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China is the most productive seaweed farming country in the world and her seaweed imports and exports have a significant impact on global seaweed trade commodities and food security. Nevertheless, few studies had delved into the main characteristics and development of China's seaweed farming industry. This paper aims to narrow this research gap by analyzing trade patterns in China's seaweed products and the international competitiveness of seaweed product by using the international market share index (IMS), the trade competitiveness index (TC), and revealed comparative advantage index (RCA) from 2002 to 2017. The results showed that Japan, the Association of Southeast Asian Nations (ASEAN), Chile, Peru, and the Republic of Korea are China's main trade partners, the total trade value of China's seaweed products has grown rapidly, its imports have gradually exceeded exports, the trade deficit continues to expand, and its competitiveness keeps decreasing. The reasons for this trend may include the changing seaweed trade commodity structure, product differentiation, increased government support in competing countries, and trade barriers among the trade partners. We propose actively expanding international trade markets, establishing a strong seaweed product processing industry to develop high value-added seaweed products, optimizing the trade commodity structure, and differentiating of seaweed products to improve the competitiveness of China's seaweed products.

Mediterranean mussel farming in Greece developed considerably during the last 40 years reaching a gross commodity product up to the limits of the country's production capacity (35–40,000 tonnes/year). Despite the achievements in the sector's growth, little or no effort has been attributed yet to risk assessment and moreover to risk management of the activity. The present effort aims at developing a working framework for the shellfish aquaculture of Greece to be used as a tool by the sector's decision makers to advance strategies for risk elimination or avoidance. The work was based on a generic risk management standard tool, the Joint Australian and New Zealand Risk Management Standard AS/NZS ISO 31000:2009 that has been adapted to the specific national characteristics of all levels of the mussel farming business -activities and the industry function. The framework supported by data sets regarding development, production, profits and losses, retrieved by surveys through distributed questionnaires or interviews during site-visits, as well as by collecting data from national and international authorities. Data input covered technology, farm size, farmer risk-attitude, risk-management strategies, risk-perceptions and socioeconomic profiles. Major risks and risk management options were identified providing aid for remediation risk policies to the stakeholders.

We examine the effectiveness of a quality assurance mechanism that relies on a third-party’s reputation. We focus on the market for fine and rare wines where auction houses intermediating between sellers and buyers have also claimed expertise to assess product authenticity. An auction house’s reputation can sustain product quality in this market only if consumers absorb relevant information and punish houses who fail at screening out counterfeits. We test the existence of such reputational effects by studying the market responses to two disclosures of houses having auctioned fake wine. We find that one house experienced negligible losses after a 2008 event involving a limited number of counterfeit bottles. However, three houses associated with a 2012 incident involving thousands of bottles are found to have suffered reputational losses. These losses are demonstrated over the following year by an average 4.5% decrease in equilibrium sales prices of all wines, and a slight decrease in sales quantities of older wines, relative to other houses.

Sensory nudge has been creating a significant impact in the field of market research with implementation of methods used in neuro marketing. These methods help in understanding the impact of sensory nudge on consumer purchase behavior. This research paper focuses on the impact of different sensory nudges due to the packaging, promotional activities, design and other motivational that influences the purchase behavior for green product. This sensory nudge stimulates the environmental consciousness of the consumers and their preference for green products over conventional products. This research work has been started with an introduction to the research topic followed by an extensive review of literature related to the subject matter. Then the conceptual framework for the study was developed followed by research hypotheses. The testing of hypotheses was carried by using specified research methodology with the help of a structured questionnaire for conducting the survey. The results from data analysis were interpreted. The major challenge in this context is to know the impact of sensory nudge derived from marketing activities that modulate consumer’s perception, attitude, and behaviour towards purchasing of green products that builds environmental sustainability. Based on the extensive review of the literature, this study addresses the causes that restrict consumers’ from using green products. This study also scrutinizes the environmental consciousness of the respondents. The research work includes analyzing the awareness level of green products and perception about the term green.

In the agriculture and healthcare industries, AI has been deployed to achieve better crop production, disease prediction, continuous monitoring, efficient supply chain management, improved operational efficiency, and reduced water waste, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving society while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machine learning models for the complexity and diversity of data in the real-world applications are being developed at the moment. This article discusses machine learning and artificial intelligence applications in agriculture, healthcare, management, and social studies. Disease Prediction, water irrigation optimization, Sales growth, profit maximization, sales forecast, inventory management, security, fraud detection, and portfolio management are some of the major uses.

In the agriculture and healthcare industries, AI has been deployed to achieve better crop production, disease prediction, continuous monitoring, efficient supply chain management, improved operational efficiency, and reduced water waste, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving society while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machine learning models for the complexity and diversity of data in the real-world applications are being developed at the moment. This article discusses machine learning and artificial intelligence applications in agriculture, healthcare, management, and social studies. Disease Prediction, water irrigation optimization, Sales growth, profit maximization, sales forecast, inventory management, security, fraud detection, and portfolio management are some of the major uses.

In the e-commerce and financial industries, AI has been deployed to achieve better customer experience, efficient supply chain management, improved operational efficiency, and reduced mate size, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving customers while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machine learning models for the complexity and diversity of data in the food industry are being developed at the moment. This article discusses machine learning and artificial intelligence applications in e-commerce, corporate management, and finance. Sales growth, profit maximization, sales forecast, inventory management, security, fraud detection, and portfolio management are some of the major uses.

It is important for marketing academics and public policymakers to understand how to persuade consumers to consume healthier food instead of unhealthy food. Based on the theories of synchrony effect and self-control, this research makes novel predictions about the time-of-day effects on (un)healthy food purchases. The results of a field study and a laboratory experiment confirm that consumers’ self-control level becomes lower in the evening, which leads consumers to buy more unhealthy products in the evening than they do in the morning. A follow-up eye-tracking experiment reveals the underlying mechanism through which self-control failure makes consumers pay more visual attention to unhealthy food and the weaker effect of attention to healthy food on purchases. The results of this study provide valuable insights not only for marketing academics but also for retailers and consumer marketing companies.

