Selection of Symmetrical and Asymmetrical Supply Chain Channels for New Energy Vehicles Under Multi-Factor Influences

Abstract

In recent years, as an important alternative to traditional gasoline-powered vehicles, new electric vehicles (NEVs) have gained widespread attention and rapid development globally. In the traditional automotive industry chain, downstream vehicle manufacturers need to master core technologies, such as engines, chassis, and transmissions. In contrast to the traditional automotive industry chain, where downstream vehicle manufacturers must master core technologies, like engines, chassis, and transmissions, the electric vehicle industry chain has evolved in a way that the development of core components is gradually separated from the vehicle manufacturers. Downstream vehicle manufacturers can now outsource key components, such as batteries, electric controls, and motors. Additionally, in terms of sales models, the electric vehicle industry chain can adopt either the traditional 4S dealership model or a direct-sales model. As the research and development of core components are increasingly separated from vehicle manufacturers, the downstream vehicle manufacturers can source components, like batteries, electric controls, and motors, externally. At the same time, they can choose to use either the traditional 4S dealership model or the direct-sales model. The underlying mechanisms and channel selection in this context require further exploration. Based on this, a mathematical model is established by incorporating terminal marketing input, product competitiveness, and after-sales service levels from the literature to solve for the optimal pricing under centralized and decentralized pricing strategies. Using numerical examples, the pricing and profit performance under different market structures are analyzed to systematically examine the impact of the electric vehicle supply chain on business operations, as well as the changes in various elements across different channels. We will focus on how after-sales services (including the spare part supply) influence the pricing strategy and profit distribution in the supply chain, aiming to provide insights into advanced manufacturing system management for manufacturing enterprises and improve the efficiency of intelligent logistics management. The research indicates that (1) The direct-sales model helps to improve the terminal marketing input, after-sales service quality, and product competitiveness for supply chain stakeholders; (2) It is noteworthy that the manufacturer’s direct-sales model also significantly contributes to lowering prices, highlighting that the direct-sales model has substantial impacts on both supply chain stakeholders and, importantly, consumers.

1. Introduction

New energy vehicles (NEVs), as a significant alternative to traditional internal combustion engine vehicles, have gained widespread attention and experienced rapid development worldwide in recent years. The main categories of NEVs include battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and hydrogen fuel cell vehicles (FCEVs). With advancements in technology and the promotion of policies, NEVs have made significant progress in market share, technological innovation, and industrial chain development. The new energy vehicle industry is an important branch of the automotive sector, reshaping the century-old structure of the traditional automotive supply chain. In the traditional automotive supply chain, downstream manufacturers must master core technologies, such as engines, chassis, and transmissions. However, in the NEV supply chain, the research and development of core components have gradually separated from the vehicle manufacturers. Downstream vehicle manufacturers can now purchase batteries, electronic control systems, and motors externally. Additionally, some smart hardware and advanced driver-assistance system (ADAS) chips can also be developed in collaboration with other companies, thus lowering the entry barriers for vehicle manufacturers and providing greater space for development. Simultaneously, industries such as charging stations and battery-swapping stations, which serve the NEV aftermarket, are expected to play an increasingly important role in the supply chain.

Traditional automakers rely on the 4S dealership network (sales, spare parts, service, and survey, which includes vehicle sales, spare parts, after-sales service, and feedback within the automotive supply chain model, hereafter referred to as the 4S dealership network) for sales. In contrast, many manufacturers in the NEV supply chain are opting for a direct-sales model, reducing intermediaries and improving supply chain efficiency. Taking globally renowned NEV brands as an example, Tesla, an American brand, adopts a direct-sales model, opening numerous flagship stores and showrooms worldwide, where consumers can directly purchase electric vehicles. Moreover, Tesla also offers online purchasing and customization services, further enhancing the convenience of buying. Mercedes-Benz, a German brand, continues to use the traditional dealership model, with a large network of dealers worldwide, where consumers can purchase Mercedes electric vehicles. At the same time, Mercedes-Benz also provides online purchasing and customization services, integrating both online and offline channels. Polestar, a Swedish brand created jointly by Volvo and Geely, uses an online direct-sales model, allowing consumers to purchase electric vehicles directly through the Polestar website. Additionally, Polestar offers offline experience centers for test drives and purchases. Hyundai, a South Korean brand, adopts a hybrid model, combining direct sales and dealerships with a wide network of both company-owned stores and dealers globally. Consumers can purchase Hyundai electric vehicles at these locations, and Hyundai also offers online purchasing and customization services, enhancing consumer choice.

In the automotive industry, the 4S dealership model and direct-sales model represent two distinct supply chain structures. These brands, through their different sales channels, demonstrate how the traditional role of retailers is diminished in the direct-sales model, where manufacturers engage directly with consumers, eliminating intermediary steps. Retailers may transform into after-sales service centers focused on support services, reflecting the diversification and flexibility of the NEV supply chain’s sales models. The rise of the direct-sales model greatly enhances the consumer shopping experience and reduces intermediary costs. According to reports from the Boston Consulting Group [1], as manufacturers increasingly adopt direct-sales models, the traditional multi-tier dealership structure is being simplified. Manufacturers optimize their supply chains, utilizing direct-to-consumer sales channels, thereby enhancing their control over sales and market dynamics. In comparison, the traditional 4S dealership model relies on intermediaries, such as dealers and retailers, to establish a more complex supply chain system, where dealers not only handle sales but also undertake after-sales services. Furthermore, research by Vehicle Empire [2] highlights how the direct-sales model impacts the intermediary roles in the supply chain, emphasizing that by removing traditional retailers, manufacturers can gain greater control over pricing, product delivery, and after-sales services. This shift in the model is redefining the way automobiles are sold and gradually changing the operational logic of the supply chain. However, Wei’s [3] studies have also shown that despite the many efficiency and cost-control benefits of the direct-sales model, the traditional 4S dealership model remains dominant in certain markets, especially where extensive service networks and local market adaptability are required. Traditional dealerships, through distributed networks, can better meet the needs of consumers in different regions, particularly in terms of service quality and market coverage.

In conclusion, the key distinction between the 4S dealership model and the direct-sales model lies in decentralization versus centralization. The former relies on collaboration among multi-level suppliers and retailers, while the latter simplifies the supply chain process, enabling manufacturers to directly control sales and services. As the market demand and technological advancements evolve, both models have their applicable scenarios and advantages, requiring manufacturers to choose the most appropriate sales channel according to specific circumstances.

In the electric vehicle supply chain, analyzing centralized and decentralized pricing is crucial for understanding the optimization of pricing strategies. Centralized pricing helps manufacturers to unify pricing strategies by reducing price conflicts and information asymmetry between supply chain members, thereby improving the coordination and market control of the supply chain. Centralized pricing can significantly reduce coordination costs within the supply chain, ensuring consistency in pricing and the overall stability of the supply chain. On the other hand, decentralized pricing provides each member of the supply chain with more flexibility. In particular, when facing different market demands and competitive environments, retailers can set independent prices based on local market conditions, which improves pricing responsiveness and market adaptability. Decentralized pricing enables retailers to quickly respond to changes in market demand, adjust prices flexibly to cope with competition, and, ultimately, enhance the overall sales performance.

By combining these two pricing strategies, the electric vehicle supply chain can choose the most appropriate pricing model based on different market demands, the specific roles of supply chain members, and the competitive environment. This approach helps to improve supply chain collaboration and market adaptability and provides theoretical support and practical guidance for optimizing pricing strategies.

In this study, we choose to incorporate marketing investment, product competitiveness, and the after-sales service level into the mathematical model of the optimized pricing strategy, primarily because these factors significantly influence pricing decisions and consumer-purchasing behaviors. Research indicates that marketing investment typically includes various components, such as advertising expenses, promotional costs, market research costs, and sales staff salaries. These investments aim to enhance brand image, expand market reach, attract customers, and stimulate sales, making them critical factors in consumers’ decision making. They can directly influence sales volume and price sensitivity, thereby having a positive impact on company performance [4]. The competitiveness of new energy vehicle products is primarily reflected in aspects such as performance, safety, and comfort. Product competitiveness determines whether the product can stand out in a highly competitive market, and a competitive product can support higher pricing [5]. In addition, the level of after-sales service is closely related to consumer loyalty. High-quality after-sales service helps companies to achieve price premiums by offering added value. These factors collectively influence the pricing strategy in the new energy vehicle supply chain. Good after-sales service is an important consideration for many consumers when purchasing a product. Many automotive brands are enhancing their warranty services and derivative services. For example, NIO offers free lifetime warranty and free roadside assistance, which not only strengthen its brand image but also greatly increase customer trust and satisfaction. Mercedes-Benz provides a long-term warranty solution that covers a wide range of potential faults, from basic coverage to extended protection. Audi has launched the “Audi Original Extended Warranty Service” in the Chinese market, offering professional and reliable warranty coverage after the original warranty period expires, thereby ensuring worry-free driving for customers. Tesla provides an extended warranty on new vehicles, including coverage for the battery and drive units, and its mobile service team offers on-site repairs and diagnostics. In conclusion, high-quality after-sales service is an indispensable part of automakers’ efforts to fulfill their product quality promises, win customer satisfaction, and maintain market competitiveness [6]. Therefore, these three factors are crucial in the model.

Based on the above background, this paper will focus on addressing the following questions:

(1)

How do the 4S dealership model and direct-sales model affect the profitability and retail prices of automobile manufacturers?

(2)

How do the 4S dealership model and direct-sales model influence terminal marketing investment, the after-sales service level, and product competitiveness?

