Key Insights: A Study of Operational Models for New Logistics Business Forms from the Perspective of the Platform Economy

In designing and laying out the logistics network, it is essential to balance retail operations with last-mile delivery. , meet the demands of high-frequency, niche‑market orders, control overall operational costs, and leverage digital and intelligent systems and equipment to progressively address issues such as volatility and time‑sensitivity throughout the supply chain.

Platform economics is an economic activity composed of interconnected units, underpinned by digital technologies and supported by both physical and virtual trading platforms, linked through intelligent networks. It bridges supply and demand across diverse sectors—including e‑commerce, social networking, and logistics—where logistics plays a pivotal role in aggregating resources and reorganizing upstream and downstream actors around the platform to establish a new retail ecosystem. The primary research focus of emerging logistics models centers on how firms leverage their respective resource advantages—such as leasing, self‑built infrastructure, and outsourcing—to deliver end‑to‑end, anytime‑anywhere integrated service solutions, while transitioning toward digital and intelligent service paradigms. These new logistics formats must, on the basis of precise alignment with commercial flows, reduce costs, optimize existing operational models, and emphasize the deep integration of key elements—people, goods, and venues—within the platform economy. Accordingly, alongside upgrading traditional logistics networks, researchers can turn to emerging areas such as instant‑delivery and fresh‑food logistics, seeking to harness smart technologies to enhance response speed and matching efficiency in real‑world operations. Building upon conventional in‑house logistics systems, they can explore modular outsourcing and develop regional, centrally managed supply chains that link central warehouses to forward‑positioned depots, with the aim of eventually establishing a diversified, closed-loop ecosystem driven by transparent and concealed warehousing, cold‑chain logistics for perishables, and intelligent logistics solutions.

Under the trends of the platform economy, traditional large‑scale e‑commerce can no longer keep pace with technological advances, rising consumer expectations, and the upgrading of industry value chains across various product categories; the “digital‑concept‑driven”… The new retail model emerging from the O2O integration of online and offline logistics is increasingly becoming mainstream. This new retail paradigm relies on a revamped logistics system to reconfigure circular value chains centered around diverse trading platforms [1], enabling B‑to‑B merchants to leverage consumer‑level data to enhance operational efficiency and explore innovative business models such as live streaming, social commerce, and mini‑programs. The key to building a new logistics system lies in optimizing logistics costs while enhancing the comprehensive capabilities of various regional consolidation hubs—covering goods collection and distribution, warehousing, transshipment, and last-mile delivery—thereby further fostering the integrated development of multimodal transport and cross-border e‑commerce logistics.

01 New Retail Drives the Optimization and Upgrading of E-commerce Logistics Models

• E-commerce logistics guidance O2O All‑Domain, All‑Time Transaction Sharing O2O e‑commerce platforms are shifting from the previously relatively simplistic online matchmaking model toward an integrated “product‑service” approach. Centered on users’ round‑the‑clock everyday scenarios, their offerings range from enhancing the online shopping experience to ensuring timely offline delivery. By identifying and addressing evolving consumer needs, these platforms aim to capture untapped “blue‑ocean” opportunities. Traditional platforms must further expand their product SKUs—minimum inventory units—to broaden choice, strengthen return‑and‑exchange policies and after‑sales support, and elevate overall shopping convenience. In the realm of offline logistics, they need to build a comprehensive, always‑on delivery ecosystem, spanning conventional full‑truckload, less‑than‑truckload, and express services all the way to next‑day and same‑day delivery, thereby upgrading the entire logistics experience. Against the backdrop of data‑driven integration across channels, this creates shared value across the new retail landscape. The areas where online and offline operations are increasingly integrated within e‑commerce platforms are illustrated in Figure 1.

Figure 1 O2O Shared-Service Region Driven by E-commerce Logistics

In the figure 1. Online merchants and users complete the first half of the commercial flow, closing the data loop, while logistics providers handle the second half—transportation along the supply chain. On the one hand, online merchants manage a large volume of fragmented orders and possess robust capabilities in data collection and analysis, enabling more objective and accurate demand forecasting and logistics planning. Meanwhile, offline retailers, through either self‑established or outsourced networks, can leverage extensive physical outlets and last‑mile delivery points, effectively unlocking the value of real‑time delivery and receipt, thereby facilitating faster door‑to‑door service at competitive logistics costs. Consequently, both O2O participants should integrate their core competencies across the upstream planning stages and downstream last‑mile delivery segments of the overall logistics supply chain, jointly realizing tangible synergies [2]. On the other hand, online brands typically offer a broad range of SKUs but face highly dispersed order volumes at all levels; in contrast, offline retailers, though limited in SKU variety, benefit from high degrees of scale and operational efficiency. With platform‑driven guidance and traffic diversion, resource sharing between O2O partners in areas such as planning, procurement, warehousing, transportation, and last‑mile delivery can further amplify the economies of scale inherent in each individual model. First, for brand owners, new logistics business models require the ability to generate timely and accurate sales forecasts and manage inventory effectively. Reducing overall logistics costs for warehousing and transportation aims to eliminate inventory whenever possible or to implement dynamic “zero-inventory” management. Secondly, from a user experience perspective, the new logistics model must address fragmented and personalized needs. Enables real-time delivery of goods and enhances customer satisfaction through services such as big data analytics, personalized information notifications, and door-to-door delivery. Finally, looking ahead to the future of smart logistics, the new logistics paradigm of the e‑commerce era must be grounded in… Technologies such as artificial intelligence (AI) and the Internet of Things are driving the automation and intelligent upgrading of various product value chains. Leveraging hardware and software technologies to advance the comprehensive integration of smart logistics.