Servitization is the activity of selling the services provided by the product rather than the product itself. It is a business model that might be environmentally superior to conventional selling. Servitization promises accessibility to the product’s functionality, pooling of consumer use, and potentially products of better design. However, it can also inflate consumption and result in a bigger environmental impact overall. In this paper, we compare servitization with traditional selling for a monopolist durable goods manufacturer from both an economic and environmental perspective. In this comparison, we define the durability of a product as the use capacity; that is, how many usages it can endure before reaching end of life. We study the firm’s durability decision, followed by the price/fee decision, and the consequent usage in the market under each model. We find that servitization produces durability levels that are robust to customer heterogeneity, and higher than selling. Overall, environmental superiority of servitization hinges on product related costs, customer heterogeneity, and market composition. It is, however, robust to varying environmental factors in the use and manufacturing phases. When we compare environmental preferability with the economic incentives of the firm, we observe that they are not always aligned.

Product returns cost retailers billions of dollars annually. We analyze return policies (RPs) with cash refunds in the presence of wardrobers. We then analyze RPs with store credit or gift cards (SC/GC) refunds. We also analyze two-option RPs in which consumers choose between cash and SC/GC refunds. We find that for cash-only RPs, the retailer uses price to attract consumers while using the refund fraction to discourage wardrobers. SC/GC can be a good alternative to cash when consumers have high valuation for SC/GC and some SC/GC are not redeemed. With high redemption rate, SC/GC can be a good alternative when consumers make hedonic purchases and the retailer has a reasonable profit margin. We also find that single-option RPs dominate two-option RPs when genuine consumers and wardrobers have the same valuation for SC/GC, and two-option RPs can dominate when the two consumer segments have heterogeneous valuation for SC/GC. When two-option RPs dominate, the retailer gives lenient SC/GC option to genuine consumers who have a high valuation for SC/GC, and uses the cash option to decrease the cost or increase the profit from wardrobers who have a low valuation for SC/GC for some ranges of problem parameters. Moreover, when consumers have continuous valuation for SC/GC, two-option RPs dominate single-option RPs in genuine-only consumer markets. With two-option RPs, the retailer uses cash to increase profit from consumers with a low valuation for SC/GC and uses SC/GC to increase profit from consumers with a high valuation for SC/GC.

This paper studies an optimal investment and benefit adjustment problem for a target benefit pension plan. The pension sponsor can adjust the benefit level to guarantee the stable operation of the plan. The pension is allowed to invest in a risk-free bond and a stock. The weighted product of the benefit outgo and pension wealth is considered in the objective function, which is taken in the form of Cobb-Douglas utility. Thus the plan takes both the benefit level and terminal wealth of the pension into account and then the benefit payment are dependent on the financial situation of the plan. By applying dynamic programming approach, we establish the corresponding Hamilton-Jacobi-Bellman equation and derive the optimal investment-benefit strategy and the value function explicitly. The verification theorem is presented and proved. Furthermore, the recursive utility is considered as an extension and we find that the elasticity of intertemporal substitution has a positive effect on the optimal benefit level. Finally, numerical examples are given and demonstrate that this pension scheme is sustainable and can provide stable and consecutive incremental benefit payment for future generations.

Five lab studies and a field study explore how power distance belief (PDB) influences consumers' preference for mass-market versus niche brands, the underlying mechanisms, and boundary conditions. We find that high (vs. low) PDB consumers are more likely to prefer mass-market over niche brands because they tend to be risk averse, which in turn increases preference for widely used brands. Consequently, the relationship between PDB and brand preferences is mediated by risk aversion. Further, when product usage contexts are risky, low (but not high) PDB consumers' preference for mass-market brands increases, whereas offering an extended money-back guarantee increases high (but not low) PDB consumers' preference for niche brands.In addition, we find that when choosing service providers (versus choosing physical goods), low (but not high) PDB consumers are significantly more likely to prefer mass-market brands over niche brands. We conclude with the implications of these findingsfor marketers, such as for their segmentation and targeting endeavors. We also provide specific tools that marketers could use, both inside and outside the store, to influence consumers' preference for mass-market versus niche brands.

In the digital age, firms continuously look for leading technologies and make products of superior quality to satisfy new and sophisticated consumers’ needs. Although this approach strives to sell a product more appealing to customers, it also renders the product more complex and difficult to use, generating the feature fatigue effect. This is characterized by feelings frustration experienced by consumers when purchasing high-tech products with many appealing features, most of which turn out to be undesired, useless, and difficult to use. We study the impact of the feature fatigue effect on a manufacturing firm’s policies in designing its product–service portfolio. We consider two periods in order to capture the effect of customers’ word-of-mouth originating from feature fatigue, which is shared with other potential customers. Therefore, we assess not only the impact of feature fatigue but also the extent to which this effect can be alleviated by after-sales service. We capture feature fatigue by first considering a base model without this effect and then comparing the manufacturer’s decisions regarding quality, pricing, and service when it has either clear or noisy effect. Our findings reveal that feature fatigue drives manufacturers to under invest in quality and to replace penetration pricing policies with skimming policies. In addition, we demonstrate that after-sales service cannot fully mitigate the detrimental effect of feature fatigue on a manufacturer’s profits.

This study examines the bullwhip effect in a manufacturing/remanufacturing supply chain consisting of one retailer, one manufacturer, one supplier, and one third-party collector. Products returned from the end users are converted to ‘as-good-as-new’ products through remanufacturing. Both manufactured and remanufactured products are used to meet the stochastic demand of the retailer. We incorporate the price of the product in the demand through an autoregressive moving average (ARMA) pricing process, realizing a market demand that evolves according to a non-standard ARMA(1,1) process. For this demand model, we employ the order-up-to inventory policy with a minimum mean square error forecasting scheme and measure the bullwhip effect at each echelon of the supply chain. We investigate the behaviour of the bullwhip effect analytically, and a numerical evaluation shows that the bullwhip effect or the anti-bullwhip effect may occur at each echelon depending on the values of the autoregressive and moving average parameters of the underlying demand process. We also investigate the effects of the remanufacturing yield, the variance of the market shock on price, and the lead times on the bullwhip effect.

A sizable research stream in marketing finds that a strong fit between a brand extension product and its parent brand encourages positive consumer responses. Yet this large body of literature fails to provide managers with specific practical guidance about how to create brand-extension fit for optimal results. The problem is a lack of understanding of what brand-extension fit really is, and there has been little work to address this issue by synthesizing the key dimensions of brand-extension fit. The current article addresses this gap by identifying the key constituent dimensions of brand-extension fit. This is an important topic because brand extensions are essential for business renewal and growth. We identify six dimensions of brand-extension fit: feature-based, function-based, resource-based, usage-occasion-based, market-based, and image-based fit. Each dimension addresses a different aspect of brand-extension fit and suggests ways for brand managers to create brand-extension fit. Less expected is that studies that use a strict subset of these dimensions overweight those fit dimensions that are included, and the associated estimated coefficients are biased. From a managerial perspective, counterfactual analysis also shows that reliance on a strict subset of these dimensions results in suboptimal decisions.

