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Research Article

Supply chain product quality control strategy in three types of distribution channels

Roles Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations School of Business, Shandong Normal University, Ji’nan, Shandong, China, School of Management, Shandong University, Ji’nan, Shandong, China

Published: April 22, 2020
https://doi.org/10.1371/journal.pone.0231699
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Based on a three-stage stackelberg dynamic game analysis, this paper constructs a product quality control strategy model for three types of distribution channels (direct channel, retail channel and mixed channel) in a three-echelon supply chain, which is composed of one manufacturer, one retailer and the final customer. This paper studies how to design a distribution channel strategy and provides a product quality control strategy. Furthermore, this paper analyzes three types of distribution channels strategy in the context of how they influence a manufacturer’s product quality decision and quality prevention strategy, a retailer’s product pricing decision and quality inspection strategy, and the final customer’s product demand decision. We compare the manufacturer’s product quality level, quality prevention effort level, wholesale price, direct sale price and the retailer’s quality inspection effort level, retail price in three types of distribution channels and determine the manufacturer’s and retailer’s expected profits function and the final customer’s consumer surplus. In addition, we introduce the distribution channels demand elasticity ratio to analyze the influence of determining the product quality control strategy. Most importantly, we conduct a numerical sample analysis that will prove the model’s effectiveness and indicate a specific application in practice.

Citation: Zhu L (2020) Supply chain product quality control strategy in three types of distribution channels. PLoS ONE 15(4): e0231699. https://doi.org/10.1371/journal.pone.0231699

Editor: Dejan Dragan, Univerza v Mariboru, SLOVENIA

Received: July 17, 2019; Accepted: March 31, 2020; Published: April 22, 2020

Copyright: © 2020 Lilong Zhu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript. No supporting information files.

Funding: This work was supported by the Humanities and Social Sciences Foundation of the Ministry of Education in China under grant No. 17YJA630147, Nature Science Foundation of Shandong Province under grant No. ZR2019MG017 and National Social Science Foundation of China under grant No. 13AGL012 to LZ.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

In recent years, with the rising of network economy and e-commerce, in addition to the traditional retail channel, more and more customers or consumers choose to purchase products from the internet channel(direct channel), which have become an important way of products sale. With the changing in customer or consumer buying behavior, more and more companies are beginning to redesign or rebuild their distribution channel structure(Chiang W et al. 2003 [ 1 ], Tsay A et al. 2004 [ 2 ], Kenji M 2017 [ 3 ], Yan W et al. 2018 [ 4 ]), Such as HP, Nike, Lenovo, in addition to focus on the traditional retail channel, have also opened up an internet channel(direct channel); Dell, MI has been focused on internet channel in the past, and now also began selling products in traditional retail channel; and Apple, Haier sell their products in the traditional retail channel and internet channel in the same time, which used a mixed channel structure. Many facts have proved that the mixed channel structure which composed of the traditional retail channel and the internet channel(direct channel), on the one hand can achieve better customer coverage and penetration(Jerath K et al. 2017 [ 5 ], Tian L et al. 2018 [ 6 ]), on the other hand may also lead to different distribution channels conflict, competition and imbalance(Chen J et al. 2017 [ 7 ], Lan Y et al. 2018 [ 8 ]).

Nowadays, more and more researchers focus on how to design a distribution channels strategy and determine a product quality control strategy in different types of distribution channels in a three echelon supply chain that is composed by one manufacturer, one retailer and the final customer, which have become one of hot research fields in supply chain management. However, nowadays the research field has three potential systemic problems: first of all, how to design different types of distribution channels structure in a three echelon supply chain(direct channel, retail channel and mixed channel); what’s more, the different types of distribution channels structure in supply chain how to influence the manufacturer’s product quality decision and quality prevention strategy, the retailer’s product pricing decision and quality inspection strategy, and the final customer’s product demand decision; above all, how to influence the manufacturer’s and retailer’s expected profits function and the final customer’s consumer surplus, and how to determine a product quality control strategy in order to eliminate “channel conflict” and “free-riding behavior”. All of these problems and difficulties have not been fully resolved, which are also important research directions for current researchers.

In this paper, we will construct a distribution channel strategy model in a three echelon supply chain that is composed of one manufacturer, one retailer and a final customer based on a three-stage stackelberg dynamic game. Furthermore, we will introduce the distribution channel demand elasticity ratio and investigate how to craft a product quality control strategy in three types of distribution channels (direct channel, retail channel, and mixed channel), which will eliminate the influence of “channel conflict” and “free-riding behavior”. Most important, we will determine the manufacturer’s product quality level, quality prevention effort level, wholesale price, direct sale price, and the retailer’s product quality inspection effort level and retail price, the manufacturer’s and retailer’s expected profits function, and the final customer’s consumer surplus. Then, we will conduct a numerical sample analysis that will indicate a specific application in practice.

2 Related literature

This paper is chiefly related to three streams of literature. The first stream is the research on how to design a distribution channels structure strategy, the different types of distribution channels structure and how to influence the product quality decision in a supply chain. Yunchuan Liu (2011) [ 9 ] established a channel model to analyze the benefits of competitive upward channel decentralization. Anastasios X (2012) [ 10 ] studied how to apply optimal newsvendor policies for a dual-sourcing channel in a supply chain. Hongyan Shi et al. (2013) [ 11 ] analyzed consumer heterogeneity and product quality and how to influence the coordination of distribution channels. Guangye Xu et al. (2014) [ 12 ] constructed a two-way revenue contract to coordinate a dual-channel supply chain. Salma Karray (2015) [ 13 ] investigated how vertical strategy and horizontal strategy influence cooperative promotions in the distribution channel. Kenji M (2016) [ 14 ] investigated the optimal product distribution strategy for a manufacturer that used dual-channel supply chains. Kinshuk J et al. (2017) [ 15 ] discussed how to make a product quality level decision in a distribution channel with demand uncertainty. Liu Yan et al. (2018) [ 16 ] provide insights on how market size uncertainty affects the optimal quality and quantity provision in distribution channels. Ranjan A and Jha J (2019) [ 17 ] investigate the pricing strategies, green quality and coordination mechanism between the members in a dual-channel supply chain.

