---
title: "The 3 Core Factors and 3 Core Links of Community Group Buying"
description: "Community group buying is a complex business, and this article argues that only three factors matter: scale, cost, and timeliness. Scale depends on price, which depends on cost and subsidies; cost depends on operations and supply side; timeliness depends on central warehouse and operations. The core links affecting cost are operations and supply side, while the core links affecting timeliness are central warehouse and operations."
author: "陈维龙"
publisher: "New Distribution"
email: "zhaobo258@gmail.com"
telephone: "+8615854817671"
published: "2021-05-16"
language: "en"
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# The 3 Core Factors and 3 Core Links of Community Group Buying

> Community group buying is a complex business, and this article argues that only three factors matter: scale, cost, and timeliness. Scale depends on price, which depends on cost and subsidies; cost depends on operations and supply side; timeliness depends on central warehouse and operations. The core links affecting cost are operations and supply side, while the core links affecting timeliness are central warehouse and operations.

**-01-**
**Why Study Core Factors and Core Links**
Chairman Mao pointed out, "If there are multiple contradictions in any process, one of them is necessarily the principal contradiction, playing the leading and decisive role, while the others are in secondary and subordinate positions. Therefore, in studying any process, if it is a complex process with more than two contradictions, we must use all our strength to find its principal contradiction. Once this principal contradiction is grasped, all problems can be readily solved."
Community group buying is a complex business. On the supply side, there are suppliers, products, processing warehouses, distribution, etc. On the warehousing and distribution side, there are five major links: shared warehouse, central warehouse, trunk logistics, grid warehouse, and group leader. On the market side, there are three major links: ground promotion, group leader, and users. On the platform side, there are product operations, traffic operations, activity operations, etc.
Because the community group buying business is too complex and at an early development stage, problems exist everywhere. Therefore, clarifying the relationships among them and identifying the core factors and core links are essential to understanding the business and guiding its development.
**This article argues that community group buying only needs to consider three factors: scale, cost, and timeliness. Scale depends on price; price depends on cost and subsidies; cost depends on operations and supply side; timeliness depends on central warehouse and operations.**
## **-02-**
## **The 3 Core Factors of Community Group Buying**
The core factors reflecting the competitiveness of community group buying are scale, cost, and timeliness. This article uses theoretical analysis, case analysis, user experience analysis, and analysis of other factors to demonstrate that **cost and timeliness** are the core factors of community group buying.
I believe the importance of the scale factor is self-evident. Anyone with a basic understanding of the community group buying industry will not doubt that scale is one of the core factors, or even the only one. If you really cannot understand the importance of scale, please refer to my article "The Situation, Characteristics, and Countermeasures of Community Group Buying from 2021 to 2023." If ranking is necessary, this article believes that scale is superior to cost, and cost is superior to timeliness.
However, this article only discusses why cost and timeliness are core factors, not other factors.
# **1. Theoretical Analysis**
In competitive strategy theory, there are three different forms of competitive strategy: differentiation strategy, focus strategy, and cost leadership strategy. This article uses the framework of competitive strategy theory to analyze the external conditions of community group buying and the competitive strategy that should be adopted.
**1.1. Differentiation Strategy** Differentiation strategy refers to providing products, services, or experiences that are significantly different from competitors to attract different customer groups. Can community group buying achieve differentiation?
In the supply link of community group buying, suppliers, products, processing, etc., are almost identical in each region. In the warehousing and distribution link, it is all standard central warehouse, grid warehouse, and group leader fulfillment models and experiences. In the user link, it is the trio of group leader, WeChat group, and mini-program. There is no obvious differentiation in any link.
There are deeper reasons why differentiation strategy is not applicable to community group buying.
Community group buying uses low prices to exchange for users' pre-order information, reversely organizing the supply side and warehousing and distribution to ensure users get lower prices within an appropriate time.
From a positioning perspective, community group buying is limited to a specific range in terms of delivery time, delivery method, price, and product range. For any giant seeking a trillion-scale market, differentiation strategy cannot be implemented.
In short, from positioning to operation, there is almost no obvious differentiation. There is only a difference between good and bad, not a difference between same and different.
**1.2. Focus Strategy** Focus strategy refers to focusing on a specific user group, a specific region, or a specific attribute of a product/service. For example, Pinduoduo in the internet case and Telunsu in the traditional industry are classic cases of focus strategy.
As mentioned in the differentiation strategy, community group buying has concentrated on user groups (price-sensitive), regions (lower-tier markets), products (fresh produce and daily necessities), and delivery experience, making further concentration difficult. Moreover, this combination of concentration is mutually supportive, and adjusting any one link will cause huge problems.
If further segmentation and focus strategy are implemented among these factors, it means handing over the largest market to competitors.
Even Pinduoduo, a typical case of focus strategy, is currently seeking broader development rather than concentrating on the narrow market of low prices and lower-tier markets.
The positioning of community group buying is already too concentrated, and for giants, further focus strategy cannot be implemented.
**1.3. Cost Leadership Strategy** Cost leadership strategy refers to maximizing cost reduction to gain competitive advantage through lower costs. Several conditions for implementing cost leadership strategy include:
  * Customers use the product or service in basically the same way or have basically the same experience.
  * The products, services, or experiences provided by the enterprise are standardized or homogeneous.
  * Customer switching costs are low.
  * There is intense price competition among competitors.
  * There are few ways for competitors to achieve differentiation.
The current community group buying perfectly meets the above five conditions.
Users consume on any platform in the same way, and the products and services provided by platforms are basically homogeneous. Users can switch between platforms at will with very low cost. There are many competitors with strong strength, competing primarily on price and subsidies, and users are very price-sensitive. Competitors basically cannot achieve differentiation unless they give up the largest market.
**1.4. Summary** Cost leadership strategy must be the core of community group buying competitive strategy, and cost is the core factor of community group buying.
# **2. Case Analogy Analysis**
We use e-commerce (Alibaba e-commerce, JD.com, Pinduoduo) and food delivery as analogies.
In the e-commerce market, JD.com focuses on product quality and delivery experience, as users who emphasize quality want better and faster delivery, differentiating from Alibaba's comprehensive and cheap approach. Pinduoduo focuses on low prices and lower-tier markets, differentiating from Alibaba.
Community group buying is further differentiation and concentration under the pattern of Alibaba, JD.com, and Pinduoduo, and cannot be further differentiated or concentrated, otherwise the potential scale is insufficient.
In the food delivery market, due to regional scale effects, everyone tries to include as many merchants as possible in the region, so the supply side is homogeneous. On the rider side, there is basically no difference except price and timeliness. There are also no significant differences on the user side and platform side. However, the combination of supply side, rider side, and user side, with refined operations and cost control, becomes the key to winning. Also, due to regional scale effects, focus strategy cannot be implemented, so cost leadership strategy is the only option.
