Manufacturing Decisions Research Articles

Data organization in laser-based powder bed fusion for metals

Data analytics (DA) and artificial intelligence (AI) have been chosen as the technologies for extracting new knowledge and making better decisions in additive manufacturing (AM) processes. They have been chosen because accurate and complete physics-based, process-simulation or mathematical models do not exist. DA and AI models should be based on measurable data collected by part and process sensors. This paper is focused on how to organize the data collected from such sensors. An example is also provided to show how to store AM-related data in a hierarchical data structure that is consistent with the data from multiple sensors. The data provides functions and properties at various stages in a product lifecycle. The associated metadata for both functions and properties are organized in the same hierarchical structure according to the relationships of machine, build, melting laser beams, process planning, in-situ monitoring, ex-situ inspection, material microstructure imaging, and mechanical testing. Sample data with metadata are stored in a file in the format of Hierarchical Data Format 5 (HDF5). The paper provides an organization of complex AM data that can support AM software tools for a variety of product lifecycle activities.

The impact of tariffs and price premiums of locally manufactured products on global manufacturers' sourcing strategies

The past decade has seen significant pressure to return manufacturing to developed countries. Such movements call for import tariffs to be raised and consumers' valuation of products made from locally sourced components increased. These factors are believed to stimulate firms operating in a single market to source components locally. However, it is not clear that they are equally effective for multinational firms that produce and sell products in multiple markets. To enrich the understanding of this issue, we build a game‐theoretical model to analyze the sourcing decisions of a global manufacturer that maintains production sites and sells products in both domestic and foreign markets in a competitive environment. The firm can choose either to source components from suppliers located in the same markets as the manufacturing sites to gain tariff savings and a price premium from consumers' higher valuation, or to source all components from a single foreign supplier to obtain a lower sourcing cost. We find that the structure of the global supply chain plays a critical role in the firm's sourcing strategy. Consumers' higher valuation of locally manufactured goods always promotes local sourcing. Raising tariffs, however, might backfire and discourage local sourcing because of the firm's global supply chain structure and the foreign supplier's strategic response to higher tariffs. Finally, local sourcing may reduce the consumer surplus even though consumers place a higher value on locally manufactured goods. This paper sheds light on recent movements to return manufacturing to developed countries and demonstrates the importance of taking the manufacturer's global supply chain structure and vertical interaction with suppliers into account.

A Decision-Making System for Low Carbon Manufacturing.

Based on the stochastic market demand of products, this paper studies the low-carbon manufacturing decisions making of manufacturing enterprises considering downward substitution and green technology input under the carbon cap-and-trade policy. The results show that the government's carbon trade policy will have a great impact on the production of manufacturing enterprises. Therefore, manufacturing enterprises must attach importance to the constraints of the government's carbon emission reduction policies. In terms of it, there are strategies for manufacturing enterprises such as adjusting the output, trading the carbon emission right, and so on. On this case, green technology input can increase the expected profit of manufacturing enterprises, especially in the case of downward substitution.

Raising funds through crowdfunding for a capital-constrained servicing manufacturer

Servitization (service-oriented manufacturing) is an emerging business model under which a manufacturer offers services derived from physical goods. A servicing manufacturer is more likely to be capital-constrained, as its revenue is monetized over a long period of time. Therefore, we explore how a capital-constrained servicing manufacturer raises funds through crowdfunding. We construct a game-theoretical model to capture the optimal decisions (quality and price) of a servicing manufacturer facing two types of customers. In our paper, the endogenous quality level is derived by tailoring the service to consumers’ needs. According to the posted quality level, customers determine their optimal usage of the service, which impacts the target of crowdfunding. We find that when both types of customers share the same product in crowdfunding, a higher proportion of high-end customers will lead to a lower quality level determined by the manufacturer. In addition, the servicing manufacturer also optimizes price decisions to induce high-type customers to make a contribution to the success of crowdfunding. These results underscore the influence of the crowdfunding mechanism on the servicing manufacturer’s decisions. However, offering a product line lets no one sacrifice interests for the success of crowdfunding. Both the manufacturer and customers obtain the highest benefits because servitization enables the manufacturer to provide customers with customized services. We argue that servitization improves the performance of crowdfunding by allowing the manufacturer to optimize operational decisions.