The second stream pertains to designing a product quality contract and establishing a quality incentive mechanism in a supply chain. Peng Ma et al. (2013) [ 18 ] created a product quality contract design for two-stage supply chain coordination through integrating manufacturer-quality and retailer-marketing efforts. Jie Zhang et al. (2014) [ 19 ] discussed a strategic pricing method with reference effects in a quality competitive supply chain. Raaid B et al. (2016) [ 20 ] analyzed the effect of adopting a dual-channel on the performance of a two-level supply chain. Chen J et al. (2017) [ 7 ] consider the supply chains can be centralized or decentralized, and demonstrate that quality improvement can be realized when a new channel is introduced. Li Wei and Chen Jing (2018) [ 21 ] develop game-theoretic models in which the retailer sells a product in two quality-differentiated brands to demonstrate that the quality difference. Zhang J et al. (2019) [ 22 ] use an analytical model to study the interrelationship between a platform’s contract choice and a manufacturer’s product quality decision.

The third stream of related literature concerns research on product quality risk sharing and the quality strategy of distribution channels in a supply chain. Zhu Lilong et al. (2011) [ 23 ] explored manufacturers’ moral hazard strategy and quality contract design in a two-echelon supply chain. Cinzia B et al. (2012) [ 24 ] discussed product quality-driven innovation with the design of a quality control contract. Christina Wong et al. (2013) [ 25 ] investigated the combined effects of internal and external supply chain integration on product quality innovation. Rui H and Lai G (2015) [ 26 ] investigated the deferred payment and inspection mechanisms for mitigating supplier’s product quality risk. Xiao T and Jim Shi (2016) [ 27 ] studied a manufacturer marketing a product and considered the pricing and channel priority strategies of dual-channel supply chain. Wang S.J et al. (2017) [ 28 ] explore interaction of channel structure with price-and quality-based competition between two manufacturers. Lin T and Jiang B (2018) [ 29 ] discussed the effects of consumer-to-consumer product sharing risk and profit on different distribution channel structure.

In this paper, first of all, we will introduce the distribution channel demand elasticity ratio and investigating how to construct a product quality control strategy model and channel coordination in three types of distribution channels (direct channel, retail channel, and mixed channel); what’s more, we consider the manufacturer’s product quality decision and quality prevention strategy, the retailer’s product pricing decision and quality inspection strategy, and the final customer’s product demand decision in a three-echelon supply chain; above all, we also establish a product quality control strategy model in three types of distribution channels to eliminate the influence of “channel conflict” and “free-riding behavior”, which will improve the manufacturer’s and retailer’s expected profits and the final customer’s consumer surplus.

The remainder of our paper is organized as follows: in section 3, we describe the model and the basic assumption; in section 4, we consider the product quality strategy in the direct channel and determine the first-best contact parameters; in section 5, we investigate the product quality strategy in the retail channel and establish the manufacturer’s and retailer’s stackelberg “leader-follower” quality control model, and we compare the contract parameter differences with the direct channel. In section 6, we investigate the product quality strategy in the mixed channel that includes a retail channel and a direct channel scenario simultaneously, and in section 7 we present a numerical example analysis to verify our model results. Finally, we provide the research conclusions and direction for future research.

3 The model and assumption

The final customer’s quality utility is θq i , and θ denotes the type of final customer; then, we assume θ ~ U [ a , b ] uniform distribution, i.e. a is the final customer lower limit of distribution quantity, b is the final customer upper limit of distribution quantity, and the corresponding final customer’s consumer surplus is ( θq i – p i ).

In this paper, the manufacturer will determine the three types of distribution channels including a direct channel, a retail channel and a mixed channel. The three-stage stackelberg game is in the following order: in stage one, the manufacturer determines the product quality level in a different distribution channel and determines the product quality prevention effort level; in stage two, the manufacturer determines the wholesale price in a retail channel or the direct sale price in a direct channel; and in stage three, the retailer determines the product quality inspection effort level and the retail price.

And then, the three types of distribution channels decision system is described as Fig 1 .

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https://doi.org/10.1371/journal.pone.0231699.g001

4 Product quality strategy in direct channel

Proposition 1 In the direct channel, with the final customer’s product demand price elasticity decreases, the manufacturer’s product quality level and direct sale price will increase, and the quality prevention effort level will also increase. In this scenario, the manufacturer’s expected profits’ function is concave; i.e. an optimal product quality level exists that will to be maximum. Then, the final customer’s consumer surplus will increase with the decreasing in the demand price elasticity.

5 Product quality strategy in retail channel

Proposition 2 In the retail channel, with the final customer’s product demand price elasticity decreases, the manufacturer’s product quality level and wholesale price will increase, and the retailer’s product retail price will also increase. In comparison with the direct channel scenario, the product quality level and the retail price will be much higher.

Based on proposition 2, we conclude that the manufacturer’s product quality level in the retail channel will be much higher than that in the direct channel scenario; the retailer’s retail price will be greater than the wholesale price, which will be also much higher than the manufacturer’s sales price in the direct channel scenario.

Based on corollary 2.1, we can infer that the manufacturer’s quality prevention effort level in the retail channel will be greater than the retailer’s quality inspection effort level, which will also be greater than the manufacturer’s quality prevention effort level in the direct channel.

Based on corollary 2.2, we conclude that the manufacturer’s expected profits in the retail channel will be greater than that in the direct channel scenario.

Corollary 2.3. CS R* ( q * R , p * R ) > CS D* ( q * D , p * D ).

Based on corollary 2.3, we conclude that the final customer’s consumer surplus in the retail channel will be greater than that in the direct channel.

6 Product quality strategy in mixed channel

Based on proposition 3, we conclude that, in the mixed channel, the manufacturer’s product quality level will be greater than which in the direct channel and less than which in the retail channel. In addition, the wholesale price will decrease, the manufacturer’s direct sale price will increase and the retailer’s retail price will decrease.

Based on corollary 3.1, we conclude that, in the mixed channel, the manufacturer’s quality prevention effort level will be greater than that in the retail channel and the direct channel, and the retailer’s quality inspection effort level will be greater than that in the retail channel, which will effectively eliminate the “free-riding behavior.

Based on corollary 3.2, we infer that, in the mixed channel, the manufacturer’s expected profits will be less than that in the retail channel but will be greater than the profits in the direct channel; additionally, the retailer’s expected profits will be greater than that in the retail channel.

Based on corollary 3.3, we can infer that, in the mixed channel, the final customer’s consumer surplus will be greater than that in the retail channel and the direct channel.