Community group buying also has no significant differences on the supply side, warehousing and distribution side, and user side, and has regional scale effects, so cost leadership strategy is the only strategy.
# **3. User Experience Analysis Framework**
The most obvious factors perceived by customers are price, timeliness, quality, and one-stop shopping, corresponding to "more, faster, better, cheaper."
**3.1. Price** The core feature of community group buying is low price and high cost-performance, which is recognized by all parties. The price of community group buying basically depends on cost and subsidies. Subsidies can be ignored, and we focus on cost, which determines price.
**3.2. Timeliness** From the perspective of products and scenarios, fresh produce is the only core category of community group buying. In past shopping behavior, users bought fresh produce for immediate use. Because community group buying offers low prices, users are willing to give up time and adopt the behavior of purchasing one day in advance.
But users still hope goods can arrive faster. If goods can arrive before noon the next day, they can catch lunch, which is the best experience—both getting price discounts and not delaying use. Timeliness can expand the consumption scenarios of community group buying—from dinner the next day to lunch the next day.
Whoever can deliver before noon the next day can create a better user experience, suitable for more consumption scenarios, and form competitiveness.
From a business format perspective, timeliness is one of the key differences between community group buying and other formats, such as e-commerce with 3-day delivery, front warehouses and home delivery with half-hour delivery, and JD.com with next-day delivery. Because of JD.com's price and category issues, there is a large space left for the next-day delivery positioning.
This article later discusses how community group buying solves the timeliness problem, and the characteristics of these solutions are the core differences between community group buying and any other format.
Therefore, timeliness is definitely one of the core factors of community group buying.
**3.3. Quality** Quality should be viewed at two levels, depending on price and fulfillment capability.
First level: Community group buying is positioned as low price and low quality, so in terms of positioning, quality is determined to be low. Moreover, the number of suppliers needed by the platform is very limited, and they are basically shared. As long as purchase prices are similar, products are similar.
Second level: Under the determined low-quality positioning, due to supply side and warehousing and distribution reasons, "low-quality" products become even lower quality, such as cheap fruits being bruised or squeezed during processing or warehousing and distribution.
Although users perceive quality very clearly, this article believes quality is not a core factor for the following reasons:
1) From a business format perspective, products of the same quality as community group buying can be purchased through other channels, such as discounted products in physical stores or products on certain low-price e-commerce platforms.
2) From the perspective of platform supply differences, the main sources affecting product quality in community group buying are operational operations in warehousing and distribution and processing, such as cold chain facilities, anti-damage packaging, product handling, transportation, and quality inspection. The business-level issues in warehouses and processing are relatively easy to solve, and there is unlikely to be a significant gap between platforms.
3) From the perspective of improvement difficulty, solving quality issues in warehousing, distribution, and procurement has a one-way, closed characteristic. Doing well in warehousing, distribution, and processing can improve quality. However, price/cost and timeliness are multi-directional and open. For example, one point to improve timeliness is to improve the timeliness of grid warehouses, which mainly depends on cost, group efficiency, and group density, which in turn depend on regional scale.
Regional scale is the ultimate indicator, an open ecosystem composed of multiple influencing factors. Therefore, improving timeliness is much more difficult than improving quality.
4) The cost of improving timeliness is higher than the cost of improving quality, meaning that efficiency related to timeliness improvement can affect cost more.
**3.4. One-Stop Shopping** From practice, as long as price and timeliness are appropriate, a small number (thousands) of SKUs can also create a large number of transactions. Moreover, according to my estimation, the SKU quantity of community group buying is on the order of ten thousand, and there will not be a big difference between platforms. For details, see my article "The Most Important Issues of Community Group Buying."
**3.5. Summary** Although from user perception, the priority is price, quality, timeliness, and SKU quantity, combined with the core characteristics of community group buying, the differences in platform supply, and the difficulty of achieving goals, this article believes that price/cost and timeliness are the core factors of community group buying. Although this article does not consider quality a core factor, because user perception is too obvious, it still needs attention.
# **4. Analysis of Other Factors/Indicators**
Besides cost and timeliness, other factors are also key assessment indicators for community group buying business development, such as average order value, number of group leaders, number of grid warehouses, group efficiency, etc. Why are they not core factors/indicators?
**4.1. Average Order Value** Average order value is an extension indicator of cost. When we analyze cost, we analyze absolute value and relative value (cost ratio). When analyzing relative value, we use average order value. Therefore, cost is the core factor, and average order value is just a variable for examining cost.
**4.2. Group Efficiency, Number of Group Leaders, Number of Users, User Retention Rate** The number of group leaders, number of grid warehouses, number of users, user retention, etc., are result indicators, which are the results of doing well in cost and timeliness.
**4.3. Others** Out-of-stock, missing goods, damage, misallocation, group leader type, sales per square meter, sorting efficiency, etc., are basic indicators at the business operation level.
# **5. Summary**
From the framework of competitive strategy, community group buying must choose cost leadership as its competitive strategy, and cost is the only factor. From user experience, combined with the core characteristics of community group buying, the differences in platform supply, and the difficulty of achieving goals, cost and timeliness are the core factors.
**In short, cost and timeliness are the core factors of community group buying.** Note: Because this article focuses on cost and timeliness, and does not discuss scale, the following content only covers six links: supply side, central warehouse/shared warehouse, trunk logistics, grid warehouse, group leader, and operations, and does not discuss the user link (traffic).
## **-03-**
## **Core Links Affecting Timeliness in Community Group Buying**
The links affecting timeliness in community group buying include supply side, central warehouse, trunk logistics, grid warehouse, and group leader.
Suppliers deliver goods to the central warehouse/shared warehouse in advance on the day of user orders. The central warehouse completes sorting and dispatches goods to grid warehouses via trunk logistics. Grid warehouses sort and deliver to group leaders via branch logistics, completing the entire fulfillment.
Before discussing timeliness, this article stipulates that the standard arrival time is before 11:00 AM the next day. Arrival before 11:00 AM ensures user experience and reflects the competitiveness and cost control of different platforms. Of course, this is from the final perspective. This article will discuss the significance of supply side, central warehouse, grid warehouse, and group leader on timeliness control from the perspectives of timeliness proportion, optimization space and difficulty, impact scope, and influencing factors, and point out the links that have a decisive impact on timeliness.
If the timeliness proportion is small, optimization space is small, impact scope is wide, influencing factors are many, and determined by other links, it means this link has no significant importance for timeliness optimization.