On the large‐scale production of a new vaccine

Large‐scale production of a new vaccine, such as the COVID‐19 vaccine, is characterized by evolving demand and production yield uncertainties. Further, following its FDA approval for efficacy and safety, a vaccine may not even be manufacturable due to its low production yield and the resulting lack of profitability. To deal with those challenges in practice, a vaccine manufacturer can (stochastically) increase the uncertain yield of a vaccine prior to launching large‐scale production by learning through small‐scale, experimental production runs about manufacturing conditions that are conducive to raising that yield. After starting large‐scale production and receiving revenues from the sale of the vaccine, the manufacturer can continue to stochastically improve vaccine yield by acquiring knowledge from real‐time production data. The two key decisions faced by a vaccine manufacturer concern the optimal timing and capacity of the vaccine's large‐scale production. To determine the structure of vaccine manufacturer's optimal decisions, we formulate and solve a stochastic, multiperiod, sequential‐decision model, while incorporating the dynamic evolution of vaccine yield uncertainty under those two yield improvement strategies. We establish the optimality of a threshold stopping policy for the timing of the large‐scale vaccine production. This policy depends in a fundamental way on the relative stochastic rates of the two yield improvement strategies. We characterize the manufacturer's optimal capacity decision and identify conditions under which optimal capacity and production yield become substitutes. We analyze the implications of our results for rendering a new vaccine large‐scale manufacturable, and bringing it to the market sooner during a pandemic period.

The Employability of Graduates of National Characteristic Discipline Programs of Study in China: Evidence from Employers

Prior studies have examined the historical evolution, multiple stakes, measurements and operation models of graduate employability, and the researches on graduate employability have gradually shifted to the perspective of employers with wider labor market uncertainty and higher education massification. However, there is still a gap in research on the demand for graduate employability by employers in national high-end equipment manufacturing that work closely with higher education in scientific research. Namely, it remains unclear what really matters in the processes of employers’ recruitment decisions in national high-end equipment manufacturing. Drawing on Yorke’s definition and CareerEDGE model, this study defines graduate employability as a set of achievements—skills, understandings and personal attributes—that makes graduates more likely to gain employment in national high-end equipment manufacturing, including “emotional intelligence”, “knowledge and skills”, “generic skills”, “work experience”, “character and personality”. Owing to the importance and arduousness of national high-end equipment manufacturing historical mission and main tasks, we argue that employers pay more attention to graduate employability in the recruitment process. Empirically examining based on 831 questionnaires from employers of national high-end equipment manufacturing in China, we show that employers prefer graduates with higher levels of “cooperative innovation ability”, “knowledge and skills”, “stress management and adaptation” within Chinese national characteristic discipline programs. Particularly, although employers in national high-end equipment manufacturing have always emphasized employees’ loyalty and dedication, “character and personality” of a graduate does not have a direct effect on employer hiring preference, but instead the effect of cooperative innovation ability and knowledge and skills are fully moderated by character and personality.

Techno‐economic, environmental, and social measurement of clean energy technology supply chains

AbstractIn addition to the criteria of reliability and cost, clean energy technologies, such as wind, solar, and batteries, need to strive to a higher standard of environmental and societal benefit along their entire supply chain. This means additional performance metrics for these technologies should be considered, such as embodied energy, embodied carbon, recycled content and recyclability, environmental impact of material sourcing, impact on land and ecosystems, materials recovery at end of life, and production through quality nonexploitive jobs with community benefit. Many commercial and emerging energy technologies have not yet been explicitly evaluated based on these environmental and social performance metrics, which presents multiple opportunities for researchers and analysts. In this paper, we review the importance and current limitations of techno‐economic and life‐cycle assessment models for research design and manufacturing decisions. We explore emerging manufacturing modeling options that could improve environmental and social performance and how they could be used to help guide research. Even with the deployment of low‐carbon energy‐generation technologies, the future of a successful clean energy transition requires collaboration between researchers, advanced manufacturers, independent standards and tracking organizations, local communities, and national governments, to ensure the financial, environmental, and social sustainability of the entire supply and manufacturing process of energy technologies.

