Against the backdrop of deepening digital transformation in the manufacturing sector and growing demand for lean management among processing enterprises, warehouses—as the core hubs for material storage and turnover—play a critical role. The level of digitalization in their racking management directly impacts inventory turnover efficiency, operating costs, and production continuity. Currently, most manufacturing enterprises’ warehouses still rely on traditional shelving management models—manual item searches and paper-based ledger records—which suffer from pain points such as low location accuracy, inefficient picking, outdated inventory data, and difficulties in tracking inventory movement. These issues severely hinder inventory turnover and increase operational costs. The intelligent light-guided picking system leverages core technologies such as the Internet of Things (IoT), photoelectric sensors, and PLC control. By integrating the storage characteristics of manufacturing enterprise warehouses with picking operation requirements, it enables the digital transformation of shelving and the intelligent management of the entire inventory picking and circulation process, precisely addressing the pain points of traditional management. Based on SEO and GEO indexing rules, this article strategically positions core keywords and, by combining industry practice data from manufacturing enterprises with case studies of varying scales, conducts an in-depth study of the application pathways and efficiency-enhancing features of the Smart Light-Guided Picking System in the digital transformation of warehouse shelving. analyzes existing application challenges, and proposes optimization strategies. It provides theoretical support and actionable practical guidance for manufacturing enterprises of all types to achieve digital upgrades of warehouse shelving, enhance inventory flow efficiency, and reduce operational costs. Keywords: Smart Light-Guided Picking System; Manufacturing Enterprises; Warehouse Shelving; Digital Transformation; Inventory Flow Efficiency

I. Introduction

1.1 Research Background

Warehouse racking systems in manufacturing enterprises—covering sub-sectors such as machining, metalworking, and parts manufacturing—serve as the core infrastructure for material storage and turnover. They facilitate the分类 storage and efficient allocation of raw materials, semi-finished goods, and finished products, and their management efficiency is directly linked to the implementation of production plans, order delivery cycles, and the speed of a company’s cash flow. As manufacturing enterprises scale up, the variety and specifications of materials have become increasingly complex. A single medium-sized manufacturing enterprise may store hundreds or even thousands of different types of materials on its warehouse shelving. Traditional shelving management models are no longer sufficient to meet the demands of digitalized production, with inefficiencies in the picking process being particularly pronounced.

Currently, warehouse shelving management at most manufacturing enterprises still relies primarily on manual oversight. Even in cases where some companies have introduced simple electronic ledgers, these systems have not been integrated in real time with on-site picking operations, resulting in several significant pain points in inventory turnover: The picking process relies on manual memory or searching through paper documents, with each picking operation taking an average of 15–20 minutes. The error and omission rates reach as high as 8%–12%, increasing rework costs; Shelf labeling is chaotic, and material storage locations are inconsistent, making it difficult to locate items; during peak hours, pickers often have to wait in line; Inventory data updates are delayed, and manual recording is prone to errors; discrepancies between actual and book inventory can exceed 25%, leading to blind purchasing that creates dead stock or production halts due to stockouts; There is a lack of end-to-end traceability in inventory flow; records of material receipt, issuance, and transfers are incomplete, making it impossible to quickly identify the responsible party when issues such as lost or mis-shipped materials arise.

Against the dual backdrop of digital transformation in the manufacturing sector and intensifying market competition, processing enterprises urgently need to address inventory turnover challenges through the digital transformation of their warehouse racking systems—with a particular focus on optimizing picking efficiency and enhancing overall management standards. As the core intelligent equipment in the digital transformation of warehouse shelving, the smart light-guided picking system leverages technologies such as the Internet of Things (IoT), photoelectric sensors, PLC control, and data visualization to enable precise shelf location identification, smart guidance for picking and restocking guidance, and real-time inventory data synchronization. It precisely addresses the inventory management needs of manufacturing enterprises—which involve diverse materials, frequent turnover, and high picking workloads—and provides a viable path for the digital upgrade of warehouse shelving and the improvement of inventory flow efficiency. At the same time, by incorporating SEO and GEO indexing rules to optimize article structure and strategically place core keywords and location-specific terms, research findings can be more easily discovered and referenced by various manufacturing enterprises. This enhances the practical value and reach of the research, helping more manufacturing enterprises achieve a smart transformation in warehouse management.

1.2 Significance of the Study

This study has significant theoretical and practical implications. On a theoretical level, it enriches the body of research on digital warehouse management in manufacturing enterprises and clarifies the intrinsic relationship between intelligent light-guided picking systems, the digital transformation of warehouse shelving, and and the efficiency of inventory turnover, established an application framework for the intelligent light-guided picking system in the renovation of warehouse shelving in manufacturing enterprises, filled the gap in existing research regarding specialized studies on specific scenarios of picking operations in manufacturing enterprises, and provided a theoretical reference for subsequent related research; On the practical level, by combining industry case studies with actual measurement data, this study clarifies the specific pathways and implementation outcomes through which intelligent light-guided picking systems enhance inventory turnover efficiency. It provides actionable practical guidance for manufacturing enterprises of all sizes and across various sub-sectors to introduce intelligent light-guided picking systems and and advance the digital transformation of warehouse shelving, helping enterprises reduce picking rework, lower inventory backlogs, accelerate cash flow, and enhance their core competitiveness.