Conversely, if the timeliness proportion is large, optimization space is large, impact scope is narrow, influencing factors are few, or other links have weak influence on this link, it means this link has significant importance for timeliness optimization.
**This article believes that in the long run, the core links affecting timeliness in community group buying are central warehouse/shared warehouse and operations, with grid warehouse as a secondary link.**
# **1. Impact of Supply Side on Timeliness**
**1.1 Timeliness Proportion** The supply side of community group buying includes suppliers, products, processing, distribution, etc.
The model of community group buying is buying today and delivering to group leaders tomorrow. While the platform sells, suppliers deliver goods. Generally, before the sales deadline, suppliers have already delivered goods to the central warehouse/shared warehouse. The core here is order forecasting, batch advance delivery, and over-delivery.
Order forecasting and over-delivery ensure no stockouts and indirectly ensure timeliness. Batch advance delivery ensures timeliness and shares the processing pressure of the central warehouse.
If it were one-time delivery, suppliers would need to deliver 1-2 hours before the sales deadline. If too early, order forecasting deviation is too large, delivering too many or too few goods. Too many significantly increase supplier costs; too few cause stockouts, which have a greater impact. Suppliers all need to deliver 1-2 hours before the deadline. Millions of items concentrated in the central warehouse would directly paralyze it. Therefore, batch advance delivery is necessary.
Even with batch advance delivery, suppliers may still have stockouts, late delivery, or over-delivery, but the problem is not that big and can be flexibly handled by the central warehouse/shared warehouse.
Through order forecasting, batch advance delivery, and over-delivery measures, the supply side generally completes all delivery 1-2 hours before the deadline, ensuring timeliness. Achieving this effect is not difficult, and almost all platforms can do it, and it cannot be advanced further.
Therefore, this article believes that the supply side has no impact on timeliness, as it is universally completed 1-2 hours before the deadline, and this cannot be improved.
Based on the above analysis, this article defines timeliness as the time from when the supplier delivers the last batch of goods to when it reaches the group leader, i.e., from 10 PM to 11 AM the next day, a total of 13 hours.
Because the supply side is not counted in timeliness, we cannot discuss the optimization space and difficulty, impact scope, and influencing factors of the supply side on timeliness.
Note: The processing capacity of the fresh produce supply side (processing warehouse) is the core link affecting supply side timeliness, but it is not discussed here.
# **2. Impact of Central Warehouse/Shared Warehouse on Timeliness**
**2.1. Differences in Central Warehouse/Shared Warehouse Among Platforms** There are differences between the Xingsheng Youxuan model and other models. Xingsheng Youxuan's model is that the central warehouse of a province is concentrated in one park, with a shared warehouse set up in the park. Other models have central warehouses distributed in different cities, and shared warehouses may not be in the same park as the central warehouse.
Different models have different impacts on timeliness (including cost). This article only analyzes the impact of each node in the warehousing and distribution link under the same model, not the impact of different models. This article analyzes the Xingsheng Youxuan model.
Note: Except for the central warehouse/shared warehouse, which is analyzed using Xingsheng Youxuan, other links are analyzed for other platforms.
**2.2. Role of Central Warehouse/Shared Warehouse in Supply Side and Warehousing and Distribution** If we compare the warehousing and distribution and supply side model of community group buying to a computer, the central warehouse is the CPU, processing all "data"; the shared warehouse is like a cache, receiving "data" to be processed at any time, maximizing CPU efficiency; suppliers and their warehouses are like hard drives, storing large amounts of "data" to be processed. The data on the hard drive (supplier goods) cannot enter the "cache" or "CPU" simultaneously; it must enter in batches in advance to prevent the central warehouse from "crashing."
Despite this design, the central warehouse cannot process millions of items in and out and transfer within one day. To further relieve pressure on the central warehouse, a three-level processing model of central warehouse/grid warehouse/group leader was established. The central warehouse only needs to do preliminary processing at the granularity of grid warehouses, then grid warehouses do secondary processing at the granularity of group leaders, and finally group leaders do tertiary processing by order.
To accommodate the three-level processing model, Xingsheng Youxuan adopted a B2B sorting operation model rather than a B2C e-commerce sorting model.
Through the establishment of "hard drives," batch advance warehousing, "cache," three-level processing, and B2B sorting operations, the central warehouse can process millions of items in and out and transfer within one day, completing all operations between 0:00 and 1:00 AM the next day, ensuring arrival by 11:00 AM. It can be said that the entire warehousing and distribution link of community group buying is designed around the central warehouse/shared warehouse, which is the core of the core.
What are the benefits of this model? We can compare the processing capacity of Xingsheng Youxuan and JD.com warehouses.
Xingsheng Youxuan's central warehouse/shared warehouse group covers about 200,000 square meters, with a daily processing capacity of about 6 million items (actually about 16 hours of processing capacity), and it is achieved with zero automation equipment.
According to a report by the Guangdong Procurement and Supply Chain Association media account in December 2020, JD.com's Dongguan Asia No.1 warehouse covers about 500,000 square meters, fully using automated sorting equipment, with a daily order processing capacity of 1.6 million orders.
If each order contains 4 items, it is equivalent to Xingsheng Youxuan's processing capacity. But JD.com's warehouse is 2.5 times larger and uses expensive automation equipment. More importantly, I guess the report's 1.6 million orders per day only includes off-shelf and outbound, with goods pre-warehoused and shelved, meaning only half of the operation process is completed.
Obviously, the community group buying warehousing and distribution model greatly improves the efficiency of warehousing and distribution operations, achieving zero inventory and next-day delivery goals.
**2.3. Timeliness Proportion** Generally, the central warehouse starts working at 10 AM on the day of user orders when supplier goods arrive. It finishes the last batch of goods outbound between 0:00 and 2:00 AM the next day, ending work.
Because timeliness is defined from 10 PM, the central warehouse takes 2-4 hours from 10 PM to 0:00-2:00 AM, accounting for 15%-30% of total timeliness (2/13-4/13). Generally, Xingsheng's central warehouse ends between 0:00 and 1:00 AM, accounting for about 23% (3/13, i.e., ending at 1 AM).
**2.4. Optimization Space and Difficulty** Because suppliers need to deliver the last batch of goods in excess before 10 PM, how much is this batch?
Generally, the second-to-last batch is 80% of the day's forecasted delivery, meaning the last batch is about 20% of the day's total. Generally, the first batch arrives at 10 AM and starts operations. From 10 AM to 10 PM, 12 hours to process 80% of goods, i.e., 6.6% per hour. Then from 10 PM to 1 AM the next day, 3 hours to process 20% of goods, i.e., 6.6% per hour.
That is, the central warehouse operates at a constant and full capacity from 10 AM to 1 AM the next day, from start to finish.