(3)

How do the effects of terminal marketing investment and the marketing investment cost coefficient influence the level of the terminal marketing investment?

(4)

How do the after-sales service level cost coefficient and terminal investment effects impact the after-sales service level?

(5)

How does the terminal marketing investment affect retail prices?

(6)

How do the terminal marketing investment, vehicle manufacturers’ after-sales service, and product competitiveness influence the profits of automobile manufacturers?

This paper constructs two supply chains: The first supply chain consists of new energy vehicle component suppliers, new energy vehicle manufacturers, a 4S dealership, and consumers. The second supply chain consists of new energy vehicle component suppliers, new energy vehicle manufacturers, and consumers. It investigates the optimization and comparison of retail prices, marketing investment levels, product competitiveness, and after-sales service levels across different channel supply chains of new energy vehicles.

The main contributions of this paper are

(1)

Constructing models for sales pricing, marketing investment levels, product competitiveness, and after-sales service levels for different channel supply chains of new energy vehicles and analyzing the comparison between supply chains, providing theoretical support for the production–sales–service decisions of each member in the new energy vehicle supply chain;

(2)

Analyzing the impacts of the sensitivity coefficients and cost investment coefficients of marketing investment levels, product competitiveness, and after-sales service levels on the optimal decision variables and profits of each member in the new energy vehicle supply chain, providing rationalized suggestions for the business decisions of supply chain participants.

2. Literature Review

In the research on supply chain channel selection, Yang Qian et al. [7] found that both price and channel competition, as well as consumer channel preferences, jointly affect the manufacturer’s supply chain model choice. Zhang et al. [8] found that a manufacturer’s choice of supply chain model depends not only on the operating costs of the channel and consumer preferences but also on the channel strategies of competitors. Other literature focuses on the unique characteristics of new energy vehicles, exploring the choice of sales supply chain models. For example, Li Sining et al. [9] constructed a supply chain decision model for the cooperation and innovation between technology service providers and new energy vehicle manufacturers, exploring the impacts of different competition modes on product pricing and profits. They found that adopting a two-tier supply chain is beneficial for reducing market prices and increasing corporate profits. Xie Jiaping et al. [10] discovered that in an environment with uncertain market demand, adopting a multi-tier supply chain helps new energy vehicle manufacturers to reduce production costs and improve profits. Cui et al. [11] were the first to study the impacts of fairness concerns on supply chain coordination under wholesale price contracts in a two-tier supply chain environment. The study concluded that when the fairness concerns of supply chain members meet certain conditions, wholesale price contracts can achieve supply chain coordination. Wang et al. [12] studied the coordination of a retailer-dominated low-carbon supply chain, considering the retailer’s fairness concerns. The research object was a single-channel sales system where manufacturers sell green products through e-commerce platforms. The study analyzed the coordination of this system under the consideration of the manufacturer’s fairness concerns and the service level of the e-commerce platform, finding that the manufacturer’s fairness concerns have no impact on the platform’s service level. Guo Jinsen et al. [13] studied the operational decision-making methods of a supply chain under three different financing models, combining loans from various financial institutions and commercial credit, in an offline and online dual-channel sales environment when both upstream and downstream companies face financial difficulties. Bi Gongbing et al. [14] considered a sales model where both online and offline sales coexist, along with consumer returns. They established two different pricing decision models: one for unified pricing and one for autonomous pricing in dual-channel and cross-channel returns. They developed optimal pricing and return strategies based on consumer purchase habits and return intentions, thereby improving operational efficiency and profit levels. Wang Yikai and Xu Wenping [15] constructed an online and offline dual-channel supply chain based on service levels, analyzing the impacts of retailer service levels in both channels on pricing decisions. Zhao Da et al. [16] considered the optimal pricing decision and financing strategy of each member in a dual-channel supply chain where all the members are risk averse and the retailer faces financial constraints. He Lihong et al. [17] studied a retailer dual-channel supply chain with a vertically integrated supplier under centralized decision making, analyzing the impacts of the supplier’s stockholding ratio and the retailer’s network channel share on related decisions, such as pricing in a supplier-dominated Stackelberg game. Li Zhiguo and Zeng Xinyu [18] established a supply chain model consisting of upstream suppliers and existing and new retailers, exploring the strategic ordering decisions of retailers under government subsidies and asymmetric information and using a signaling game model to compare the ordering decisions in scenarios with and without government subsidies. Wang Le et al. [19] studied when an online retailer in a dual-channel supply chain with multiple offline retailers would decide to implement live-streaming sales, considering the difference in consumers’ perceived product quality across sales channels. Wang Yuyan et al. [20] explored the optimal pricing and service decisions in a dual-channel supply chain where the manufacturer engages in online direct sales and offline retail distribution, considering consumers’ differing perceptions of product quality across channels. Gong Wenwei et al. [21] considered the impacts of three types of power structure differences on supply chain members’ decisions and profits when manufacturers are overly confident. Dan et al. [22] studied the optimal decision making in a dual-channel supply chain consisting of manufacturers and retailers in product competition with warranty and value-added services bundled together. They also considered the optimal decision making of supply chain members in a dual-channel supply chain consisting of a manufacturer, an online store, and a physical retailer under uncertain demand and inventory constraints [23]. Similar to this study, some research investigates the optimal pricing and service decisions in a dual-channel supply chain with a manufacturer engaging in online direct sales and offline retail distribution [21]. However, this paper mainly considers the channel comparison between online direct sales and offline retail, which does not align with the actual sales model of new energy vehicles. Furthermore, this paper not only considers after-sales service and pricing issues but also incorporates product competitiveness, making it more aligned with the current status of the new energy vehicle market.

In the study of after-sales services and pricing strategies in the automotive industry, numerous studies indicate that good after-sales service can effectively increase consumers’ purchase intentions and loyalty, attract new customers, and retain existing ones, thus bringing greater economic benefits to companies. In today’s market environment, many manufacturers no longer focus solely on the price of the product but instead begin to seek other competitive advantages. One such strategy is to offer comprehensive and competitive warranty services to meet consumers’ expectations and needs [24]. Guajardo et al. [25] studied the impacts of warranty duration and after-sales service quality on consumer car demand. Yang Zichao et al. [26] considered the issue of manufacturers’ after-sales derivative services being undervalued by customers after outsourcing and constructed a dual-objective decision model aimed at customer utility and profit maximization under centralized and decentralized decision making. They designed a cost-sharing and value-sharing supply chain coordination contract. Fu et al. [27] investigated the relationship between the warranty period and product reliability in the secondary market. Zhang et al. [28] explored optimal strategies and profit comparisons for manufacturers in a dual-channel supply chain where the retailer, third-party provider, or both share the outsourcing service responsibilities. Yang Zichao et al. [29] constructed a game model for after-sales services in the two modes of in house and outsourcing, providing recommendations for manufacturers’ decisions on whether to handle after-sales services internally or outsource them. Esmaeili et al. [30] studied the optimal decisions of a supply chain consisting of manufacturers offering warranty services and agents providing three types of warranty services in non-cooperative and semi-cooperative game scenarios. Song Han et al. [31] explored the moral hazard and incentive issues in R&D-outsourcing contracts when service providers face financial constraints. Bian et al. [32] studied the retailer’s warranty plan choice and optimal warranty pricing decisions in the context of traditional extended warranties and trade-in extended warranties. Padmanabhan and Rao [33] analyzed the impacts of consumers’ risk preferences, product reliability, and the introduction of consumer moral hazards on automotive manufacturers’ after-sales service policies and their optimization. Jiang and Zhang [34,35] examined the impacts of service plans on manufacturers’ after-sales services. Hünecke and Gunkel [36] investigated the impacts of after-sales services, in the high-end automotive industry, on brand loyalty.

Soares et al. [37] reviewed the research progress in electric vehicle supply chain management, covering areas such as pricing strategies, supply chain coordination, and inventory management, and provided guidance for future research directions. Fan, Z.-P. et al. [38] studied the vertical cooperation and pricing strategies between manufacturers and retailers in the electric vehicle supply chain under brand competition, analyzing the impacts of different cooperation models on the overall supply chain profit and the interests of various parties. Ma, J. [39] explored the coordination issues between centralized and decentralized pricing strategies in a competing supply chain where retailers offer extended warranty services, analyzing the impacts of different channel structures on supply chain profit and efficiency. Hua Ke [40] investigated the pricing decision problem of competing retailers in a two-echelon supply chain under uncertain conditions, examining how different pricing strategies affect the overall performance of the supply chain.

In the research on marketing input levels, Zhang Juliang et al. [41] established a market demand function that incorporates sales efforts. Ma [42] and others studied a two-level supply chain consisting of a manufacturer and a retailer, where both product quality and sales effort simultaneously influence market demand. They analyzed the impacts of different channel power structures on the production decisions of supply chain members. Gao Juhong [43] established a closed-loop supply chain decision model led by the retailer and investigated how product greenness and retailer sales efforts, both influencing the market demand, affect supply chain decisions. Wang et al. [44] examined pricing and advertising investment decisions in a two-level supply chain consisting of a manufacturer and a retailer. They found that when both the manufacturer and retailer offer price discounts, the scenario where the manufacturer dominates the supply chain power is more favorable to the manufacturer. Similar to this study, which involves a two-level supply chain consisting of a manufacturer and a retailer, [45] where both product quality and sales effort influence the market demand, the impacts of different channel power structures on the production decisions of supply chain members are also analyzed [42]. However, the difference in this study lies in the inclusion of an upstream parts supplier as one of the supply chain members, which enhances the generality of the model. Additionally, this study not only focuses on the production decisions of supply chain members but also considers the impact of pricing on consumers from the consumer’s perspective.