02 The Progressive Upgrading of E-commerce Logistics Models

E-commerce parcel delivery volume and growth rate are both slowing. As the new retail concept expands the e‑commerce market, brands are increasingly adopting new approaches to logistics management. The likelihood of a sharp surge in SKU‑level order volumes is steadily increasing. Leveraging their extensive experience and accumulated service capabilities, third‑party logistics providers can deliver greater transaction‑driving value across the entire supply chain. From the e‑commerce platform’s perspective, the primary focus of logistics services is end‑consumers, typically delivered via full‑truckload, less‑than‑truckload, and express delivery—among which express delivery plays a pivotal role by providing direct last‑mile delivery, making it more familiar and readily accepted by users. In addition to annual commercial‑flow statistics, express‑delivery volume has long served as one of the key indicators for gauging the overall health of the logistics sector. Between 2013 and 2022, nationwide express‑delivery volume consistently exceeded 100 billion parcels per year, as illustrated in Figure 2.

Figure 2 China’s Express Delivery Volume and Annual Growth Rate, 2013–2022. Data sources: National Bureau of Statistics, State Post Bureau, and Huaxin Consulting.

Figure 2 In 2013, the annual volume of express delivery services was only around 9.1 billion parcels. Over the following decade, it grew steadily year after year, surpassing 110 billion parcels by 2022. According to the State Post Bureau, the e‑commerce logistics sector now boasts a peak daily handling capacity exceeding 700 million parcels, with per capita annual parcel volume approaching 80 items. The COVID‑19 pandemic in 2020 further catalyzed rapid growth in both e‑commerce and the logistics industries. However, alongside this expansion, the growth rate of express delivery volume began to decelerate sharply from 2021 onward, with year‑on‑year growth at just 2.1% in 2022. This indicates that the logistics sector is approaching resource saturation; beyond increasing investment in infrastructure and hardware, it will also require the integration of cutting‑edge technologies to expand logistical capacity. Building on this, traditional models must be reimagined by aligning all links in the logistics supply chain with new retail trends, thereby fostering the emergence of new logistics business formats. 2. E-commerce Logistics Model Upgrading Driven by New Retail Under the new retail paradigm, the upgrading of consumption is driving product requirements toward “direct sourcing from the place of origin.” The shift in standards for “express direct‑delivery product traceability” has prompted many merchants to secure product variety and quality through direct sourcing from domestic production regions and cross‑border e‑commerce channels, thereby serving as an external driver for the upgrading of logistics models. First, last-mile delivery is evolving toward an “in-store‑warehouse integration” model within the hour. The traditional e-commerce logistics model is primarily based on The new logistics system, driven by the platform economy, primarily relies on “RDC (Regional Distribution Center) urban warehouses for last‑mile delivery” and “omni‑channel warehouses offering standard express delivery.” It strengthens instant‑delivery services and integrates stores with forward‑stocking warehouses, establishing a point‑to‑point, door‑to‑door terminal‑delivery network and a rapid pick‑up‑and‑drop‑off system. This upgrades the previous day‑long delivery timeframe to one measured in hours or even minutes, as illustrated in Figure 3.