To improve timeliness, processing capacity can be increased between 10 PM and 1 AM, for example, processing 10% or even 15% per hour, then the central warehouse operation end time shortens to 11 PM to 0:00 AM, i.e., only 1-2 hours.
Although this optimizes timeliness, it significantly increases costs because once efficiency is increased to 10% or 15% per hour, there will be significant resource idleness between 10 AM and 10 PM, with resource utilization of about 54%-71%.
If you want to fully utilize central warehouse/shared warehouse resources, the central warehouse link will inevitably end between 0:00 and 1:00 AM.
In summary, the central warehouse/shared warehouse cannot optimize timeliness and timeliness proportion unless costs are significantly increased, trading cost for timeliness.
Note: Saying there is almost no optimization space for the central warehouse/shared warehouse here is for Xingsheng Youxuan. Other platforms can use ending operations between 0:00 and 1:00 AM as a comparison to see their own gaps.
**2.5. Impact Scope** The downstream of the central warehouse is trunk logistics, grid warehouses, group leaders, and users in the region; the upstream is suppliers in the region. If this link has problems, the entire chain is paralyzed.
From the role of the central warehouse, we can find that the processing efficiency of the central warehouse is the core and bottleneck of the whole. If the central warehouse is not smooth or has low processing capacity, under the pressure of millions of items, fulfillment timeliness will explode with delays. A clear example is the case in 2020 when a platform's central warehouse was blocked, causing several days of inability to fulfill orders smoothly.
**2.6. Influencing Factors** Scale, SKU quantity, product attributes, in-warehouse operations, warehouse settings, supply side, site selection, etc., will significantly affect the timeliness of the central warehouse.
As scale increases (after a certain scale), warehouse settings need to become larger, increasing the distance for warehouse personnel operations, slowing timeliness.
As SKU quantity increases, the difficulty of manual product finding increases, processing efficiency decreases, and timeliness slows.
The heavier and larger the product, the harder it is to handle, reducing processing efficiency and slowing timeliness.
In-warehouse operations include operation processes, employee skills, and management, which gradually go from unfamiliar to familiar, making timeliness faster and stable at a certain level.
The supply side includes whether batch advance delivery is used, delivery timeliness, and whether over-delivery is used.
Site selection is analyzed in the trunk logistics section below.
Scale is determined by multiple links; SKU quantity and product attributes are determined by operations; in-warehouse operations, warehouse settings, and site selection are determined by the central warehouse/shared warehouse link.
**2.7. Summary** The central warehouse/shared warehouse has a huge impact on timeliness. Even in extreme conditions, it accounts for more than 20%, and this is achieved only when upstream and downstream fully serve the central warehouse. It can be said that the central warehouse is the top priority in the supply side and warehousing and distribution link. If the central warehouse fails, the whole game is lost.
# **3. Impact of Trunk Logistics on Timeliness**
**3.1. Timeliness Proportion** Trunk logistics refers to the transportation link from the central warehouse to grid warehouses, including the time to unload at grid warehouses.
Because the central warehouse operates in batches, it starts sending the first batch to grid warehouses at 2 PM and arrives around 0:00 AM the next day. Grid warehouses can start working at 0:00-1:00 AM, so trunk logistics does not occupy overall timeliness. Moreover, the timeliness of trunk logistics is only related to distance, about 60-80 km/h.
If it is the Xingsheng Youxuan model of central warehouse/shared warehouse, trunk logistics takes longer because goods are delivered to the provincial capital's central warehouse/shared warehouse and then sent to grid warehouses across the province.
If it is other models, trunk logistics takes shorter because goods are delivered in advance to several prefecture-level central warehouses/shared warehouses, and only need to be delivered from the local city to local or neighboring city grid warehouses. But this model increases supplier delivery costs, which are usually borne by suppliers but ultimately added to the price and subsidized by the platform.
Once the central warehouse/shared warehouse model is fixed, the timeliness of trunk logistics is a fixed constant with almost no change.
**3.2. Optimization Space and Difficulty** The timeliness of trunk logistics is basically a constant, with almost no optimization space or negligible.
**3.3. Impact Scope** Trunk logistics in a region are independent of each other and do not affect each other. Therefore, trunk logistics only affects the timeliness of grid warehouses on its own route, with a relatively small impact scope.
There is one situation: each trunk route departs from the central warehouse, and congestion may occur in the central warehouse city, causing overall delays.
**3.4. Influencing Factors** Generally, only two factors affect trunk logistics: 1. Central warehouse site selection, causing overall trunk logistics delays. 2. Grid warehouse site selection, causing local trunk logistics delays. Because grid warehouses need to balance proximity to users to reduce costs and delays caused by urban congestion, this is a difficult problem to balance, or an unsolvable problem, and all platforms are the same.
Central warehouse site selection is determined by the central warehouse/shared warehouse link; grid warehouse site selection is determined by the grid warehouse link (negligible).
**3.5. Summary** In summary, trunk logistics has a significant impact on timeliness, but under the same model, the timeliness of trunk logistics is basically unchanged. Even if there are changes, they are determined by the central warehouse link, and trunk routes in a region are independent with small impact scope. Therefore, in the long run, the impact of trunk logistics on timeliness can be ignored.
# **4. Impact of Grid Warehouse on Timeliness**
**4.1. Timeliness Proportion** Generally, grid warehouses receive some goods around 0:00 AM the next day and can start working. This article takes 1:00 AM the next day as the start time for grid warehouse operations, connecting with the end time of central warehouse operations. Until 11:00 AM the next day, when all goods are delivered to group leaders, it is the end time. Therefore, grid warehouses take 11 hours, accounting for 77% of timeliness.
**4.2. Optimization Space and Difficulty** Grid warehouse time is divided into sorting time and delivery time. The sorting link duration is basically fixed. Even if sorting worker efficiency improves significantly, sorting workers need to work at least 6-7 hours.
Because sorting workers work at night, if they come for a short time and leave after 3-4 hours, how is the salary calculated? The hourly wage for working 6-7 hours is much lower than for working 3-4 hours. To maximize benefits, they must work 6-7 hours.
In addition, if sorting workers are efficient enough and numerous enough to finish in 3-4 hours, would drivers deliver goods around 4 AM, knocking on store owners' doors or leaving goods on the roadside? Drivers must start delivering around 7 AM when store owners begin opening.
Therefore, the sorting link duration is fixed, about 6-7 hours. Thus, the remaining time for the delivery link is also fixed, about 4-5 hours.
The processing scale of a grid warehouse is about 10,000-30,000 items per day, only about one-hundredth of the central warehouse's processing scale, so processing difficulty is low and improvement difficulty is low. Even if grid warehouse processing capacity is insufficient, it will not be as difficult to recover as the central warehouse, nor cause serious impact.