Guarantee credit financing (GCF), allows banks to offer loans to capital-constrained retailer (he) based on the guarantee contracts offered by the manufacturer (she). Therefore, this study investigates the signaling role of guarantee contracts when the retailer is less informed about its product's market potential. In particular, we explore a joint contract of guarantee credit and production quantity to deliver the manufacturer's demand information. We add a framework to the capital-constrained supply chain with asymmetric demand information under GCF. Using a signaling game to capture the demand information asymmetry, we identify the strategic interaction between the retailer and the manufacturer under GCF. Also, we investigate the value of GCF when both GCF and trade credit financing (TCF) are viable. We find that the single guarantee contracts cannot signal the manufacturer's demand information when the production cost is less than a threshold, otherwise, only the separating equilibria survive the intuitive criterion. Besides, the findings reveal that the demand information asymmetry can either benefit or harm the retailer or the manufacturer, but cannot benefit both of them. In addition, GCF remains an attractive financing option when both GCF and TCF are viable. A joint contract of guarantee credit and production quantity can be used to deliver the demand information if the guarantee contracts are invalid. Our paper helps to explain the impact of asymmetric demand information on supply chain partners’ decisions under GCF and sheds light on the design and use of guarantee contracts.

A delivery mix that includes delivery from stock and drop-shipping is of interest to many internet retailers. We consider a retailer serving a time- and price-sensitive market with two substitutable products that differ in the guaranteed delivery time and price, an express product (delivered from the stock) and a regular product (drop-shipped). In case of stockout, customers may switch from the express product to the regular product. We study how to differentiate the products in terms of delivery times and prices and how to determine the stock level to maximize the retailer's expected profit while satisfying service constraints. We solve different variants of the problem and derive insights into the optimal retailer's strategy. In addition, we study the impact of stockout-based substitution. This paper is the first to investigate time- and price-based differentiation along with inventory decisions for a retailer who relies on a hybrid distribution to satisfy a time- and price-sensitive demand subject to stockout-based substitution. When prices and stock are fixed, in addition to minimum and maximum time differentiations, a medium differentiation strategy may be optimal but depends on the stock level. When only prices are fixed, there exists a price differentiation limit below which a minimum time differentiation is optimal, and above which only the express product should be offered. For the general model, numerical experiments show that a higher stockout-based substitution leads to greater time differentiation (which is consistent with the results of previous models) and more stock. However, this would not impact the price differentiation.

Given the increased purchasing powers in overseas markets and the existence of purchasing agents, international brands may reconsider their entering strategies. In this paper, we use a game-theoretical approach to investigate the strategy of an international brand to carve out a niche in overseas markets with considering of purchasing agents and multinational tax rules. At the beginning, the brand serves the domestic market only, and the purchasing agent acts as a domestic consumer but resells the product to the overseas market. To enter the overseas market, the brand can choose to operate an overseas store itself or sell through a cross-border e-commerce platform, with the existence of the purchasing agents. We derive the optimal strategies for the brand and the impacts of purchasing agents. Our results show that the brand prefers to enter the overseas market once the two overseas strategies are profitable but sticks to domestic market under certain circumstances, which depends on the tax policy and consumers’ valuation discount of buying from an agent. For the optimal overseas strategies of the brand, selling through a platform is more likely to be a profitable option than managing an overseas store. When both overseas strategies are profitable, the brand’s optimal choice depends on tax rules only. Interestingly, the brand does not always have the motivation to drive the purchasing agents out of the overseas market by adjusting retail prices. The existence of purchasing agents may help achieve a win-win situation for the brand and consumers under certain conditions.

Humanitarian aid organizations generally need to decide how to allocate their limited budget among different materials for disaster preparedness and disaster response. The budget can either be used before the disaster for stocking decisions under uncertainty or it can be used after the disaster to satisfy the required demand at a higher cost when the uncertainty is resolved. In this study, we aim to decide how many of each type of material to procure before and after the disaster considering the budget limitations. For this purpose, we first develop single product models with and without budget constraints using a newsvendor approach. Then, we build a two-stage stochastic model with budget constraint for the two-product case and determine its optimal solution by first solving the second stage problem to allocate the remaining budget among the products after the disaster. Then, we solve the first stage problem using KKT conditions for the resulting constrained nonlinear optimization problem. Finally, we extend our analysis for multiple product cases and propose approximation algorithms utilizing the optimal solutions derived for the single and two-product cases. We provide managerial insights using our results through detailed numerical experiments, and also present a case study with real life data. It is observed that, contrary to expectations, higher quantities need to be purchased before the disaster when there is a limited budget, as compared to the unlimited budget case. When multiple products are considered, it is important to consider the interactions between the products, especially when the budget is moderate.

Researchers have recently begun investigating how visual elements affect brand positioning. However, little is known about the effect of brand typeface features on brand premiumness. This paper proposes and verifies that letter case affects consumers’ perceived brand premiumness. Eight experiments, including one eye-tracking experiment, reveal that consumers perceive brands that use all uppercase letters (“uppercase brands”) as more premium than those that use all lowercase letters (“lowercase brands”). We refer to this effect as the “uppercase premium effect.” This effect is induced by the perceived conspicuousness of uppercase brands, a process which in turn is moderated by the product's social visibility. The effect is reversed for consumers who prefer subtle signals (“inconspicuous consumers”) because these consumers are likely to perceive a conspicuous uppercase brand as gaudy. Whereas status-motivated consumers are more likely to choose uppercase brands due to the increased premiumness perceptions, the increment in such perceptions does not further influence the purchase decisions of consumers indifferent to expressing status. We conclude with a discussion of theoretical and practical implications and offer suggestions for further research.