The structure of this paper is arranged as follows: First, the processes of parts procurement, production, sales, and after-sales service in the electric vehicle supply chain are described. Then, a supply chain model is established, and the equilibrium solutions are derived. Finally, through theoretical proofs and numerical examples, the paper analyzes the impacts of electric vehicle terminal-marketing input, product competitiveness, and manufacturer after-sales services on the optimal decision variables and profits of each member in the electric vehicle supply chain with different channel structures. This analysis provides insights for the promotion of electric vehicles, channel selection, and enhancement of after-sales services and product competitiveness. It also offers reasonable suggestions for pricing strategy optimization and profit distribution issues.

3. Model Description, Symbols, and Assumptions

3.1. Model Description

This paper primarily considers two sales channels: The first is the traditional 4S dealership model used by conventional automotive companies, where the supply chain consists of upstream parts suppliers, automobile manufacturers, and retailers. The upstream parts manufacturers are responsible for providing components, such as batteries and motors; the automobile manufacturer is responsible for the overall vehicle production; and the retailer is responsible for vehicle sales. The second is the direct-sales model, which is more common in the current NEV industry. In this model, the supply chain consists of upstream parts suppliers and automobile manufacturers, with the automobile manufacturer establishing its own direct-sales stores to serve as the retailer. The two channel models are illustrated in Figure 1.

In Figure 1, “Manufacturer (Retailer)” refers to the role of the automobile manufacturer in the direct-sales model. In this model, the manufacturer not only undertakes the production of vehicles but also acts as the retailer, directly selling products to end consumers without relying on traditional dealership networks. This structure enables manufacturers to have greater control over pricing, marketing, and after-sales services, thereby improving the supply chain efficiency and consumer experience.

3.2. Explanation of the Model’s Symbols

The symbols used in the model are explained in

Table 1. Explanation of the symbols in the model.

Model Parameter	Parameter Description
D	Potential market demand
b	Consumer price sensitivity
m	Effect of after-sales service level on sales
n	Effect of terminal marketing investment on sales
$\beta$	Effect of product competitiveness on sales
${\pi }_M,{\pi }_R,{\pi }_S$	Profits of the manufacturer, retailer, and upstream parts supplier
D	Vehicle sales volume of the manufacturer
h	Terminal marketing cost investment coefficient
k	Product competitiveness cost coefficient
j	After-sales service level cost investment coefficient
p (decision variable)	Vehicle retail price
g (decision variable)	Terminal marketing investment level
$\theta$ (decision variable)	After-sales service level, where $\alpha$ represents the market average, and $\theta -\alpha$ represents the difference between the manufacturer’s after-sales service level and the market average
w (decision variable)	Wholesale price of the vehicle
c (decision variable)	Vehicle manufacturer’s component procurement cost
t (decision variable)	Production cost of the upstream parts supplier
f (decision variable)	Product competitiveness, where v represents the market average, and $f-v$ represents the difference in product competitiveness between the selected parts supplier and the market average

.

3.3. Assumptions

For simplicity, the following assumptions are made in the model:

Assumptions 1.

Consumers are willing to choose manufacturers with higher product competitiveness, better after-sales service, and more convenient purchasing options, within a reasonable price range. Therefore, borrowing from the linear demand function model, it is assumed that the sales volume of the manufacturer is a function of the price, terminal marketing investment, product competitiveness, and after-sales service level. In addition, a represents the market’s capacity, $\alpha$ represents the market’s after-sales service level average, and v represents the market product’s competitiveness average:

$$D=a-bp+m\left(\theta -\alpha \right)+ng+\beta \left(f-v\right).$$

(1)

Assumptions 2.

Product competitiveness refers to consumer concerns controlled by upstream parts suppliers, such as the vehicle’s delivery cycle and electric vehicle’s range. The cost of the product competitiveness is borne by the upstream parts supplier.

Assumptions 3.

The potential demand of the market, a, is sufficiently large: $D>0$.

Assumptions 4.

In the 4S dealership model, the marketing investment cost is borne by the retailer (4S dealership), while in the direct-sales model, the marketing investment cost is solely borne by the manufacturer. The after-sales service cost of the car manufacturer is borne by the automobile manufacturer, regardless of the sales model.

Based on the above assumptions, the profit functions for the retailer, manufacturer, and upstream parts suppliers in the traditional 4S dealership model are as follows:

$${\pi }_{R,4s}=\left(p-w\right)D-{\displaystyle \frac{1}{2}}h{g}^2,$$

(2)

$${\pi }_{M,4s}=\left(w-c\right)D-{\displaystyle \frac{1}{2}}j{\theta }^2,$$

(3)

$${\pi }_{S,4s}=\left(c-t\right)D-{\displaystyle \frac{1}{2}}k{f}^2.$$

(4)

The profit functions for the manufacturer and upstream parts supplier in the direct-sales model are as follows:

$${\pi }_M,z=\left(p-c\right)D-{\displaystyle \frac{1}{2}}j{\theta }^2-{\displaystyle \frac{1}{2}}h{g}^2,$$

(5)

$${\pi }_{S,z}=\left(c-t\right)D-{\displaystyle \frac{1}{2}}k{f}^2.$$

(6)

4. Model Construction and Solution

4.1. Traditional 4S Store Model

For the traditional 4S store model, we first derive the equilibrium solutions for the retailer’s decision variables, the vehicle retail price (p) and the terminal marketing investment level (g), when the retailer maximizes its profit. Substituting the demand function (1) into Equation (2), we obtain

$${\pi }_{R,4s}=\left(p-w\right)\left(a-bp+m\left(\theta -\alpha \right)+ng+\beta \left(f-v\right)\right)-{\displaystyle \frac{1}{2}}h{g}^2.$$

(7)

The Hessian matrix of ${\pi }_{R,4s}$ with respect to the decision variables (p and g) is

$$H{\pi }_{R,4s}=\left|\begin{array}{cc}-2b& n\\ n& -h\end{array}\right|.$$

(8)

According to the parameter assumptions, we know that when certain conditions hold, there exists a unique p and g for the retailer’s profit function that maximizes ${\pi }_{R,4s}$. To calculate the maximum value of ${\pi }_{R,4s}$, we take the first-order partial derivatives of Equation (7) with respect to $p_{4s}$ and $g_{4s}$ as follows:

$${\displaystyle \frac{\partial {\pi }_{R,4s}}{\partial p_{4s}}}=a+gn-2bp+bw-m\alpha +f\beta -v\beta +m\theta ,$$

(9)

$${\displaystyle \frac{\partial {\pi }_{R,4s}}{\partial g_{4s}}}=-gh+n\left(p-w\right).$$

(10)

By solving the system of Equations (9) and (10), we obtain

$$p_{4s}={\displaystyle \frac{ah+bhw-n^{2}w+h\left(-m\alpha +f\beta -v\beta +m\theta \right)}{2bh-n^2}},$$

(11)

$$g_{4s}={\displaystyle \frac{n\left(-a+bw+m\alpha -f\beta +v\beta -m\theta \right)}{-2bh+n^2}}.$$

(12)

Using the reverse substitution method, we then find the equilibrium solutions for the decision variables of the manufacturer when maximizing profits. Rewriting Equation (3), we obtain

$${\pi }_{M,4s}=\left(w-c\right)\left(a-bp+m\left(\theta -\alpha \right)+ng+\beta \left(f-v\right)\right)-{\displaystyle \frac{1}{2}}j{\theta }^2.$$

(13)

Taking the second-order partial derivative of ${\pi }_{M,4s}$ with respect to $\theta$, we obtain ${\displaystyle \frac{{\partial }^2{\pi }_{M,4s}}{{\partial {\theta }_{4s}}^2}}=-j<0$ and ${\displaystyle \frac{{\partial }^2{\pi }_{M,4s}}{\partial {w_{4s}}^2}}={\displaystyle \frac{-2b(bh+2n^2)}{2bh-n^2}}<0$. Therefore, the after-sales service level ($\theta$) and the wholesale price (w) have a maximum value. By substituting Equations (11) and (12) into Equation (13), and solving the system of equations ${\displaystyle \frac{\partial {\pi }_{M,4s}}{\partial {\theta }_{4s}}}=0$ and ${\displaystyle \frac{\partial \pi M4s}{\partial w4s^2}}=0$, we obtain

$$w_{4s}={\displaystyle \frac{2b^{2}chj+aj\left(2bh-n^2\right)+j{n}^2\left(m\alpha -f\beta +v\beta \right)-b\left(ch{m}^2+cj{n}^2+2hjm\alpha +2hj\left(-f+v\right)\beta \right)}{b\left(4bhj-h{m}^2-2j{n}^2\right)}},$$

(14)

$${\theta }_{4s}={\displaystyle \frac{hm\left(-a+bc+m\alpha -f\beta +v\beta \right)}{-4bhj+h{m}^2+2j{n}^2}}.$$

(15)

Substituting w and $\theta$ into (11) and (12), we obtain $p\ast$ and $g\ast$, respectively, as follows:

$$p_{4s}={\displaystyle \frac{b^{2}chj+aj\left(3bh-n^2\right)+j{n}^2\left(m\alpha -f\beta +v\beta \right)-b\left(ch{m}^2+cj{n}^2+3hjm\alpha +3hj\left(-f+v\right)\beta \right)}{b\left(4bhj-h{m}^2-2j{n}^2\right)}},$$