Figure 3. Advancement and Upgrading of E-commerce Logistics Models

Figure 3. New‑retail logistics emphasizes transforming the traditional “physical storefront plus warehouse” model, striving to shift from fixed inventory to zero inventory and ensuring that products remain in continuous, dynamic logistical flow from the point of origin to the end user. However, conventional practices such as centralized consolidation, unified cross‑regional fulfillment, and the RDC model are not being phased out; instead, they are integrated—based on the specific characteristics of different product categories (e.g., fresh produce, agricultural and sideline products, light industrial goods) and emerging transaction formats like live streaming and group buying—into a diversified “traditional‑plus‑upgrade” logistics framework [3]. Secondly, logistics facilitates the optimal selection of products. Logistics enterprises need to integrate existing transport capacity at the packaging and shipping stage, enabling brand owners to source high‑quality, competitively priced products across domestic and international touchpoints. In this process, logistics visibility helps carriers strengthen their end‑to‑end supply chain oversight and enhance their ability to identify and rectify errors. ① Visualized monitoring of the logistics process, enabling end-to-end visibility through surveillance devices deployed across warehousing and transportation; ② Visualization of logistics data, leveraging real-time logistics information to generate interactive dashboards that enhance operational efficiency by optimizing routes, timing, and resource allocation; ③ Visualized logistics decision-making, reengineering workflows and data analysis across all logistics stages, and using simulation tools to render decision outcomes in a visual format. Finally, enhance reverse logistics capabilities by leveraging omnichannel consumption and diversification. Reverse logistics refers to the logistics process in which customers return goods to the brand, primarily involving product returns and repairs, and encompasses items such as footwear, headwear, and apparel. For mainstream e‑commerce categories such as 3C products, cosmetics, and toys, repair and return requests are concentrated on high‑value items like electronics, digital devices, and luxury goods such as watches and jewelry. As the platform economy underscores the fragmentation and personalization of consumer demand, logistics service providers must prioritize enhancing their reverse‑logistics capabilities. Specifically, this entails: ① offering door‑to‑door pickup, smart label‑management services, and notifications for returns and re‑shipping after repairs; ② providing convenient in‑store and collection‑point options, including expedited delivery and time‑limited pickup; and ③ leveraging smart devices to strengthen security during the transport of high‑value goods, while refining specialized insurance and claims‑handling mechanisms to progressively address the pain points associated with lost parcels. II. Multidimensional Network Development in New Logistics Business Models The platform economy has heightened market attention to the efficiency of commercial logistics and fulfillment. To cover all aspects of consumers’ daily lives, an efficient logistics system is essential—one that seamlessly integrates online pricing and channel advantages with offline experiences and service strengths. 4]. As the overall growth rate of the logistics industry slows—illustrated in Figure 2—and as trunk‑line operational efficiency becomes increasingly standardized, new logistics models must deepen the design of innovative network architectures to expand customer touchpoints, optimize route planning, and implement differentiated network operations for various types of cargo. (1) Enhancing Timeliness and Optimizing Outlet Design in the New Logistics Network When designing a new logistics network, the key consideration is the delivery lead time for different modes of transport—such as full truckload, cold chain, less-than-truckload, and express services. The overall network should be structured around trunk‑line transportation and planned based on critical time milestones at each stage, ensuring both reasonable promised delivery windows for customers and effective handling of reverse logistics, including returns and exchanges that cannot be processed within standard timelines. In branch‑line operations, further efficiency gains can be achieved through strategies like small‑vehicle frequent service, direct routing, and two‑leg transfer connections. At the design stage of intermediate transshipment hubs and last‑mile delivery points, tailored branch‑line networks should be developed according to cargo type, optimizing parcel pickup, drop‑off, and last‑mile delivery while eliminating inefficient practices such as head‑haul, circuitous routes, and redundant transport paths. 5], in addition to meeting timeliness requirements, it is also necessary to consider the coverage area of each delivery outlet, as well as the distribution of shipment volumes and logistics costs between the central transshipment hub, the LTL consolidation points, and individual courier outlets, in order to determine the optimal outlet network layout based on cost-effectiveness, as illustrated in Figure 4.

Figure 4. Logistics Network Routing Models and Multi‑Network Operations for Diverse Product Types

The design of logistics networks for diverse product categories should be tailored to their origins and characteristics, with separate planning and network operations. For example, light industrial goods and agricultural products have different time‑sensitivity requirements, and the customer bases for cold‑chain and LTL shipments also differ. However, in practice, these can be integrated within a multi‑dimensional network framework, enabling unified deployment. First, for time‑critical express deliveries and fresh‑produce items—where customers are widely dispersed and delivery locations are scattered—small‑vehicle transport, pre‑positioned warehouse transshipment, and coordinated pickup and last‑mile delivery by couriers and riders can be employed. Second, for LTL and full‑truckload shipments that prioritize volume over timeliness and require specialized equipment for loading and unloading, courier‑network hubs should be concentrated in designated areas such as logistics parks and warehousing markets, facilitating point‑to‑point service. Third, while express networks cover relatively small areas and offer flexible delivery, large‑item and LTL networks necessitate strategically located core distribution centers with broad coverage, operating under a centralized pickup‑and‑delivery model. Yet, when selecting site locations, if express services and LTL/full‑truckload operations overlap significantly in a given region, a shared network infrastructure can be adopted, allowing for unified capacity management. In the emerging logistics landscape, as network deployment becomes more widespread, users’ reliance on any single hub will gradually diminish; at the same time, service speed will draw closer to end‑consumers. By transforming last‑mile logistics from a point‑based to a more area‑wide approach, and by diversifying service offerings, we can better meet evolving customer needs. At the consumer‑end touchpoints, full‑node connectivity and end‑to‑end delivery are achieved, with flexible deployment of consolidation hubs, drop‑off/pickup stations, AI‑enabled smart lockers, and other formats to handle logistics volume and extend the network [6]. As intelligent technologies continue to be adopted, the construction of next‑generation logistics networks will increasingly leverage smart algorithms and cloud computing to optimize and flexibilize last‑mile networks—covering aspects such as vehicle dispatch, warehouse site selection, and workforce allocation. By integrating hub locations and inventory levels, these systems can route delivery personnel and vehicles more efficiently, while intelligently allocating capacity to meet consumers’ “profile‑based” needs. Moreover, smart networks will enhance overall algorithmic performance and information synchronization across nodes in areas like demand forecasting, map analytics, and precision navigation, thereby further refining the design of new logistics networks and enabling greater visualization of their practical implementation. (II) The Three-Dimensional Network Structure of “Sky, Earth, and People” in New Logistics Business Models The new logistics network encompasses small-, medium-, and large-scale logistics networks, Warehousing equipment, Delivery and Pickup Points Under intelligent guidance, with full‑area coverage, operators must reduce overall supply‑chain costs and achieve digital optimization in warehouse layout, route network design, and order picking and consolidation. This enables the establishment of a comprehensive “Sky–Ground–People” network architecture, as illustrated in Figure 5. The Sky Network is an online logistics data‑sharing platform that connects all parties in the supply chain, coordinates third‑party logistics providers, and progressively mitigates issues such as warehouse congestion and delivery delays during peak seasons. The Ground Network comprises warehousing and distribution facilities at various levels across the country, facilitating the exchange and feedback of real‑world operational data and insights from diverse logistics models to the online system. Meanwhile, the People Network focuses on building physical service capabilities for end users, ensuring adequate service coverage and timely fulfillment of daily delivery orders.