Although the timeliness of grid warehouses in sorting and delivery is fixed, the current contradiction is that grid warehouse efficiency is not high enough, resulting in failure to complete fulfillment before 11 AM.
**4.3. Impact Scope** The impact of grid warehouses on timeliness is limited to the local area covered by the grid warehouse, about 15 kilometers in radius. As scale increases, this scope will become narrower. In extreme cases of scale expansion, a grid warehouse can only affect the delivery timeliness of 1 community and 3 kilometers (each community has 3 groups, group efficiency 300 items).
As scale expands and order density increases, grid warehouse timeliness will passively and spontaneously improve. Moreover, timeliness between grid warehouses is independent and has no impact on each other.
Therefore, the impact and improvement significance of grid warehouses on timeliness are far lower than that of the central warehouse.
**4.4. Influencing Factors** Grid warehouse timeliness is mainly determined by sorting worker efficiency and driver delivery efficiency.
Sorting worker efficiency is determined by proficiency, SKU quantity, and product attributes. The more proficient the sorting worker, the fewer SKUs, and the smaller and lighter the products, the higher the sorting efficiency.
Driver delivery efficiency is determined by group efficiency, group density, and product attributes. The higher the group efficiency, the higher the group density, and the smaller and lighter the products, the higher the delivery efficiency.
Proficient workers are determined by the grid warehouse link; group efficiency, group density, SKU quantity, and product attributes are determined by the operations link.
Therefore, grid warehouse timeliness is jointly determined by grid warehouse and operations.
**4.5. Summary** Even in extreme cases, grid warehouses account for 77% of timeliness, and in current conditions, the proportion is even higher. But grid warehouses are not the core link for the following reasons:
  * Grid warehouse timeliness optimization has a limit; the best is to finish delivery before 11 AM the next day, and earlier is meaningless.
  * The delivery link also has an upper limit; delivery can only start around 7 AM the next day.
  * The core factors determining grid warehouse efficiency are not in the grid warehouse link but in the operations link.
# **5. Impact of Group Leader on Timeliness**
When goods are delivered to the group leader, it can be considered that goods have reached the user. This is consistent with e-commerce fulfillment experience and user cognition. Therefore, group leaders have almost no impact on timeliness.
The only two factors affecting timeliness are the group leader's opening time and store location, which affect the delivery efficiency of grid warehouse drivers, as discussed in the grid warehouse section. These two factors are also relatively easy to solve.
# **6. Summary**
The supply side, group leader, and trunk logistics have almost no impact on timeliness; the central warehouse has a significant impact, especially after scale increases; grid warehouses have a certain impact.
The core link determining central warehouse timeliness is the central warehouse link and operations. The core factors determining grid warehouse timeliness are the grid warehouse link and operations.
**Therefore, in the long run, the core links affecting timeliness in community group buying are central warehouse/shared warehouse and operations, with grid warehouse as a secondary link.**
## **-04-**
## **Core Links Affecting Cost Factors in Community Group Buying**
The core links affecting cost factors in community group buying include supply side, central warehouse, grid warehouse, group leader, and operations. This article will discuss the significance of central warehouse, grid warehouse, group leader, supply side, and operations for cost control, and point out the links that have a decisive impact on cost.
This article will analyze the significance of each link in cost from four perspectives: cost proportion, optimization space and difficulty, leverage effect, and influencing factors.
Leverage effect refers to the optimization of this link having a significant impact on the cost optimization of other links, meaning optimizing this link can achieve a leverage effect.
Influencing factors refer to the factors that affect the cost changes of this link. This means that if there are fewer influencing factors or they are limited to this link, then this link must be optimized to solve the problem, and optimization of other links is ineffective for this link's cost optimization.
If the cost proportion is small, optimization space is small, leverage effect is weak, influencing factors are many, and determined by other links, it means this link has no significant importance for cost optimization.
Conversely, if the cost proportion is large, optimization space is large, leverage effect is strong, influencing factors are few, or other links have weak influence on this link, it means this link has significant importance for cost optimization.
**This article believes that operations and supply side are the core links for cost optimization. Grid warehouse and group leader are secondary, and central warehouse and trunk logistics have the least value for cost optimization.**
# **1. Impact of Central Warehouse/Shared Warehouse on Cost**
**1.1. Cost Proportion** The central warehouse/shared warehouse accounts for about 1%-2% of total cost, including the shared warehouse. If only the central warehouse is counted, it is less than 1%.
**1.2. Optimization Space and Difficulty** The central warehouse's role in the supply side and warehousing and distribution link is the "CPU," the core bottleneck, always running at full capacity. Therefore, further improvement is very difficult. Even if improved by 30%, the cost reduction contribution is only 0.3%, because the central warehouse cost proportion is only 1%.
The core goal of the central warehouse/shared warehouse is to ensure the smooth operation of the entire chain and guarantee timeliness. Cost optimization is not the goal of the central warehouse/shared warehouse.
**1.3. Influencing Factors** The cost of the central warehouse/shared warehouse mainly consists of rent, equipment, and labor. The core factors affecting cost are rent utilization, equipment utilization, and labor utilization. Because this link is too early and primitive, basically manual operations, human factors determine the utilization of the central warehouse/shared warehouse.
Scale, SKU quantity, product attributes, in-warehouse operations, warehouse settings, site selection, etc., will significantly affect the timeliness of the central warehouse.
As SKU quantity increases, the difficulty of manual product finding increases, processing efficiency decreases, and cost increases.
The heavier and larger the product, the harder it is to handle, reducing processing efficiency and increasing cost.
In-warehouse operations include operation processes, employee skills, and management, which gradually go from unfamiliar to familiar, reducing cost and stabilizing at a certain level.
Scale is determined by multiple links; SKU quantity and product attributes are determined by operations; in-warehouse operations and warehouse settings are determined by the central warehouse/shared warehouse link.
**1.4. Leverage Effect** Optimizing central warehouse/shared warehouse costs has almost no impact on other links' costs, with almost no leverage effect.
**1.5. Summary** The cost proportion of the central warehouse/shared warehouse is extremely small, and optimization space is limited. From the perspective of Xingsheng Youxuan's central warehouse/shared warehouse, its core significance is to ensure timeliness and smooth operation, not to optimize cost.
# **2. Impact of Trunk Logistics on Cost**
The cost of trunk logistics is basically calculated by kilometers. Generally, 1 kilometer costs 1 yuan, plus 0.1-0.2 yuan gross profit, the outsourcing cost of trunk logistics is 1.1-1.2 yuan/km, a fixed cost.