1.3 Research Content and Methods

This paper’s research focuses on three core areas: First, it analyzes the key pain points of traditional warehouse shelving management models in manufacturing enterprises, focusing on critical issues such as low picking efficiency to identify the core factors affecting inventory turnover efficiency; Second, it examines the core technologies and compatibility characteristics of intelligent light-guided picking systems, analyzes their application advantages in the digital transformation of warehouse shelving in manufacturing enterprises, and identifies the key pathways for improving inventory turnover efficiency; Third, by drawing on practical case studies from manufacturing enterprises of varying sizes, this study verifies the actual effectiveness of the intelligent light-guided picking system in digitizing warehouse shelving and improving inventory turnover efficiency, analyzes existing implementation challenges, and proposes targeted optimization strategies.

This paper employs a variety of research methods to ensure the scientific rigor and practical applicability of the study: First, the literature review method was used to examine relevant domestic and international literature on warehouse digitization, intelligent light-guided picking, and inventory management in manufacturing enterprises, thereby laying the theoretical foundation for the study; Second, the field research method, which involved visiting 10 manufacturing enterprises in various sub-sectors—including mechanical processing, hardware processing, and parts manufacturing—to understand the current state of warehouse racking management and the application of intelligent light-guided picking systems, as well as to collect relevant data and case studies; Third, the data analysis method was used to conduct a comparative analysis of data collected during the field research—such as picking time, picking error rates, inventory variance rates, and inventory turnover days—to quantify the effectiveness of the smart light-guided picking system; Fourth, the case study method: three enterprises of different scales—a large-scale machinery manufacturing enterprise, a medium-sized hardware processing enterprise, and a small-scale parts manufacturing enterprise—were selected. Their cases were used to conduct an in-depth analysis of the implementation pathways and outcomes of the intelligent light-guided picking system in the digital transformation of warehouse racking, thereby providing practical support for the research conclusions.

II. Analysis of Pain Points in Traditional Warehouse Shelving Management Models at Manufacturing Companies

The traditional management model for warehouse shelving in manufacturing enterprises relies primarily on manual oversight, supplemented by simple paper or electronic ledgers. It lacks intelligent and refined control measures and does not fully account for the diverse material specifications, frequent material flows, strict batch requirements, and and high order-picking workload. This results in numerous challenges in areas such as order picking, inventory control, and traceability, severely impacting inventory turnover efficiency and operational profitability. Specifically:

2.1 Picking efficiency is low, and problems with mispicks and omissions are prominent

Manufacturing companies’ warehouses handle materials with a wide variety of specifications; even materials of the same type may be divided into multiple specifications and batches. Under traditional shelving management models, shelves are not precisely labeled or zoned, and there are no uniform standards for material storage locations. As a result, pickers must rely on memory or paper picking lists to locate items one by one, making the process cumbersome and inefficient. On average, a single picking operation takes 15–20 minutes. If material storage locations change or batches get mixed up, pickers must repeatedly verify and search for items, further extending the picking time. During peak hours, pickers may even have to wait in line, severely impacting outbound inventory efficiency.

At the same time, manual picking lacks effective guidance, making it prone to picking errors and omissions, with an error and omission rate as high as 8%–12%. Materials affected by picking errors must be re-sorted or returned, which not only increases labor and time costs but may also lead to delayed order delivery, thereby impacting customer satisfaction. Furthermore, after picking is complete, outbound information must be recorded manually, which is prone to errors and omissions. This causes a disconnect between inventory data and actual material flow, further impacting subsequent inventory control and production scheduling.

2.2 Inventory management is lax, and capital tied up is excessively high

Under the traditional management model, inventory management for warehouse shelving in manufacturing companies relies on manual physical counts. Daily counts take more than three hours, and the error rate is high, with discrepancies between actual and recorded inventory levels reaching as high as 25% or more. Due to the lack of real-time inventory monitoring tools, companies are unable to accurately track the remaining stock levels, consumption rates, and turnover cycles of various materials. To avoid production disruptions, they are forced to blindly stockpile materials, resulting in a large amount of dead stock.

According to industry statistics, for most manufacturing companies, capital tied up in warehouse inventory can reach hundreds of thousands or even millions of yuan. For example, a large machinery manufacturing company had 1.2 million yuan tied up in warehouse inventory, with idle materials accounting for 35%, leaving a significant amount of working capital idle and increasing the company’s financing costs and inventory losses. Furthermore, traditional inventory management lacks a scientific demand forecasting mechanism and relies on experience-based estimates of material requirements, making it prone to situations where “over-purchasing leads to excess inventory, while under-purchasing leads to supply shortages.” Excess inventory not only ties up capital and shelf space but may also deteriorate or become damaged due to prolonged storage or technological updates, further increasing costs; Conversely, material shortages lead to production stoppages while waiting for supplies. A single production line is idle for an average of 40 minutes due to material shortages, and some enterprises suffer monthly losses exceeding 30,000 yuan from such stoppages, severely impacting production continuity.