7 Numerical example

In this paper, we assume a manufacturer that sells computer components through a retailer (retail channel) or internet online system (direct channel) or through a mixed channel to the final customer. The parameters are described as follows: k = 2, η m = η r = 1, α = 60, θ ~ U (0,60), T = 120, ε = {2.5, 3.0}, β R ~ [2.5, 3.5]. We use numerical computing by Matlab 7.0 and obtain the results, as shown in Tables 1 – 4 .

https://doi.org/10.1371/journal.pone.0231699.t001

https://doi.org/10.1371/journal.pone.0231699.t002

https://doi.org/10.1371/journal.pone.0231699.t003

https://doi.org/10.1371/journal.pone.0231699.t004

Based on Table 1 , we conclude that, in the direct channel, the manufacturer’s product quality level, the quality prevention effort level and the direct sale price will increase, and the customer’s consumer surplus will also increase with the decreasing in the product demand price elasticity, which will benefit the manufacturer and the final customer when the distribution channel demand elasticity ratio decreases.

Based on Table 2 , we can infer that, in the retail channel, the final customer’s consumer surplus will increase with the increasing in product demand price elasticity. Compared with the direct channel, the manufacturer’s product quality level, the quality prevention effort level and the expected profits will increase, and the customer’s consumer surplus will also increase.

Based on Tables 3 and 4 , Figs 2 and 3 , we conclude that in the mixed channel compared with that in the direct channel and the retail channel, the manufacturer will determine a transfer payment to eliminate channel conflict, the manufacturer’s quality prevention effort level and retailer’s quality inspection effort level will increase, which will effectively eliminate the “free-riding behavior”, the manufacturer’s expected profits will be higher than which in the direct channel and less than which in the retail channel; in addition, the retailer’s expected profits and the final consumer surplus will increase.

https://doi.org/10.1371/journal.pone.0231699.g002

https://doi.org/10.1371/journal.pone.0231699.g003

8 Conclusions and future research

In this paper, we construct a product quality control model of three types of distribution channel (direct channel, retail channel and mixed channel) in a three-echelon supply chain, which is comprised by one manufacturer, one retailer and the final customer, and then we discuss how to design a distribution channel strategy and craft a quality control strategy. Furthermore, our paper analyzes three types of distribution channel strategies regarding how to influence the manufacturer’s product quality decision and quality prevention strategy, the retailer’s product pricing decision and quality inspection strategy, and the final customer’s product demand decision. We compare the product quality level in three types of distribution channels and solve the manufacturer’s and retailer’s expected profits functions and the final customer’s consumer surplus. In addition, we introduce the distribution channel demand elasticity ratio to analyze the influence of determining the product quality control strategy.

Our paper demonstrates that, in the direct channel, the manufacturer’s product quality level, the quality prevention effort level and the direct sale price will increase, and the customer’s consumer surplus will also increase with the decreasing in the products demand price elasticity. In addition, in the retail channel which is compared with the direct channel scenario, the manufacturer’s product quality level, the wholesale price, the quality prevention effort level and expected profits will increase, and the retailer’s retail price, the quality inspection effort level and the customer’s consumer surplus will be much higher. In the mixed channel, the manufacturer will determine the transfer payment to eliminate channel conflict, the manufacturer’s quality prevention effort level and the retailer’s quality inspection effort level will increase, which will effectively eliminate the “free-riding behavior”. In addition, the manufacturer’s expected profits will be higher than that in the direct channel and less than that in the retail channel. The retailer’s expected profits and the final consumer surplus will also increase, and our conclusions will be a strong complement to the research field. Most importantly, we conduct a numerical sample analysis that demonstrates the model’s effectiveness and the conclusions’ correctness and will also indicate a specific application in practice.

In further research, we will assume that the manufacturer’s quality prevention effort level and the retailer’s quality inspection effort level have incomplete information regarding how to craft a product quality control strategy in three types of distribution channels; then, we will also attempt to construct a multi-stage, repeat and asymmetry information dynamic game model and analyze the distribution channel strategy regarding how to influence the manufacturer’s and retailer’s expected profits function, the final customer’s consumer surplus and social welfare.

Acknowledgments

The authors are grateful to the referees for their valuable comments and their helps on how to improve the quality of our paper.

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Supply chain product quality control strategy in three types of distribution channels

1 School of Business, Shandong Normal University, Ji’nan, Shandong, China

2 School of Management, Shandong University, Ji’nan, Shandong, China

Associated Data

All relevant data are within the manuscript. No supporting information files.

1 Introduction

2 Related literature

3 The model and assumption

In this paper, we establish a three-echelon supply chain structure that consists of a risk-neutral manufacturer and retailer, and the final customer. The manufacturer first makes decision of the product quality. q i is the manufacturer’s product quality level; furthermore, i ∈ { D , R , MC } denote the direct channel, the retail channel and the mixed channel respectively. The product quality cost function is C i ( q i ) = k q i 2 / 2 ( k is the manufacturer’s production technology elasticity); so, we assume C i ' ( q i ) > 0 , C i " ( q i ) > 0 and C i (0) = C i ′(0) = 0, C i ′(+∞) = +∞, i.e. C i ( q i ) is the convex function of increasing marginal cost. λ m is the manufacturer’s product quality prevention effort level, and λ m ∈[0,+∞); then, we can obtain that the manufacturer’s product quality prevention level is ( 1 − e − λ m ) . Furthermore, ( 1 − e − λ m ) ∈ [ 0 , 1 ] , and the corresponding manufacturer’s quality prevention cost function is ( 1 − e − λ r ) C m ( λ m ) = η m λ m , η m is the manufacturer’ quality prevention cost elasticity. The parameter w is the manufacturer’s wholesale price, P D is the direct sale price in a direct channel, and T is the manufacturer’s transfer payment to the retailer in order to eliminate the manufacturer’s and retailer’s channel conflict.

The retailer purchases the product from the upstream manufacturer and makes decision of the product quality inspection. λ r is the retailer’s quality inspection effort level, and λ r ∈ [ 0 , + ∞ ) ; then, the retailer’s product quality inspection level is ( 1 − e − λ r ) . Furthermore, ( 1 − e − λ r ) ∈ [ 0 , 1 ] , and the corresponding retailer’s quality inspection cost function is ( 1 − e − λ r ) C r ( λ r ) = η r λ r , η r is the retailer’s quality inspection cost elasticity. The parameter p R is the retailer’s retail price.

The final customer’s product demand function will be D i ( q i ) = α − β i p i / q i ; α denotes the market maximum demand, and β i is the product demand price elasticity.

And then, the three types of distribution channels decision system is described as Fig 1 .