Today, in the market with ever-changing consumer preferences, three-dimensional (3D) printing is becoming an overwhelming trend. In this paper, we explore the use of 3D printing in mass customization (MC) programs. We consider the case when 3D printing brings extra self-design fun to consumers, which is highlighted in MC practices of the auto company BMW and the furniture company Poltrona Frau, and also changes the cost formula (i.e., the marginal product variety cost) of the MC product. In addition, the roles played by the risk attitudes of the MC manufacturer and consumers, consumer returns, as well as consumers’ time sensitive behaviors, are also uncovered. We find that under the case with a low consumer's willingness to pay for the traditional “ready-made product variety enjoyment” (RPVE), even the maximized product variety level and the additional self-design fun cannot make the 3D printing based MC more profitable. In addition, compared to the markets with risk-averse and risk-neutral consumers, the 3D printing based MC can increase more MRBs and a higher consumer surplus by simultaneously highlighting the high self-design fun and the high RPVE in a market with risk-seeking consumers. The high flexibility and responsiveness of 3D printing also shows its advantages in remanufacturing the consumer returned MC items and enhancing MC programs’ overall lead-time. Finally, applying 3D printing in creating molds can also help the MC manufacturer tackle demand uncertainties. These findings all provide a good reference to the application of 3D printing in MC operations.

We consider a large group of consumers who decide whether or not to buy a durable good offered by a firm. Without previous experience with the product, consumers rely on the ratings of past purchasers to evaluate the product goodwill and make optimal decisions. The consumers have heterogeneous intrinsic rating behaviors and preferences. For example, the population may include avid fans who tend to purchase at early stages and over-rate the product. What are the firm’s optimal pricing and marketing strategies in the face of such heterogeneous market? To answer this question, we model the problem as a Stackelberg mean-field game, with the firm acting as leader and the consumers as followers. We determine the conditions under which a Stackelberg equilibrium exists and provide a numerical scheme to compute the optimal prices, marketing activities, aggregate rating, and market share. We provide some numerical examples to illustrate the type of insights that can be obtained with our model. For instance, we show that it is useful to compare the fairness of different rating aggregators, and can anticipate herding behavior triggered by biased raters.

We study the production/inventory competition problem between two manufacturers each adopting the co-production system to process a raw material into two products in fixed proportions that are substitutable between the manufacturers. We first consider the one-period problem where the demands for the two manufacturers’ products are uncertain and independent. The unmet demand of one manufacturer's product can be met by the other manufacturer's leftover stock of the same product, if available, and is lost otherwise. The manufacturers compete for the substitute demands by choosing their own purchase/processing quantities. We show the existence of the unique Nash equilibrium processing quantity, and examine the impact of competition by comparing the total equilibrium quantity of the game with that of the centralized-decision case. We then study the multi-period production/inventory competition problem in which the manufacturers make decisions in each period according to the initial inventory levels of the products and the rivals’ decisions. We show the existence and uniqueness of the Nash equilibrium strategy and that the equilibrium strategy has the simple base-stock structure. We then consider the case where the manufacturers’ demands are correlated. In addition, studying the case where the manufacturers are capacitated, we show that the unique Nash equilibrium exists and has a modified base-stock structure. We further generalize the results to the infinite-period case and the case with more than two products. Finally, numerical studies are conducted to investigate the impacts of the model parameters on the equilibrium outcomes and get some managerial insights from the analytical findings.

Customer loyalty programs are often used by retailers as an important marketing tool. As an essential element of loyalty programs (LPs), the reward amount invested by the retailer is often linked to retailer's profit and customer behavior. We consider a retailer who sells a type of product in two periods to strategic customers. The retailer makes inventory ordering and loyalty program investment decisions. Customers decide whether to purchase immediately (in period 1) to receive the product and reward, or to wait for a markdown discount but face the risk of stockout. We have focused on three main questions: (1) How does the loyalty reward investment decision impact strategic customers’ behavior? (2) What are the optimal decisions and profits for the retailer when investing in a loyalty program? (3) What are the factors influencing the reward investment decision? We find that investing in the loyalty program induces more customers to make an early purchase, leading to a higher inventory ordering decision and more profit for the retailer. The benefits from reward investments are significantly affected by the product's cost and customers’ valuation of the product. Our results shed light on the reward investment decision of LPs. For products with high profit margins and market-determined prices (valuation approximates price), the retailer should invest in a loyalty program to achieve better profits.

The top online marketplaces in the world, such as Alibaba, Amazon, eBay and others, sold $2.7 trillion in 2020, or 62% of global web sales that year. Despite the promise of unfettered access to customers, the reality is that most sellers struggle with unprecedented unauthorized “rogue resellers.” These often anonymous, third party individuals and firms may not provide the quality experiences aligned with customers’ expectations of a brand and often market their goods in competition with trusted partners. This requires a systematic battle plan in order to effectively protect the seller’s brand equity, service promise and authorized distribution network. We describe the channel war that is at work and the forces fueling its development. We explain how rogue reseller activities drive down pricing, increase channel competition and conflict, impact customer experience, and harm brand equity. We recommend revising product policies, procedures and agreements to begin to differentiate products from those sold by unauthorized parties. We also recommend a legal strategy to complement these efforts. Critical steps include regular and active enforcement, as well as implementing services, benefits and quality controls for the brand’s products that cannot be replicated by unauthorized sellers and requiring authorized distributors and retailers to maintain those benefits and controls.

This paper develops a model for a cold chain operated among a single product manufacturer and multiple retailers to minimize the total cost of the chain. A distinguishing feature of the cold chain is that the quality of the product degrades with time. The storage temperature and time influence the degradation rate. In this context, quality can be measured using multiple distinct indices. A global stability index is used to quantify the overall quality. The non-Arrhenius model is exclusively applied to measure the quality degradation during the transportation and at a retailer's storage as the product moves in the cold chain. Optimal replenishment times for the manufacturer and retailers are decision variables and the cold chain's total cost includes the cooling cost, carbon emission cost, and loss of value in addition to the traditional supply chain costs. It is found that for a given manufacturer's replenishment time, the total cost is convex on each retailer's replenishment time. Hence, an algorithm is developed based on this property to find the optimal replenishment times for the manufacturer and retailers. This paper also investigates how specific cold chain parameters influence optimal decisions and the total cost. The analyses show that the resulting costs will be significantly higher if quality degradation is ignored in a cold chain.

Combination products with a wide range of clinical applications represent a unique class of medical products that are composed of more than a singular medical device or drug/biological product. The product research and development, clinical translation as well as regulatory evaluation of combination products are complex and challenging. This review firstly introduced the origin, definition and designation of combination products. Key areas of systematic regulatory review on the safety and efficacy of device-led/supervised combination products were then presented. Preclinical and clinical evaluation of combination products was discussed. Lastly, the research prospect of regulatory science for combination products was described. New tools of computational modeling and simulation, novel technologies such as artificial intelligence, needs of developing new standards, evidence-based research methods, new approaches including the designation of innovative or breakthrough medical products have been developed and could be used to assess the safety, efficacy, quality and performance of combination products. Taken together, the fast development of combination products with great potentials in healthcare provides new opportunities for the advancement of regulatory review as well as regulatory science., Graphical abstract Image 1, Highlights • Full overview of the origin, definition and designation of combination products. • Regulatory review and evaluation of the safety and efficacy of combination products. • Regulatory science and its future perspectives of combination products.