The only way to significantly reduce trunk logistics costs is to solve the problem of empty return trips. I personally think this problem is unsolvable. Even if solved, other platforms can adopt it, and cost becomes another constant.
Therefore, the impact of trunk logistics on cost can be ignored, because whether high or low, this cost is basically rigid and cannot be optimized.
# **3. Impact of Grid Warehouse on Cost**
**3.1. Cost Proportion** Currently, community group buying giants pay about 0.55 yuan per item to grid warehouses, and grid warehouse cost accounts for 7.9% (average order value 7 yuan).
**3.2. Optimization Space and Difficulty** This article estimates the extreme cost of grid warehouses is about 0.27 yuan per item. See the appendix at the end for the specific calculation method.
To achieve the extreme state, the following constraints must be met simultaneously:
  * On the driver side: reach 100 yuan per trip, each delivery takes 1.3 hours, and can deliver 3 times in the morning.
  * On the sorting worker side: reach 300 items per hour, 25 yuan per hour.
  * On the site rent side: reach 30,000 items per 1,000 square meters, 15 yuan/month/square meter.
  * On the franchisee side: reach monthly investment of 250,000 yuan, net profit of 45,000 yuan, net profit per item of 0.05 yuan.
After completing the above difficult constraints, only 0.28 yuan per item is optimized, reducing cost proportion from 7.9% to 3.9%, saving 4%. I believe the reasonable cost limit for grid warehouses should be around 0.34 yuan per item, with a cost proportion of 4.9%, and extreme situations should not be pursued. (The appendix at the end discusses detailed data for extreme situations; after seeing the data, you will understand that the extreme model is almost impossible to achieve, or it is a matter of several years later.)
Because the threshold for grid warehouse scale effects is low, cost influencing factors are many, and optimization space is small, this article believes that the cost difference between platforms' grid warehouses will not exceed 0.1 yuan, within a few cents.
**3.3. Influencing Factors** Grid warehouse cost consists of driver cost, sorting worker cost, rent and other costs, and franchisee profit.
Sorting worker cost is determined by sorting worker efficiency, which is determined by proficiency, SKU quantity, and product attributes. Driver cost is determined by driver delivery efficiency, which is determined by group efficiency, group density, and product attributes. Rent and other costs can be considered fixed parameters. Franchisee profit can be considered a fixed profit margin after deducting all costs.
Proficient workers are determined by the grid warehouse link; group efficiency, group density, SKU quantity, and product attributes are determined by the operations link.
Therefore, grid warehouse cost is jointly determined by grid warehouse and operations links.
**3.4. Leverage Effect** People compare grid warehouses to food delivery riders. This article uses food delivery riders as an example to analyze the leverage effect of grid warehouse cost optimization.
The core of food delivery cost optimization is rider cost (large optimization space and leverage effect). Rider cost is determined by the number of orders and distance per delivery, which depends on order density and merchant density per unit area. Therefore, the core of optimizing food delivery cost is optimizing both supply and demand sides plus optimizing delivery route algorithms. So optimizing rider cost is actually the core lever of the "user-rider-merchant" triangle, with significant leverage.
The core of grid warehouse cost optimization is also the number of orders and distance per driver delivery, which depends on order density per unit area (group efficiency and group density), but is unrelated to merchant density. In community group buying, within a province, the number of suppliers on the same day does not exceed 1,000. They directly supply the entire province's goods, generally distributed in various areas of the provincial capital. Therefore, grid warehouse cost optimization will not affect the supply side.
Because grid warehouse cost proportion and absolute value are too small, users will not switch platforms for a few cents. But food delivery cost proportion and absolute value are large enough, and differences between platforms will cause some users to switch to more favorable platforms. That is, grid warehouses have no impact on the demand side either.
The grid warehouse link cannot act as a lever for supply and demand like riders; it can only simply optimize cost.
**3.5. Summary** Because grid warehouse cost optimization space is small, cost differences between platforms are small, there is no leverage effect, and most costs are passively optimized, grid warehouses cannot play a decisive role like "riders." In the long run, optimizing grid warehouse costs has limited significance.
# **4. Impact of Group Leader on Cost**
**4.1. Cost Proportion** Group leader cost generally accounts for about 10% of total revenue. Different products have different commission ratios. Generally, high-profit products have high commission rates, and vice versa. Commission ratios also vary by period; generally, early periods have high commission rates, and vice versa. Group leader commission is also related to unit price. The situation is complex and not discussed here.
**4.2. Optimization Space and Difficulty** Before discussing group leader cost, it is necessary to clarify the function and value of group leaders, and discuss cost based on value.
Group leaders generally perform functions such as pulling groups for traffic, forwarding activities, group user operations, after-sales, credit empowerment, and self-pickup point fulfillment. Self-pickup point fulfillment is the most core and basic function and value of group leaders, and is indispensable. Self-pickup point fulfillment includes receiving goods, sorting and packing, categorizing, short-term storage, providing frozen and refrigerated services, providing necessary plastic bags, and some group leaders also need to provide home delivery.
In terms of fulfillment value, the role of group leaders is equivalent to Cainiao stations. Generally, Cainiao stations charge 0.4 yuan per package for storage (fulfillment function). Therefore, based on the principle of value and cost equivalence, group leader income should not be less than 0.4 yuan per item.
From the perspective of overall group leader income, the extreme average group efficiency is 310 items per day, as discussed in the appendix, so group leaders can earn 124 yuan per day. That's pretty good.
Therefore, group leader cost of 0.4 yuan per item is the extreme cost.
**4.3. Influencing Factors** Fulfillment service, expansion and operation service, product value/profit, product size/weight, group efficiency, etc., affect the cost of the group leader link.
Fulfillment service, expansion and operation service are related to group leaders; product value/profit, product size/weight, and group efficiency are basically determined by operations, not group leaders.
**4.4. Leverage Effect** Optimizing cost in the group leader link has almost no impact on other links, so there is no leverage effect.
**4.5. Summary** In summary, no matter what, group leader cost should not be less than 0.4 yuan per item. A reasonable situation should be between 0.5-0.6 yuan per item (also depends on product value and operations, may increase). Currently, group leader cost is about 0.7 yuan per item.
Therefore, the ultimate cost proportion optimization for group leaders is from 10% to 5.7% (average order value 7 yuan), and a reasonable situation is from 10% to 7.1%.
# **5. Impact of Supply Side on Cost**
**5.1. Cost Proportion** Generally, platforms add about 20% to the supplier's quoted price as the selling price. Therefore, it can be simply considered that the supply side accounts for 80% of total cost. Group leaders account for less than 10%, and warehousing and distribution account for about 10%. This data is not precise because it is greatly related to platform, stage, and other factors. Generally, the supply side cost proportion can be considered 70%-80%.