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Object name is pone.0231699.g001.jpg

4 Product quality strategy in direct channel

In the direct channel, the manufacturer sells its product to the final customer directly through an internet or online ordering system; then, the manufacturer determines the product quality level, the quality prevention effort level and the direct sale price. Therefore, the manufacturer’s expected profits’ function model is as follows.

The manufacturer’s decision variables are the product quality level q D , the quality prevention effort level λ m and the direct sale price p D .

Proof. Based on the stackelberg game analysis, this paper will use the backward induction method to solve the equation. Thus, using the first-order and second-order optimal condition with respect to p D in formula (1) yields the following:

Then, we substitute Eq (4) into formula (1) and use first-order and second-order optimal conditions with respect to λ m , which yields the following:

Therefore, we derive that

Based on the above analysis, we conclude that p D and λ m D is the manufacturer’s first-best sales price, and the quality effort level occurs with a direct channel.

Thereafter, we use the first-order and second-order optimal conditions with respect to q D in Eq (5) , which yields the following:

Combine Eqs (9) and (10) , we derive that

We substitute Eq (11) into Eqs (4) and (7) , respectively, to obtain the following

Thereafter, we substitute Eqs (11) and (12) into formula (1), and we obtain that

Therefore, we can describe the final customer’s consumer surplus as follows:

Based on proposition 1, we conclude that, in the direct channel, the manufacturer’s product quality level, the direct sale price and the quality prevention effort level will increase with a decrease in the final customer’s product demand price elasticity. In addition, the manufacturer’s expected profits function is concave, and q D * and ∏ M D * ( q D * ) is the manufacturer’s optimal quality level and maximum expected profits. Thereafter, the final customer’s consumer surplus will increase with the decrease in demand price elasticity.

5 Product quality strategy in retail channel

In the retail channel, the manufacturer sells product to the retailer, which will determine a product quality inspection strategy and then sell the product to the final customer. The manufacturer determines the product quality level, the quality prevention effort level and the wholesale price, and the retailer determines the quality inspection level and the retail price. Therefore, the manufacturer’s and retailer’s stackelberg “leader-follower” control model is described as follows:

Therefore, formula (15) is the manufacturer’s expected profits function; formula (16) is the retailer’s expected profits function.

Proof. In this paper, we still use the backward induction method to solve the model. Thus, we use the first-order optimal condition with respect to p R and λ r in formula (16), which yields the following:

We substitute Eq (19) into formula (15) and use the first-order optimal condition with respect to w , and we obtain that

We substitute Eq (21) into formula (20) and use the first-order optimal condition with respect to λ m , which yields the following:

Thereafter, we use first-order and second-order optimal conditions with respect to q R in formula (22), which yields the following:

Therefore, we obtain that

We substitute Eq (25) into formula (21) and (23) and rearrange as follows:

Therefore, we substitute Eqs (25) and (26) into formula (19) and rearrange as follows:

Based on the assumption condition and the Y.C Liu (2011) and Salma Karray (2015) research results, we assume β D = εβ R , where ε is the demand elasticity ratio in a different distribution channel condition and ε >1. The demand price elasticity for the direct channel will be greater than for the retail channel; i.e., the final customers are more sensitive to price in the direct channel. Samar K.M (2008) earlier had proved that η m = η r .

We compare Eqs (25) , (26) and (27) with Eqs (11) and (12) , respectively, as follows:

Corollary 2.1 λ m R * > λ r R * > λ m D * ( ε > 2).

Proof. We compare the manufacturer’s quality prevention effort level and the retailer’s quality inspection effort level in the retail channel with that in the direct channel; then, we determine that

Corollary 2.2 ∏ M R * ( q R * ) > ∏ M D * ( q D * ) .

Proof. We substitute formula (25), (26) and (27) into Eqs (15) and (16) ; then, we find that

Therefore, we compare formula (28) with (29) to obtain that

Based on corollary 2.2, we conclude that the manufacturer’s expected profits in the retail channel will be greater than that in the direct channel scenario.

Corollary 2.3. CS R* ( q * R , p * R ) > CS D* ( q * D , p * D ).

Proof. The final customer’s consumer surplus in the retail channel will be described as follows

Therefore, we compare formula (30) with (14) to obtain

We obtain C S R * > C S D *

Based on corollary 2.3, we conclude that the final customer’s consumer surplus in the retail channel will be greater than that in the direct channel.

6 Product quality strategy in mixed channel

In the mixed channel, the manufacturer may sell a product to the final customer directly through an online ordering system or sell wholesale to the retailer who will continue to sell the product to the final customer. Thereafter, the manufacturer will determine a transfer payment to the retailer to eliminate the channel conflict. Therefore, the manufacturer determines the product quality level, the quality prevention effort level, the wholesale price and the direct sale price, and the retailer determines the quality inspection level and the retail price. The manufacturer and the retailer’s stackelberg “leader-follower” control model can be described as follows:

Therefore, formula (31) is the manufacturer’s expected profits function, formula (32) is the retailer’s expected profits function, and T is the transfer payment.

Proposition 3 In the mixed channel, in comparison with a direct channel and a retail channel scenario, the manufacturer’s product quality level will be greater than which in the direct channel and less than which in the retail channel, i.e. q D * < q M C * ≤ q R * , the wholesale price will decrease, i.e. W M C * ≤ w R * , and the manufacturer’s direct sale price will increase, i.e. p D M C * > p D * . In addition, the retailer’s retail price will decrease, i.e. p R M C * ≤ p R * .

Proof. We still use the backward induction method to solve the model. Thus, we use the first-order optimal condition with respect to p R and λ r in formula (32), which yields the following:

We substitute formula (36) into formula (31), which yields the following

Therefore, we use the first-best condition w and p D in formula (37) and obtain that

Thereafter, we substitute formula (38) into formula (37), which yields the following

We use the first-best condition λ m and first-best and second-best condition q M C and obtain that

Therefore, we substitute formula (42) into formula (36), (38) and (40) and obtain that

We compare the product quality level, the wholesale price, the sales price and the retail price in the mixed channel, with which in the direct channel and the retail channel scenario yields the following:

Then we can infer that w M C * ≤ w R * , P D * < p D M C * and p R M C * ≤ p R * .

Corollary 3.1 λ m M C * ≥ λ m R * > λ m D * , λ r M C * ≥ λ r R * .