Platform-hardware business models (e.g., integrating a software technology platform into a hardware device such as a smartphone) have been widely applied in various industries. This study investigates the response of competing original equipment manufacturers (OEMs) when the developer strategically updates the platform quality. Consumers have two-dimensional valuations of the product: one valuation for the device provided by the OEM and another valuation for the platform provided by the developer. The developer offers two types of agreements: acceptance and rejection of preinstallation of core services that profit the developer. OEMs can preinstall the core services and pay nothing to use the platform, or they can pay a wholesale price for the platform and reject the installation of the core services. We propose a game-theoretic model to examine the strategic agreement choices of dominant and weak OEMs when the developer strategically offers an updated platform quality to enhance a weak OEM's competitiveness. We find that the developer benefits from the OEMs’ acceptance only when the exogenous wholesale price for the dominant OEM is low and the cost difference is moderate. Also, when the research and development (R&D) efficiency is high, an increase in the cost difference induces a conversion from acceptance to rejection for the dominant OEM, and an opposite outcome for the weak OEM. One OEM's installation acceptance may enhance or weaken the advantage of the other OEM's acceptance for the consumer surplus, depending on the value of the R&D efficiency. Several extensions are examined to complement the basic model, and implications behind each strategic choice are revealed.

High-utility itemset mining has evolved as an essential and captivating research topic. It aims to extract the patterns/itemsets having high utility value; hence, they are called high utility itemsets (HUIs). From a business perspective, a utility can be the benefit associated with the sale of a particular item or the usefulness or satisfaction that a customer experiences from a product. The economic utilities are helpful to evaluate the drivers behind a customer’s purchase decision. The advances in information technology have enabled us to access the datasets related to various domains like health care, stock market, market-basket, education and bioinformatics. Companies strive to increase the utility value of their products and share their customer’s transactions data to extract high utility patterns to achieve global customer insights. However, this can lead to massive security and privacy risk if their competitors misuse the patterns that can disclose their confidential information. Privacy-preserving utility mining (PPUM) is a branch of privacy-preserving data mining (PPDM) that presents various algorithms which intend to hide sensitive high utility itemsets (SHUIs) and maintain a balance between utility-maximizing and privacy-preserving. In this paper, two SHUIs hiding algorithms, MinMax and Weighted, are proposed with three variants of each algorithm. Experiments on various datasets show that proposed algorithms perform better than the existing SHUIs hiding algorithms as fewer distortions of non-sensitive knowledge occur. This study uses six performance evaluating metrics to assess the proposed algorithms against compared algorithms.

Background aims The novelty of cell and gene therapies (CGTs) has introduced unique supply chain challenges and considerations not seen by chemically synthesized (small-molecule) drugs. These complexities increase during the clinical phases, where drug safety and efficacy milestones are still underdeveloped. For example, for autologous therapies such as chimeric antigen receptor T-cell therapies, in which the treatment is developed from the patient's own cells, supply chain management plays an integral role in chemistry, manufacturing and control processes. Supply chain management requires proactive planning because of the strict cold chain requirements and time sensitivity of CGTs. This research examines strategies and responses to challenges experienced by industry stakeholders (e.g., sponsors and manufacturers) during the implementation phases of clinical supply chain management. This research further evaluates the adequacy of the current regulatory framework for distribution and supply chain management of CGTs in the US. Methods A survey methodology was used to query subject matter experts from the biopharmaceutical industry who were familiar with the clinical supply management of CGTs in the US. The survey instrument was developed using an implementation framework and disseminated electronically to mid- and senior-level subject matter experts who had experience with clinical trials, supply chain management and CGTs. Results A total of 128 respondents accessed the survey, and 105 respondents answered at least one question. Seventy-five respondents completed the survey. Results showed that a lack of harmonization in regulations across the supply chain, limited resources, challenges with vendor management, high costs and complexities in the supply chain due to product specificity and customization proved to be impediments for the industry. In addition, the coronavirus disease 2019 pandemic had a significant impact on supply chain implementation. The results revealed that less than half of the respondents had business continuity plans in place. These challenges increased for smaller and mid-size organizations. Thirty percent of small and mid-size organizations were less prepared to scale up than larger companies. Conclusions Suggestions from industry stakeholders were to adopt and enforce Good Distribution Practices in the US (81%), pre-plan distribution strategies with internal and external stakeholders along the supply chain and develop agile systems and robust processes end to end. Hurdles in scaling up and scaling out from the clinical to commercial phases for time- and temperature-sensitive CGT products make it difficult to predict the supply chain's long-term feasibility. Although there are initiatives to improve these impediments, such as improving industry partnerships and creating global CGT transportation standards, there are still regulatory knowledge gaps present for CGTs. Therefore, it is essential to establish a baseline and foundation for CGT supply chains extending beyond the loading dock.

Improved supply chain optimization strategies can play a major role in addressing global food security and safety in years to come. In particular, tighter safety regulations, changing consumer quality requirements and more stringent market competition call upon integrated supply chain decision-making frameworks that explicitly consider product quality control. This effort requires metrics of quality that accurately reflect product physico-chemical properties, as well as consumer purchasing preferences. However, a critical challenge linked to embedding the complex dynamics of the evolution of product quality in time within supply chain models is the large-scale nature of the ensuing optimization problems, which are computationally intractable even for moderate-size, single-item systems. In the present work, we introduce a computationally efficient optimal production and distribution planning framework for perishable products having multiple quality attributes that evolve in time as a function of environmental conditions during shipment and storage. We also propose a model reduction strategy and a decomposition framework that enhance the scalability of our approach. We perform extensive numerical simulations using different network instances to validate our theoretical findings, as well as to demonstrate the advantages of the proposed supply chain management scheme.