Obviously, the supply side is the largest cost item and should be the key link for cost optimization.
**5.2. Optimization Space and Difficulty** To analyze the optimization space and difficulty of the supply side, it is necessary to understand the current situation and possible states of the supply side. Because modeling with data analysis is too complex, only an overview is provided here. Data analysis is available in my membership service.
Currently, suppliers in community group buying are mainly second-tier suppliers. Besides product costs, main costs include business expenses (delivery fees, business coordination, warehousing, sorting and packing), inventory and losses, and procurement corruption. Business expenses, inventory, and losses are the main reasons why most suppliers lose money or do not make money. If these two types of costs can be controlled within a reasonable range and mainstream suppliers are upgraded to first-tier, 5%-20% of costs can be optimized.
**5.3. Influencing Factors** Factors affecting supply side costs include: scale, quality, business expenses (delivery fees, business coordination, warehousing, sorting and packing), inventory and losses, supplier capability, procurement, brand, etc.
Scale is the result of multiple links. Quality is basically determined by the positioning of community group buying, with little change.
Delivery fees, business coordination, warehousing, and sorting and packing costs are stepwise fixed costs. Once invested, they must reach a corresponding scale to be amortized, otherwise costs are high.
Generally, the larger the scale, the lower the cost, and there is a minimum scale (related to products and other factors, almost impossible to measure). During development, operations break through by configuring traffic and focusing on developing core suppliers.
Inventory and losses are basically determined by operations. Currently, platform operations give little consideration to supplier inventory, which is the core reason for inventory and losses.
Because misallocation, missing goods, and damage in warehousing and distribution are secondary and short-term reasons, they can be controlled within an acceptable range after a period of development. This is the largest expense source after business expenses, especially for fresh produce.
Supplier capability and procurement basically depend on scale, so they do not need to be considered separately.
Procurement corruption is also a major issue affecting supply side costs. The common solution in all industries is organization, system, and management, which is part of enterprise capability.
Brand has a certain premium. In categories with weak brand, white-label or factory products can be used to reduce costs. This is jointly determined by operations and procurement.
Scale is the core factor determining supply side costs, but in the process from small to large, operations need to break through and gradually cultivate a suitable supply side.
Besides product costs, business expenses, inventory, and losses are the two largest cost sources on the supply side. Only through operations can these two types of costs be significantly optimized.
**5.4. Leverage Effect** The supply side connects the central warehouse/shared warehouse and operations. For example, delivery fees, business coordination, and warehousing are greatly related to the central warehouse/shared warehouse model. Some platforms set up multiple central warehouses in the same province, causing these costs to multiply. Inventory and losses are greatly related to operations and central warehouse, such as over-stocking leading to unrecoverable returned goods, causing losses.
**5.5. Summary** The supply side cost proportion is very high, with large optimization space, complex influencing factors, and a certain leverage effect, so it is a core link.
Note: Besides supplying goods, the supply side also provides product selection and pricing services, which are of great significance.
# **6. Impact of Operations on Cost**
This article defines operations as product operations, traffic operations, and activity operations.
**6.1. Cost Proportion** Because community group buying is still in the early development stage, and due to difficulties in defining activity costs and data acquisition, this article cannot provide a numerical value for operations cost proportion. However, based on e-commerce and retail experience, operations cost proportion may be higher than 10%, reaching 20%, far exceeding the cost proportions of grid warehouses, central warehouses, and group leaders.
**6.2. Optimization Space and Difficulty** Because operations costs cannot be defined at the current stage, optimization space and difficulty cannot be analyzed.
**6.3. Influencing Factors** Operations experience, operations capability, user perception, ground promotion layout, etc., will affect operations costs. In the short term, ground promotion layout will significantly affect operations and its cost/effectiveness. In the medium and long term, operations experience, operations capability, and user perception will significantly affect operations cost/effectiveness.
**6.4. Leverage Effect** When discussing the impact of central warehouse, grid warehouse, group leader, and supply side on cost, the key indicator of average order value frequently appears. This means optimizing average order value can affect the cost of the entire warehousing and distribution link, as well as supplier revenue and profit. Therefore, average order value is the core indicator affecting costs across the entire chain.
Average order value means the average selling price per item, and this indicator is basically driven by operations. Operations can increase average order value through user segmentation, product segmentation and specifications, activities, coupons, etc.
If average order value increases, the cost proportions and profits of group leaders, grid warehouses, central warehouses, supply side, and the entire chain will significantly improve.
The following table shows the impact of improvements in operations, group leaders, grid warehouses, central warehouses, and supply side on the cost proportion of each link:
As long as operations start and increase average order value from 7 yuan to 12 yuan, from the perspective of cost proportion, it can reach the extreme levels of group leaders, grid warehouses, and central warehouses, and this impact is simultaneous on all links.
If efforts are made separately in each link, only that link can be improved. Currently, Xingsheng's average order value is about 12 yuan, so this is definitely not the extreme level.
Operations can also affect the absolute costs of each link, not just relative costs (cost proportion) by increasing average order value. For example, operations affect group density and group efficiency, which have a decisive impact on the absolute cost of grid warehouses.
Therefore, the operations link has a significant impact on cost optimization.
Note: Operations are not limited to the impact of average order value; there are also retention, purchase frequency, etc. This article only uses one important indicator to illustrate the importance of operations.
**6.5. Summary** Operations costs account for a relatively high proportion, have a significant leverage effect, significantly improve cost optimization in each link, and are almost unaffected by other links. Therefore, the operations link is the core link for cost optimization.
Note: Besides average order value, I am trying to establish an indicator system and data for evaluating operations. For details, see my membership service.
# **7. Summary of Core Links Affecting Cost Factors in Community Group Buying**
**Based on cost proportion, optimization space, and leverage effect, operations and supply side are the core links for cost optimization. Grid warehouse and group leader are secondary, and central warehouse and trunk logistics have the least value for cost optimization.**
## **-05-**
## **The 3 Core Factors and 3 Core Links of Community Group Buying**
Community group buying only needs to focus on three factors: scale, cost, and timeliness. Whoever does well in these will have a competitive advantage.
The decisive factor affecting scale is price, and the core factor affecting price is cost.
The decisive links affecting cost are operations and supply side, followed by grid warehouse, then group leader, and finally central warehouse and trunk logistics.
The decisive links affecting timeliness are central warehouse, followed by grid warehouse (core in operations), and supply side and trunk logistics have basically no impact.
**In one sentence: Community group buying only needs to look at three factors: scale, cost, and timeliness. Scale depends on price; price depends on cost and subsidies; cost depends on operations and supply side; timeliness depends on central warehouse and operations.**
**Note:** Because the scale factor is too important, if only one factor is considered for community group buying, it is scale. A separate article will be published later.