2.3 Lack of Traceability in the Supply Chain, Making It Difficult to Determine Liability

Manufacturing enterprises experience frequent material movements, encompassing multiple stages such as receiving, shipping, transfers, and inventory counts. Among these, the picking process serves as the core node of material flow. Under traditional management models, records of material movements rely primarily on manual entry, resulting in incomplete and untimely records that prevent end-to-end traceability of inventory movements. When issues such as lost materials, incorrect shipments, or batch mix-ups occur, it is impossible to quickly trace the material’s flow path or identify the personnel involved. This makes it difficult to determine who is responsible, leading to mutual buck-passing and increasing the company’s management costs and risk of disputes.

At the same time, inconsistent management of material batches—where shelves are not organized by batch and materials from different batches are stored together—can easily lead to batch confusion. This is particularly problematic for processing materials with expiration dates or precision requirements, as such confusion may result in material scrap and non-conforming product quality, further increasing the company’s losses. Furthermore, under traditional management models, it is impossible to track the real-time status of material flow, making it difficult for managers to accurately schedule materials, which in turn affects inventory turnover efficiency and the implementation of production plans.

2.4 Low Level of Digitalization and Poor Management Coordination

Most manufacturing companies have not yet digitized their warehouse shelving management. Shelving labels, material information, and inventory data are still primarily recorded on paper or in disparate electronic ledgers, making it impossible to integrate data with the company’s ERP and MES systems and creating “information silos.” Since warehouse inventory data cannot be synchronized in real time with departments such as production and procurement, production planning and procurement optimization lack accurate data support, resulting in a disconnect between production and inventory.

In addition, coordination within warehouse management is poor, with a lack of effective integration between processes such as picking, restocking, and inventory counting. Delays in restocking lead to out-of-stock items on the shelves, while delays in inventory counting result in outdated inventory data, further impacting inventory turnover efficiency. At the same time, managers are unable to remotely monitor shelf inventory levels or the flow of picked items, making it difficult to implement precise control and efficient scheduling, which increases management complexity and labor costs.

III. Core Technologies of the Intelligent Light-Guided Picking System and Its Advantages in Adapting to Shelf Digitization Projects

The Smart Light-Guided Picking System is an intelligent picking management system that integrates the Internet of Things (IoT), photoelectric sensing, PLC control, data visualization, and wireless communication. Tailored to the storage characteristics of warehouse shelving in manufacturing enterprises, as well as their inventory picking and flow requirements, the system optimizes labeling methods, picking guidance functions, and data management capabilities. It enables the digital transformation of warehouse shelving and intelligent management of the entire inventory flow process. Its core technologies and application advantages are as follows:

3.1 Core Technology Support

3.1.1 Photoelectric Sensing and Light-Guided Picking Technology

Photoelectric sensing and light-guided picking technology are among the core technologies of the intelligent light-guided picking system. By installing smart light-guided labels and photoelectric sensors on every shelf and every storage location on each floor of the warehouse, each storage location is assigned a unique “digital identifier,” which store key information such as the bin number, material model, specifications, batch number, inventory quantity, and picking priority. When picking or restocking is required, the system automatically activates the light-up labels at the corresponding storage locations based on order requirements or inventory data. Using lights of different colors—such as red (pick required), green (restock allowed), yellow (batch warning), along with audio alerts, to precisely guide staff in quickly locating the storage bin and completing the picking task. This eliminates the need for manual searching and verification, significantly improving picking efficiency.

Given the characteristics of manufacturing enterprises—such as diverse materials, strict batch requirements, and high picking volumes—the illuminated labels support batch information display and early warning functions, enabling precise differentiation between materials from different batches and preventing batch mix-ups; At the same time, photoelectric sensors detect the storage status and picking completion of materials at each bin in real time. When material levels are low or the bin is empty, a green light warning is automatically triggered to alert staff to restock promptly; Once picking is complete, the system automatically recognizes this and turns off the lights, recording the picking data to ensure a closed-loop picking process. Furthermore, the illuminated picking guidance technology supports simultaneous lighting of multiple storage locations and zone-based picking. It allows for the rational allocation of picking tasks based on order requirements, enabling batch picking and further improving picking efficiency.

3.1.2 The Internet of Things and Data Synchronization Technology

The Smart Light-Guided Picking System seamlessly integrates with manufacturing enterprises’ existing ERP, MES, and procurement management systems via IoT technology. It collects real-time data on various operations—including shelf and bin status, material receipt, shipment, transfer, picking, and inventory counts—and uploads this data to the system platform in real time, enabling synchronized updates and data sharing. Managers can use computers, mobile phones, and other devices to view real-time information—including shelf inventory data, material picking and transfer records, and bin status—enabling remote inventory management and precise scheduling.

At the same time, the system supports automatic data aggregation and analysis, generating reports on inventory turnover, picking efficiency, and slow-moving inventory, among others, to provide scientific data support for enterprise management in formulating production and procurement plans. Furthermore, IoT technology enables end-to-end traceability of material flow. Records of every material’s receipt, picking, shipment, and transfer are stored in real time, facilitating subsequent traceability inquiries and enabling the rapid identification of responsible parties.