The aggravation of environmental issues makes green manufacturing become inevitable and fuzzy uncertainties prevail in supply chain management. To further promote the development of green supply chain, this paper considers a two-echelon green supply chain models with governmental interventions composed of one supplier and one retailer under fuzzy uncertainties, in which the parameters of demand function and manufacturing cost are all characterized as fuzzy variables. Then the equilibrium decisions of the expected value and chance-constrained programming models are derived considering the different risk references of the supplier and the retailer. At the end, numerical examples are presented to demonstrate the theoretical underpinning of proposed models. Analytical results indicate that the supplier and the retailer can obtain different equilibrium decisions by adjusting different confidence levels. The equilibrium decisions reflect the risk attitudes of the supplier and the retailer to the uncertainties in supply chain system and their different predictions on possible level. Then, whether the supplier as a leader has the first-mover advantage is related to the risk attitudes of the supplier and the retailer when dealing with the uncertainties of the green supply chain system. Moreover, the strong governmental interventions can coordinate not only the conflict between pricing and green level decisions, but also the contradiction between the consumers and the supplier. In addition, the retailer may be the main puller of the green supply chain in developing the green product.

Abstrac The aim of this study is to examine modal usage for shippers with limited transportation options to assess rail and truck quality of service. While service quality is an important consideration in shippers' mode choice decisions, freight studies often assume that shippers minimize total transportation costs subject to shipping rates. We test the cost minimization hypothesis using data on wheat shipments for individual grain elevators in the U.S. Great Plains where shippers are limited to rail and truck services with no direct access to navigable waterways and find that shippers' use a higher share of rail than they would if shipping rates alone determined their use of rail. This observation, in tandem with recent shipper surveys showing higher than expected ratings of service quality, suggests that shippers’ modal choice of rail likely stems from a perception of higher quality of service provision. Moreover, this suggests that railroads have provided high service quality despite perpetual captivity concerns. Our results support the idea that even in markets with limited intermodal competition, other types of competition, including geographic, product, and intramodal competition are sufficient to compel railroads to provide a level of service that meets or exceeds expectations.

We consider a fast-fashion brand that cooperates with a luxury brand, jointly launching a co-branded product. The impacts of the co-branding on the two brands’ original product lines are uncertain, and each brand can be either risk averse or risk neutral. Consumers, driven by exclusivity or conformity, are classified as either snobs or conformists. The equilibrium retail price, quality investment, and investment support are derived. The optimal market-targeting strategy for the fast-fashion brand as marketer of the co-branded product, is identified. We show that the fast-fashion brand is willing to give up either the conformist or the snob market under certain conditions, even when it has sales in both markets. In addition, given a particular market-targeting strategy, the fast-fashion brand benefits from its own risk aversion if the cost of risk is low, but the luxury brand is always worse off; the luxury brand may benefit from the fast-fashion brand’s risk aversion, however, if the market-targeting strategy is changed. Both brands are worse off when the luxury brand is risk averse.

We study how to design an original equipment manufacturer’s (OEM’s) direct selling channel in a multi-market setup such that the OEM experiences sustained business growth without sacrificing its brand customers’ profits. In this paper, we consider an OEM producing for a brand customer that operates in two markets: the domestic market in which the OEM resides and the international market (i.e., other mature markets). The OEM can offer its brand customers at a discount price in exchange for using the excess capacity to produce products under the OEM’s own brand and then sell these products through its channel. We build a game theoretical model in a Stackelberg setting in which the OEM is the leader and the brand is the follower, and determine their optimal pricing decisions and the associate profits under two different dual channel settings (i.e., one in which the brand cannot flexibly change its retail price and one in which it can). Contrary to the first-order intuition of market cannibalization, we find that the OEM direct selling channel can be a win-win-win strategy for the brand (gaining higher profit margin in the international market), the OEM (gaining a higher market coverage in the domestic market), and consumers in the domestic market (buying the product at a cheaper price). Such insights are generally robust, even when we consider a cost for the OEM’s selling channel, and/or consider a price constraint enforced by the brand, such that the OEM avoids selling the same product at too low a price.

The demand decreases as the price increases. Also, the demand rate for fresh goods depends on its freshness, which can be assessed by its sell-by date. Furthermore, a large pile of consumer goods displayed in a supermarket often provokes consumers to buy more due to its freshness, variety, or visibility. With larger business transactions, the supplier usually requests a good-faith deposit (or an advance payment) to discourage the order cancelation. On the other hand, the retailer likes to hold some portion of the purchase amount until the business transaction is completed and satisfactory (or a credit payment). Consequently, an advance-cash-credit (ACC) payment scheme, which is a combination of advance, cash, and credit payments, becomes commonly used in business transactions. In this study, we explore an inventory system in which: demand curve depends on unit price, displayed volume, and sell-by date; and the supplier and the retailer agree with an ACC payment scheme. The objective for grocery stores is to maximize the total profit by finding its price and order cycle simultaneously. We derive theoretical results, and then propose an algorithm. Finally, we perform sensitivity analyses to gain the following managerial insights: (i) An increase in stock efficiency significantly reduces selling price and cycle time while tremendously raising total profit. (ii) Credit payment generates the lowest selling price but the highest profit among all payment types. (iii) An increase in shelf life through preservation technology investment significantly raises total profit while slightly increasing the unit price and cycle time.

Grit—or perseverance in striving for long-term goals despite adversity, failure, and challenge (Duckworth et al., 2007)—has remained a focus in education and psychology for over a decade. Existing research, however, has yet to explore how grit might affect and guide consumption. In this article, we identify grit as an influential factor. Through one field study and three experiments, we reveal that individuals higher (vs. lower) in grit are less likely to choose and purchase indulgent foods. This occurs because individuals higher in grit perceive avoiding indulgent foods as more goal-relevant. However, changing the descriptive properties of the food using just-below (e.g., 199 vs. 200) calorie information can entice gritty consumers toward indulgence. Overall, these results show that grit impacts consumers’ product choices, and that businesses should consider grit when marketing and selling products that vary according to indulgence.