Proof. We compare the manufacturer’s quality prevention effort level and the retailer’s quality inspection effort level in the mixed channel with which in the direct channel and the retail channel scenario and obtain the following:

By Corollary 2.1, we obtain that λ m R * > λ m D * ; then, we can infer that λ m M C * ≥ λ m R * > λ m D * .

Corollary 3.2 ∏ M R * ( q R * ) > ∏ M M C * ( q M C * ) > ∏ M D * ( q D * ) , ∏ R M C * ( q M C * ) > ∏ R R * ( q R * ) .

Proof. We substitute formula (42), (43) and (44) into Eqs (31) and (32) , which yields the following:

Therefore, we compare formula (49) and (50) with formula (13), (28) and (29) and obtain that

Corollary 3.3 C S M C * ( q M C * ) > C S R * ( q R * ) > C S D * ( q D * ) .

Proof. The final customer’s consumer surplus in a mixed channel will be described as follows

Thereafter, we compare formula (52) with formula (14) and (30) and obtain that

We find that C S M C * > C S R * > C S D * .

Based on corollary 3.3, we can infer that, in the mixed channel, the final customer’s consumer surplus will be greater than that in the retail channel and the direct channel.

7 Numerical example

Based on Tables Tables3 3 and and4, 4 , Figs Figs2 2 and and3, 3 , we conclude that in the mixed channel compared with that in the direct channel and the retail channel, the manufacturer will determine a transfer payment to eliminate channel conflict, the manufacturer’s quality prevention effort level and retailer’s quality inspection effort level will increase, which will effectively eliminate the “free-riding behavior”, the manufacturer’s expected profits will be higher than which in the direct channel and less than which in the retail channel; in addition, the retailer’s expected profits and the final consumer surplus will increase.

An external file that holds a picture, illustration, etc.
Object name is pone.0231699.g002.jpg

8 Conclusions and future research

Acknowledgments

The authors are grateful to the referees for their valuable comments and their helps on how to improve the quality of our paper.

Funding Statement

This work was supported by the Humanities and Social Sciences Foundation of the Ministry of Education in China under grant No. 17YJA630147, Nature Science Foundation of Shandong Province under grant No. ZR2019MG017 and National Social Science Foundation of China under grant No. 13AGL012 to LZ.

Data Availability

PLoS One. 2020; 15(4): e0231699.

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Supply Chain Product Quality Control Strategy in Three Types of Distribution Channels

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Editor's initial comments to the paper:

The paper deploys a three-stage Stackelberg dynamic game analysis and constructs a product quality control strategy model for three types of distribution channels (direct channel, retail channel, and mixed channel) in a three-echelon supply chain, which is composed of one manufacturer, one retailer and the final customer. Furthermore, the paper studies how to design a distribution channel strategy and provides a product quality control strategy. Here, three types of distribution channels strategy in the context of how they influence a manufacturer’s product quality decision and prevention strategy, a retailer’s product pricing decision and quality inspection strategy, and the final customer’s product demand decision, are analyzed. The subject of this research is up-to-date and fundamentally interesting for scholars from the field of SCM and OR.

The paper, in general, roughly satisfies all rigor requirements that are demanded from Plos One. The red clue remains more or less consistent all over the paper, while the derivations of equations seem to be adequate.

However, the editor has detected some major issues based on his own perception, the review of the additional reviewer, and the Plos One criteria for the papers, which should be corrected prior to the further publishing process.

Comments of the AE:

1. It is not clear enough emphasized what the main contribution of the paper is, i.e., what has been done new, if compared with the work of the other researchers. Here, the borderline should be clearly highlighted, while the main contributions (novelties, originality) should be more explicitly given in the introduction and other places, where necessary.

2. Figure 1 should be improved in the sense of informative content meaning that the reader immediately understands the main point without even looking at the corresponding text.

Comments regarding the Plos One criteria for the papers:

There exists a certain doubt whether the paper meets PLOS ONE criteria for papers that describe new methods or software for applications. Specifically, these reports must meet the criteria of utility, validation, and availability, which are described in detail at http://journals.plos.org/plosone/s/submission-guidelines#loc-methods-software-databases-and-tools . On this basis, the AE warns the authors to reconsider these facts and give the rationale and/or explanation fortified with the facts, let be in the paper itself or in the answers to reviewers or cover letter.

Comments of Reviewer #1

Summary (see reviewer 1 comments.pdf):

Additional comments are available in the file: reviewer 1 comments.pdf.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Partly

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Reviewer #1: Yes

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Reviewer #1: No

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Reviewer #1: 1. The language of the article should be more concise and accurate.

2. It is better to complement the differences between this study and the existing research literature.

3. The conclusions of the study for the the objectives mentioned in the abstract as ‘This paper studies how to design a distribution channel strategy and provides a product quality control strategy’ are not very clear.

4. In this article, we can not see how consumer utility and consumer behavior affect product market demand.

5. In Mixed Channel, how does the product demand between the two channels affect each other?

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Submitted filename: Editor - initial review.docx

Submitted filename: Reviewer 1 Comments.pdf

Author response to Decision Letter 0

Responses to Editors and Reviewers

Dear Professor Dejan Dragan and Reviewers,

Thank you very much for your suggestions and critical comments about our paper submitted to PLOS ONE (Manuscript ID: PONE-D-19-20228). The revised title is “Supply Chain Product Quality Control Strategy in Three Types of Distribution Channels”.

We are also thankful to the reviewers for their critical reading and valuable comments on the manuscript. Those comments were very helpful for providing direction for our further studies. We have tried our best to revise our manuscript according to the comments. Attached, please find the revised version, which we would like to resubmit for your kind consideration. The main revised parts are marked in blue in the paper. The following is a detailed explanation how we have complied with the editor’s and reviewers’ suggestions.

Responds to the editor’s comments:

Comment #1:

Response: Thank you very much for the highly praises and the valuable suggestions. Our paper constructs a product quality control strategy model for three types of distribution channels (direct channel, retail channel and mixed channel) in a three-echelon supply chain which use a three-stage stackelberg dynamic game analysis, and then, analyzes three types of distribution channels strategy in the context of how they influence a manufacturer’s product quality decision and quality prevention strategy, a retailer’s product pricing decision and quality inspection strategy, and the final customer’s product demand decision. Most importantly, we conduct a numerical sample analysis that will prove the model’s effectiveness and indicate a specific application in practice.