This article frequently mentions efficiency and labor, which are the core constraints on cost and timeliness optimization. Currently, the informatization and automation levels of each link in community group buying are very low. If automation and informatization levels can be improved, costs can be further reduced.
However, due to the special nature of operations, most links cannot be automated, such as the driver side, where automation cannot improve efficiency. Which links cannot be automated and which may be automated will be introduced later.
From a time perspective, I believe that automation equipment will not be widely applied until the end of 2022, playing a role in improving costs and efficiency.
Although the timeliness and cost optimization value of most links is relatively small, it does not mean they should not be optimized or less optimized. Because this article points out the timeliness/cost proportion and optimization space under extreme conditions, there is still much to do in the current state.
In addition, community group buying adopts a cost leadership strategy, which means adhering to comprehensive, full-chain, and all-link cost optimization, not just optimizing important links.
This article points out the core links to help everyone understand the role and status of each link in cost optimization, and to treat each link differently.
As a third-party observer, how to observe the three factors of scale, cost, and timeliness to determine the competitiveness of each platform? What indicators should be observed? Do different development stages focus on different points? How should current data and problems be understood? This article only provides the extreme cost calculation model for grid warehouses. What are the evaluation indicators for the supply side and operations link?
Join my membership, and I will sort out various indicators and internal logic for you, and provide monthly data on supply side, central warehouse/shared warehouse, grid warehouse, operations, etc., to help you track platform development. Membership services also include monthly expert interviews, with experts including me and an industry expert (supplier/grid warehouse/shared processing warehouse).
**Appendix - Grid Warehouse Extreme Cost Calculation Model**
Grid warehouse cost consists of driver cost, sorting worker cost, rent and other costs, and franchisee profit. Calculating their extreme costs separately can obtain the grid warehouse extreme cost model.
**Driver Cost Extreme Model** For drivers, their daily income is rigid; if below this level, they will not do it. Therefore, the driver cost constraint comes from driver income and business efficiency. If delivery efficiency is high enough, drivers can deliver one more trip in the same time, obtaining the same or more income.
**Driver Income** From 7 AM to 11 AM, if each trip only takes 1.3 hours, they can deliver 3 trips, earning 300 yuan in the morning (100 yuan per trip). Afternoon and evening time can be used for other business. Drivers can accept this. In fact, Huolala transport drivers need at least 100 yuan to complete transportation and loading/unloading within 1.3 hours.
**Business Efficiency** Each van's full load is 900 items. To deliver within 1.3 hours, there must be high group efficiency and group density, and the average distance between grid warehouse and group leaders must be short enough. The following assumptions must be met:
  * Driving time: average one-way distance per driver is about 3 km, round trip 6 km, taking 40-50 minutes;
  * Unloading and handover time: average extreme group efficiency is 310 items, each unloading 150 items, taking 3-5 minutes. 900 items total about 18-30 minutes.
  * Grid station loading time: 5 minutes.
One delivery takes 70-80 minutes, and from 7 AM to 11 AM, 3 deliveries can be made.
Further analysis of the extreme group efficiency and group density: Assume the industry ceiling is 800 million items per day, average order value is 10 yuan, and the industry scale ceiling is 2.5 trillion yuan, which is an optimistic estimate given by all parties. Therefore, 800 million items per day is an optimistic estimate.
Excluding Taiwan, Hong Kong, Macau, Xinjiang, Tibet, Qinghai, Inner Mongolia, which are not suitable for community group buying development, and most of the population in first-tier cities like Beijing, Shanghai, and Shenzhen, community group buying can cover about 1.28 billion people.
Therefore, per capita 0.625 items per day. Assuming 4 group leaders per 1,000 households, generating 1,250 items (2 people per household), the extreme group efficiency is about 310 items.
Further analysis of driver driving time: If group efficiency is 310 items, each community has 3 groups, meaning each delivery can only serve 1 community. According to front warehouse density—3 km per warehouse—drivers need to deliver 3 km each time, taking about 30 minutes.
Because grid warehouses are three times larger than front warehouses, it is not easy to find a 3 km per warehouse warehouse in the city for grid warehouses, so it can be assumed to be 5 km per warehouse.
Because rural population density is much lower than urban, setting 3 km per warehouse is also unreasonable; at least 5 km per warehouse is needed.
Therefore, driver driving time is expected to be between 40-50 minutes.
**Summary** The above parameters such as unloading and handover time, driving time (distance), group efficiency, and group density are relatively extreme assumptions. Under these conditions, drivers can obtain reasonable income, ultimately achieving the state of 900 items with only 100 yuan cost.
**Sorting Worker Cost Extreme Model** For sorting workers, their daily income is rigid; if below this level, they will not do it. Therefore, the sorting worker cost constraint comes from sorting worker income and business efficiency. If business efficiency is high enough, sorting workers can complete more business in the same time, obtaining the same or more income.
**Sorting Worker Income** Each sorting worker earns 25 yuan per hour, working from 0:00 AM to 6-7 AM every day, earning 150-175 yuan per day. Working every day without rest, they can earn 4,500-5,250 yuan per month. Considering night work, no rest all month, and the intensity of 300 items per hour, a monthly income of about 5,000 yuan is a very low cost.
**Warehouse Rent Cost and Other Cost Explanation** Other costs include utilities, business, and other expenses. Assuming 300 yuan per day, with 30,000 items per day, it can be counted as 0.01 yuan per item. Rent cost is basically fixed.
**Franchisee Profit Extreme Model** Franchisee profit consists of business scale and per-item income, but the profit level is constrained by the franchisee's costs. For example, if a franchisee needs to invest 500,000 yuan annually, the annual net profit is unlikely to be less than 100,000 yuan, otherwise they could choose to work or do other business.
We assume a grid warehouse with 30,000 items per day requires about 11 drivers (3 trips) and 14 sorting workers. A boss employing 25 employees, if annual net profit is not 500,000 yuan (about 0.05 yuan per item profit), it is extremely uneconomical.
From a capital investment perspective, to handle these businesses, the grid warehouse boss needs to spend 267,000 yuan per month. If each item earns 0.05 yuan net profit, monthly net profit is 45,000 yuan, equivalent to a senior white-collar income. This is a condition that cannot be lower.
Note: The above content only applies when driver delivery vehicles do not change significantly, such as from vans to large trucks or other transport tools. Grid warehouses have not achieved a high degree of automation. However, even if these changes occur in the future, the role of grid warehouses in the entire chain is as described in the main text, not a core link.
Source: Chen Weilong's Telescope (ID: yiguojiren) Author: Chen Weilong
Tips will be paid 400-2000 yuan once adopted.


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