BACKGROUND: The health benefits related to intake of whole grain foods are well established. Consumption of whole grains in the US population is low, and whole grain content can vary greatly depending upon the specific products that are purchased. OBJECTIVES: To examine the proportion of products purchased by US households containing whole grain and refined grain ingredients using time-specific food composition data, and examine whether purchases differ between income, race or ethnicity, and household make-up. METHODS: Nationally representative Nielsen Homescan 2018 data were used. Each barcoded product captured in Nielsen Homescan 2018 was linked with ingredient information using commercial nutrition databases in a time-relevant manner. Packaged food products containing whole grain ingredients, refined grain ingredients, neither, or both were identified. The percentage of packaged food products containing whole grain and refined grain ingredients purchased by US households was determined overall, by demographic subgroup, and by food category. RESULTS: The proportion of packaged food purchases containing refined grain ingredients was significantly higher than whole grain ingredients (30.9% compared with 7.9%; P 90% of bread purchases contained refined grain ingredients, and the 5 categories with the largest proportion of whole grain ingredients contributed to

Official prices are commonly publicized by firms as reference prices to influence consumers’ valuation of products. By characterizing the reference effect as a general nondecreasing concave function of the official price, we study the timing of pricing decisions in dual distribution channels where a manufacturer distributes the product by a direct channel and an indirect retail channel. We investigate two fundamental pricing policies wherein the manufacturer and the retailer put forward their selling prices simultaneously (ST) or sequentially (SQ). Counter intuitively, our analytical results demonstrate that despite facing a more severe downstream price competition, the manufacturer should charge a higher (rather than a lower) official price in the ST setting than in the SQ setting. In addition, with the reference effect of official price, the ideal case for the manufacturers that operate dual distribution channels is to let the direct channel focus on promoting products and let the retail channel function be the major selling channel, an idea which is supported by the practices of Nike, Apple, and many other leading brands around the world. Furthermore, our numerical examples disclose that the dual-channel efficiency under both decentralized cases (i.e., ST and SQ) increases in the magnitude and variability of the reference effect and the retail channel cost, but decreases in the unit direct channel cost. Finally, we extend our model to incorporate an online direct channel and an offline retailer. We find that, under all of CE, ST and SQ scenarios, the shipping fee charged in the online direct channel will reduce the manufacturer’s total profits by lowering her optimal official prices and direct-sales quantities simultaneously. Managerial implications of this study are discussed.

Background The rise of investigative and commercially available cell therapy products adds a new dynamic to academic medical centers; that is, the management of patient-specific cell products. The scope of cell therapy has rapidly expanded beyond in-house collection and infusion of cell products such as bone marrow and peripheral blood transplant. The complexities and volumes of cell therapies are likely to continue to become more demanding. As patient-specific “living drugs,” cell therapy products typically require material collection, product provenance, transportation and maintenance of critical quality attributes, including temperature and expiration dates. These requirements are complicated by variations in product-specific attributes, reporting requirements and interactions with industry not required of typical pharmaceuticals. Methods To manage these requirements, the authors set out to establish a framework within the Immune, Progenitor and Cell Therapeutics Lab, the Current Good Manufacturing Practice facility responsible for cell manufacturing at Mayo Clinic Rochester housed within the Division of Transfusion Medicine. The authors created a work unit (biopharmaceutical unit) dedicated to addressing the specialized procedures required to properly handle these living drugs from collection to delivery and housing the necessary processes to more easily integrate externally manufactured cell therapies into clinical practice. Results The result is a clear set of expectations defined for each step of the process, with logical documentation of critical steps that are concise and easy to follow. Conclusions The authors believe this system is scalable for addressing the promised growth of cell therapy products well into the future. Here the authors describe this system and provide a framework that could be used by other centers to manage these important new therapies.

This paper analyzes partnerships between foreign and domestic firms as a source of signaling advantages, considering the case of virtual codesharing in aviation. Virtual codesharing is an important type of cooperative agreement between airlines that does not change any real product attributes and hence provides the opportunity to identify this signaling effect. Roughly 75% of the flights in trans-Atlantic and Pacific markets involves codesharing. Typically, the consumer buys the ticket with the domestic airline, while a foreign airline operates the flight. Analyzing individual-level choice data from a representative sample of Australian air travelers, we find that the average consumer is willing to a pay a premium between 4 and 5.5% of the ticket price when a flight by a foreign carrier is codeshared with the national carrier Qantas. When flying to a less familiar destination, risk averse consumers are willing to pay a premium about two times higher than non-risk averse consumers, suggesting that partnerships have a strong impact on consumer valuation through reducing perceived risks and uncertainties associated with alien carriers. By this mechanism, home country bias may spillover to foreign partners of domestic firms.

Protected geographical indications (PGIs) are an increasingly popular strategy for securing price premiums on food products in the interest of preserving associated rural livelihoods, however they often fail to show clear benefits for small or otherwise marginalized farmers. Their environmental record for supporting low-impact farming practices is also mixed. This case study of the PGI for Spanish almond-based desserts Turron de Jijona and Turron de Alicante details a yet unstudied form of such failures, where manufacturers seeking to scale production removed the requirement to source their primary ingredient (almonds) from the region's low-input farms and opted for intensively produced ingredients imported from abroad. This uprooting of turron from its agroecological origins highlights how the GI designation creates tension between the need to legitimate a product as rooted in traditional agrarian lifeways and the incentive to expand exports by industrial means. While previous studies show how such tension can result in agricultural intensification benefitting larger landholders within a given region, I argue this inequity and ecological risk operates at a global scale. Importantly, the turron case reveals that “quality” of a product is a moving target shaped not only by culinary legacies but also forged by a global political economy of agriculture suffering chronic overproduction. Finally, the role of the state in structuring PGI institutions more inclusively and adjudicating conflicts among actors along the value chain is fundamental to the capacity of these programs to achieve social and environmental goals.

The micro, small, and medium-sized business (MSMEs) industry is the economy's growth engine. Micro-Small and Medium Enterprises have a greater Labour strength than large businesses. Micro-Small and Medium Enterprises account for most jobs in any sector. Waste reduction, quality improvement, time management, environmental sustainability, and growth measures are all major issues for Micro-Small and Medium Enterprises. As a result, any business needs a good team and a long-term business model to succeed. One of the most reliable designs for product and quality development, waste reduction, and problem-solving is the 8Ds methodology. As a result, the goal of this 8Ds methodology is to adopt and investigate a simple sustainable work method that aids in the reduction of time decay and waste. Micro-Small and Medium Enterprises have a high rate of manufacturing defects, which leads to company failure and a decline in sales and revenue. The 8Ds work method entails forming a proactive team to tackle these questions. The primary goal of this research paper is to identify a problem with both the gearbox and vibrator in the well-known tobacco industry and to use the Eight Disciplines(8D) methodology to minimize breakdown time and repair costs of the machinery. All these details were gathered from various industrial processes to research and evaluate the 8d methodology. The 8d approach, root cause analysis, and fishbone diagram were also used to solve the problem described above. After changing the gear material and using lubricants between the gear and the vibrator, the breakdown time was reduced. Following a successful implementation of the changes, the break manufacturing time of the tobacco bundle was reduced by 29 percentage and defects were reduced by 72 percentage as well as the equipment cost of maintenance.