After-sales service and warranty decisions of a durable goods manufacturer

We study a monopolistic durable goods manufacturer that also provides a base warranty and after-sales services to her customers through a designated retailer. The customers evaluate the total cost of ownership of the product when they make the purchasing decision. However, they can foresee the future costs only to a certain extent. In this setting, we find the manufacturer’s product price decision followed by the spare parts wholesale and retail price decisions made by the manufacturer and the retailer, respectively. As a benchmark, we study the centralized system where the manufacturer and the retailer are integrated. We also study an alternative model; i.e., a third party model, where the manufacturer is not active in the after-sales business and does not offer a warranty, and these services are carried out by an independent retailer. We find that when the manufacturer controls her after-sales channel, she gives away the product for free and extracts all her profit from the after-sales services. In fact, the extent of the warranty coverage is irrelevant, and does not affect the manufacturer’s profit or the consumer surplus. Especially when customers are myopic with very limited foresight into future costs, the manufacturer will prefer to control her after-sales market. From the customer perspective, however, the third party model tends to be the better choice.

INN: An Interpretable Neural Network for AI Incubation in Manufacturing

Both artificial intelligence (AI) and domain knowledge from human experts play an important role in manufacturing decision making. Smart manufacturing emphasizes a fully automated data-driven decision-making; however, the AI incubation process involves human experts to enhance AI systems by integrating domain knowledge for modeling, data collection and annotation, and feature extraction. Such an AI incubation process not only enhances the domain knowledge discovery but also improves the interpretability and trustworthiness of AI methods. In this article, we focus on the knowledge transfer from human experts to a supervised learning problem by learning domain knowledge as interpretable features and rules, which can be used to construct rule-based systems to support manufacturing decision making, such as process modeling and quality inspection. Although many advanced statistical and machine learning methods have shown promising modeling accuracy and efficiency, rule-based systems are still highly preferred and widely adopted due to their interpretability for human experts to comprehend. However, most of the existing rule-based systems are constructed based on deterministic human-crafted rules, whose parameters, such as thresholds of decision rules, are suboptimal. Yet the machine learning methods, such as tree models or neural networks, can learn a decision rule based structure without much interpretation or agreement with domain knowledge. Therefore, the traditional machine learning models and human experts’ domain knowledge cannot be directly improved by learning from data. In this research, we propose an interpretable neural network (INN) model with a center-adjustable sigmoid activation function to efficiently optimize the rule-based systems. Using the rule-based system from domain knowledge to regulate the INN architecture not only improves the prediction accuracy with optimized parameters but also ensures the interpretability by adopting the interpretable rule-based systems from domain knowledge. The proposed INN will be effective for supervised learning problems when rule-based systems are available. The merits of the INN model are demonstrated via a simulation study and a real case study in the quality modeling of a semiconductor manufacturing process. The source code of this work is hosted here: https://github.com/XiaoyuChenUofL/Interpretable-Neural-Network .

Impact of competition type on a competitive manufacturer's preference of decision timing

In this paper, we consider the impact of competition type (i.e., quantity competition vs. price competition) to study a manufacturer's preference for decision timing when she establishes a direct channel: she can choose to decide the retail price or quantity in the direct channel earlier than, concurrently with, or later than the retailer. We derive the competitive manufacturer's equilibrium outcomes and its most preferred decision timing in both quantity competition and price competition models. We identify two effects when the manufacturer establishes the direct channel: the wholesale price effect, the manufacturer may reduce the wholesale price to support the retailer's demand, and the threat effect, the manufacturer uses the direct-selling option to influence the retailer's decision. We find that the wholesale price effect and the threat effect vary with the competition type and the manufacturer's decision timing. If the manufacturer makes the retail decision earlier than the existing retailer, the wholesale price effect even does not exist, regardless of the competition type. As to the threat effect, we find it vanishes if quantity competition is assumed and the manufacturer makes the quantity decision before the retailer, while it exists in price competition regardless of the decision timing. If the selling cost is low, the manufacturer has incentives to establish a direct channel and prefers making the retail quantity decision after the retailer in quantity competition, and the retailer and consumers may benefit from the manufacturer's decision. In price competition, the manufacturer prefers deciding the retail price earlier than the retailer, and consumers benefit from encroachment. However, contrary to the literature, we show that the retailer is worse off with encroachment.