(16)

$$g_{4s}={\displaystyle \frac{jn\left(-a+bc+m\alpha -f\beta +v\beta \right)}{-4bhj+h{m}^2+2j{n}^2}}.$$

(17)

Next, using backward induction, we find the equilibrium solution for the upstream parts supplier’s decision variable when the profit is maximized. We rewrite (4) as

$${\pi }_{S,4s}=\left(c-t\right)\left(a-bp+m\left(\theta -\alpha \right)+ng+\beta \left(f-v\right)\right)-{\displaystyle \frac{1}{2}}k{f}^2.$$

(18)

At this point, the decision variable is the product’s competitiveness (f). Taking the second-order derivative with respect to f, we obtain ${\displaystyle \frac{{\partial }^2{\pi }_{S,4s}}{\partial f_{4s^2}}}=-k<0$. Therefore, the manufacturer’s profit function has a maximum value. Substituting Equations (15)–(17) into Equation (18) and setting ${\displaystyle \frac{\partial {\pi }_{S,4s}}{\partial f_{4s}}}=0$, we obtain

$$f_{4s}\ast ={\displaystyle \frac{hj\beta \left(-a+bt+m\alpha +v\beta \right)}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}}.$$

(19)

Similarly, setting ${\displaystyle \frac{\partial {\pi }_{S,4s}}{\partial c_{4s}}}=0$, we obtain

$$c_{4s}\ast ={\displaystyle \frac{k\left(4bhj-h{m}^2-2j{n}^2\right)\left(a+bt-m\alpha \right)+k\left(-4bhj+h{m}^2+2j{n}^2\right)v\beta -bhjt{\beta }^2}{b\left(8bhjk-4jk{n}^2-h\left(2k{m}^2+j{\beta }^2\right)\right)}}.$$

(20)

Similarly, we substitute the obtained values of $f\ast$ and $c\ast$ into Equations (14)–(17), yielding the final parameter values that do not include decision variables, and we obtain the following results:

$$p_{4s}\ast ={\displaystyle \frac{ak\left(7bhj-h{m}^2-3j{n}^2\right)+b^{2}hjkt+k\left(h{m}^2+3j{n}^2\right)\left(m\alpha +v\beta \right)-b\left(jk{n}^{2}t+h\left(km\left(mt+7j\alpha \right)+7jkv\beta +jt{\beta }^2\right)\right)}{b\left(8bhjk-4jk{n}^2-h\left(2k{m}^2+j{\beta }^2\right)\right)}},$$

(21)

$$g_{4s}\ast ={\displaystyle \frac{jkn\left(-a+bt+m\alpha +v\beta \right)}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}},$$

(22)

$$w_{4s}\ast ={\displaystyle \frac{ak\left(6bhj-h{m}^2-3j{n}^2\right)+2b^{2}hjkt+k\left(h{m}^2+3j{n}^2\right)\left(m\alpha +v\beta \right)-b\left(jk{n}^{2}t+h\left(km\left(mt+6j\alpha \right)+6jkv\beta +jt{\beta }^2\right)\right)}{b\left(8bhjk-4jk{n}^2-h\left(2k{m}^2+j{\beta }^2\right)\right)}},$$

(23)

$${\theta }_{4s}\ast ={\displaystyle \frac{hkm\left(-a+bt+m\alpha +v\beta \right)}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}}.$$

(24)

Consequently, the profits of the retailer, manufacturer, and upstream parts supplier are as follows:

$${\pi }_{R,4s}={\displaystyle \frac{h{j}^2{k}^2\left(2bh-n^2\right){\left(-a+bt+m\alpha +v\beta \right)}^2}{2{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}},$$

(25)

$${\pi }_{M,4s}={\displaystyle \frac{hj{k}^2\left(4bhj-h{m}^2-2j{n}^2\right){\left(-a+bt+m\alpha +v\beta \right)}^2}{2{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}},$$

(26)

$${\pi }_{S,4s}={\displaystyle \frac{hjk{\left(-a+bt+m\alpha +v\beta \right)}^2}{16bhjk-2\left(2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}}.$$

(27)

4.2. Manufacturer’s Direct-Sales Model

The solution approach follows that of the traditional sales model: first, determine the equilibrium solutions for the manufacturer’s optimal retail price (p), terminal marketing investment level (g), and after-sales service level ($\theta$) that maximize its profit. In this case, the manufacturer also acts as a retailer. Thus, the manufacturer’s profit function, as given in Equation (5), is rewritten as follows:

$${\pi }_{M,z}=\left(p-c\right)\left(a-bp+m\left(\theta -\alpha \right)+ng+\beta \left(f-v\right)\right)-{\displaystyle \frac{1}{2}}j{\theta }^2-{\displaystyle \frac{1}{2}}h{g}^2.$$

(28)

The Hessian matrix of the manufacturer’s profit function with respect to the decision variables p, g, and $\theta$ is $H{\pi }_{M,z}=\left|\begin{array}{ccc}-2b& n& m\\ n& -h& 0\\ m& 0& -j\end{array}\right|$. According to the assumed parameter conditions, we know that $-2b<0$, $-h>0$, and $-j<0$, ensuring the existence of a unique set of values for p, g, and $\theta$ that maximize the manufacturer’s profit function. By maximizing the manufacturer’s profit function and setting the first-order derivatives of Equation (28) with respect to p, g, and $\theta$ to zero, we solve the system of three equations and obtain the equilibrium values of p, g, and $\theta$ as follows:

$$p_z={\displaystyle \frac{ahj+bchj-ch{m}^2-cj{n}^2-hjm\alpha +hj\left(f-v\right)\beta }{2bhj-h{m}^2-j{n}^2}},$$

(29)

$$g_z={\displaystyle \frac{jn\left(-a+bc+m\alpha -f\beta +v\beta \right)}{-2bhj+h{m}^2+j{n}^2}},$$

(30)

$${\theta }_z={\displaystyle \frac{hm\left(-a+bc+m\alpha -f\beta +v\beta \right)}{-2bhj+h{m}^2+j{n}^2}}.$$

(31)

Next, using backward induction, we determine the equilibrium decision variable for the upstream component supplier that maximizes its profit. Substituting the demand function into Equation (6) yields

$${\pi }_{S,z}=\left(c-t\right)\left(a-bp+m\left(\theta -\alpha \right)+ng+\beta \left(f-v\right)\right)-{\displaystyle \frac{1}{2}}k{f}^2.$$

(32)

where the decision variable is the product’s competitiveness (f). Taking the second-order derivative of ${\pi }_{S,z}$ with respect to f gives

$${\displaystyle \frac{{\partial }^2{\pi }_{S,z}}{\partial f_s^2}}=-k<0$$

indicating that the manufacturer’s profit function has an optimal solution concerning the after-sales service level (f). By substituting Equations (29)–(31) into Equation (32) and solving ${\displaystyle \frac{\partial {\pi }_{S,z}}{\partial fz}}=0$, we obtain

$$f_z\ast ={\displaystyle \frac{hj\beta \left(-a+bt+m\alpha +v\beta \right)}{-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2}}.$$

(33)

Similarly, solving ${\displaystyle \frac{\partial {\pi }_{S,z}}{\partial c}}=0$ yields

$$c_z\ast ={\displaystyle \frac{k\left(2bhj-h{m}^2-j{n}^2\right)\left(a+bt-m\alpha \right)+k\left(-2bhj+h{m}^2+j{n}^2\right)v\beta -bhjt{\beta }^2}{b\left(4bhjk-2k\left(h{m}^2+j{n}^2\right)-hj{\beta }^2\right)}}.$$

(34)

Thus, the equilibrium expressions for each decision variable are as follows:

$$p_z\ast ={\displaystyle \frac{ak\left(3bhj-h{m}^2-j{n}^2\right)+b^{2}hjkt+k\left(h{m}^2+j{n}^2\right)\left(m\alpha +v\beta \right)-b\left(jk{n}^{2}t+h\left(km\left(mt+3j\alpha \right)+3jkv\beta +jt{\beta }^2\right)\right)}{b\left(4bhjk-2k\left(h{m}^2+j{n}^2\right)-hj{\beta }^2\right)}},$$

(35)

$$g_z\ast ={\displaystyle \frac{jkn\left(-a+bt+m\alpha +v\beta \right)}{-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2}},$$

(36)

$${\theta }_z\ast ={\displaystyle \frac{hkm\left(-a+bt+m\alpha +v\beta \right)}{-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2}}.$$

(37)

By substituting Equations (33)–(37) into Equations (5) and (6), the profits of the manufacturer and the upstream component supplier in the direct-sales model supply chain are derived as follows:

$${\pi }_{M,z}={\displaystyle \frac{hj{k}^2\left(2bhj-h{m}^2-j{n}^2\right){\left(-a+bt+m\alpha +v\beta \right)}^2}{2{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^2}},$$

(38)

$${\pi }_{Sz}=-{\displaystyle \frac{h^3{j}^3{k}^3\left(2bhj-h{m}^2-j{n}^2\right){\beta }^2{\left(-a+bt+m\alpha +v\beta \right)}^4}{2{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^4}}.$$

(39)

5. Result Analysis

Proposition 1.

In the two supply chain models, the manufacturer’s after-sales service level (θ) is affected by different sales channels. The after-sales service level is higher in the direct sales channel.

