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The purpose of this paper is to introduce a new probability density function having both unbounded and bounded support with a wider applicability. While the distribution with bounded support on [0, 1] has applications in insurance and inventory management with ability to fit risk management data on proportions better than existing bounded distributions, the same with unbounded support is used as a lifetime model and is considered as an attractive alternative to some existing models in the reliability literature.

The new density function, called modified exponential-geometric distribution is derived from the exponential-geometric distribution introduced by Adamidis and Loukas (1998). The support of the density function is shown to be both unbounded and bounded depending on the values of one of the shape parameters. Various properties of the density function are studied in detail and the parameters are estimated through maximum likelihood method of estimation. A number of applications related to reliability, insurance and inventory management are exhibited along with some useful data analysis.

A single probability distribution with both unbounded and bounded support, which does not seem to exist in the reliability literature, is introduced in this paper. The proposed density function exhibits varying shapes including U-shape, and the failure rate also shows increasing, decreasing and bathtub shapes. The Monte Carlo simulation shows that the estimates of the parameters are quite stable with low standard errors. The distribution with unbounded support is shown to have competitive features for lifetime modeling through analysis of two data sets. The distribution with bounded support on [0, 1] is shown to have application in insurance and inventory management and is found to t data on proportions related to risk management better than some existing bounded distributions.

The authors introduce an innovative probability distribution which contributes significantly in insurance and inventory management besides its remarkable statistical and reliability properties.

This paper explores the effectiveness of using blockchain technology to solve financial constraints faced by small- and medium-sized suppliers in a capital-constrained supply chain.

To characterize the impact of blockchain on credit period and enterprise credit level, the study formulates a newsvendor model to analyze a supply chain in which a financially constrained supplier sells products to a financially sound manufacturer, subject to uncertain demand. The study investigates three repayment methods: the benchmark case without blockchain and two blockchain-enabled cases with the hybrid repayment mode and single repayment mode (SRM), respectively. The study derives and compares the equilibria under each repayment method to characterize their impact.

When the bank interest rate is low and the carbon cap is also low, choosing to implement blockchain technology leads to higher profitability for the manufacturer than not utilizing it. Within the framework of blockchain technology, when comparing the two repayment models, the manufacturer exhibits a preference for SRM. Furthermore, under specific conditions of the bank interest rate, blockchain technology can effectively facilitate consensus among supply chain members in terms of channel selection.

The results derived in this paper provide novel managerial implications to the capital-constrained members in terms of pricing decisions and order quantity under demand uncertainty considering blockchain technology, which transfers the creditor's rights to the bank and shortens the collection time. In addition, blockchain technology enables efficient and intelligent collaborative development of supply chains, which can reduce carbon emissions during the transportation of goods.

Few studies incorporate blockchain technology into supply chain finance, and this paper considers the credit period and capital's time value for a capital-constrained supplier facing the adoption of blockchain technology within a stochastic demand environment.

The increasing prevalence of a wide range of infectious diseases, as well as the underwhelming results of vaccination rates that may be traced back to problems with vaccine procurement and distribution, have brought to the fore the importance of vaccine supply chain (VSC) management in recent years. VSC is the cornerstone of effective vaccination; hence, it is crucial to enhance its performance, particularly in low- and middle-income countries where immunization rates are not satisfactory.

In this paper, the authors focus on VSC performance improvement of India by proposing supply contracts under demand uncertainty. The authors propose three contracts – wholesale price (WSP), cost sharing (CS) and incentive mechanism (IM) for the government-operated immunization program of India.

The authors' findings indicate that IM is capable of coordinating the supply chain, whereas the other two contracts are inefficient for the government. To validate the model, it is applied to a real-world scenario of coronavirus disease 2019 (COVID-19) in India, and the findings show that an IM contract improves the overall efficiency of the system by 23.72%.

Previous studies focused mainly on the influenza VSC industry within developed nations. Nonetheless, there exists a dearth of literature pertaining to the examination of supply contracts and their feasibility for immunization programs that are administered by the government and aimed at optimizing societal benefits. The authors' findings can be beneficial to the immunization program of India to optimize their VSC cost.

Based on the traditional buyback model, this paper aims to propose a new buyback method – the variable buyback contract – to solve the serious inventory backlog in the current economic situation.

In this paper, the authors further study the buyback problem in a two-level supply chain with uncertain demand. Such a problem can be found in many research papers, which also use the Stackelberg game model. They put forward many factors that affect the buyback price, including risk preference, random arrival of consumers, etc. Different from the existing research, the authors propose another factor that may affect supply chain buyback – the retailer's remaining inventory to study the buyback contract.