Figure 5. The Three-Dimensional Network Structure of “Sky, Ground, and People” in New Logistics Business Models

In the practical construction of a three-dimensional network, logistics enterprises need to take the ground network as the central axis. It integrates the sky network, the human network, and the entire trunk line with its various branch lines, delivering real-time data to each node. This provides upstream brand owners with a comprehensive suite of logistics options while enhancing downstream platform advantages and customer loyalty [7]. The fragmented delivery of traditional e‑commerce parcels will gradually give way to more consolidated, large‑scale operations, boosting logistics efficiency and reducing costs in the process. Fragmented parcels are collected, picked, packed, and delivered by separate logistics providers, making it difficult to achieve economies of scale; moreover, they suffer high damage rates during long‑distance transport. By contrast, the three‑dimensional network enables merchants to stock up in advance based on sales forecasts, employ less‑than‑truckload or full‑truckload allocation to cut transportation costs, and shorten the time between order placement and delivery. They can also outsource to other logistics providers to move the shipping point closer to the customer, so that even if a shipment is damaged, inventory can be quickly reallocated from a nearby warehouse without needing to notify the manufacturer. Under this three‑dimensional network, goods are uniformly routed to a regional hub warehouse. Before selling, merchants use the sky network’s database to forecast local demand, then deploy inventory at the regional warehouse to minimize transit and last‑mile delivery times. Subsequently, the most suitable delivery method is matched to each SKU and executed by the human network. For example, a merchant in Sichuan could leverage real‑time market intelligence to predict sales in Shanghai and pre‑position products at a major East China warehouse. Once the online platform processes payment, the merchant can immediately transmit the order information to the logistics provider’s network system. If a vehicle happens to be traveling from the East China warehouse to an area near the Shanghai buyer, the sky network will instruct the warehouse to complete intelligent picking and packaging, then dispatch the carrier to pick up the shipment—allowing the entire process to be completed within the same day. (3) Smart logistics networks drive the emergence of new logistics business models. Smart logistics is rapidly increasing its share within the emerging logistics sector. The smart logistics network architecture comprises multiple modules, including smart mapping, intelligent wearable devices, smart operations, AI‑based recognition, and drones, which are clearly outlined in Figure 6. This intelligent network facilitates information interconnectivity across the supply chain, broadens scenario coverage, and empowers on‑site business processes. Supported by cloud computing, functions such as automated site selection, route planning, unmanned delivery, and chip packaging will also be progressively deployed in real‑world applications.

Figure 6. The Main Network Architecture and Components of Smart Logistics

The primary focus of an intelligent logistics network is to address : ① The costs of logistics automation and intelligent equipment are trending downward, while the operational efficiency and throughput of individual process stages far exceed those of manual labor, necessitating a shift in roles from humans to machines; ② Various e‑commerce and lifestyle platforms are pivoting toward new retail models, with price competition giving way to service‑based competition. As a result, overall fulfillment efficiency across the logistics supply chain has reached new heights, driving growing demand for smart networks and advanced equipment; ③ Smart networks must integrate logistics connectivity, AI, and cloud computing to enable intelligent data collection, optimal facility layout, and high‑quality task allocation, thereby realizing automated and diversified applications at both the production and sales ends. For example, in the intelligent delivery module, technologies such as GPS systems and route‑optimization models can assign various types of orders within the system to vehicles best suited to their locations and load capacities, digitizing delivery information and enabling intelligent decision‑making. Real-time route displays, navigation tracking, and other information‑query functions further facilitate seamless coordination with warehousing centers to complete logistics tasks [8]. In the intelligent packaging module, smart networks accurately capture product characteristics and quality requirements, distinguishing between agricultural and industrial goods, recording the packaging process along the workflow, and leveraging technological tools to collect, analyze, and share data on the geographic distribution of production sites and sales outlets. During intelligent loading and unloading, based on cargo packaging, storage location, and shipment volume, drones, autonomous vehicles, smart shuttles, and automated sorting systems are deployed to reposition goods and adjust their storage states, optimizing the three‑dimensional and dynamic processes of loading/unloading, stacking, sorting, and inbound/outbound operations.