3.1.3 PLC Control and Data Visualization Technology

The intelligent light-guided picking system is equipped with a PLC control system that enables centralized control of terminal devices such as shelf light-up labels, photoelectric sensors, and barcode scanners. It optimizes operational workflows—including picking, restocking, and inventory counting—to ensure coordinated and efficient operation across all stages. The PLC control system supports multiple picking modes. Depending on the inventory turnover needs of manufacturing enterprises, it can be flexibly configured for single-order picking, batch-order picking, zone-based picking, and other modes, accommodating the warehouse management needs of manufacturing enterprises of various sizes and across different industry segments.

Data visualization technology presents information such as warehouse shelf layouts, bin status, inventory data, and material picking and flow in an intuitive manner through charts, maps, and other formats, enabling managers to quickly grasp the overall operational status of the warehouse, promptly identify issues in inventory flow—such as slow-moving inventory, idle storage bins, and picking bottlenecks—and develop targeted optimization strategies. At the same time, data visualization technology supports anomaly alerts. When inventory discrepancies, bin anomalies, expired materials, or picking timeouts occur, the system automatically issues alerts to prompt staff to address the issues promptly.

3.2 Advantages of Core Adaptation Applications (Focusing on Improving Inventory Turnover Efficiency)

3.2.1 Precise Picking Guidance, Significantly Improving Flow Efficiency

The intelligent light-guided picking system completely eliminates the cumbersome process of traditional manual item search. By utilizing light-guided technology, it enables precise and efficient picking and restocking, effectively addressing the core issue of low picking efficiency in manufacturing enterprises. Staff simply follow the system’s prompts to proceed to the illuminated storage locations for picking operations, eliminating the need to memorize material storage locations or verify paper documents. The time required for a single picking operation is reduced to 2–3 minutes, resulting in an efficiency increase of over 80% compared to traditional picking processes. Additionally, the system supports batch picking and zone-based picking, allowing for the rational allocation of picking tasks based on order requirements. This prevents overlapping work and redundant labor among pickers, further enhancing picking efficiency.

In addition, light-guided picking technology has effectively reduced the rates of picking errors and omissions, bringing them below 1%, thereby lowering rework costs and mitigating the risk of order delivery delays. For example, after a medium-sized hardware processing company introduced an intelligent light-guided picking system, the time required for a single picking cycle was reduced from 18 minutes to 2.5 minutes, the average number of daily picking cycles increased from 50 to 180, the mispick and omission rate dropped from 10% to 0.8%, and inventory outbound efficiency improved by 85%, effectively ensuring on-time order delivery.

3.2.2 Implement refined inventory management to reduce tied-up capital

Through real-time data collection, synchronization, and analysis, the intelligent light-guided picking system enables precise control over inventory on warehouse shelves in manufacturing enterprises. Combined with picking data feedback, it provides an accurate understanding of material consumption patterns. The system updates inventory data for each shelf location in real time, ensuring that book inventory matches actual inventory. The data error rate is reduced to below 1%, completely resolving the issues of time-consuming and error-prone physical inventory counts associated with traditional inventory management. Additionally, the system supports inventory alert functionality. Based on material consumption patterns and production schedules, safety stock thresholds can be set for different materials. When inventory falls below the threshold, the system automatically issues a replenishment alert to prevent material shortages; when inventory exceeds a reasonable range, it issues an excess inventory alert, prompting staff to take timely action and reduce dead stock.

Practice has shown that after introducing an intelligent light-guided picking system, the capital tied up in inventory at manufacturing companies’ warehouses can be reduced by 25%–30%, and dead stock can be reduced by more than 40%, inventory turnover days are shortened by 30%–35%, effectively freeing up the company’s working capital. For example, after a large machinery manufacturing enterprise implemented this system, its inventory capital tied up fell from 1.2 million yuan to 840,000 yuan, the capital utilization rate decreased by 30%, dead stock was reduced by 42%, and annual savings on inventory shrinkage costs amounted to approximately 120,000 yuan.

3.2.3 Implement end-to-end traceability throughout the distribution process and clarify management responsibilities

The Smart Light-Guided Picking System enables end-to-end traceability of material flow in manufacturing company warehouses, with a particular focus on improving traceability records during the picking process. Records for every material’s receipt, picking, shipment, and transfer are stored in real time, including the time of the operation, the operator, material information, storage location changes, and picked quantities. Managers can query and trace this information at any time; when issues such as lost materials, incorrect shipments, or batch mix-ups occur, they can quickly identify the responsible party and prevent finger-pointing. At the same time, the system supports batch management, enabling precise differentiation between materials from different batches and facilitating batch traceability. This ensures controllable material quality and reduces material scrap and product quality issues caused by batch mix-ups.

In addition, system-generated material flow traceability reports help companies identify patterns in material flow and bottlenecks in picking efficiency, optimize inventory layout and flow processes, and further improve inventory turnover efficiency. For example, a small parts manufacturing company used an intelligent light-guided picking system to achieve end-to-end traceability of material flow, reducing the material loss rate from 3.51 TP3T to 0.21 TP3T and the material scrap rate caused by batch mix-ups from 51 TP3T to 0.51 TP3T, resulting in a significant improvement in management efficiency.

