management system. It is responsible for task decomposition, route optimization, access control, data analytics, and anomaly alerts, enabling intelligent management and control of all operations in the line-side warehouse; Third is the execution layer, which includes hardware devices such as smart electronic tags, LED indicator lights, barcode scanners, and touchscreen terminals. This layer directly handles on-site operations such as picking, restocking, and inventory counting, enabling human-machine collaboration.

3.3 Key Advantages of the PTL System for Integration with Line-Side Warehouses

Compared to traditional manual methods, the PTL light-guided picking system offers comprehensive advantages in addressing the pain points of line-side warehouse operations, with its core strengths manifesting in four key areas. First, it features an extremely streamlined workflow, simplifying the traditional, multi-step, complex process into three steps: “look at the light—pick the item—confirm.” This eliminates the wasted time spent manually searching for items and verifying documents, significantly improving operational efficiency. Second, precision with zero errors: light-guided positioning, visual quantity display, and real-time system verification eliminate picking errors and omissions at the source, achieving a picking accuracy rate of 99.1% or higher. Third, standardized operations: Unified workflows and operating standards reduce reliance on employee experience, enabling new employees to become productive quickly and ensuring consistent operational quality. Fourth, real-time data: Inventory data is synchronized immediately upon task completion, enabling real-time visibility and traceability of line-side warehouse inventory and supporting precise material management.

IV. Practical Implementation Strategies for Light-Guided Picking (PTL) in Online Peripheral Warehouse Picking and Restocking

Taking into account the operational scenarios and production rhythms of the shop floor’s line-side warehouse, we have implemented a targeted PTL light-guided picking system to optimize the picking process and restructure the replenishment mechanism. This has resulted in more efficient picking, more accurate replenishment, and smarter management, thereby comprehensively enhancing the operational efficiency of the line-side warehouse’s logistics operations.

4.1 Optimization of the Rapid Picking Process for PTL-Based Line-Side Warehouses

Leveraging the PTL system to restructure the line-side warehouse picking process, we have completely eliminated paper documents and achieved fully automated operations throughout the entire process. The specific process consists of four steps. Step 1: Automatic task generation. Based on MES production orders and material requirements at each workstation, the system automatically breaks down the bill of materials for each workstation. By integrating this with the inventory distribution in the line-side warehouse, it intelligently divides picking tasks and plans the optimal picking route to minimize redundant movement. Step 2: Intelligent Light Guidance. The system dispatches picking tasks to the corresponding storage locations; the indicator light for the target location automatically illuminates, and electronic labels precisely display the material model, quantity to be picked, and corresponding production workstation, eliminating the need for manual verification. Step 3: Fast and Accurate Picking. Operators follow the light guidance to pick materials sequentially, strictly adhering to the quantities displayed on the tags. The system verifies the picked quantities in real time and issues immediate alerts for over-picks or under-picks. Step 4: Real-time data synchronization. Once a batch of materials has been picked, the operator touches the label to confirm. The system automatically deducts the quantity from the line-side warehouse inventory, updates the material outbound records, and synchronizes the material flow data with the production system, ensuring full traceability throughout the process.

The optimized picking process completely eliminates inefficient tasks such as manually searching for items and comparing documents. The time required to pick an item from a single storage location has been reduced from 3–5 minutes to less than 10 seconds, significantly improving picking efficiency. At the same time, the system intelligently plans picking routes to minimize unnecessary movement by operators, resulting in a significant increase in daily output per person. This perfectly meets the flexible picking requirements of high-variety, small-batch production.

4.2 Building a PTL-Based Smart Replenishment System for Line-Side Inventory

To address the lag associated with traditional passive replenishment, we have leveraged the real-time monitoring capabilities of the PTL system to establish an intelligent replenishment system characterized by “predictive alerts, proactive replenishment, and on-demand adjustment.” First, the system monitors the remaining stock levels of materials in the line-side warehouse and the consumption rates at workstations in real time. Companies can set safety stock thresholds based on production needs; when material inventory falls below the threshold, the system automatically triggers a replenishment alert, the corresponding storage bin light flashes as a reminder, and a replenishment task is generated. Second, the system intelligently prioritizes replenishment based on production line work order schedules and material consumption patterns, giving priority to supplying materials for core workstations and urgent orders to prevent production stoppages due to material shortages. Finally, operators follow the light cues to retrieve the corresponding materials from the central warehouse to replenish the line-side warehouse. Once replenishment is complete, the system automatically updates the inventory data, forming a closed-loop management process of “inventory monitoring—alert triggering—intelligent replenishment—data update.”