Comment #2:

It is not clear enough emphasized what the main contribution of the paper is, i.e., what has been done new, if compared with the work of the other researchers. Here, the borderline should be clearly highlighted, while the main contributions (novelties, originality) should be more explicitly given in the introduction and other places, where necessary.

Response: Thank you very much for the valuable suggestions. We rewrite and emphasize the paper’s main contributions in every paragraph in “2 Related Literature”, just like: (1) Our paper differs from the existing literature by introducing the distribution channel demand elasticity ratio and investigating how to construct a product quality control strategy model and channel coordination in three types of distribution channels (direct channel, retail channel, and mixed channel) by providing a new perspective. (2) Our model contributes to the product quality control strategy research by constructing a distribution channel model in a three-echelon supply chain which is composed of one manufacturer, one retailer and the final customer based on a three-stage stackelberg dynamic game. Then, the model considers the manufacturer’s product quality decision and quality prevention strategy, the retailer’s product pricing decision and quality inspection strategy, and the final customer’s product demand decision. (3) we also establish a product quality control strategy model in three types of distribution channels to eliminate the influence of “channel conflict” and “free-riding behavior”, which will improve the manufacturer’s and retailer’s expected profits and the final customer’s consumer surplus.

Comment #3:

Figure 1 should be improved in the sense of informative content meaning that the reader immediately understands the main point without even looking at the corresponding text.

Response: Thank you very much for the valuable suggestions. We redraw the Figure 1 Three types of distribution channels decision system which improve in the sense of informative content meaning that the reader immediately understands the main point of our paper.

Figure 1 Three types of distribution channels decision system

Responds to the reviewer’s comments:

The language of the article should be more concise and accurate.

Response: Thank you very much for the valuable suggestions. We made necessary revisions and language editing in the manuscript according to your suggestions, and the English has also been edited by Wiley English Language Editing Services.

It is better to complement the differences between this study and the existing research literature.

The conclusions of the study for the objectives mentioned in the abstract as ‘This paper studies how to design a distribution channel strategy and provides a product quality control strategy’ are not very clear.

Response: Thank you very much for the valuable suggestions. With the changing in customer or consumer buying behavior, more and more companies are beginning to redesign or rebuild their distribution channel structure, Such as HP, Nike, Lenovo, in addition to focus on the traditional retail channel, have also opened up an internet channel(direct channel); Dell, MI has been focused on internet channel in the past, and now also began selling products in traditional retail channel; and Apple, Haier sell their products in the traditional retail channel and internet channel in the same time, which used a mixed channel structure. In our paper, we will construct a distribution channel strategy model in a three echelon supply chain that is composed of one manufacturer, one retailer and a final customer based on a three-stage stackelberg dynamic game. Furthermore, we will introduce the distribution channel demand elasticity ratio and investigate how to craft a product quality control strategy in three types of distribution channels (direct channel, retail channel, and mixed channel), Most important, we will determine the manufacturer’s product quality level, quality prevention effort level, wholesale price, direct sale price, and the retailer’s product quality inspection effort level and retail price, the manufacturer’s and retailer’s expected profits function, and the final customer’s consumer surplus.

Comment #4:

In this article, we can not see how consumer utility and consumer behavior affect product market demand.

Response: Thank you very much for the valuable suggestions. In “3 The Model and Assumption”, we describe that the final customer’s quality utility is, and denotes the type of final customer, is the manufacturer’s product quality level; furthermore, denote the direct channel, the retail channel and the mixed channel respectively; then, we assume ～ uniform distribution, and the corresponding final customer’s consumer surplus is.

Therefore, in direct channel, we can describe the final customer’s consumer surplus as = = (14)

In retail channel, we can describe the final customer’s consumer surplus as

In mixed channel, we can describe the final customer’s consumer surplus as

Comment #5:

In Mixed Channel, how does the product demand between the two channels affect each other?

Response: Thank you very much for the valuable suggestions. In our paper, we describe the mixed channel that the manufacturer sells their products in the traditional retail channel by the retailer, in the same time, the manufacturer sells their products to the final customer in direct channel (internet channel). The final customer’s product demand function will be ; denotes the market maximum demand, and is the product demand price elasticity. Therefore, In mixed channel, the manufacturer and the retailer’s stackelberg “leader-follower” control model can be described as follows:

The product demand in retail channel as

The product demand in direct channel as

is the manufacturer’s product quality level in mixed channel, is the product demand price elasticity in retail channel, is the product demand price elasticity in direct channel, is the manufacturer’s wholesale price, is the retailer’s retail price, is the direct sale price in a direct channel, the manufacturer’s product quality cost function in direct channel is .

We have tried our best to improve the manuscript and made some substantial changes and necessary deletions according to the editors’ and reviewers’ comments. We earnestly appreciate the editors’ and reviewers’ professional work and hope that the corrections will make our manuscript suitable for publication in PLOS ONE. We are looking forward to receiving comments from reviewers in the future.

Once again, thank you very much for your valuable comments and suggestions.

Best wishes.

Yours sincerely,

Lilong Zhu (Correspondence)

E-mail: moc.621@8002gnoliluhz

Tel: + 86 13853193366, Fax: + 86 531 8618 2769

School of Management, Shandong University, Ji’nan Shandong, 250100, China

College of Business, Shandong Normal University, Ji’nan 250014, Shandong, China

Submitted filename: Response to Reviewers.doc

Decision Letter 1

16 Mar 2020

PONE-D-19-20228R1

==============================

Please see below

We would appreciate receiving your revised manuscript by 29.3.2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #3: Yes

3. Has the statistical analysis been performed appropriately and rigorously?

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #2: Really, the paper has been greatly improved as all comments provided by all other reviewers in the first round.

Reviewer #3: 1. It is a very nice thing that the new literature review part is added. But it seems to be to be too detailed. It will be better that the author just mentioned 2~3 related papers and on top of that, provide their own contributions. Moreover, those contributions should be more precise, instead of "..by providing a new

perspective.", the author need to be more specific about what exactly the new perspective is.

2. The model and assumption need to be more concise and concrete. For example, when the author mentioned the customer type follow a U[a,b], it needs more clarify about the value means and the types they are referring to. It will be better if the author check the model and assumption part one more time and provide additional explanation if necessary

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Reviewer #2: Yes: Ali Wagdy Mohamed

Reviewer #3: No

Author response to Decision Letter 1

25 Mar 2020

Thank you very much for your suggestions and valuable comments about our paper submitted to PLOS ONE (Manuscript ID: PONE-D-19-20228R1). The manuscript’s title is “Supply Chain Product Quality Control Strategy in Three Types of Distribution Channels”.