03 Store‑Warehouse Layout and Process Redesign in Instant Logistics under the Platform Economy

• Real-Time Logistics Process Optimization and Strategies for Addressing Challenges   Instant logistics is a new logistics model that relies on platform‑based operations, with no intermediate warehousing and primarily offers door‑to‑door, real‑time delivery. It has evolved from traditional networked transportation and integrated warehousing‑logistics models, expanding its service area and product range through same‑city delivery. At present, it primarily serves B‑to‑B brand merchants, with gradual expansion to the consumer (C) end as new logistics business models mature. The instant‑logistics dispatch process comprises four key stages, as illustrated in Figure 7: after a user places an order, the transaction platform receives the order data, aggregates the quantities and addresses of all participating brands, and publishes the resulting logistics information online—either by operating its own delivery network or leveraging crowdsourced third‑party capacity. Based on the system’s estimated time windows for pickup, delivery, and final handover, the platform assigns couriers to collect the package on schedule, ultimately completing door‑to‑door delivery within the expected timeframe [9]. However, instant logistics currently faces several challenges across diverse business segments. In high‑volume e‑commerce scenarios, there is significant overlap between in‑house delivery systems, third‑party courier services, and B2C‑oriented delivery and errand‑running operations. Optimizing the dispatch workflow to address such overlaps and uncertainties remains a critical priority. Furthermore, the rapid growth of instant‑order volumes and the dispersed nature of customer addresses have led to uneven distribution of delivery capacity, resulting in pronounced imbalances—excess capacity during off‑peak periods and shortages during peak hours—thus undermining overall utilization efficiency.
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• Figure 7 Optimization of Real-Time Logistics Dispatch Processes and Their Influencing Factors

Optimization of real-time logistics dispatch processes and their influencing factors. First, in terms of process optimization, real-time logistics must keep pace with advancements in information technology. First, both existing and untapped market capacities are vast, requiring precise forecasting across factors such as volume, scale, and delivery locations. Second, maintaining controllability throughout the logistics process entails designing stable capacity‑sourcing channels and ensuring rational resource allocation. Third, driven by digitalization, it is essential to make the objective conditions influencing logistics more manageable, visualizing and standardizing delivery‑zone delineation in alignment with merchant distribution and service‑coverage areas. At present, while instant‑logistics is experiencing rapid growth, it also faces several key challenges: First, regarding product categories, instant‑logistics services are predominantly concentrated in food delivery, fresh agricultural products, and retail‑supermarket segments. As the variety and styles of goods expand, the requirements for overall operational conditions and service performance—such as quality, timeliness, and temperature control—vary significantly, thereby increasing the complexity of last‑mile delivery. Second, in terms of package dimensions, instant‑logistics primarily handles small parcels; however, under the platform economy, demand for larger items is also rising, making it increasingly difficult to match delivery needs with appropriate vehicle capacities and further complicating the logistics process [10]. To address these issues, targeted adjustments can be implemented: first, establish shared data flows—online platforms can aggregate user and product‑sales data, leveraging this information to align product attributes with logistics‑capacity data, and automatically match orders with available transport resources; second, standardize delivery protocols by clearly defining technical and transportation standards for different product categories and enforcing them, thereby enhancing operational efficiency through a standardized delivery system from order placement to final receipt; third, enhance user experience by improving the effectiveness of instant‑logistics services, clarifying each stakeholder’s priorities, and boosting platform user engagement. (II) Industrial Chain Layout for On-Demand Delivery in the O2O Model 1. Integrated Store-and-Warehouse Layout for Instant Delivery An integrated store-and-warehouse model leverages smart devices, electronic tags, and intelligent networks to deliver a seamless multi‑channel shopping experience—combining online virtual shelves with in‑store physical products. Stores can feature automated sorting zones, autonomous delivery vehicles, and IoT‑enabled conveyor systems, while delivery services—operated either in‑house or through crowdsourced networks—ensure same‑day, hour‑level delivery within urban areas covering distances of 0 to 3 kilometers. This integrated layout can be categorized into two distinct types. The first type is open storage. , it is necessary to leverage large-format stores or forward‑location warehouses to connect the various retail outlets, See figure. As shown in Figure 8, the “store‑level sorting + last‑mile delivery” model can shorten delivery times to meet the time‑sensitive demands of orders for agricultural products, fresh produce, food delivery, pharmaceuticals, and more. Before a customer places an online order, the automated system first forecasts demand volume and product categories, then selects timely bestsellers and high‑demand items. These are shipped from production sites to large regional stores for sorting; items requiring cold‑chain storage are routed to forward‑stocking warehouses, while retail‑ready products are sent to neighborhood stores, transforming what was once long‑haul transportation into short‑distance, retail‑oriented delivery. The second type is a hidden compartment. , Under the integrated store-and-warehouse model, the initiator of logistics activities is the B-end, not the C-end. Moreover, logistics services are integrated and offered to consumers as an integral part of the product. , Driven by the market‑driven mechanism spurred by the platform economy, logistics enterprises are continuously enhancing their on‑demand delivery services characterized by small order volumes and high delivery frequency [11]. The “bright warehouse” represents the initial phase of integrated store‑warehouse planning, while the “dark warehouse” constitutes the subsequent stage of concrete implementation. Its primary function is to leverage forward‑positioned warehouses as direct touchpoints connecting with customers; using real‑world storage locations as service radii, it employs intelligent analytics combined with user preferences to recommend optimal delivery routes. While completing deliveries, it also provides reverse‑logistics services, further solidifying an hour‑level delivery system comprising trunk‑line warehousing and distribution, dark warehouses, and instant logistics, thereby laying the groundwork for the expansion of the platform economy. An efficient “bright‑warehouse + dark‑warehouse” delivery network can capitalize on O2O advantages by establishing retail outlets between urban central warehouses and end users. Beyond traditional retail consumption, these outlets can offer a range of value‑added services. In addition to in‑store pickup, customers can opt for instant delivery—whether online or offline—raising delivery efficiency from daily or hourly to minute‑level. 2. The Rise of Proximity E‑Commerce and Its Impetus for Instant Logistics Proximity e‑commerce is a novel platform‑based transaction model emerging within the new retail landscape,