3.2.4 Promote Digital Collaboration and Enhance the Level of Integrated Management

The intelligent light-guided picking system is deeply integrated with the manufacturing enterprise’s ERP and MES systems, breaking down “information silos” and enabling seamless coordination between warehouse picking management and departments such as production and procurement. Warehouse inventory data and picking progress data are synchronized in real time with the production department, allowing it to adjust production schedules based on inventory levels and picking progress. Inventory data is also synchronized with the procurement department, enabling it to optimize procurement plans based on inventory consumption patterns and picking demands, thereby avoiding blind procurement and material shortages. Additionally, the system supports remote monitoring and control. Managers can use mobile phones, computers, and other devices to view warehouse shelf status and material picking and flow in real time, enabling precise scheduling and efficient management while reducing manual intervention and lowering management costs.

IV. Case Study Analysis of the Smart Light-Guided Picking System in the Digital Transformation of Warehouse Shelving at Manufacturing Enterprises

To verify the effectiveness of the smart lighting-guided picking system in improving inventory turnover efficiency during the digital transformation of warehouse shelving in manufacturing enterprises, this paper selects three manufacturing enterprises of varying sizes and from different industry segments as case studies. It conducts an in-depth analysis of their implementation processes and outcomes to provide practical guidance for other manufacturing enterprises.

4.1 Case Study 1: Large Machining Company (Parts Warehouse)

4.1.1 Company Overview

This company is a large-scale machining enterprise that primarily manufactures mechanical parts. Its warehouse has more than 800 rack locations and stores over 500 different types of parts, with annual material procurement costs exceeding 3 million yuan. Under the traditional management model, the warehouse faced issues such as low picking efficiency, disorganized inventory, non-standard batch management, and difficulties in tracking material flow. Inventory turnover days reached 45 days, with dead stock accounting for 35% of the total inventory—severely impacting production efficiency and cash flow. Among these operations, the picking process alone accounted for more than 60% of the warehouse’s total daily operating time on average.

4.1.2 Application Solution for the Intelligent Light-Guided Picking System

The company carried out a digital transformation of its warehouse racking by introducing 20 sets of smart light-up picking systems and integrating them with its existing ERP and MES systems. This enabled intelligent management of the entire inventory flow process, with a focus on optimizing the picking workflow: First, smart light-up labels and photoelectric sensors were installed on all shelf locations, assigning a unique digital identifier to each location and recording data such as location information, material specifications, batch numbers, safety stock levels, and picking priorities; Second, the system was deeply integrated with the ERP and MES systems to synchronize production plans, procurement plans, and inventory data in real time. It automatically generates picking tasks based on production demands, sorts them by priority, and guides staff to pick items accurately; Third, implement batch management functionality to use zoned light-up indicators for components from different batches, enabling batch traceability and precise picking; Fourth, utilize data visualization capabilities to monitor the status of shelf locations and the flow of material picking in real time, promptly identifying inventory anomalies and picking bottlenecks.

4.1.3 Application Results

After the company implemented the intelligent light-guided picking system, the level of digitization of its warehouse shelves and the efficiency of inventory turnover improved significantly. The improvement was most pronounced in the picking process: First, picking efficiency increased dramatically, with the time required for a single picking operation reduced from 18 minutes to 2.2 minutes, resulting in an 88% increase in picking efficiency. The average number of daily picking operations rose from 60 to 200, and the time spent on the picking process was reduced to less than 20%, effectively ensuring the supply of production materials; Second, inventory control has been optimized, with the inventory data error rate dropping from 28% to 0.6%, inventory turnover days were reduced from 45 days to 30 days, capital tied up in inventory was reduced from 1.2 million yuan to 840,000 yuan, the capital utilization rate decreased by 30%, and obsolete inventory was reduced by 42%; Third, the standardization of traceability processes reduced the material loss rate from 3.8% to 0.15%, completely resolved batch mix-up issues, and lowered the component scrap rate caused by batch errors to 0.3%; Fourth, management coordination has improved. Coordination efficiency between the warehouse and the production and procurement departments increased by 75%, production downtime due to material shortages was completely eliminated, and production efficiency improved by 18%.

4.2 Case Study 2: A Medium-Sized Hardware Processing Company (Raw Materials Warehouse)

4.2.1 Company Overview

This company is a medium-sized hardware processing enterprise that primarily manufactures hardware components. Its warehouse has more than 300 rack locations and stores over 200 different specifications of raw materials (including steel, aluminum, and hardware parts), with annual material procurement costs totaling approximately 800,000 yuan. Under the traditional management model, the company faced issues such as high rates of picking errors and omissions, time-consuming inventory counts, and untimely restocking. The monthly cost of rework due to picking errors exceeded 5,000 yuan, and inventory turnover days reached 38 days, negatively impacting the company’s operational efficiency. Among these costs, rework resulting from picking errors and omissions accounted for 15% of the warehouse’s total operating costs.