Battery R&D decision of electric vehicle manufacturer considering government subsidy

PurposeIn this paper, the authors aim to consider the manufacturer's battery research and development (R&D) decision under subsidy. The supply chain includes two manufacturers, which produce substitutable electric vehicles, and a battery supplier. One of the manufacturers can choose to develop batteries or buy batteries. The authors assume consumers do not have enough trust in the manufacturer-made battery.Design/methodology/approachStackelberg game is made use of to study the battery R&D strategy of the manufacturer under the incentive of government subsidies. This paper makes a comparative analysis on six situations, then the authors get some conclusions and give some managerial insights.FindingsThe results show that subsidy strategies do not necessarily reduce actual payments when the manufacturer does not research and develop batteries. The retail prices and actual payments are closely related to the substitutability and total cost advantage of product. The authors also find consumer trust positively affects the demand of the electric vehicles using the manufacturer-made batteries and then affects the manufacturer's battery R&D decision. When consumers have low trust in manufacturer-made battery, subsidy can bring greater sales and make R&D more profitable than procurement, so that the manufacturer chooses R&D. This study's findings also suggest consumer subsidy is always better for the government.Originality/valueDistinguished from previous studies, the authors discuss the decision-making of component research, and introduce various government subsidy strategies and consumer trust to study their roles in the manufacturer's battery R&D choice.

Rapid translation of a cellular therapeutic from research to clinic

Cell therapies face regulatory and manufacturing challenges long before they are accessible to patients. Even as therapies prove effective and safe, raw material and manufacturing decisions made during early development can either stall progression or rapidly accelerate it. Utilizing scalable manufacturing processes and translation friendly raw materials during product development​ of MSCs and extracellular vesicle (EV) therapies will enable cell therapy developers to accelerate leading candidates into clinical trials, ultimately enabling more curative treatments to reach patients.Ashish Patel is a Clinical Senior Lecturer in the School of Cardiovascular Medicine & Sciences at King’s College London and Consultant Vascular and Endovascular Surgeon at Guy’s and St Thomas’ NHS Foundation Trust, London, UK. His British Heart Foundation-funded PhD, focusing on monocyte/macrophage biology in tissue remodelling, was awarded in 2013. He completed his Higher Surgical Training in General & Vascular Surgery as a Clinical Academic Lecturer at King’s College London & Guy’s & St Thomas’ NHS Foundation Trust. During this time, he received an Academy of Medical Sciences Grant and the Circulation Foundation’s George Davies Visionary Award. Dr Patel’s research interests are focused on the bench-to-bedside development and translation of novel cardiovascular advanced therapies, particularly for the treatment of peripheral arterial disease. He is an investigator within the BHF Centres of Regenerative Medicine and a Phase I accredited Principal Investigator at Guy’s & St Thomas’ NHS Foundation Trust. His research is funded by the Royal College of Surgeons, Rosetrees Trust, Advanced Therapies Network and the Frances and Augustus Newman Foundation.Joseph Candiello is a bioengineering expert with years of experience working at the nexus of biomaterials, biophysics, and organoid engineering. During 10+ years in academic research, he focused primarily on engineering a stem cell based vascularized islet organoid system for diabetes research as well as previous studies understanding the biophysical properties of developing interstitial tissues during his PhD in Bioengineering at the University of Pittsburgh. Prior to his PhD, Joe received his BSc from the Pennsylvania State University in Engineering Science and Mechanics, where he currently serves on the department's Industry & Professional Advisory Council. Dr. Candiello joined RoosterBio as a Field Application Scientist in 2017 before his current role as Senior Product Manager helping to bring new and innovative products to the market to support MSC based therapeutics.

Efficiency framework to assess aeronautic composite panel production: Tracking environmental and process performance

This study focuses on the characterization of the production process of a composite sandwich panel for an aircraft structure. Two curing alternatives were compared, namely hot-press and autoclave. A holistic assessment was conducted applying the Total Efficiency Framework, which combines both process efficiency and environmental performance analyses into a single index score to support manufacturing decision. The study provides inventory data, collected at laboratory scale regarding materials, energy consumption, and process operation for composite panel production, which are seldom available. This foreground data was used to quantify the process efficiency, based on lean design tool, and to estimate the potential environmental impacts, using Life Cycle Assessment methodology to determine the eco-efficiency of the production process. The results suggested that the autoclave curing outperforms the hot-press alternative in terms of efficiency, eco-efficiency, and environmental performance. Regarding the total efficiency index results for maximum productivity, the results show a difference of 12% between the two alternatives, indicating potential competitive advantages in an industrial setting.

Carbon Emission Constraint Policy in an OEM and Outsourcing Remanufacturer Supply Chain with Consumer Preferences.