Figure 8 The Layout of Public and Private Warehouses in O2O Instant Delivery

By leveraging a downstream end‑service network within the supply chain, we can meet the needs of stores within a radius of… Logistics demand for high-frequency, real-time orders within a 0–3 km radius: near-field e‑commerce exerts significant influence on traditional wide-area e‑commerce across multiple dimensions, including market share, consumer mindsets, and logistics services. As illustrated in Figure 9, near-field e‑commerce has relocated regional distribution centers (RDCs) closer to end‑users, positioning them as near‑zone DCs. According to data from various logistics platforms, nationwide near-field delivery orders have surpassed an average of 30 million per day, further blurring the boundaries of offline logistics, dismantling information silos, and shifting competitive dynamics from marketing scale and channel depth to rapid identification and fulfillment of consumer needs.

Figure 9 Differences Between Wide-Area and Near-Field E-commerce and Breakdown of the Delivery Process

With the rise of self-media, The rise of private-domain traffic, such as WeChat official accounts Moreover, businesses must shift from a traffic‑centric mindset to a user‑centric one, moderating their heavy reliance on traditional platforms and increasingly leveraging private‑domain traffic. A growing number of small and micro‑enterprises will gradually enter the near‑field e‑commerce space. The decentralization of commercial flows requires robust social logistics capacity, while near‑field e‑commerce is accelerating the integration of online‑to‑offline (O2O) models. Statistics show that, between 2020 and 2022, delivery time has become one of the market’s primary concerns, with nearly 50% of users explicitly demanding timely service. To address this, software solutions should integrate intelligent, real‑time dispatch systems to connect local instant‑delivery services. Near‑field e‑commerce can also provide real‑time insights into weather forecasts, supply‑and‑demand dynamics, and other relevant information. Whether it’s instant delivery, on‑site delivery, or same‑city direct delivery, merchants can accurately share inventory‑planning data—such as stock levels and projected order volumes—with couriers and riders, ensuring that each immediate order is matched with the most suitable transport resources through optimal routing [12]. In addition, hardware infrastructure driven by new‑infrastructure initiatives will serve as a powerful catalyst for reducing costs and boosting efficiency in instant delivery. For example, vigorous development of smart self‑pickup devices—beyond the well‑established parcel lockers used in broader e‑commerce—can be extended to sectors like fresh‑food and food‑delivery within near‑field e‑commerce, which could actively explore shared smart pickup‑locker models. Various refrigerated fresh‑goods can be delivered via cold‑chain logistics and picked up from intelligent refrigerated lockers, creating contactless instant‑delivery scenarios that effectively mitigate mismatches between delivery times and consumers’ schedules.

04 Diversified Operations and New Business Models in Fresh-Product Logistics

The business volume of fresh-food e-commerce has also experienced explosive growth in recent years. A key requirement is maintaining product freshness, which has made fresh‑produce logistics—particularly cold‑chain transportation—an integral part of the emerging logistics landscape. Fresh‑produce logistics providers operate across both B2B and B2C segments: B2B carriers primarily serve large‑scale e‑commerce platforms, leveraging open warehouses to serve traditional channels such as origin sites, farmers’ markets, and supermarkets, with a mix of proprietary operations and specialized services; B2C players focus on last‑mile, proximity‑based channels, encompassing third‑party logistics as well as storefronts and fulfillment centers that directly engage end consumers. In addition, there is crowdsourced C2C logistics, which caters mainly to instant‑delivery needs, using digital platforms to connect merchants with independent delivery capacity, thereby creating a multi‑to‑multi delivery network. (1) The fresh‑food market is driving steady growth in demand for cold‑chain logistics. Fresh products primarily include meat products, fruits and vegetables, dairy products, aquatic products, frozen foods, and certain pharmaceuticals. Based on the cold-chain circulation rates of various product categories and relevant statistical data. In 2020, the national demand for cold-chain logistics of fresh produce was 265 million tons, rising to 275 million tons in 2021, reflecting a steady growth trend. In terms of annual growth rate, the pace slowed from 14% in 2020 to 3.8% in 2021, primarily due to factors such as the pandemic, which contributed to a downward trajectory. Beyond these external influences, the deceleration is also partly attributable to the saturation of both hardware and software capacity in the logistics market. Detailed data are presented in Figure 10.