We are also thankful to the reviewers for their critical reading and valuable comments on the manuscript. Those comments were very helpful for providing direction for our further studies. We have tried our best to revise our manuscript according to the comments. Attached, please find the revised version, which we would like to resubmit for your kind consideration. The main revised parts are marked in red in the paper. The following is a detailed explanation how we have complied with the editor’s and reviewers’ suggestions.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

• A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

• An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Response: Thank you very much for the highly praises and the valuable suggestions for this manuscript. According to the editor’s and reviewers’ suggestions, we have completed all the revision which each point raised by the academic editor and reviewers, the file is labeled 'Response to Reviewers'. We have completed a marked-up copy of our manuscript that highlights changes made to the original version, the file is labeled 'Revised Manuscript with Track Changes'. We also have completed an unmarked version of our revised paper without tracked changes, the file is labeled 'Manuscript'.

Response: Yes, I would like to make the peer review history publicly available.

If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Response: Thank you very much for the valuable suggestions. We have addressed and completed all the revised comments in the new 'Manuscript'.

Is the manuscript technically sound, and do the data support the conclusions?

Response: Thank you very much for the valuable suggestions. In this manuscript, all the data support the conclusions.

Has the statistical analysis been performed appropriately and rigorously?

Response: Thank you very much for the valuable suggestions.

Have the authors made all data underlying the findings in their manuscript fully available?

Is the manuscript presented in an intelligible fashion and written in standard English?

Comment #6:

Review Comments to the Author.

Response to Reviewer #2: Thank you very much for the valuable suggestions. We have revised and completed all comments provided by the editor and reviewers in the first round.

Response to Reviewer #3: Thank you very much for the valuable suggestions.

1. We have revised the literature review part, which is chiefly related to three streams of literature, it seems not to be too detailed, which is mentioned the related papers and on top of that, provide their own contributions. Moreover, those contributions should be more precise as line 150-160.

2. We have revised model and assumption more concise and concrete in line 174-l96.

For example, The final customer’s quality utility is , and denotes the type of final customer; then, we assume Ɵ~U[a, b] uniform distribution, i.e. a is the final customer lower limit of distribution quantity, b is the final customer upper limit of distribution quantity, and the corresponding final customer’s consumer surplus is .

We have checked the model and assumption part one more time, and then, provided additional explanation more clearly and accurately.

Sincerely yours,

Decision Letter 2

31 Mar 2020

PONE-D-19-20228R2

Dear authors,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Additional Editor Comments (optional):

The improvements have been noticeable so that the paper deserves to be published in the Plos One.

Acceptance letter

Dear Dr. Zhu:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Organizing and managing channels of distribution

Published: March 1999
Volume 27 , pages 226–240, ( 1999 )

Cite this article

Gary L. Frazier 1

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During the past three decades, tremendous strides have been made in our understanding of how firms should organize and manage their channels of distribution. Still, we have barely touched the surface of all the managerial issues that need to be addressed. A variety of research needs still exist regarding constructs and issues examined in prior channels research. Furthermore, many issues of managerial importance relating to the organization and management of channels of distribution have received no attention in empirical research. The purpose of this article is to provide a perspective on how channels research should proceed in the future to promote the most progress. It is hoped that the article will help to shape the future direction of marketing thought with regard to channels of distribution and its fundamental domain.

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Gary L. Frazier , Ph.D., holds an endowed chair appointment as the Richard and Jarda Hurd Professor of Distribution Management in the Marshall School of Business Administration at the University of Southern California (USC), Los Angeles. His research interests have focused on the structuring and management of channels of distribution around the world. He is especially interested in how channel relationships can be efficiently and effectively coordinated to create value for the channel’s customers. He has conducted research studies on channels of distribution in Europe and India, as well as in the United States. He has edited 10 books and proceedings, and has published more than 40 research article, including 15 in the Journal of Marketing and the Journal of Marketing Research . He heads the Program in Distribution Management at USC, supported by distributors with more than $10 billion in annual sales. He has won several awards for his teaching. He also has had the opportunity to consult and teach for a number of major corporations.

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Frazier, G.L. Organizing and managing channels of distribution. J. of the Acad. Mark. Sci. 27 , 226–240 (1999). https://doi.org/10.1177/0092070399272007

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Please note you do not have access to teaching notes, research paper: distribution channel decisions in import consumer goods markets.

Logistics Information Management

ISSN : 0957-6053

Article publication date: 1 June 1998

Develops an additional theoretical extension on channel decisions to the basic transaction cost model (TCM) by combining insights from production economy and industrial structure analysis methods with the existing TCM approach. The data for this study were obtained from survey questionnaires of 119 importing firms in Korea. The empirical results showed that production economy and industrial structure approach with previous TCM extended explanatory power of research model on channel style decisions.

Channel management
Organizational theory
Production economics
Transaction costs

Kim, Y. (1998), "Research paper: distribution channel decisions in import consumer goods markets", Logistics Information Management , Vol. 11 No. 3, pp. 178-187. https://doi.org/10.1108/09576059810218982

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Determinants of Margins in the Distribution Channel: An Empirical Investigation

In this paper we describe how margins in the channel vary over time within a product category and identify the market, manufacturer, and retailer characteristics that explain this variation. To obtain the equilibrium margins, we explicitly model the behavior of the various agents in the marketplace. Because the behavior of the agents changes in response to changes in the economic environment, we observe shifts in the total channel margins and the way they are split between the channel members. We explain this variation by examining the impact of directly measurable factors on total margins in the distribution channel and the share of these margins that manufacturers and retailers obtain. We illustrate the proposed approach using data for the ground coffee category in Germany. Our empirical analysis demonstrates that while the market-level factors affect total margins in the channel, size and other characteristics of manufacturers and retailers have a larger impact on the way margins are split. Our findings have immediate implications for the product portfolios offered by manufacturers, the positioning of store brands, and the retail service level.

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Distribution of Market Power, Endogenous Growth, and Monetary Policy

Yumeng Gu, Sanjay R. Singh

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2024-09 | March 28, 2024

We incorporate incumbent innovation in a Keynesian growth framework to generate an endogenous distribution of market power across firms. Existing firms increase markups over time through successful innovation. Entrant innovation disrupts the accumulation of market power by incumbents. Using this environment, we highlight a novel misallocation channel for monetary policy. A contractionary monetary policy shock causes an increase in markup dispersion across firms by discouraging entrant innovation relative to incumbent innovation. We characterize the circumstances when contractionary monetary policy may increase misallocation.