Proof. 

$$\begin{array}{cc}\hfill & {\theta }_{4s}\ast ={\displaystyle \frac{hkm\left(-a+bt+m\alpha +v\beta \right)}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}}\hfill \\ \hfill & {\theta }_z\ast ={\displaystyle \frac{hkm\left(-a+bt+m\alpha +v\beta \right)}{-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2}}\hfill \\ \hfill & {\displaystyle \frac{{\theta }_{4s}\ast }{{\theta }_z\ast }}={\displaystyle \frac{\frac{hkm\left(-a+bt+m\alpha +v\beta \right)}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}}{\frac{hkm\left(-a+bt+m\alpha +v\beta \right)}{-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2}}}\hfill \\ \hfill & ={\displaystyle \frac{-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}}\hfill \\ \hfill & =1+{\displaystyle \frac{2jk\left(2bh-n^2\right)}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}}\hfill \\ \hfill & j>0;k>0;n>0;-a+bt+m\alpha +v\beta <0;\hfill \\ \hfill & {\theta }_{4s}\ast ={\displaystyle \frac{jkn\left(-a+bt+m\alpha +v\beta \right)}{-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2}}>0\hfill \\ \hfill & -8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2<002bh-n^2>0;\hfill \\ \hfill & 0<{\displaystyle \frac{{\theta }_{4s}\ast }{{\theta }_z\ast }}<1;{\theta }_{4s}\ast >0;{\theta }_z\ast >0\hfill \\ \hfill & {\theta }_{4s}\ast <{\theta }_z\ast \hfill \end{array}$$

Based on Proposition 1, it can be concluded that the direct-sales model shortens the supply chain hierarchy, reduces the time for logistics and information transfer, lowers costs, and improves resource utilization efficiency, thereby enhancing the manufacturer’s after-sales service level. The after-sales service function not only covers product maintenance but also contributes to the brand promotion and customer experience. It significantly affects the customer perception and satisfaction, which, in turn, influence product sales. Therefore, the direct-sales model helps to improve the manufacturer’s after-sales service level. □

Proposition 2.

The product’s competitiveness (f) is influenced by different sales channels. Among the two models, the product’s competitiveness (f) is higher in the direct-sales model.

Proof. 

The proof is similar to that of Proposition 1 and is, thus, omitted. □

In the direct-sales model, the manufacturer acts as the retailer, eliminating intermediate stages (such as distributors and wholesalers), which reduces the time for information and logistics transfer. This helps to improve the product competitiveness of upstream component suppliers. Moreover, direct contact with end customers allows the manufacturer to better understand the market demand and communicate more accurately with suppliers, thereby enhancing supply reliability. Acting as the retailer also enables the manufacturer to better control resources, inventory, production planning, and logistics, which optimizes resource allocation and reduces supply uncertainty, thus improving the product’s competitiveness.

Proposition 3.

In both supply chain models, the manufacturer, as the central player in the supply chain, is influenced by several parameters.

Proof. 

${\pi }_{M,z}-{\pi }_{M,4s}={\displaystyle \frac{hj{k}^2\left(2bhj-h{m}^2-j{n}^2\right){\left(-a+bt+m\alpha +v\beta \right)}^2}{2{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^2}}-{\displaystyle \frac{hj{k}^2\left(4bhj-h{m}^2-2j{n}^2\right){\left(-a+bt+m\alpha +v\beta \right)}^2}{2{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}}$

$={\displaystyle \frac{1}{2}}hj{k}^2{\left(-a+bt+m\alpha +v\beta \right)}^2(-{\displaystyle \frac{-2bhj+h{m}^2+j{n}^2}{{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^2}}+{\displaystyle \frac{-4bhj+h{m}^2+2j{n}^2}{{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}})$

${\displaystyle \frac{1}{2}}hj{k}^2{\left(-a+bt+m\alpha +v\beta \right)}^2>0$

When $-{\displaystyle \frac{-2bhj+h{m}^2+j{n}^2}{{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^2}}+{\displaystyle \frac{-4bhj+h{m}^2+2j{n}^2}{{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}}>0$

We have ${\pi }_{M,z}>{\pi }_{M,4s}$

When $-{\displaystyle \frac{-2bhj+h{m}^2+j{n}^2}{{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^2}}+{\displaystyle \frac{-4bhj+h{m}^2+2j{n}^2}{{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}}<0$

We have ${\pi }_{M,z}<{\pi }_{M,4s}$.

Thus, the proof is completed. Proposition 3 indicates that the manufacturer’s profit is influenced by different factors in different sales channels. Because of the marginal effects, the manufacturer’s profit in the direct-sales model is not necessarily greater than that in the traditional 4S store model. □

Proposition 4.

The vehicle retail price (p) is closely related to terminal marketing investment on sales (n). A higher cost coefficient leads to a lower after-sales service level.

Proof. 

Let the retail price in the traditional sales model supply chain be $p_z$ and the terminal marketing investment level in the direct-sales model supply chain be $p_{4S}$. Then

$$\begin{array}{cc}\hfill & {\displaystyle \frac{\partial p_z}{\partial n}}={\displaystyle \frac{jk{n}^2\left(-a+bt+m\alpha +v\beta \right)\left(4bjk+2k{m}^2+3j{\beta }^2\right)}{b{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}}\hfill \end{array}$$

(40)

$$\begin{array}{cc}\hfill & {\displaystyle \frac{\partial p_{4s}}{\partial n}}={\displaystyle \frac{j^{2}k{n}^2\left(-a+bt+m\alpha +v\beta \right)\left(2bk+{\beta }^2\right)}{b{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^2}}\hfill \end{array}$$

(41)

From previous proofs and the relevant parameter settings, we know that both of these derivatives are less than 0. □

Proposition 4 indicates that the terminal marketing investment cost coefficient is negatively correlated with the retailer’s price. The marketing cost coefficient refers to the expenses invested by a company in promoting products, increasing brand exposure, and improving sales, including advertising costs, promotional fees, and salesperson salaries. The negative correlation arises because as the product price increases, the market demand may decrease, resulting in higher marketing costs. This requires the company to balance the relationship between the cost and marketing investment, develop reasonable sales plans and marketing strategies, and optimize marketing efficiency to reduce costs and achieve both sales and reputational benefits.

Proposition 5.

The after-sales service level (θ) is closely related to the cost input coefficient (j). A higher cost coefficient leads to a lower after-sales service level.

Proof. 

Let the after-sales service level in the traditional sales model be denoted as ${\theta }_{4S}$ and in the direct-sales model as ${\theta }_z$. Then

$${\displaystyle \frac{\partial {\theta }_{4S}}{\partial m}}=-{\displaystyle \frac{hkm\left(-a+bt+m\alpha +v\beta \right)\left(-8bhk+4k{n}^2+h{\beta }^2\right)}{{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}}$$

Given the previous proofs and parameter settings, we have

$$-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2<0$$

and

$$2hk{m}^2>0$$

which implies $-8bhjk+4jk{n}^2+hj{\beta }^2<0$ Dividing both sides by $j>0$, we obtain

$$-8bhk+4k{n}^2+h{\beta }^2<0$$

Because $hkm>0$ and $-a+bt+m\alpha +v\beta >0$, it follows that

$${\displaystyle \frac{\partial {\theta }_{4S}}{\partial m}}<0$$

This proposition demonstrates a negative relationship between the after-sales service level and the cost coefficient associated with maintaining that level. The cost coefficient (j) reflects the comprehensive cost involved in providing after-sales service, including personnel, equipment, and maintenance expenses. A higher cost coefficient often indicates inefficiencies within the service system or overinvestment in certain areas, which can lead to the misallocation of resources and, ultimately, reduced service performance. Moreover, higher service costs may crowd out other operational budgets, further deteriorating the overall firm efficiency.

From a managerial perspective, companies should optimize their cost structure and ensure efficient allocation of after-sales service resources. Although controlling costs is crucial, firms must also ensure that cost reductions do not compromise service quality. □

Proposition 6.

The product’s competitiveness (f) is negatively related to the cost coefficient (k) associated with maintaining that competitiveness. As the cost coefficient increases, the product’s competitiveness decreases.

Proof. 

Let the product’s competitiveness (measured based on the terminal marketing input level) in the traditional model be $f_{4S}$ and in the direct-sales model be $f_z$. Then

$$\begin{array}{cc}\hfill & {\displaystyle \frac{\partial f_{4S}}{{\partial }_k}}=-{\displaystyle \frac{hj\left(-8bhj+2h{m}^2+4j{n}^2\right)\beta \left(-a+bt+m\alpha +v\beta \right)}{{\left(-8bhjk+2hk{m}^2+4jk{n}^2+hj{\beta }^2\right)}^2}}\hfill \end{array}$$

(42)

$$\begin{array}{cc}\hfill & {\displaystyle \frac{\partial f_z}{{\partial }_k}}=-{\displaystyle \frac{hj\left(-4bhj+2h{m}^2+2j{n}^2\right)\beta \left(-a+bt+m\alpha +v\beta \right)}{{\left(-4bhjk+2hk{m}^2+2jk{n}^2+hj{\beta }^2\right)}^2}}\hfill \end{array}$$

(43)

From the prior assumptions and parameter settings, both expressions are negative, i.e.,

$${\displaystyle \frac{\partial f_{4S}}{{\partial }_k}}<0,{\displaystyle \frac{\partial f_z}{{\partial }_k}}<0$$

This proposition indicates that higher cost coefficients related to maintaining the product’s competitiveness diminish the actual competitive advantage. In supply chain systems, the product’s competitiveness is often closely tied to the upstream supplier’s ability to deliver components reliably and consistently. A high cost coefficient (k) reflects increased efforts and expenses to stabilize the supply chain—such as safety stock, backup sourcing, or emergency logistics—suggesting underlying inefficiencies. □

Consequently, when firms allocate substantial resources to mitigate supply risks or quality issues, their margins shrink, and their competitive position weakens. To address this, upstream suppliers should strive to improve delivery reliability and production efficiency. Meanwhile, firms should strengthen supplier management practices and foster long-term partnerships that promote cost efficiency without compromising product performance. The Sensitivity analysis comparison between two scenarios are explained in

Table 2. Sensitivity analysis comparison between two scenarios: the 4S dealership model vs. the direct-sales model.