Figure 10 2014–2021 China Cold-Chain Logistics Demand Statistics Data Source: China Federation of Logistics and Purchasing

With a picture Taking 2019 as an example, the total volume of fresh‑produce cold‑chain logistics for the year reached 233 million tons, with a growth rate of 23.3%—an increase of over 44 million tons compared to 2018. Among these, fruit accounted for approximately 54.8 million tons, vegetables for 64.89 million tons, meat products for about 45 million tons, aquatic products for 38.23 million tons, and dairy and frozen foods for more than 29.3 million tons. The product mix is relatively balanced, and the requirements for each link in the fresh‑produce supply chain are broadly consistent. Despite this overall growth in demand, the domestic fresh‑produce cold chain still faces two major challenges: first, its coverage and overall logistical efficiency remain at a developmental stage, lagging behind international standards. Statistics show that in Europe and North America, the cold‑chain penetration rate for fresh produce has exceeded 95%, while Japan has achieved 100% cold‑chain distribution for poultry products; by contrast, China’s cold‑chain penetration for comparable fresh items remains between 50% and 70%. Second, performance levels are comparatively low: in Europe and North America, spoilage rates during cold‑chain transport of fruits and vegetables are only 1%–2%, and in Japan they stay below 5%; meanwhile, domestic spoilage rates for poultry, seafood, and fresh produce still hover around 10%–20%. Fresh‑produce logistics requires temperature‑controlled transportation services. Compared with ambient‑temperature logistics, upstream pre‑cooling and packaging, as well as downstream low‑temperature sorting and last‑mile delivery, present significantly greater technical and operational challenges. Consequently, the capital investment required for pre‑cooling equipment, specialized vehicles, cold storage facilities, and other infrastructure is also higher. To address these issues, it is essential to design diversified logistics operating models that can build a comprehensive fresh‑produce supply chain and extend its reach into third- and fourth‑tier cities, particularly to production regions. (II) Diversification of Logistics Models in the Fresh-Produce Supply Chain Single-format fresh‑food retail stores do not offer home delivery services. This has resulted in consumers developing a high degree of reliance on online platforms. Traditional platforms typically rely on delivery logistics or pre‑stocking at origin warehouses, with cold‑chain distribution via regional hub warehouses or direct shipment from the place of production—delivery cycles generally range from 2 to 4 days. This centralized warehousing model entails longer delivery times and relatively higher per‑unit logistics costs; although it can sustain a certain market share, that share has already declined to a low level [13]. New‑generation fresh‑produce logistics platforms have begun adopting diversified warehousing and delivery models. They establish cold‑chain storage facilities within the service radius of urban sorting centers to shorten receiving times, leveraging high volumes of routine orders to cover logistics construction and operating expenses. Figure 11 illustrates five distinct fresh‑produce logistics warehousing and delivery models: centralized warehousing, forward‑stocking warehouses, “store‑front‑warehouse” setups, same‑store multi‑warehouse arrangements, and supermarket‑retailer collaborations. These diversified models cater to different product categories and time‑sensitive requirements. They may utilize traditional origin warehouses and central hubs for consolidated shipping, employ e‑commerce visible warehouses alongside partner hidden warehouses as forward‑stocking depots, or leverage retail outlets’ free‑storage spaces to enable same‑store cross‑warehouse deliveries. Different warehousing types correspond to distinct delivery modes. As shown in Figure 11, while minute‑level delivery remains unattainable in the short term, all models except centralized warehousing can keep delivery times within half a day. When combined with supermarket‑retailer coordination, once goods reach supermarket stores, online sales data feeds order generation, which is then fulfilled through proprietary delivery services or third‑party crowdsourced networks, providing door‑to‑door service tailored to convenience‑seeking, high‑spending consumers and complementing conventional logistics. By contrast, “store‑front‑warehouse” or forward‑stocking approaches involve logistics providers using cold‑chain networks supplemented by refrigerated containers and ice packs to maintain freshness throughout the distribution channel. Although this approach incurs higher channel costs, it is particularly well suited to mid‑to‑high‑end fresh products such as seafood, organic fruits and vegetables, and cross‑border imports, often achieving half‑day delivery. It serves both standard, regularly delivered items and premium‑segment offerings. Under the retail‑store‑warehouse model, many customers simultaneously purchase multiple categories of fresh produce, even from different origins. E‑commerce platforms or logistics operators can establish distribution hubs in major and medium‑sized cities, leveraging digital analytics to proactively restock and dispatch goods to local warehouses. Once an order is placed, items are picked directly from the nearest warehouse or dispatched accordingly, keeping the entire fresh‑produce supply chain in rapid motion. This enables one‑stop fulfillment of online orders, aggregating diverse product categories into a single delivery, thus offering users a seamless, one‑stop shopping experience comparable to that of brick‑and‑mortar supermarkets.