This replenishment system has enabled a transition from “reactive replenishment” to “proactive forecasting,” completely resolving issues such as replenishment delays, production downtime due to material shortages, and over-replenishment. It precisely matches dynamic material demands on the production line, effectively balances inventory in line-side warehouses, reduces the risk of material backlogs and shortages, and ensures the continuous and stable operation of the production line.

4.3 Supporting Optimization Measures for the Implementation of the PTL System in the Line-Side Warehouse

To maximize the efficiency advantages of the PTL light-guided picking system, it is necessary to optimize the basic management of the line-side warehouse to achieve a deep integration of technology, processes, and management. First, optimize the storage location layout. Reorganize the line-side warehouse storage locations based on material usage frequency, size specifications, and workstation compatibility requirements. Place high-frequency materials in areas that facilitate efficient operations, and use PTL tags to ensure “one item per location” and “one light per code,” thereby guaranteeing smooth operations. Second, standardize material management by unifying storage specifications and size labeling, and standardizing the processes for material receipt, issuance, replenishment, and inventory counting to align with PTL’s intelligent operation standards. Third, provide staff training by conducting specialized sessions on new system operation, anomaly handling, and equipment maintenance, enabling operators to master streamlined workflows and quickly adapt to the intelligent operation model. Fourth, a data operations and maintenance mechanism has been established. Leveraging the PTL system’s big data analytics capabilities, we regularly analyze material consumption patterns and operational efficiency data to continuously optimize inventory thresholds, replenishment schedules, and storage location layouts, thereby achieving an upgrade to lean management.

V. Empirical Analysis of the Operational Efficiency of the PTL Light-Guided Picking System

To visually verifyPTL with Light-Guided SortingThe value of improving efficiency in shop floor side-line warehouses: Based on data from the implementation of intelligent upgrades at multiple manufacturing companies’ shop floor side-line warehouses, this analysis compares the differences in key metrics between traditional manual operations and the PTL intelligent operation model across five dimensions—picking efficiency, picking accuracy, replenishment timeliness, labor costs, and inventory management—to quantify the results of these implementations.

In terms of picking efficiency, the traditional manual method took an average of about 18 minutes per order, with each worker picking 350 lines per day and covering an average of more than 14 kilometers of unnecessary walking distance daily. After implementing the PTL (Light-Guided) picking system, the average picking time per order was reduced to 7 minutes, individual picking efficiency increased by more than 62%, the average number of rows picked per person per day rose to 567, and the distance traveled for non-productive movements was reduced to less than 5.8 kilometers. overall picking efficiency improved by 50%–80%, with even more significant gains in complex scenarios featuring a high density of SKUs. At the same time, line-change and material preparation efficiency increased by 40%–60%, effectively addressing the industry-wide pain point of time-consuming line changes in flexible production.

In terms of picking accuracy, traditional manual picking is subject to human error, resulting in a comprehensive error rate of approximately 3%–5%, which can easily lead to production disruptions. After implementing the PTL system—which features light-guided positioning, intelligent quantity verification, and full-process monitoring— the picking error rate has dropped below 0.1%, and the picking accuracy rate has stabilized at over 99%. This has virtually eliminated production downtime and material waste caused by mispicks or missed picks, significantly reducing production quality risks.

In terms of replenishment and production support, the traditional reactive replenishment model has an average response time of over 30 minutes, leading to frequent monthly production stoppages due to material shortages. The PTL intelligent proactive replenishment model enables real-time inventory alerts, reduces replenishment response times to less than 5 minutes, and improves replenishment efficiency by more than 70%. This completely resolves production line downtime caused by material shortages, significantly increases effective production time, and greatly enhances production continuity. At the same time, the precise on-demand replenishment model has reduced material backlog in the line-side warehouse by 40%, effectively freeing up production space in the workshop.

In terms of labor and management costs, the PTL system’s streamlined and standardized operational model significantly reduces reliance on staff experience. The onboarding period for new employees has been shortened from 1–2 months to 3–5 days, and training costs have been substantially reduced. At the same time, for the same workload, the number of personnel required for logistics operations in the line-side warehouse can be reduced by 30%–40%, effectively lowering the company’s labor costs. In terms of inventory management, the time required for manual inventory counts has been reduced by more than 70%, and the rate of inventory accuracy has increased to over 99.5%, completely resolving the issues of data inaccuracies and disorganized management associated with traditional models.