Variable	m	n	$\mathit{\beta }$	h	k	j
p	――	+	――	-	――	――
g	――	+	――	-	――	――
$\theta$	+	――	――	――	――	-
f	――	――	+	――	-	――

Note: “+” represents a positive correlation, “-” represents a negative correlation, and “――” represents no correlation.

.

6. Numerical Simulation

This section conducts a numerical simulation to study the above model. The simulation is performed using MATLAB R2016b software, which is employed to solve the system of equations and analyze the results in different scenarios. The simulation allows for the sensitivity analysis of the model’s parameters and provides insights into the behavior of the supply chain under various conditions. Refer to reference [46] for the method used in the numerical simulation section. Based on the research assumptions and referring to the parameter settings in the literature [38,46,47,48,49], the common supply chain parameters are set at $a=100, b=2, t=20, \alpha =0.4$, and $v=0.8$.

6.1. The Effect of the Terminal Marketing Investment and the Impact of the Marketing Cost Coefficient on the Level of the Marketing Investment

Figure 2 illustrates the effect of the terminal marketing investment (n) on the terminal marketing investment level (g). The analysis explores the sensitivity of the terminal marketing investment level to the effect of the marketing investment. Without loss of generality, the other parameters are assumed as follows:

Figure 2 shows a positive correlation between n and g, and for the given parameters and any given value of n, it is always true that $g_z>g_{4S}$.

As shown in the figure, as n increases, the gap between the terminal marketing input levels continues to widen. This indicates that compared to the traditional sales model, in the direct-sales model, retailers invest more in terminal marketing. This suggests that in the entire supply chain, retailers bear the responsibility for marketing, while parts suppliers and manufacturers focus on production and after-sales services. Retailers’ marketing investments are driven by profit motives and cost control. When the marketing input effect is low, retailers increase their investment to boost sales. However, as the marketing effect improves, retailers become more conservative to maintain a balance between input and output. Compared to the direct-sales model, the traditional 4S store model has lower growth in marketing investment, as retailers need to control marketing costs to avoid eroding profits, which further widens this gap. Additionally, as the marketing input effect increases, every additional unit of investment by the manufacturer in the direct-sales model yields increasing market returns, which further enhances the manufacturer’s willingness to invest.

In the traditional retail model, although retailers also benefit from the improved marketing input effect, their market share and brand influence are limited by regional or other factors. Excessive marketing input leads to diminishing marginal returns. Therefore, retailers tend to be more cautious with marketing investment, which increases the gap between the two models.

Figure 3 shows the impact of the terminal marketing input cost coefficient (h) on the terminal marketing input level (g). The figure demonstrates a negative correlation between h and g, and for any given value of h, $g_z>g_{4S}$ always holds.

The relationship between the terminal marketing input level and h in different channel models is quite complex. When h is below a certain threshold, the gap between the two models continues to widen, while when h exceeds this value, the gap gradually narrows, approaching zero. This indicates that when the terminal marketing input cost coefficient is low, retailers in the traditional retail model experience greater marginal returns on marketing investments, so they tend to increase their investments, widening the gap with the direct-sales model. At the same time, the retailer’s direct-sales profits can better cover the marketing input costs, providing more motivation for the retailer to invest more in marketing. When the terminal marketing input cost coefficient exceeds a certain threshold, the marginal benefit of the marketing investment decreases for retailers in the traditional retail model, and they begin to face diminishing marginal returns. As costs increase, retailers’ marketing investments gradually decrease, approaching those in the direct-sales model, and could even approach zero. This suggests that under high-cost conditions, retailers neither in the traditional model nor in the direct-sales model can generate enough profit to cover marketing expenses, leading them to reduce investments to maintain a profit balance.The effect of the terminal marketing investment and the impact of the marketing cost coefficient on the level of the marketing investment are explained in

Table 3. The effect of the terminal marketing investment and the impact of the marketing cost coefficient on the level of the marketing investment.

Influencing Factor	Scenario 1: Impact of an Increase in n	Scenario 2: Effect of Changes in the Terminal Marketing Input Cost Coefficient (h)
Difference in Terminal Marketing Input	As n increases, the gap in the terminal marketing input widens, with retailers in the direct-sales model investing more.	As h changes, when h is below a certain threshold, the gap increases; when h exceeds this value, the gap gradually narrows, approaching zero.
Retailers’ Marketing Behavior	Retailers’ marketing investment is driven by profit motives and cost control; when the marketing effect is low, investment increases, but as the marketing effect improves, retailers become more conservative.	When h is low, retailers in the traditional retail model experience greater marginal returns on marketing investments, so they are inclined to increase investments. When h is high, the marginal benefit decreases, and they become more conservative.
Difference Between a Traditional 4S Store and a Direct-Sales Model	In the traditional sales model, retailers’ marketing investments grow more slowly, widening the gap with those in the direct-sales model.	As h increases, retailers’ marketing investments in the traditional model decrease, approaching those in the direct-sales model.
Manufacturer’s Willingness to Invest in Marketing	As the marketing input effect increases, the manufacturer’s willingness to invest in the direct-sales model increases.	In the traditional retail model, as h increases, the marginal benefit of the marketing investment decreases, potentially reducing the manufacturer’s willingness to invest.
Cost and Profit Relationship	Retailers in the direct-sales model can better cover marketing costs, motivating higher investment.	In high-cost scenarios, retailers neither in the traditional model nor in the direct-sales model can generate enough profit to cover marketing expenses, leading to reduced investments to maintain a profit balance.

.

6.2. The Effect of the After-Sales Service Cost Input Coefficient and the Terminal Marketing Effect on the Level of the After-Sales Service

Figure 4 shows the effects of the after-sales service on sales (m) and the after-sales service level ($\theta$). Without loss of generality, the other parameters are set at $h=1, k=0.15, n=0.15,$$\beta =0.15, j=0.15$, and $m=0.15$. Figure 4 demonstrates a positive correlation between m and $\theta$, and for any given value of m, ${\theta }_z>{\theta }_{4S}$ always holds.

As shown in the figure, as m increases, the gap between the two models in terms of the after-sales service level continues to widen. This phenomenon indicates that the direct-sales model places more emphasis on after-sales service. In the direct-sales model, the manufacturer directly handles after-sales services, and the quality of the after-sales service directly impacts the brand reputation and customer loyalty. Therefore, manufacturers are more willing to improve after-sales service levels to ensure customers receive high-quality experiences, thus driving sales growth. As the effect of after-sales service on sales increases, the return on investment in after-sales service also increases, further motivating the manufacturer to enhance service levels, leading to an increasing gap between the two models.

One reason for this situation in the traditional retail model is the decentralization of the after-sales service and cost control. In the traditional retail model, although after-sales service is the responsibility of the manufacturer, the actual service experience is often provided by various regional 4S stores, which leads to service fragmentation and inconsistencies in service quality. Moreover, in the traditional retail model, manufacturers focus more on cost control to avoid eroding profits, so the improvement in after-sales service levels is relatively conservative. As the effect of the after-sales service on sales increases, manufacturers find it difficult to achieve the same level of improvement as that in the direct-sales model.

Figure 5 shows the impact of the after-sales service cost coefficient (j) on the after-sales service level ($\theta$). Figure 5 demonstrates a negative correlation between j and $\theta$, and for any given value of j, ${\theta }_z>{\theta }_{4S}$ always holds.

The relationship between the after-sales service level and j in different sales models is more complex. When $\theta$ is below a certain threshold, the gap between the two models continues to widen. However, when $\theta$ exceeds this value, the gap gradually narrows and approaches zero. This pattern of widening and then narrowing the gap as the after-sales service cost input coefficient changes can be explained from the perspectives of cost efficiency and marginal service returns.

This indicates that when the after-sales service cost input coefficient is low, the cost of improving the after-sales service level is lower. In this case, for manufacturers in the direct-sales model, as they are directly facing the consumers, they tend to increase their service investments to enhance customer satisfaction and brand loyalty. In the traditional retail model, manufacturers are not directly interacting with consumers, and, therefore, the drive to improve service levels is relatively weak. For low cost-input coefficients, manufacturers in the direct-sales model can more easily improve after-sales service levels, leading to an expanding gap between the two models.

When the cost input coefficient becomes high, the gap between the two models begins to shrink, indicating that as the after-sales service cost input coefficient rises beyond a certain threshold, the marginal cost of improving service levels increases. At this point, manufacturers in the direct-sales model will become more cautious, as the increasing cost of improving service levels may eventually outweigh the returns. Traditional retailers, who have been more conservative with their service investments, are less likely to significantly alter their service levels. However, because both models are facing increased costs, the gap between them gradually narrows. Additionally, as the cost coefficient increases, the marginal returns from service improvement gradually decrease, and manufacturers in both models begin to reduce their investments to avoid situations where high investment levels result in low returns. As a result, whether in the direct-sales or 4S store model, when marginal returns decrease to a certain level, the after-sales service levels in both models gradually converge.The effect of the after-sales service cost input coefficient and the terminal marketing effect on the level of the after-sales service are explained in

Table 4. The Effect of the after-sales service cost input coefficient and the terminal marketing effect on the level of the after-sales service.