Carbon emission reduction has been a consensus goal for most countries to achieve environmental sustainability. The use of carbon emission trading policies has been generally considered by the governments. Remanufacturing, as an effective way to reduce carbon emission, is incorporated together with the tool of carbon emission policy to construct a low-carbon supply chain in this paper. We analyze the carbon emission reduction and profit maximization problem among enterprises of original equipment manufacturers (OEMs) and their outsourcing remanufacturers, integrating the impact of the carbon emission constraint policy and the carbon market. Considering consumer preferences on low-carbon products and recycling rates of waste products, we construct a Stackelberg game model (dominated by the OEM) and analyze the impact of a carbon emission constraint policy on sales price, volume, carbon emission, and revenue of new and remanufactured products in the supply chain system. The results suggest that the upper bound set by the government on carbon emission for enterprises positively affects sales volume of new products and negatively affects sales prices of both products. Moreover, the discount rate of carbon emission constraint negatively affects sales volume of new products and positively affects sales prices of both products. Notably, the carbon emission constraint policy has impacts on the production decisions of both manufacturers on an economic scale. When the upper bound of carbon emission is equal to a certain threshold, the OEM could obtain the greatest revenue. The results provide a new perspective for the government to attain the goal of carbon emission reduction and not sacrifice economic growth. Managers in outsourcing remanufacturers and OEMs could also be implicated from our results to collaborate in allocating remanufacturing orders to achieve win-win opportunities between them.

Warranty or education?: An analysis of marketing strategy choices for remanufactured products

PurposeImplementing effective marketing strategies can reduce consumers' perceived risks and promote the development of the remanufacturing market. The paper aims to explore the impact of two marketing strategies on the remanufacturing decisions of an original equipment manufacturer (OEM) and investigates the design of the optimal warranty service (WS) and consumer education (CE) strategies. Moreover, the authors also examine the optimal marketing strategy choices using the three criteria of OEM: profitability, consumer surplus and environmental impact (EI).Design/methodology/approachThe authors develop a stylized model by game theory, which characterizes how an OEM that produces and sells both new and remanufactured products (RPs) should design the optimal remanufacturing marketing activity, and how to choose between different remanufacturing marketing strategies. Moreover, consumer's utility theory is used to describe consumers' perception of different remanufacturing marketing strategies to derive product demand.FindingsThe results reveal that increasing the warranty and education levels will not always improve the firm's profitability; the result depends heavily on the size of the functionality-oriented consumer (FOC) segment. Remanufacturing marketing strategies might harm the OEM, consumers and environment under certain conditions. Moreover, the optimal marketing strategy selections are jointly influenced by the FOC segment and the new production cost. There exist triple-win regions in which the OEM should not hesitate to perform WS and CE.Originality/valueFew studies focus on the design and choice of remanufacturing marketing strategies, especially considering the role of consumer perceived behavior. This research contributes the behavioral remanufacturing marketing management and provides managerial implications for the implementation of OEM remanufacturing marketing strategy.

Driving Mechanism for Manufacturer's Decision of Green Innovation: From the Perspectives of Manager Cognition and Behavior Selection.

From the perspectives of manager cognition and behavior selection, this paper analyzes the cognitive basis of manufacturer’s green innovation and discovers that the embodied cognition of the manager has an important influence on the selection of green innovation behavior. Next, the behavior activation in the four stages of manufacturer’s green innovation, namely, initiation, termination, change, and solidification, was analyzed, and two behavior selections were proposed: the adaptive legitimacy with institutional logic as the cognitive starting point and the strategic legitimacy with efficiency logic as the cognitive starting point. On this basis, the authors examined four types of manufacturer decisions of green innovation (compliance, selection, creation, and control) driven by manager cognition and behavior selection. The examination reveals how should the manager, facing the growing environmental pressure, form a correct cognition, select the right behavior, and make the proper green innovation decision, which promotes the green, sustainable development of manufacturers.