Article Citation

Gu, Yumeng, and Sanjay R. Singh. 2024. “Distribution of Market Power, Endogenous Growth, and Monetary Policy,” Federal Reserve Bank of San Francisco Working Paper 2024-09. Available at https://doi.org/10.24148/wp2024-09

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AI generates high-quality images 30 times faster in a single step

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Three by two grid of AI-generated images, with small black illustrated robots peeking from behind. The images show a scenic mountain range; a unicorn in a forest; a vintage Porsche; an astronaut riding a camel in a desert; a sloth holding a cup, dressed in a turtleneck sweater; and a red fox in a spacesuit against a starry background.

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In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models , iteratively adding structure to a noisy initial state until a clear image or video emerges. Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have introduced a new framework that simplifies the multi-step process of traditional diffusion models into a single step, addressing previous limitations. This is done through a type of teacher-student model: teaching a new computer model to mimic the behavior of more complicated, original models that generate images. The approach, known as distribution matching distillation (DMD), retains the quality of the generated images and allows for much faster generation.

“Our work is a novel method that accelerates current diffusion models such as Stable Diffusion and DALLE-3 by 30 times,” says Tianwei Yin, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and the lead researcher on the DMD framework. “This advancement not only significantly reduces computational time but also retains, if not surpasses, the quality of the generated visual content. Theoretically, the approach marries the principles of generative adversarial networks (GANs) with those of diffusion models, achieving visual content generation in a single step — a stark contrast to the hundred steps of iterative refinement required by current diffusion models. It could potentially be a new generative modeling method that excels in speed and quality.”

This single-step diffusion model could enhance design tools, enabling quicker content creation and potentially supporting advancements in drug discovery and 3D modeling, where promptness and efficacy are key.

Distribution dreams

DMD cleverly has two components. First, it uses a regression loss, which anchors the mapping to ensure a coarse organization of the space of images to make training more stable. Next, it uses a distribution matching loss, which ensures that the probability to generate a given image with the student model corresponds to its real-world occurrence frequency. To do this, it leverages two diffusion models that act as guides, helping the system understand the difference between real and generated images and making training the speedy one-step generator possible.

The system achieves faster generation by training a new network to minimize the distribution divergence between its generated images and those from the training dataset used by traditional diffusion models. “Our key insight is to approximate gradients that guide the improvement of the new model using two diffusion models,” says Yin. “In this way, we distill the knowledge of the original, more complex model into the simpler, faster one, while bypassing the notorious instability and mode collapse issues in GANs.”

Yin and colleagues used pre-trained networks for the new student model, simplifying the process. By copying and fine-tuning parameters from the original models, the team achieved fast training convergence of the new model, which is capable of producing high-quality images with the same architectural foundation. “This enables combining with other system optimizations based on the original architecture to further accelerate the creation process,” adds Yin.

When put to the test against the usual methods, using a wide range of benchmarks, DMD showed consistent performance. On the popular benchmark of generating images based on specific classes on ImageNet, DMD is the first one-step diffusion technique that churns out pictures pretty much on par with those from the original, more complex models, rocking a super-close Fréchet inception distance (FID) score of just 0.3, which is impressive, since FID is all about judging the quality and diversity of generated images. Furthermore, DMD excels in industrial-scale text-to-image generation and achieves state-of-the-art one-step generation performance. There's still a slight quality gap when tackling trickier text-to-image applications, suggesting there's a bit of room for improvement down the line.

Additionally, the performance of the DMD-generated images is intrinsically linked to the capabilities of the teacher model used during the distillation process. In the current form, which uses Stable Diffusion v1.5 as the teacher model, the student inherits limitations such as rendering detailed depictions of text and small faces, suggesting that DMD-generated images could be further enhanced by more advanced teacher models.

“Decreasing the number of iterations has been the Holy Grail in diffusion models since their inception,” says Fredo Durand, MIT professor of electrical engineering and computer science, CSAIL principal investigator, and a lead author on the paper. “We are very excited to finally enable single-step image generation, which will dramatically reduce compute costs and accelerate the process.”

“Finally, a paper that successfully combines the versatility and high visual quality of diffusion models with the real-time performance of GANs,” says Alexei Efros, a professor of electrical engineering and computer science at the University of California at Berkeley who was not involved in this study. “I expect this work to open up fantastic possibilities for high-quality real-time visual editing.”

Yin and Durand’s fellow authors are MIT electrical engineering and computer science professor and CSAIL principal investigator William T. Freeman, as well as Adobe research scientists Michaël Gharbi SM '15, PhD '18; Richard Zhang; Eli Shechtman; and Taesung Park. Their work was supported, in part, by U.S. National Science Foundation grants (including one for the Institute for Artificial Intelligence and Fundamental Interactions), the Singapore Defense Science and Technology Agency, and by funding from Gwangju Institute of Science and Technology and Amazon. Their work will be presented at the Conference on Computer Vision and Pattern Recognition in June.

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Playboy image from 1972 gets ban from ieee computer journals, use of "lenna" image in computer image processing research stretches back to the 1970s..

Benj Edwards - Mar 29, 2024 9:16 pm UTC

On Wednesday, the IEEE Computer Society announced to members that, after April 1, it would no longer accept papers that include a frequently used image of a 1972 Playboy model named Lena Forsén. The so-called " Lenna image ," (Forsén added an extra "n" to her name in her Playboy appearance to aid pronunciation) has been used in image processing research since 1973 and has attracted criticism for making some women feel unwelcome in the field.

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Supply chain product quality control strategy in three types of distribution channels

1 Introduction

2 Related literature

3 The model and assumption

4 Product quality strategy in direct channel

5 Product quality strategy in retail channel

6 Product quality strategy in mixed channel

7 Numerical example

8 Conclusions and future research

Acknowledgments

Supply chain product quality control strategy in three types of distribution channels

Associated Data

1 Introduction

2 Related literature

3 The model and assumption

4 Product quality strategy in direct channel

5 Product quality strategy in retail channel

6 Product quality strategy in mixed channel

7 Numerical example

8 Conclusions and future research

Acknowledgments

Funding Statement

Data Availability

Decision Letter 0

Author response to Decision Letter 0

Decision Letter 1

Author response to Decision Letter 1

Decision Letter 2

Acceptance letter

Organizing and managing channels of distribution

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