Influencing Factor	Scenario 1: Impact of an Increase in m	Scenario 2: Effect of Changes in the After-Sales Service Cost Input Coefficient (j)
Difference in After-Sales Service Level	As m increases, the gap in the after-sales service level widens, with the direct-sales model placing more emphasis on after-sales service.	As j changes, when j is low, the gap between the models increases, and as j exceeds a certain threshold, the gap decreases, approaching zero.
Manufacturer’s Behavior in the Direct-Sales Model	Manufacturers in the direct-sales model are more willing to improve after-sales service to enhance the brand reputation and customer loyalty.	When j is low, the direct-sales model can easily improve the after-sales service, leading to a widening gap with the traditional model. As j increases, the direct-sales model becomes more cautious in service improvements because of rising marginal costs.
Retailer’s Behavior in the Traditional Retail Model	In the traditional retail model, after-sales service is more decentralized and cost controlled, leading to slower improvement in service quality.	As the cost input coefficient (j) increases, the traditional model’s service level improvement becomes more conservative, with manufacturers focusing on cost control rather than service enhancement.
Cost and Profit Relationship	As the after-sales service impact increases, manufacturers in the direct-sales model are more motivated to invest in service improvements to boost sales.	With higher service cost input coefficients, the traditional model’s marginal returns decrease, leading to less investment in service improvements, narrowing the gap between the two models.
Marginal Return on Service Investments	When the effect of the after-sales service on sales increases, the return on investment in after-sales service also increases, further encouraging investment.	As the cost input coefficient rises, the marginal benefit of the service investment decreases, causing manufacturers to reduce investments to avoid high-cost low-return scenarios.

.

6.3. The Impacts of the Terminal Marketing Input Effect and Marketing Input Cost Coefficient on the Retail Price

Figure 6 shows the impact of the terminal marketing input effect (n) on the retail price (p). Without loss of generality, the other parameters are set at $h=2, k=0.2, n=0.2,$$\beta =0.2, j=0.2$, and $m=0.2$.

From the figure, it can be seen that when the marketing input effect is small, the retail price of the vehicle in the traditional 4S store model is relatively high. As the marketing input effect increases, the retail price in the direct-sales model gradually increases, eventually surpassing that in the traditional model. This suggests that when the marketing input effect is small, marketing activities have limited impact on sales, and consumers rely more on the offline experience and services provided by retailers. In the traditional 4S store model, retailers share the fixed costs and service expenses, and to ensure profits, they mark up the product prices, leading to higher retail prices. In contrast, in the direct-sales model, the manufacturer sells directly to consumers, eliminating retail intermediaries and additional markups. Therefore, when the marketing input effect is low, the direct-sales model tends to have a lower price, attracting consumers through price competition.

As the marketing input effect increases, the market’s sensitivity to marketing activities also rises. Manufacturers in the direct-sales model need to invest more resources in market promotion, and these costs are ultimately reflected in the retail price. Manufacturers in the direct-sales model raise the price to pass on some of the marketing costs to consumers in order to maintain a reasonable profit margin. When the marketing input effect reaches a certain intensity, the manufacturer in the direct-sales model bears all the marketing costs and seeks to enhance the product’s added value (e.g., brand premium) to attract consumers. This leads to a gradual price increase, eventually surpassing that in the 4S store model. At this point, the direct-sales model relies on high prices and high-level marketing investment strategies to meet the demands of high-end consumers and market competition.

When the marketing input effect is small, the 4S store model results in higher prices because of retailers’ markup strategies. However, as the marketing input effect increases, the manufacturer in the direct-sales model raises the price gradually to cover higher marketing costs, eventually surpassing that in the 4S store model. This reflects the different pricing mechanisms and cost pass-through characteristics of the two models in varying market environments.

Figure 7 shows the impact of the marketing input cost coefficient (h) on the retail price (p). The figure demonstrates that when the terminal marketing input cost coefficient is low, the retail price in the traditional 4S store model is relatively low. As the marketing input cost coefficient increases, the retail price in the direct-sales model gradually decreases, eventually falling below that in the traditional model.

This indicates that when the marketing input cost coefficient is low, manufacturers and retailers invest relatively little in marketing activities, resulting in lower cost pressure. In the 4S store model, retailers can achieve sales with low marketing costs, using existing channels and customer resources, leading to lower retail prices. In the direct-sales model, manufacturers must bear the marketing expenses themselves, and even with low costs, they still need to cover basic operating expenses through retail prices, which makes the price relatively higher.

As the marketing input cost coefficient increases, the cost pressure in the direct-sales model decreases more quickly. When the marketing input cost coefficient increases, the cost per unit of marketing input rises significantly. In the direct-sales model, manufacturers manage their marketing budgets more efficiently, using strategies like precision marketing to improve efficiency and avoid excessive spending. As costs are optimized, product prices can gradually decrease. In the 4S store model, retailers, in order to ensure their own profits, pass the additional cost pressure onto the retail price, which leads to a gradual increase in prices. On the other hand, the direct-sales model does not involve retailers, and the entire supply chain becomes more streamlined, with greater flexibility in cost control. When faced with high marketing input cost coefficients, manufacturers can further reduce costs by internal optimization or eliminating unnecessary steps, thus lowering the retail price. In contrast, the 4S store model requires coordinating the profit distribution with retailers, and increasing costs could lead to price hikes, diminishing competitiveness.

In conclusion, when the marketing input cost coefficient is low, the 4S store model can meet market demand at a lower cost, resulting in lower retail prices. However, as the cost coefficient increases, the direct-sales model, with its flexible cost management and efficient pricing strategy, gradually reduces the retail price, while the 4S store model sees its price rise because of the uneven cost distribution. This shift reflects the different adaptability and competitive strategies of the two models in high-cost environments.The effect of the terminal marketing input effect and the marketing input cost coefficient on the retail price are explained in

Table 5. The impacts of the terminal marketing input effect and the marketing input cost coefficient on the retail price.

Factor	Scenario 1: Impact of an Increase in n	Scenario 2: Impact of an Increase in the Marketing Input Cost Coefficient (h)
Retail Price Gap	As n increases, the retail price in the direct-sales model gradually surpasses that in the traditional 4S store model.	As h increases, the retail price in the traditional 4S store model gradually rises, while the direct-sales model’s price gradually decreases.
Price Formation Mechanism	When the terminal marketing effect is small, the retail price in the traditional 4S store model is higher because of retailer markup, whereas the direct-sales model’s price is lower.	As the marketing input cost coefficient (h) increases, the direct-sales model lowers its price through cost optimization, while the 4S store model’s price increases because of rising costs.
Manufacturer Behavior	When the terminal marketing effect is small, the direct-sales model has a lower price to attract consumers through price competition; as the effect increases, the price gradually rises and surpasses that in the traditional model.	As h increases, manufacturers in the direct-sales model need to raise prices to cover marketing costs.
4S Store vs. Direct-Sales Price Changes	When the marketing effect is small, the 4S store model has a higher price; as the effect increases, the direct-sales model’s price surpasses that in the 4S store model.	As h increases, the retail price in the 4S store model rises because of retailer markup, while the direct-sales model lowers its price through cost management optimization.
Cost and Price Relationship	With a small effect, the direct-sales model attracts consumers through lower prices; with a large effect, the price increases.	As h increases, the retailer markup leads to a higher price in the 4S store model, while the direct-sales model lowers its price through internal optimization.

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6.4. The Impacts of the Marketing Input Effect, Terminal Input Effect, and Product’s Competitiveness on Manufacturers’ Profits

In this section, the impacts of the terminal marketing input effect, after-sales service-level effect on sales, and product’s competitiveness on manufacturers’ profits are analyzed. Without loss of generality, the parameters are set at $h=2, k=0.2, m=0.2, n=0.2, \beta =0.2$, and $j=0.2$. Figure 8 shows the sensitivity of the manufacturers’ profits to the marketing input effect (n).

Figure 8 illustrates that when the terminal marketing input effect is small, the profit of the manufacturer in the traditional 4S store model is higher. As the marketing input effect increases, the profit in the direct-sales model gradually increases, eventually surpassing that in the traditional model. When a certain critical value is reached, the profit of the manufacturer in the traditional 4S store model will again exceed that in the direct-sales model. This indicates that when the marketing input effect is small, the marginal return on terminal marketing is low. In the traditional 4S store model, the retailer shares the marketing costs, resulting in lower overall investments and higher profits for the manufacturer. In contrast, in the direct-sales model, the manufacturer bears all the marketing costs alone. However, because of the small marketing effect, the marginal returns on investments are insufficient to offset the high costs, resulting in lower profits.

As the marketing input effect increases, the direct-sales model gradually takes the lead because the sensitivity of the market to marketing activities increases, and the returns on marketing investments for manufacturers become higher. In the direct-sales model, the manufacturer directly controls marketing activities, allowing all the returns on investment to be converted to profits, without the need to share profits with retailers. This leads to a gradual increase in profits, eventually surpassing those in the 4S store model.

Once a critical value is reached, the 4S store model regains the advantage. This shows that when the marketing input effect reaches a certain threshold, excessive marketing investment leads to diminishing marginal returns, and the growth rate of sales begins to slow down. In the direct-sales model, the manufacturer continues to increase investments to maintain competitiveness. However, because the manufacturer bears all the marketing costs, the efficiency of the investments decreases, and profits decline. In contrast, in the traditional 4S store model, retailers share the marketing costs, reducing the cost pressure on the manufacturer and benefiting from the retailers’ marketing activities. Therefore, when the marketing effect is too large and marginal returns decline, the manufacturer’s profit in the 4S store model exceeds that in the direct-sales model.