Game changer or threat: The impact of 3D printing on the logistics supplier circular supply chain

3-Dimensional Printing (3DP) has been widely used in the circular supply chain. Still, most of the literature focused on addressing only the manufacturer's adoption of 3DP and how it influences the supply chain. A growing number of non-manufacturer (e.g., logistic suppliers) have adopted 3DP, but its impact on manufacturers and customers is still underexplored. This paper uses a two-player single-period supply chain model, supported by an in-depth interview, to investigate how the logistics supplier's 3DP adoption impacts the circular supply chain in the spare parts aftersales market. The findings show that cost reduction of 3DP does not always benefit the logistics supplier. Still, this finding opens a new revenue stream for the logistics supplier and the integrated supply chain. Further, the manufacturer can financially benefit from such adoption only when the cost of 3DP production is relatively high. Interestingly, the logistics supplier can use 3DP adoption as a game-changer to become a new "green manufacturer", thereby posing a strategic threat that can influence the traditional manufacturer's decisions regarding financial benefits. Customers can also enjoy more surplus when logistics suppliers adopt low-cost 3DP. This study is one of the first to investigate how non-manufacturer 3DP adoption impacts the circular supply chain.

Research on single/cooperative emission reduction strategy under different power structures.

This paper develops low-carbon decisions in a two-echelon supply chain considering consumers' low-carbon preference and cap-and-trade (C&T) regulation. Two different power structures are considered, including manufacturer-dominated (MD) and retailer-dominated (RD) cases. The single emission reduction (SER) mode where only the manufacturer invests in low-carbon technology and the cooperative emission reduction (CER) mode where the manufacturer invests in low-carbon technology and the retailer invest in low-carbon promotion are investigated respectively. It is found that a relatively loose C&T regulation helps to promote the cooperation of supply chain enterprises. Under both MD and RD cases, CER mode is always a rational choice for supply chain enterprises. Under SER mode, the manufacturer's profit will not always decrease when he loses the dominant position. However, the RD case is profitable for the retailer and the supply chain. Under CER mode, the dominant role is important for both the manufacturer and the retailer. However, the profit of the supply chain under RD case may be lower than that under MD case. Through numerical analysis, we found that the fluctuation of carbon price has a more significant impact on the manufacturer's emission reduction decision under CER mode than that under SER mode. In addition, with the increase of unit carbon price, the RD case performs better than the MD case in promoting supply chain's low-carbon level and profit.

Impact of carbon emission reduction on supply chain model with manufacturing decisions and dynamic lead time under uncertain demand

Consignment stock policy plays a crucial role in stock management to accomplish more significant benefits for supply chain players. Accordingly, in this research, we developed an inventory model based on the vendor managed inventory and consignment stock agreement by delivering a single item from the single vendor to a single buyer under controllable lead time. Manufacturing industries are a major source of worldwide greenhouse gas emissions. Consumption of larger energy through non-renewable sources increases the use of fossils and coal. Though, this produces more waste and has a negative impact on the environment. In contrast to earlier research, logarithmic investment is employed to control the amount of carbon generated throughout the manufacturing process. The demand rate is treated as a pentagonal fuzzy number to account for the uncertainty. The primary goal of this study is to maximize gross profit with various realistic factors and reduce carbon emissions from the production process. Five numerical experiments, comparison and sensitivity analysis are performed to validate the proposed model. Finally, the benefits of variable pricing and investing in carbon reduction will assist industry managers in effectively managing pricing and inventory strategies to adopt a sustainable supply chain in an uncertain environment.

For Better or For Worse: Impacts of IoT Technology in e‐Commerce Channel

Internet of Things (IoT) technology utilizes sensors and other internet‐enabled devices to collect and share data. It is widely regarded as a disruptive technology that brings tremendous opportunities to supply chain members. This study uses a game‐theoretical model to study an e‐commerce setting in which an online platform provides IoT infrastructure and a manufacturer sells its products on the platform. Our work examines the interaction among the manufacturer's IoT investment decision, the platform's choice of pricing models, and the platform's transfer payment strategy. We solve the model analytically and obtain several interesting findings. Our study shows that the manufacturer in a wholesale pricing model is more likely to invest, and invests more, in IoT technology than in an agency one. One surprising finding is that both the manufacturer and the channel performance could be hurt by an increase in IoT technology value in certain situations. Also surprisingly, even having the option of investing in IoT technology by the manufacturer can make both the manufacturer and the channel performance worse off. Therefore, the advancement of IoT technology might not benefit either manufacturers or the whole industry, although e‐commerce platform giants and the news media have been advocating the benefits of IoT technology enthusiastically in recent years. Our results should concern both device manufacturers who contemplate adopting or have adopted IoT technology and policymakers who are interested in overall channel performance.