First, the authors found that under the variable buyback contract, there is an optimal retail price, wholesale price and an optimal range of parameter settings for the buyback price. Second, the proposed Pareto-optimal solution for system improvement can achieve supply chain coordination. Third, under some conditions, the variable buyback contract is better than the wholesale price contract and fixed-price buyback contract.

First, this is the first paper to discuss to measure the buyback price with the retailer's remaining inventory. Second, the proposed buyback contract can help decision-makers to choose the optimal improvement strategies. Third, this contract has a certain practical significance, which can effectively alleviate the current inventory backlog problem.

The online direct selling mode has been widely accepted by enterprises in the O2O era. However, the dual-channel (online/offline, forward/backward) operations of the closed-loop supply chain (CLSC) changed the relationship between manufacturers and retailers, thus resulting in channel conflict. The purpose of this paper is to take a dual-channel operations of CLSC as the research target, where a manufacturer sells a single product through a direct e-channel as well as a conventional retail channel; the retailer are responsible for collecting used products in the reverse supply chain and the manufacturer are responsible for remanufacturing.

The authors build a benchmark model of dual-channel price and service competition and take the return rate, which is considered to be related to the service level of the retailer, as the function of the service level to extend the model in the reverse SC. The authors then analyze the optimal pricing and service decision under centralization and decentralization, respectively. Finally, with the revenue-sharing factor, wholesale price and recycling price transfer payment coefficient as contract parameters, the paper also designs a revenue-sharing contract led by the manufacturer and explores in what situation the contract could realize the Pareto optimization of all players.

In the baseline model, the results show that optimal price and service level correlate positively in centralization; however, the relation relies on consumers’ price sensitivity in decentralization. In the extension model, the relationship between price and service level also relies on the relative value of increased service cost and remanufacturing saved cost. When the return rate correlates with the service level, a recycling transfer payment can elevate the service level and thus raise the return rate. Through analyzing the parameters in revenue-sharing contract, a point can be reached where lowering the wholesale price and raising the transfer payment coefficient will promote retailers to share revenue.

Many enterprises establish the dual-channel distribution system both online and offline, which need to understand how to resolve their channel conflict. The conflict is especially strong in CLSC with remanufacturing. The result helps the node enterprises realize the coordination of the dual-channel CLSC.

It takes into account the fact that there are two complementary relationships, such as online selling and offline delivery; used product recycling and remanufacturing. The authors optimize the strategy of product pricing and service level in order to solve channel conflict and double marginalization in the closed-loop dual-channel distribution network.

The purpose of this paper is to study the impact of differentiation price which has been utilized to segment demand, but results in imperfect segmentation. The use of a differentiation price is among the most widely used Revenue Management (RM) techniques to segment a firm’s demand to augment profitability.

Mathematical models are developed for a firm’s RM which use a differentiation price to categorize its market demand into two segments. Three distinct demand situations are considered: price-dependent deterministic demand, price-dependent stochastic demand whose distribution is known and price-dependent stochastic demand whose distribution is unknown. Models are analyzed to determine optimal joint control of a firm’s pricing and inventory decisions for each market segment.

The analysis of the firm’s RM model has shown that revenue is jointly concave in pricing and order quantity. In most demand situations, closed-form mathematical expressions for optimal pricing and inventory are obtained.

In RM models developed in this paper, a firm only selects a differentiation price. Thus, an optimal selection of the differentiation price along with the pricing and inventory decisions may lead to an additional profitability which has not been explored in this research.

The findings reported are relevant to RM managers and practitioners and help them to calibrate their optimal revenues by segmenting markets using a differentiation price.

This paper provides a quantitative perspective of a firm’s decision on the use of the differentiation price and the market response.

The paper provides a firm’s optimal decision on pricing and inventory when it experiences demand leakage due to categorizing its market demand into two segments using a differentiation price.

Almost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a fault-free software is not possible. Also due to market competition, firms do not want to delay their software release. But early release software comes with the problem of user reporting more failures during operations due to more number of faults lying in it. To overcome the above situation, software firms these days are releasing software with an adequate amount of testing instead of delaying the release to develop reliable software and releasing software patches post release to make the software more reliable. The paper aims to discuss these issues.

The authors have developed a generalized framework by assuming that testing continues beyond software release to determine the time to release and stop testing of software. As the testing team is always not skilled, hence, the rate of detection correction of faults during testing may change over time. Also, they may commit an error during software development, hence increasing the number of faults. Therefore, the authors have to consider these two factors as well in our proposed model. Further, the authors have done sensitivity analysis based on the cost-modeling parameters to check and analyze their impact on the software testing and release policy.