Figure 11 Major Logistics and Warehousing Types and Distribution Models in the Downstream of the Fresh-Produce Supply Chain

• New business models in fresh-food logistics ———Forward Warehouse A前置仓 is a small-scale warehouse located in the geographic area closest to consumers. Unlike traditional logistics, which relies on warehouses or shipping hubs located far from target markets,前置仓 (pre‑positioned warehouses) significantly reduce product spoilage for fresh‑food items. In contrast, conventional logistics typically ships from central warehouses to urban distribution centers before reaching consumers; these central facilities are often sited in lower‑cost suburban areas, resulting in long transit times and higher losses due to distance. By contrast, with a前置仓 model, fresh products can be pre‑forecasted and delivered directly to communities near customers. Once an order is placed, items are picked and dispatched straight from the nearest前置仓, covering a radius of roughly 0–3 kilometers and ensuring delivery within two hours—with potential for gradual expansion to 5–10 kilometers in the future. The specific supply chain is illustrated in Figure 12. Under this model, the bulk of logistics costs are concentrated in last‑mile operations and delivery, so warehouse locations must be as close as possible to end users. Moreover, fully cold‑chain‑enabled pre‑shipping minimizes response time after an order is placed and optimizes last‑mile delivery costs.
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• Figure 12 Fresh-Product Logistics: Origin Warehouses – Regional Warehouses – Front-End Warehouses Logistics Chain

Traditional fresh produce is typically delivered via single-item shipping. Meanwhile, front‑end warehouses enhance efficiency through a multi‑stage transportation network that links production sites with wholesale markets, urban regional warehouses, and community distribution centers. Throughout the process, large‑batch, consolidated shipments are prioritized, employing a backbone‑first, decentralized consolidation approach to ensure high delivery efficiency across the fresh‑produce cold chain—thereby addressing, to a certain extent, the longstanding challenge of maintaining product freshness [14]. Site selection for front‑end warehouses is relatively straightforward, emphasizing high density and broad coverage; locating such facilities in non‑street‑front areas within densely populated neighborhoods can reduce leasing costs. Since these warehouses do not require brick‑and‑mortar retail spaces, their physical footprint remains modest, offering strong flexibility in site choice and high replicability. Moreover, the frequent consumption pattern of fresh products generates robust foot traffic, making it easier to attract customers. From both quality and time perspectives, front‑end warehouses deliver rapid response times and add significant value to overall logistics services. While in‑store pickup of fresh goods entails time constraints, hour‑level delivery from front‑end warehouses affords users greater scheduling flexibility. However, fresh products typically carry low profit margins, so from a developmental standpoint, front‑end warehouse operations should expand beyond fresh items into non‑perishable and everyday consumer goods. The typical SKU count ranges between one and two thousand; to offer a broader assortment, either larger leased spaces must be secured or an improved appointment‑based delivery model implemented. At present, front‑end warehouses operate in a nascent, fragmented stage. Given the pronounced regional characteristics of fresh products, inventory levels stocked at these facilities still fall short of those maintained by traditional supermarkets, and procurement costs remain slightly higher as well. Looking ahead, the operation of front‑end warehouses will inevitably require smart, digital solutions. These facilities serve not only as end‑point touchpoints with consumers but also as localized, distributed operational and data‑processing hubs. Future big‑data analytics will enable more precise consumer profiling and more accurate forecasting of logistics metrics. Based on varying consumption patterns across different regions, urban sorting centers will adjust the quantity and variety of replenishments delivered daily to each community distribution center. Through granular, personalized analysis powered by big data, consumer behavior can be digitized, while continuously refined replenishment algorithms extend short‑term sales averages to weekly, monthly, or even annual baselines. Coupled with a smart logistics system that factors in variables such as time, weather, and purchasing habits, this integrated “product selection + algorithm + logistics” framework will minimize uncertainty throughout the entire front‑end warehouse supply chain.

5 Conclusion

The development of the platform economy has prompted supply-chain‑related enterprises to proactively develop a variety of new logistics business models. In a supply-and‑sales system dominated by new retail, sourcing has shifted toward origin‑based procurement and cross‑border direct sourcing. By integrating O2O with offline warehouse‑store operations, the process of receiving goods, displaying them, warehousing, sorting, and delivering is streamlined, boosting logistics efficiency while enhancing space utilization and customer satisfaction. On the commercial side, mobile‑device‑driven purchasing, in‑store QR‑code scanning for payment, and a hybrid model of in‑house and outsourced logistics are employed to minimize delivery times within urban areas, extending the reach of front‑end warehouses and other physical‑store concepts to suburban regions and third- and fourth‑tier cities. At present, this emerging logistics paradigm remains in the growth phase of its lifecycle. Beyond external factors such as infrastructure and transportation conditions, significant challenges persist on the soft‑power front—ranging from aligning logistics planning with community‑level service points to precisely managing consumers’ perceptions of key logistics elements. Therefore, in designing and laying out the logistics network, it is essential to balance retail operations with last-mile delivery. , meet the demands of high-frequency, niche‑market orders, control overall operational costs, and leverage digital and intelligent systems and equipment to progressively address issues such as volatility and time‑sensitivity throughout the supply chain.