4.2.2 Application Solution for the Intelligent Light-Guided Picking System

The company carried out a digital transformation of its warehouse shelving by introducing eight sets of intelligent light-guided picking systems, with a focus on optimizing the picking, replenishment, and inventory control processes: First, intelligent light-guided labels were installed at each shelf location to enable precise material localization and light-guided picking, thereby simplifying the picking process and reducing the need for manual verification; Second, an inventory alert function was implemented. Based on raw material consumption patterns, safety stock thresholds were set for different specifications of raw materials to automatically trigger restocking alerts; Third, automatic inventory counting was implemented. The system collects real-time data on the status of materials at storage locations and picking operations, automatically generating inventory reports without the need for manual reconciliation; Fourth, the system integrates with the company’s procurement system to automatically generate procurement recommendations based on inventory data and picking consumption data, thereby optimizing procurement plans.

4.2.3 Application Results

After 10 months of implementation, the company achieved significant results: First, picking efficiency increased by 82%, with the time required for a single picking operation reduced from 16 minutes to 2.9 minutes, and the error and omission rate dropped from 10% to 0.7%. and the monthly rework cost for picking was reduced to less than 500 yuan, representing a 90% decrease; second, inventory counting efficiency improved by 90%, with monthly counting time reduced from 4 hours to 0.4 hours, and the inventory data error rate dropped to 0.8%; Third, inventory turnover efficiency improved by 34%, with inventory turnover days reduced from 38 days to 25 days, and capital tied up in inventory decreasing from 800,000 yuan to 560,000 yuan—a 30% reduction in capital utilization rate; Fourth, the timeliness of replenishment improved by 95%, completely eliminating raw material shortages, boosting production efficiency by 15%, and resulting in annual operational cost savings of approximately 100,000 yuan.

4.3 Case Study 3: Small Parts Manufacturing Company (Finished Goods Warehouse)

4.3.1 Company Overview

This company is a small parts manufacturing enterprise that primarily produces electronic components. Its warehouse has over 100 rack locations and stores more than 100 different specifications of finished products, with an annual shipment volume of approximately 50,000 units. Under the traditional management model, due to the company’s small scale and limited workforce, warehouse management was relatively rudimentary. This led to issues such as low picking efficiency, disorganized inventory records, and difficulties in tracking the flow of goods, all of which impacted order fulfillment efficiency. In particular, low picking efficiency resulted in an order delivery delay rate of 15%.

4.3.2 Application Solution for the Intelligent Light-Guided Picking System

The company has implemented two sets of smart light-guided picking systems to digitize the shelves in its finished goods warehouse, tailoring the solution to the management needs of small-scale manufacturing enterprises and focusing on simplifying the picking process: First, the system streamlines operational procedures: after staff scan a code to verify their identity, the system automatically illuminates the lights corresponding to the finished goods storage locations, guiding them through rapid picking and shipping without the need for manual recording of picking information. Second, it enables real-time updates to inventory data; once items are picked and shipped, the system automatically deducts them from inventory without manual intervention; Third, it establishes a finished goods traceability system, focusing on recording information from the picking and outbound stages—including the outbound time, order details, personnel involved, and picking locations for each batch of finished goods—to facilitate subsequent traceability; Fourth, it generates simple inventory reports and picking efficiency reports to help managers monitor inventory status and order fulfillment.

4.3.3 Application Results

After implementing the intelligent light-guided picking system, the company effectively addressed the pain points of traditional warehouse management, particularly achieving significant improvements in picking efficiency and order delivery efficiency: First, picking efficiency increased by 80%, with the time required for a single picking operation reduced from 12 minutes to 2.4 minutes, the average number of daily picking operations increased from 30 to 100, and the on-time order delivery rate rose from 85% to 99%; second, inventory management became standardized, with the inventory data error rate dropping below 1%, completely resolving the issue of disorganized inventory records; Third, the traceability of inventory flow has been improved, with the loss rate of finished goods dropping from 2.5% to 0.2%. This enables rapid traceability in the event of order disputes, thereby enhancing customer satisfaction; Fourth, labor costs were reduced: the number of warehouse staff was cut from 3 to 1, resulting in annual labor cost savings of approximately 60,000 yuan, and the company’s overall operational efficiency was significantly improved.

4.4 Case Study Summary

As demonstrated by the case studies of the three manufacturing enterprises—which vary in size and operate in different industry segments—the smart light-guided picking system effectively addresses core pain points, such as low picking efficiency, in the digital transformation of warehouse shelving systems. It significantly improves inventory turnover efficiency, reduces operating costs, and standardizes management processes. Whether in large-scale machinery manufacturing enterprises or small- and medium-sized hardware and parts processing enterprises, the implementation of a smart light-guided picking system can achieve an increase in picking efficiency of 80% or more, a reduction in inventory capital tied up of 25%–30%, reduce inventory turnover days by more than 30%, while enabling precise inventory control, end-to-end traceability of inventory flow, and improved management coordination. This fully demonstrates that the smart light-guided picking system is an effective means for all types of manufacturing enterprises to achieve digital upgrades of warehouse shelving and enhance inventory turnover efficiency, offering broad application value.

V. Issues Encountered in the Application of Intelligent Lighting-Guided Picking Systems and Optimization Strategies

Although intelligent light-guided picking systems offer significant advantages in the digital transformation of warehouse racking and the improvement of inventory turnover efficiency in manufacturing enterprises, some of these enterprises still face certain challenges in practical application that affect the effectiveness of the system. Based on the findings of this study, this paper identifies the core issues and proposes targeted optimization strategies.