From the proposed model, the authors found that it is better to release early and continue testing in the post-release phase. By using this model, firms can get the benefits of early release, and at the same time, users get the benefit of post-release software reliability assurance.

The authors are proposing a generalized model for software scheduling.

This study aims to identify factors influencing the bank failure rate in the USA over the period from 1970 to 2014 with an emphasis on economic/financial factors on the one hand and on banking legislation on the other hand. Regarding the latter, this study empirically investigates four major banking statutes: the Community Reinvestment Act of 1977; the Depository Institutions Deregulation and Monetary Control Act of 1980; the Federal Deposit Insurance Corporation Improvement Act of 1991; and the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. After adopting the technique of generalized method of moments (GMM), a robustness check in the form of autoregressive conditional heteroskedasticity (ARCH) is undertaken. Overall, the estimations imply that the bank failure rate was a decreasing function of the percentage growth rate of real gross domestic product (GDP) and the real interest rate yields on both three-month US Treasury bills and 30-year fixed-rate mortgages and an increasing function of the real cost of funds. In addition, there is strong evidence that the bank failure rate was increased by provisions in the Community Reinvestment Act of 1977 and the Depository Institutions Deregulation and Monetary Control Act of 1980, whereas the bank failure rate was decreased as a result of provisions in the Federal Deposit Insurance Corporation Improvement Act of 1991 and the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. Finally, there also is evidence that higher federal budget deficits elevated the bank failure rate.

After modeling the bank failure rate as a function of financial/economic variables and banking legislation, the times series from 1970 to 2014 is estimated by GMM and then by the ARCH techniques.

The results of the GMM and ARCH estimations imply that the bank failure rate in the US was a decreasing function of the percentage growth rate of real GDP as well as the real interest rate yields on both three-month US Treasury bills and 30-year fixed-rate mortgages and an increasing function of the real cost of funds. Furthermore, there is strong empirical support indicating that the bank failure rate was elevated by various provisions in the Community Reinvestment Act of 1977 and in the Depository Institutions Deregulation and Monetary Control Act of 1980, while the bank failure rate was reduced by certain provisions in the Federal Deposit Insurance Corporation Improvement Act of 1991 and the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. There also is evidence that higher federal budget deficits increased the bank failure rate.

This study is the most contemporary (1970-2014) analysis of potential causes of the bank failure rate in the USA. The study also may be the first to apply the GMM and GARCH models to the problem. Also, some interesting policy implications are provided in the Conclusion.

The purpose of this paper is to compare four life data models, namely the exponential and the Weibull models, and their corresponding generalized versions, q-exponential and q-Weibull models, by means of one practical application.

Application of the models to a practical example (a welding station), with estimation of parameters by the use of the least squares method, and the Akaike Information Criterion (AIC).

The data of the example considered in this paper is divided into three regimes, decreasing, constant and increasing failure rate, and the q-Weibull model describes the bathtub curve displayed by the data with a single set of parameters.

The simplicity and flexibility of the q-Weibull model may be very useful for practitioners of reliability analysis, and its benefits surpasses the inconvenience of the additional parameter, as AIC shows.

The q-Weibull model is compared in detail with other three models, through the analysis of one example that clearly exhibits a bathtub curve, and it is shown that it can describe the whole time range with a single set of parameters.

As productions show characteristics of multi-varieties and small batch in a recent new product system, it is more difficult to acquire its failure rate data. With the help of expert experience information, the authors can get the interval estimation of failure rate data under different methods, so how to make the interval convergence with the new information is an important problem to be solved. The paper aims to discuss this issue.

In this paper, the concept of generalized standard grey number is used to characterize the multi-source heterogeneous uncertainty failure rate data into a unified framework. Then, the engineering construction method is used to calculate the average failure rate and build the grey exponential distribution reliability function, whose image is presented as the possible region of the two-curve envelope.

Further, according to the normal distribution assumption of the regional convergence based on the information supplement, the convergence problem of the reliability function is transformed into the convergence of the area of the curve envelope region, and construct the multi-objective programming model with the minimum envelope area and the lowest total cost of information acquisition, acquire the conclusion that the failure rate is equal to the nuclear of the average failure rate when the envelope region converges.

Through the case analysis of the equipment ejection system of the Harbinger system, five groups of results are obtained by Matlab simulation, which verify the rationality and feasibility of the model described in this paper.