VI. Common Issues and Optimization Strategies for PTL System Implementation

Implementation in the WorkplacePTL with Light-Guided SortingDuring the system implementation process, factors such as hardware compatibility, workflow integration, and personnel adaptation can lead to issues such as poor system integration, equipment malfunctions, and inadequate operational adaptation. By drawing on practical experience, we have identified core issues and developed optimization strategies to ensure the long-term, stable operation of the system.

6.1 System Data Integration Issues

Some companies have outdated MES, WMS, and ERP systems that are incompatible with the PTL system’s data interfaces, resulting in issues such as delays in work order data synchronization and gaps in material information integration, which in turn cause abnormalities in the issuance of work tasks. Optimization Strategy: Before implementation, conduct a comprehensive assessment of existing systems and customize compatible interfaces to enable seamless data exchange across multiple systems; establish a data synchronization monitoring mechanism to track data transmission status in real time, promptly resolve issues related to data delays or missing data, and ensure accurate task generation and timely data updates.

6.2 High Hardware Failure Rate

The production environment in the workshop is characterized by dust, vibration, and temperature fluctuations. Long-term use can lead to poor contact and sensor malfunctions in PTL electronic tags and indicator lights, which in turn can affect work progress. Optimization Measures: Select industrial-grade PTL hardware that is waterproof, dustproof, and shock-resistant to accommodate the workshop’s complex operating conditions; establish a regular equipment inspection and maintenance system; conduct pre-shift checks of equipment operating status daily; perform regular dust removal and maintenance; and promptly replace faulty hardware to ensure stable equipment operation.

6.3 Improper Operator Procedures

Some veteran employees are accustomed to traditional manual work methods and resist operating intelligent systems, leading to issues such as non-compliant operations and arbitrarily skipping confirmation procedures, which result in distorted data records. Optimization Measures: Conduct tiered training, with a focus on explaining the convenience and value of the system to veteran employees to change their perceptions of the work process; establish standardized operating procedures that clearly define workflows and reward and penalty mechanisms; and provide on-site supervision to standardize work practices and ensure that all employees follow standardized procedures.

6.4 Unreasonable Storage Location Planning

When implementing the PTL system, some companies failed to optimize their existing storage layout. Materials were placed haphazardly, and the height arrangement was unreasonable, resulting in poor visibility of the lighting guidance and negatively impacting operational efficiency. Optimization Strategy: Based on material usage frequency, weight, specifications, and operational workflows, redesign standardized storage locations to organize materials by category and zone. This ensures unobstructed lighting guidance with no blind spots, thereby maximizing the system’s operational advantages.

VII. Conclusions and Outlook

As a core component of manufacturing logistics, on-line warehouses directly impact a company’s production efficiency, production costs, and production stability through their order-picking and restocking efficiency. The pain points associated with traditional manual operations—including low efficiency, high error rates, slow response times, and data inaccuracies—can no longer meet the evolving demands of smart manufacturing and flexible production.PTL with Light-Guided SortingLeveraging its core strengths—visual guidance, intelligent control and management, standardized operations, and data-driven traceability—this technology fundamentally resolves the key challenges associated with picking and replenishment in line-side warehouses. By optimizing the picking process, restructuring the intelligent replenishment system, and refining supporting management mechanisms, it can achieve a 50% or greater increase in picking efficiency, a picking accuracy rate exceeding 99.1%, a 70.1% improvement in replenishment response efficiency, and a reduction in labor costs of 30.1% or more—delivering significant results that comprehensively support the lean and intelligent upgrade of enterprise shop floor logistics.

Compared to traditional retrofit solutions, the PTL light-guided picking system offers the advantages of low implementation costs, short retrofit cycles, strong adaptability, and rapid results. It enables quick improvements in quality, cost reduction, and efficiency gains for line-side warehousing operations without requiring large-scale modifications to workshop hardware, making it suitable for manufacturing enterprises of all sizes—large, medium, and small. Against the backdrop of the industry’s ongoing advancement toward smart manufacturing, PTL light-guided picking technology will gradually integrate with AI algorithms, IoT sensors, and automated material handling equipment to enable advanced functions such as intelligent forecasting of material demand, fully automated optimization of work paths, and unmanned picking and restocking. This will further drive the transformation of shop floor logistics from “human-machine collaboration” to “intelligent unmanned operations.”

For manufacturing companies, implementing the PTL (Light-to-Pick) system is not only an upgrade to the line-side warehouse operating model but also a crucial step toward digital transformation and lean management. Companies need to tailor their system implementation plans and supporting management systems based on their specific production environments, material characteristics, and operational workflows. By doing so, they can fully leverage the value of technology, continuously optimize logistics efficiency, strengthen the foundation for smart manufacturing, and enhance their core production competitiveness.