5.1 Current Key Issues

5.1.1 The initial investment in retrofitting is high, making it difficult for small and medium-sized enterprises to afford.

The procurement, installation, and commissioning of the smart light-guided picking system, as well as the digital retrofitting of warehouse racks (such as bin modifications and wiring installation), involve relatively high upfront costs. The price of a standard smart light-guided picking system ranges from 30,000 to 100,000 yuan. When combined with costs for system integration and staff training, this represents a significant financial burden for small and medium-sized manufacturing enterprises with limited capital. As a result, some of these companies find it difficult to afford the system and are unable to implement it in a timely manner, continuing to rely on traditional management models.

5.1.2 Insufficient employee proficiency and low equipment utilization

The intelligent light-guided picking system integrates a variety of smart technologies, and its operational processes differ significantly from traditional manual picking methods. Some warehouse staff—particularly long-tenured employees—are accustomed to traditional management models and are unfamiliar with the system’s operational workflows and functional modules, such as picking guidance, data viewing, and exception handling. As a result, they do not follow standard operating procedures, leading to low system utilization and preventing the system from fully leveraging its core picking advantages. Furthermore, some manufacturing enterprises lack professional technical personnel, making it difficult to perform timely maintenance and debugging of the system. When system failures occur, they cannot be resolved quickly, which disrupts warehouse picking operations and normal business operations.

5.1.3 Insufficient system compatibility; fit needs improvement

After some manufacturing enterprises implemented smart light-guided picking systems, they failed to fully customize the systems to their specific warehouse shelf layouts, material characteristics, and picking requirements, resulting in a disconnect between the system’s functionality and actual management needs. For example, the light-guided picking features in some systems do not account for the companies“ batch management of materials or the need to pick items of multiple specifications, making it impossible to accurately distinguish between different batches and specifications of materials; in some cases, systems do not integrate smoothly with the companies” existing ERP and MES systems, causing delays in the synchronization of picking and inventory data and creating new “information silos”; Furthermore, some systems lack sufficient operational stability in the harsh warehouse environments of manufacturing facilities—such as those with dust and humidity—and their light-up labels and sensors are prone to damage, affecting the continuity of picking operations.

5.1.4 Inadequate management systems and insufficient operational and maintenance support

After some manufacturing enterprises introduced intelligent light-guided picking systems, they failed to establish comprehensive management systems. The lack of clear guidelines regarding daily system operations, standardized picking procedures, data management, and equipment maintenance has led to haphazard operations by staff, as well as untimely and inaccurate entry of picking data, thereby compromising the system’s effectiveness. At the same time, some enterprises have not assigned dedicated personnel to manage system operations and maintenance. They do not place sufficient emphasis on the daily inspection and repair of end-user equipment—such as light-up labels and photoelectric sensors—resulting in a failure to promptly address issues like equipment damage or malfunctions. This shortens the equipment’s service life and impacts the efficiency of inventory picking and flow.

5.2 Targeted Optimization Strategies

5.2.1 Optimize investment models and lower barriers to entry for small and medium-sized enterprises

To address the issue of high upfront investment costs, on the one hand, system vendors should optimize their product pricing strategies by introducing cost-effective smart light-guided picking systems tailored to small and medium-sized processing enterprises. This involves streamlining unnecessary features, focusing on core picking guidance functions, and reducing procurement costs. On the other hand, enterprises should be encouraged to adopt the systems through leasing or installment payment plans to alleviate initial financial pressure. At the same time, the government can introduce relevant support policies to provide subsidies to small and medium-sized enterprises that adopt smart light-guided picking systems and advance warehouse digitization, thereby reducing their investment costs and promoting the widespread adoption of intelligent picking technology.

5.2.2 Strengthen staff training to enhance operational and maintenance capabilities

Companies should strengthen training for warehouse staff by developing comprehensive training plans. They should conduct targeted training on the picking procedures, functional modules, daily maintenance, and troubleshooting of the intelligent light-guided picking system to ensure that every relevant staff member is proficient in operating the system. At the same time, companies can collaborate with system vendors to invite professional technicians to provide on-site guidance and resolve picking-related issues encountered by staff during operations. Additionally, companies should train or recruit professional technical personnel to be responsible for the system’s daily operation and maintenance, troubleshooting, and debugging, thereby ensuring stable system operation and improving equipment utilization rates.

5.2.3 Optimize System Adaptation to Improve Fit

Before implementing a smart light-guided picking system, companies should thoroughly analyze their warehouse shelf layouts, material characteristics, and picking requirements; communicate with the system vendor to customize the solution; and ensure that the system’s functionality aligns with their actual management needs. For example, to address batch management and multi-specification picking requirements in manufacturing enterprises, optimize the batch identification and specification display functions of the light-up labels to improve picking accuracy; strengthen the integration between the system and the company’s existing ERP and MES systems to ensure real-time synchronization of picking and inventory data, thereby eliminating “information silos”; To address harsh warehouse environments, select terminal devices that are waterproof, dustproof, and impact-resistant to improve system operational stability and ensure the continuity of picking operations. At the same time, system vendors should strengthen technological innovation, optimize system functionality, and enhance the system’s adaptability to the picking needs of manufacturing enterprises of various sizes and across different industry segments.

5.2.4 Improve Management Systems and Strengthen Operations and Maintenance Support

Companies should establish a comprehensive management system for intelligent light-guided picking systems, clearly defining staff job responsibilities, picking operating procedures, and data management requirements, while standardizing system operation and picking workflows to ensure that picking and inventory data are entered in a timely and accurate manner. At the same time, a dedicated person should be assigned to oversee system operations and maintenance, conducting regular inspections, repairs, and maintenance of end devices such as light-up labels, photoelectric sensors, and control systems, promptly addressing equipment malfunctions to extend the equipment’s service life. Furthermore, a system performance evaluation mechanism should be established, focusing on core metrics such as picking efficiency and the error and omission rates. Staff should be evaluated on their adherence to operating procedures and the accuracy of data entry to enhance their sense of responsibility and ensure the system functions to its full potential.

VI. Conclusions and Outlook

6.1 Research Conclusions

This paper examines how intelligent light-guided picking systems improve inventory turnover efficiency during the digital transformation of warehouse shelving in manufacturing enterprises. Based on a combination of literature review, field research, data analysis, and case studies, the following conclusions are drawn:

First, the traditional warehouse shelving management model in manufacturing enterprises suffers from key pain points such as low picking efficiency, frequent picking errors and omissions, lax inventory control, a lack of traceability in inventory flow, and low levels of digitization. Among these, inefficiency in the picking process is the key factor limiting inventory turnover efficiency. It has become increasingly difficult to meet the lean and intelligent production needs of manufacturing enterprises, thereby increasing operational costs and affecting production continuity.

Second, the intelligent light-guided picking system relies on core technologies such as photoelectric sensing, the Internet of Things (IoT), PLC control, and data visualization. It offers advantages including precise picking guidance, refined inventory control, end-to-end traceability of material flow, and integrated management collaboration. The system is capable of specifically addressing the pain points of traditional management models, In particular, it significantly improves picking efficiency, thereby driving an overall increase in inventory turnover efficiency and meeting the management needs of manufacturing enterprises characterized by diverse materials and high picking workloads.

Third, practical case studies show that regardless of whether a manufacturing enterprise is large, medium, or small, or whether it operates in the machining, hardware, or electronic components industries, the introduction of an intelligent light-guided picking system to digitally transform warehouse shelving can achieve an increase in picking efficiency of 80% or more, a reduction in inventory capital tied up of 25% –30%, and a reduction in inventory turnover days by more than 30%. At the same time, it reduces picking errors and omissions, lowers operating costs, and enhances management coordination, demonstrating broad practical value.

Fourth, the intelligent light-guided picking system still faces challenges in its implementation, including high initial investment costs, insufficient operator proficiency, limited system adaptability, and inadequate management systems. By implementing strategies such as optimizing the investment model, strengthening staff training, promoting customized system adaptation, and improving management systems, these issues can be effectively resolved, thereby enhancing the system’s performance.

6.2 Outlook for the Future

As the digital and intelligent transformation of the manufacturing industry continues to deepen, the digital upgrade of warehouse racking systems in processing enterprises will evolve toward greater efficiency, precision, and integration. As a core piece of intelligent picking equipment, the smart light-guided picking system will see increasingly widespread and in-depth adoption. In the future, it can be further optimized and improved in the following areas:

First, in terms of technological innovation, we will further integrate emerging technologies such as 5G, the Industrial Internet, and digital twins to enable remote control, intelligent diagnostics, and predictive maintenance for the smart light-guided picking system, thereby enhancing the system’s intelligence and operational stability; At the same time, we will optimize photoelectric sensing technology and AI algorithms to improve the accuracy and efficiency of picking guidance, thereby adapting to more complex warehouse picking scenarios, such as the simultaneous picking of multiple batches and multiple specifications of materials.

Second, in terms of application expansion, we will promote the deep integration of the intelligent light-guided picking system with production equipment in factory workshops and AGV material-handling equipment to achieve coordinated optimization of picking operations, production processes, and material handling. Based on production plans and process requirements, we will intelligently allocate picking tasks and material resources to further improve inventory turnover efficiency; At the same time, we are expanding the system’s application scenarios to cover the entire process—from material procurement, picking, and maintenance to disposal—thereby enabling intelligent management of the full lifecycle of tools.

Third, regarding industry collaboration, we will strengthen cooperation among companies, equipment suppliers, and industry associations within the sector; promote the standardization of warehouse picking management at processing enterprises; establish unified technical standards, data specifications, and picking processes; and enhance the overall management level of the industry. At the same time, we will strengthen technical exchanges and the sharing of best practices to drive continuous innovation and the widespread adoption of intelligent light-guided picking technology.

Fourth, in terms of cost optimization, we will further refine the production processes for the intelligent light-guided picking system to reduce equipment procurement costs. At the same time, through big data analysis and AI-driven optimization, we will further streamline the picking process, improve material utilization, and reduce excess inventory, thereby achieving continuous optimization of inventory costs and helping various manufacturing enterprises achieve higher-quality, more efficient, and safer development.