When the marketing input effect is small, the 4S store model has an advantage because of cost sharing. When the effect increases, the direct-sales model gains an advantage because of higher returns on investments. However, when the marketing effect becomes too large and marginal returns diminish, the 4S store model regains the advantage because of cost sharing and resource distribution. This reflects the dynamic competitive characteristics of both models under different market conditions.

Figure 9 illustrates that when the effect of the after-sales service on sales is small, the profit of the manufacturer in the direct-sales model is higher. As the effect of the after-sales service on sales increases, the profit in the traditional 4S store model gradually increases and eventually surpasses that in the direct-sales model.

From Figure 9, it can be observed that when the effect of the after-sales service on sales is small, consumers pay less attention to the after-sales service. At this stage, the manufacturer in the direct-sales model can directly control the quality and cost of the after-sales service without the need to share or coordinate profits with retailers. In contrast, in the 4S store model, the manufacturer cannot directly manage the after-sales service at the terminal, requiring support from retailers and sharing certain management and coordination costs, which reduce profits.

As the effect of the after-sales service on sales increases, the consumer demand for high-quality after-sales service increases significantly. In this case, retailers in the 4S store model can use their decentralized service network to provide convenient services to consumers in more regions, meeting the market demand. Manufacturers, in this model, can expand after-sales service coverage at a lower cost by relying on the resources of retailers, thus enhancing the overall competitiveness and profit. Meanwhile, the cost pressure in the direct-sales model should not be overlooked. As the effect of the after-sales service on sales increases, manufacturers in the direct-sales model need to expand their own after-sales service network, which involves high fixed-asset investments, as well as personnel, technology, and logistics costs. As these costs rise and exceed a certain limit, profit growth in the direct-sales model will be significantly restricted, potentially even falling below that in the 4S store model. Therefore, when the after-sales service effect is small, the direct-sales model has an advantage because of its direct control and low-cost structure. However, as the effect increases, the 4S store model, relying on its distributed network and cost-sharing mechanism, gradually demonstrates superior service expansion capabilities and profit potential, eventually surpassing those of the direct-sales model. This reflects the adaptability differences of the two models under varying market demands.

Figure 10 illustrates that when the effect of the product’s competitiveness on sales is small, the profit of the manufacturer in the direct-sales model is higher. As the effect of the product’s competitiveness on sales increases, the profit in the traditional 4S store model gradually increases and eventually surpasses that in the direct-sales model.

This suggests that when the effect of the product’s competitiveness on sales is small, the market is less sensitive to the product’s performance or innovation, and consumers are more likely to consider the price and basic functionality. In this case, the direct-sales model allows the manufacturer to achieve higher profits at a lower cost by simplifying the supply chain structure, reducing product prices, and interacting directly with consumers. On the other hand, the division of labor in the 4S store model also contributes to this phenomenon. As the effect of the product’s competitiveness on sales increases, consumers become more focused on product innovation and technological differentiation. Retailers in the 4S store model can better convey product value through terminal promotions and services, such as test drives, product demonstrations, and personalized recommendations, which increase consumers’ willingness to purchase. Manufacturers rely on retailers to enhance the product’s market penetration, thus driving sales growth and increasing profits.

Within a certain range of product competitiveness effects, manufacturers need to make significant investments in R&D, production, and market education to maintain competitiveness. In the direct-sales model, the manufacturer bears all the costs and cannot share the sales pressure with retailers. Although product sales may increase, the high investment costs weaken profits, leading to poor profit performance. In the same range, in the 4S store model, retailers share a part of the market promotion and channel maintenance costs, and their own network helps to rapidly expand the product’s market share. Sales increase significantly, and the manufacturer’s cost pressure is relatively lower, leading to better profit performance.

When the effect of the product’s competitiveness on sales is small, the direct-sales model gains a profit advantage by simplifying the supply chain. As the effect increases, the 4S store model gradually gains a profit advantage because of its channel coverage and division of labor. At the same time, within a certain range, the direct-sales model’s profits decline because of high investment costs, while the 4S store model’s profits increase because of retailers sharing costs and driving sales. This reflects the different adaptive capabilities of the two models in response to costs and competition under different market conditions.The effect of the terminal marketing input effect, after-sales service-level effect, and product’s competitiveness on manufacturers’ profits are explained in

Table 6. The impacts of the terminal marketing input effect, after-sales service-level effect, and product’s competitiveness on manufacturers’ profits.

Variable	Small Effect (Direct Model’s Advantage)	Increased Effect (4S Store Model’s Advantage)	Change After Critical Value
Terminal Marketing Input Effect	The traditional 4S store model’s profits are higher because of shared marketing costs.	The direct model gradually prevails because of higher marketing returns.	An excessive marketing input effect causes diminishing returns, leading to 4S store profits exceeding those of the direct model again.
After-Sales Service-Level Effect	The direct model has lower costs because of direct control over after-sales services.	The 4S store model improves the service quality through distributed service networks, reducing costs and enhancing market competitiveness.	As the after-sales service effect strengthens, the 4S store model gradually prevails, while the direct model faces high fixed and operating costs.
Product Competitiveness Effect	The direct model lowers costs by simplifying the supply chain, leading to higher profits.	The 4S store model enhances product promotions and market penetration, gradually surpassing those in the direct model.	The 4S store model becomes dominant, especially with high R&D investments, as retailers share a part of the costs, reducing the manufacturers’ cost pressure.

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7. Conclusions

This paper comprehensively considers the impacts of the product’s competitiveness, after-sales service level, and terminal marketing investment on vehicle sales. The upstream parts suppliers bear the cost of the product’s competitiveness, the car manufacturers are responsible for after-sales service costs, and retailers are responsible for the cost of the marketing investment. By constructing corresponding models, the first supply chain adopts the traditional 4S store sales model, consisting of upstream parts suppliers, car manufacturers, and retailers. The upstream parts manufacturers are responsible for providing key components, such as batteries and motors; the car manufacturers are responsible for the overall manufacturing of the car’s body; and the retailers are responsible for the sales of the vehicles. The second supply chain follows the direct-sales model, consisting only of upstream parts suppliers and car manufacturers. This model does not have traditional car retailers; instead, the car manufacturers set up direct-sales stores to also serve as retailers. This study compares the manufacturer’s profits in these two different sales channels. The overall conclusions are as follows:

• Overall, the manufacturer’s direct-sales model benefits supply chain stakeholders by improving terminal marketing investment levels, after-sales service levels, and the product’s competitiveness;
• It is noteworthy that the manufacturer’s direct-sales model also has a significant effect on reducing the retail price, indicating that the direct-sales model not only has a substantial impact on the supply chain stakeholders but also is important for consumers;
• For manufacturers located at the center of the supply chain, it is essential to balance the relationship between costs and benefits and choose different channel models appropriately, based on product sales.

Based on the findings of this study and relevant management principles, the following management recommendations are proposed for practical implementation by enterprises:

• Optimize the supply chain structure: Advance a channel model that suits the specific enterprise; enhance control over marketing, after-sales service, and the product’s competitiveness; and improve the customer experience and brand loyalty. Establish efficient communication and resource integration capabilities to respond promptly to the market demand and create a competitive advantage for the company;
• Develop precise marketing strategies: Increase the positive impacts of marketing investment on sales. Enterprises should design targeted marketing plans based on target markets and consumer behavior. At the same time, they should carefully control the marketing investment cost coefficient to avoid diminishing returns caused by excessive investments. Using big-data analysis of consumer behavior can help to improve the investment–output ratio;
• Strengthen after-sales service management: High-quality after-sales service directly affects consumer satisfaction and loyalty. Enterprises should strengthen the coverage and consistency of their after-sales service network, especially in the direct-sales model, where service quality should be considered as a part of the brand’s core competitiveness. This can be achieved by optimizing service processes, improving service efficiency, and training employees;
• Enhance the product’s competitiveness: The product’s competitiveness is a key factor in the supply chain system and directly influences consumers’ purchasing decisions. Manufacturers should increase investment in research and development and maintain close collaboration with upstream suppliers to ensure product quality and technological innovation. In the electric vehicle industry, improving the battery range and intelligent capabilities will become the core focus;
• Balance costs and benefits: The costs of investments in terminal marketing, after-sales service, and the product’s competitiveness should be balanced with the benefits. Enterprises can use a multi-level performance evaluation system to monitor the relationship between inputs and returns in real-time, ensuring the maximization of the resource utilization.

This analysis provides insights into the promotion of electric vehicles, channel selection, and enhancement of after-sales services and the product’s competitiveness. It also offers reasonable suggestions for pricing strategy optimization and profit distribution issues.

The main contributions of this paper are twofold: (1) developing decision-making models for pricing, marketing investment, product competitiveness, and after-sales service in different NEV supply chain channels and providing theoretical guidance for production, sales, and service decisions; (2) Examining the effects of sensitivity and cost coefficients on the optimal decisions and profits, offering practical insights for supply chain participants.

This study focuses on a simple supply chain game with complete information. However, in real-life situations, supply chain structures and the external environment are often quite complex and influenced by many factors. Therefore, the factors considered and the assumptions made in this study have certain limitations. Future research will incorporate more real-world factors, such as the inventory-holding costs of retailers, into the supply chain model. Additionally, this study only considers the after-sales service costs of manufacturers, without addressing the after-sales service costs of other supply chain members. Future research will include the sharing proportion of after-sales service costs in the supply chain model.