Packaging accounts for the largest demand for global plastic consumption and around 60% is for food and beverage packaging. The amount of packaging is increasing rapidly due to the expansion of retailer industries, especially in supermarkets and convenience stores. Plastic recycling strategies for food packaging in retailer industries need to be developed, but the current consumption and recycling status is not clear. To address this knowledge gap, this study quantifies the food packaging generation from major supermarket and convenience store chains in Taipei, Taiwan. We focus on the composition, recycling habit, and the recycling status to evaluate the packaging recyclability and major pathways for material losses. Based on our field survey and analysis, the total amount of plastic packaging generation was 21846.04 tons in 2020 with 64.99% of it being recycled. We defined the recycling rates as the continued product of Recyclable Content Ratio of the packaging itself, Sorting Accuracy Ratio of consumers, and Re-granulated Ratio in the final treatment facilities. These three ratios for major food categories were also presented to identify the hotspots of material losses. Our results suggest that to promote plastic food packaging recycling, identifying the limitations in different stages and designing corresponding strategies is crucial.

Industry 4.0 encourages the combination of latest technologies and manufacturing systems. The Additive Manufacturing (AM) may play a fundamental role in Industry 4.0 as it has capability of turning a 3D model data into a product without much human intervention. Moreover, the requirement for eliminated tools and fixtures and material waste make it suitable for industry of the future. The 3D model data, generated using modeling software is utilized in order to fabricate a three-dimensional object. However, the modeling software has some limitations regarding the conversion of the 2D digital image into a 3D model. It makes difficult direct additive manufacturing of 2D digital image. Although, there are some online resources available for this purpose. However, the detailed discussion on the algorithm is not available. Therefore, this article presents an approach for additive manufacturing of lithophane using 2D digital image. It mainly concentrates on the development of Graphical User Interface (GUI) for generating point cloud data and STL using a 2D digital image in MATLAB environment. The developed algorithm is discussed in detail for lithophane fabrication. The proposed approach takes 2D digital image from the user and converts it into 3D model data. Furthermore, the material deposition tool path is also prepared using generated 3D model data. Finally, the proposed approach is validated and tested by performing additive manufacturing. The obtained results have shown the conformity between the additive manufactured lithophane and given a 2D digital image. The obtained results of the current study may be useful in industry 4.0 for fashion and jewelry products.

The exponential growth road network in our country coupled with large scale industrialization and exodus of population towards the metros and Tier II cities has led to need to develop road network in our country. Bitumen is the most abundantly used raw material for construction of roads which is a bye product obtained from crude oil refineries which is a very limited resource in our country. The dependence of the bitumen sucks huge amount of our foreign reserves thus adversely affects our economy. India is a fast developing country and roads are considered to be life line of the economy of our country. Successive governments have laid emphasis on building the roads, airports, highways etc. due to which the requirement of bitumen is increasing exponentially. Thus there is a need to find alternate materials which can replace bitumen to the maximum possible extent and also be cost effective. To deal with the problem, study on use of epoxy as partial replacement to bitumen in flexible pavement is considered in the present work. Various experiments in labs were conducted to investigate the effect of epoxy resin and bitumen mix and the results were analyzed using Marshall Stability method and then compared with the regular bitumen roads. The results obtained indicate substantial gain in strength thus prompting the use of epoxy as a replacement of bitumen.

With the enormous growth of the population, the intense improvement of the electronic industry, and the ever-incrementing use of mobile phones in today's life, the proper disposal of waste mobile phones has been of paramount importance. Consequently, the tremendous volume of end-of-life (EOL) mobile phones worldwide calls for a sustainable management system to decide on waste mobile phone recovery to minimize the cost and environmental impact. This paper presented a decision problem, namely, EOL option determination, to decrease mobile phone waste. Thus, this study suggests a decision support system (DSS) for the iPhone mobile phone (a subset of the electronics industry) in the EOL phase. Customers’ pervading use of social media has led to these platforms being used as a rich data source to extract information. Thus, the proposed DSS analyzes the Twitter databases, collecting mobile phone defect information from the customers. We suggest an ontology-based text mining and a data mining-based technique through the self-organizing map (SOM) for information discovery from the Twitter data. Finally, a multi-objective mathematical model based on sustainability dimensions is developed to correct EOL decisions based on the defective mobile phone components analyzed from Twitter data. The proposed DSS helps manufacturers when a product is returned and decide for proper EOL processes. This study provides a novel insight and can serve as a valuable reference for solving waste management problems using social media data.

Recent improvements in the fused filament fabrication (FFF) technique have extended its usage for the manufacturing of finished products in a variety of engineering fields. However, some challenges are to be overcome including moderate mechanical, thermal, and physical properties of finished products. This paper reviews ways of improving these properties during three phases of manufacturing namely pre-processing phase, in-situ/on-site processing phase, and post-processing phase. A systematic review of these three phases is not much explored in the additive manufacturing process and hence there is a lot of scope for innovation in improving the quality of the finished product. Results of the review are finally concluded and it is found the approaches presented here in this paper resulted in increasing the performance of the fabrication process and allow for the fabrication of products with better mechanical, thermal and physical capabilities.

As the world trade conflict intensifies in the photovoltaic (PV) industry recently, it would be important and timely to investigate the impact of the countervailing measure on the global PV supply chains and the non-conventional trade policy to mitigate the worsening trade effect. A simplified dual international competing PV supply chain system is developed to formulate supply chain game-theoretical models for the investigation. It is found that export subsidy (ES) scenario creates the highest total demands, supply chain profits and social welfare but at the cost of the importing country before it escalates to the most serious countervailing (CD) scenario. The key findings echo the classical trade conflict studies that any unfair trade policy will benefit only the country exercising the policy and will eventually lead to escalation of the trade conflict and bring economic damages to the trading stakeholders. A theoretical policy analysis is conducted to explore the impact on the supply chain when the export subsidizing country shares its economic gains with the importing country who might be planning to levy the countervailing duty on the imported solar product. The finding shows the gain sharing policy is possible to offset the economic loss and hostility of the importing country but still create more economic gains for the export subsidizing country. The gain sharing concept, though still in its theoretical phase, provides a more sustainable policy than the conventional retaliation approach which hinders the global solar supply chain growth, and prolongs the current head-on trade conflict between largest world economies.

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing companies.To support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies.