Production Policy Research Articles

A modified reliability acceptance sampling plan with warranty return rate consideration under Weibull‐distributed accelerated lifetime data

AbstractWarranty is a standard and essential policy for today's products. The period and cost of warranty are majorly affected by the reliability of products. Therefore, validation and verification of product reliability are required to control warranty costs and sustain the brand reputation. In this paper, we propose a modified reliability acceptance sampling plan (MRASP) for product reliability validation. The operating characteristic function of the MRASP is derived by considering the accelerated time‐to‐failure data with Type‐II censoring, which follows a Weibull distribution. To minimize the number of failures required for the accelerated life test and consider the sampling risk constraints, we constructed an optimization model to determine the optimal plan design of the proposed MRASP. Following the optimal plan design, practitioners can set the number of failures required in the accelerated life test to estimate the lifetime capability of the submitted lot and decide the lot disposition using the acceptance benchmark. In addition, the lifetime capability estimation can be used to infer the warranty return rate and warranty cost of the lot, which is valuable for practitioners to formulate the warranty budget. Finally, we illustrate a real‐world case to demonstrate the practicality and applicability of the proposed method.

Gender, entrepreneurship and social policy in tourism: tying the knot

This article, and the special issue, seek to unpack the gendered nature of entrepreneurial pathways, specifically in relation to the role of social policies. We achieve this aim by first conceptualising gender, entrepreneurship, and social policy, to highlight the need to generate a stronger research agenda on the role of social policy within gender and tourism entrepreneurship research. We next outline an overarching framework for delineating the intersection of gender, entrepreneurship, and social policy, based on a critical review of existing studies, as well as by situating the papers in this special issue. We present this discussion through three thematic framings: (1) gender and entrepreneurship, (2) gender and social policy and (3) entrepreneurship and social policy. In conclusion, we discuss the implications for social policy and practice, and in doing so call for a research agenda that situates social policy more centrally within considerations of gender and tourism entrepreneurship.

The Front-Runner Approach—Facilitating Progressive Product Policy by Using Information from EU Product Databases

The European Commission has recently announced two guiding principles for EU product policy: First, product policy shall ensure that the performance of front-runner products in terms of sustainability becomes the norm, and second, the effectiveness of the current Ecodesign legislative framework is going to be significantly improved. Within this paper, already existing front-runner approaches and recent and ongoing product policy-making processes were reviewed. Based on the results, an EU front-runner approach is outlined. The presented approach (i) refers to performance levels of the best products already available on the market, (ii) aggregates information in existing databases, and (iii) works semi-automated. Together, all three attributes have a high potential to facilitate and accelerate the specification of appropriate minimum requirements for products at the EU level. This way, EU policymakers can deliver on the core objectives of the Ecodesign legislative framework much better. The basic mechanism and its legal entrenchment of the approach are illustrated for the energy efficiency of energy-related products. In addition, the Front-Runner Approach can be applied to any product group in the scope of the upcoming Ecodesign for Sustainable Products Regulation and to a wide range of product-related minimum requirements, such as durability, reparability, or recycled content. The study’s objective is to suggest a tailor-made and dynamic approach to keep the EU product legislation up to date using innovative technology based on the investigation of current regulations and identify the gap. Experiences from three international case studies suggest that a front-runner approach to setting energy-performance standards can drive innovation and reduce energy consumption via promoting energy-efficient products; transparency about available products is one of the key factors and can be established by a database. The EU front-runner approach comprises extending the existing energy label database (or making use of the digital product passport) and introducing a legislative procedure that triggers changes in the energy efficiency requirements in the specific EU regulations if the database shows that a certain threshold value is reached. Challenges such as limited EU staff capacities and opportunities such as increased dynamic are discussed.

Study on Economic Strategy of Green Finance Promoting the Development of New Energy Industry in Shandong Province

With global climate change and energy security issues becoming increasingly prominent, new energy industry(NEI) has become an important field to promote sustainable development. The purpose of this study is to explore the economic strategy of green finance(GF) in promoting the development of NEI in Shandong Province. Through in-depth research on the policy framework and practical operation of GF, we find that Shandong Province has made remarkable achievements in this field. Green financial policy covers many aspects, such as financial incentives, financial product innovation and policy guidance, which provides strong support for the NEI. On the practical level, the Shandong provincial government has established a close cooperative relationship with financial institutions, and strengthened financial institutions' support for new energy projects through joint loans and risk sharing. Shandong province has made a series of remarkable achievements in the economic strategy of GF promoting the development of NEI, which has laid a solid foundation for future sustainable development. However, it is still necessary to further strengthen supervision and innovate financial products in order to better lead the NEI to the road of high-quality development.

Optimization of a preventive maintenance strategy for a product with limited repair times

This paper proposes a base warranty and replacement warranty strategy for repairable products. It should be emphasized that the product failure rate is low in the early stage of the base warranty. Therefore, in order to reduce the manufacturer’s maintenance cost, the manufacturer does not carry out preventive maintenance in the early stage of the base warranty, and carries out regular preventive maintenance in the later stage of the base warranty. At the same time, if the number of product failures in the base warranty exceeds the specified limit, the manufacturer must replace products for customers free of charge according to the warranty contract. After the product is replaced, if the number of product failures does not exceed the limit, the manufacturer will continue to provide customers with the remaining base warranty period of the original product, and the warranty period will continue to be extended for a period of time. By minimizing the expected cost rate, it is shown that the optimal maintenance policy for warranty products is unique. Finally, we give a numerical example and study the effects of periodic preventive maintenance and other relevant parameters on the optimal solution.

A priority queueing-inventory approach for inventory management in multi-channel service retailing using machine learning algorithms

PurposeNowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.Design/methodology/approachThis paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.FindingsTo investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.Originality/valuePrioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Factors influencing the adoption of Internet of Things (IoT) in university libraries: a systematic literature review (SLR)

PurposeFor the provision of smart library services to end users, tools of the Internet of Things (IoT) play a significant role. The study aims to discover the factors influencing the adoption of IoT in university libraries, investigate the impact of IoT on university library services and identify challenges to adopt IoT applications in university libraries.Design/methodology/approachA systematic literature review was carried out to address the objectives of the study. The 40 most relevant research papers published in the world’s leading digital databases were selected to conduct the study.FindingsThe findings illustrated that rapid growth in technology, perceived benefits, the networked world and the changing landscape of librarianship positively influenced the adoption of IoT in university libraries. The study also displayed that IoT supported library professionals to initiate smart library services, assisted in service efficiency, offered context-based library services, provided tracking facilities and delivered effective management of library systems. Results also revealed that a lack of technical infrastructure, security and privacy concerns, a lack of technological skills and unavailability of policy and strategic planning caused barriers to the successful adoption of IoT applications in university libraries.Originality/valueThe study has provided theoretical implications through a valuable addition to the current literature. It has also offered managerial implications for policymakers to construct productive policies for the implementation of IoT applications in university libraries for the attainment of fruitful outcomes. Finally, the study provides a baseline for understanding the adoption of IoT in academic libraries.

How to promote AI in the US federal government: Insights from policy process frameworks

When it comes to routine government activities, such as immigration, justice, social welfare provision and climate change, the general perception is that the US federal government operates slowly. One potential solution to increase the productivity and efficiency of the federal government is to adopt AI technologies and devices. AI technologies and devices already provide unique capabilities, services, and products, as demonstrated by smart homes, autonomous vehicles, delivery drones, GPS navigation, Chatbots such as OpenAI's ChatGPT and Google's Bard, and virtual assistants such as Amazon's Alexa. However, incorporating massive AI into the US federal government would present several challenges, including ethical and legal concerns, outdated infrastructure, unprepared human capital, institutional obstacles, and a lack of social acceptance. How can US policymakers promote policies that increase AI usage in the face of these challenges? This will require a comprehensive strategy at the intersection of science, policy, and economics that addresses the aforementioned challenges. In this paper, we survey literature on the interrelated policy process to understand the advancement, or lack thereof, of AI in the US federal government, an emerging area of interest. To accomplish this, we examine several policy process frameworks, including the Advocacy Coalition Framework (ACF), Multiple Streams Framework (MSF), Punctuated Equilibrium Theory (PET), Internal Determinants and Diffusion (ID&D), Narrative Policy Framework (NPF), and Institutional Analysis and Development (IAD). We hope that insights from this literature will identify a set of policies to promote AI-operated functionalities in the US federal government.

On the Control Policy of a Queuing–Inventory System with Variable Inventory Replenishment Speed

This paper considers a make-to-order production–inventory system that comprises a production facility and an inventory warehouse. Customers arrive at the facility to place an order, and the orders are processed using the first-come-first-served (FCFS) discipline. The warehouse supplies inventory items (raw materials) for the production process, and the warehouse inventory is replenished by internal production. The speed of internal production can be controlled through additional costs. If the inventory level drops to zero, the unmet demand waits in the facility until the inventory is replenished. During the stockout period, newly arriving demand is lost. The stationary joint probability of unmet demands and inventory items is derived, and a cost model is constructed. The optimal control policy for internal production is investigated to minimize the cost per unit time of the system. The experimental results show that such a production speed adjustment could reduce costs by up to 42% compared to the cases without the adjustment.

Modeling & Simulation for assessing production policies: a real case study from a manufacturing company in Canada

Within the complex domain of production management, the careful choice of a highly effective scheduling policy can significantly impact the timely completion of deliveries. This study explores a practical example undertaken at a manufacturing company in Canada, employing the sophisticated features of the AnyLogic simulation program to analyze and evaluate two commonly used production policies: Shortest Production Time (SPT) and Earliest Due Time (EDT). The main aim is to determine which of these tactics consistently results in greater rates of prompt deliveries, a crucial aspect for both customer happiness and operational efficiency. In order to ascertain the credibility and resilience of the simulation results, a comprehensive statistical analysis was performed utilizing the Minitab software, with particular emphasis on the main effect plots. The initial findings suggest notable disparities in performance indicators between the two policies, with one policy exhibiting a distinct advantage in terms of its adherence to delivery timetables. This study not only presents a quantitative evaluation of SPT and EDT policies, but also gives valuable insights that might assist decision-makers in the manufacturing sector in choosing the most suitable policy to ensure prompt delivery.

Adopting new ARIMA double layering technique in make-to-stock production policy

In supply chain management, the employed forecasting algorithm plays a very vital and crucial role in how a particular business performs well in the market. This study proposes a time series based forecasting model to help in designing an effective make-to-stock supply chain mechanism for tackling the large number of suppliers (suppliers diversion) and products (items diversion). The proposed technique extends the traditional ARIMA model to ARIMA Double Layering Technique (DLT) where the algorithm is implemented in two layers: first one is to obtain the number of orders level, and the other layer is implemented at the quantity level. The systematic implementation procedure of our approach is illustrated through a real case study. The accuracy evaluation and consistency of the results show that ARIMA -DLT provides superior forecasting when compared with ML-based models which provides insightful managerial views to other similar forecasting problems.

УПРАВЛІННЯ ТОВАРНИМИ ЗАПАСАМИ ТОРГІВЕЛЬНИХ ПІДПРИЄМСТВ В СУЧАСНИХ УМОВАХ

The article is devoted to peculiarities of trade enterprises’ inventory management, which allows a modern company to maintain a balance between demand satisfaction and inventory storage costs. Stocks perform three functions: accumulation; protection against price changes and inflation; cost management. When determining the optimal balance between costs associated with keeping inventories one should determine that the sum of all relevant costs must be minimal. The main indicators that allow to determine efficiency of inventory management are considered. The ratio between the dynamics of stocks and the volume of sold products was analyzed on the example of wholesale trade enterprises (period 2013-2022). The main ways of more efficient use of stocks are proposed. Necessity of developing the enterprise’s product policy has been proven and its main stages have been determined. Peculiarities of the model of economically justified order size, which are used to optimize the size of production stocks and stocks of finished products are considered. Models of inventory management with a fixed period and a fixed volume are considered, the choice between them depends on a number of factors, including quantity of stocks, their price and importance as well as volume of the order. During the formation of the inventory management model, it is necessary to take into account specifics of business, limitations of the external environment, and the subjectivity of management decision-making. The main problem that arises when solving the task of stock management is the development of an effective and reliable system of their management, which should provide smooth production process, its realization maintaining the optimal stocks volume and minimizing expenses. In general, effective inventory management makes possible to reduce the duration of the production cycle and decrease current costs for their storage; to release financial funds from the current turnover in order to reinvest in other assets; to reduce production losses as a result of eliminating the shortage of production stocks; to accelerate the turnover of stocks, reduce the risk of their loss and reduce the costs of their storage. Managing inventory levels is essential in order to establish and support a company's long-term competitive advantage.

Production control problem for multi-product multi-resource make-to-stock systems

Most of today's production systems are working with parallel production resources to increase throughput rate due to the increase in high variability in demand and product mix. Effective control and performance evaluation of such systems is of paramount importance to minimize production and inventory-related costs. We examine a production-inventory system featuring parallel production resources capable of producing various products. In many industries such as automotive, white goods, electronics, and paint, multiple/parallel production resources are widely used to produce the ideal amount and satisfy incoming demands for distinct products. In this study, shortage cost is not restricted to only one type and both lost sales and backordering cases are analyzed. In order to analyze the optimal production policies' behavior, we initially formulate dynamic programming models for both lost sales and backordering systems, treating them as Markov Decision Processes. Subsequently, we solve these models using the value iteration algorithm. Given the challenges posed by the curse of dimensionality in the value iteration algorithm, we suggest alternative heuristic production policies. These policies extend the existing ones described for multi-item single-resource make-to-stock (MTS) systems to accommodate multiple resources. We construct simulation models to assess the efficacy of the heuristic policies, conducting comparisons of their performance against both the optimal policy and among one another. To the best of our knowledge, there has been no exploration of scenarios involving multiple production resources concurrently manufacturing distinct products in a MTS environment. Hence, this study serves as an extension to the examination of multi-item, multi-production resource MTS systems.

1975 – International Women’s Year and the Exhibitions Organised on this Occasion in Warsaw: A Case Study on State Feminism and Art Through a Revisionist Lens

The article centres around International Women’s Year IWY (1975) and the celebrations of this international event in Warsaw – the capital city of the People’s Republic of Poland. Based on archival documents held in the Department of the 20th and 21st Century Visual Arts Documentation at the Institute of Art, Polish Academy of Sciences, correspondence and press reviews, this text describes plans and realisations of art exhibitions related to IWY. This case study aims to add to the growing body of research regarding IWY, especially to the research focused on the eastern side of the Iron Curtain. It adopts a revisionist approach to analysing the intersection of cultural policy and women’s emancipation in the state-socialist context. The 1970s is seen as a unique moment when feminist art rooted in the western second wave met state-socialist initiatives for women’s art.

Inaction in Urban Climate Policy Undermines Health.

This paper investigates the critical intersection of urban climate policy and public health, emphasizing the pressing need for integrated strategies to address the intertwined challenges of climate change and health in urban settings. Despite cities being central to global emissions and energy consumption, a significant gap exists in the incorporation of health considerations into urban climate strategies, as evidenced by the analysis of urban content in Nationally Determined Contributions (NDCs). The paper highlights the Coalition for High Ambition Multilevel Partnerships (CHAMP) initiative and the forthcoming Intergovernmental Panel on Climate Change (IPCC) Special Report as pivotal moments for integrating climate and health agendas. However, it also points out the underwhelming response from cities in adopting comprehensive climate strategies, which undermines potential public health benefits. With substantial investments required to bridge the gap in health-focused climate resilience, the paper calls for a redefined approach to urban climate policy. This approach should prioritize health outcomes, leverage opportunities like the 15-Minute City concept, and foster the development of infrastructures that support both climate resilience and public health. The upcoming NDC revision cycle is identified as a critical opportunity for embedding health imperatives into urban climate strategies, emphasizing the need for a holistic perspective that views urban areas as ecosystems where climate and health are intricately connected. This comprehensive view aims to promote policies that are mutually reinforcing, thus contributing to healthier, more livable cities.

A Dynamic Model for Analyzing Investment Policies in Iran's Oil Production and Its Scenarios

various related scenarios. The study aims to identify and assess different financial and investment policies that could enhance investment attraction in Iran's oil industry. Methodology: The study employs system dynamics and content analysis to develop the model. The statistical population for the content analysis includes upstream oil industry documents and related articles on investment. In the quantitative section, employees, managers, and experts in the oil industry were selected as the sample population. Data analysis was conducted using Vensim software, and nine different scenarios were developed to evaluate financial and investment policies. Findings: The results revealed that intermediary financing, with an increase of 128.854 units, was identified as the most attractive policy. Debt-based financing, with an increase of 108.333 units, and financing based on underground reserves, with an increase of 69.580 units, followed. These findings highlight the importance of diversifying financial resources and utilizing innovative financial structures in Iran's oil industry. Conclusion: The study concludes that a combination of different financial resources and the application of diversified financial policies can significantly contribute to attracting more investments and reducing financial risks in Iran's oil industry. These findings align with previous studies on financial and investment policies and provide valuable insights for policymakers in Iran's oil sector.

Joint Discount and Replenishment Parametric Policies for Perishable Products

We consider a joint discount and replenishment problem in a discrete periodic review fashion for the sale of a perishable product, characterized by limited deterministic shelf life, replenishment lead times, and stochastic demand. Customers decide what to buy according to a linear discrete choice model, balancing price and perceived quality, uniquely determined by the residual shelf life. The decisions we consider are: How many new items to order, the age of the items to be discounted, and how much discount to offer. In this context, we compare a set of policies mixing the constant order policy and the base stock one with some easy discounting policies, optimizing their parameters using a simulation-based optimization framework. To evaluate their performance in terms of revenue and quantity of scraped items, we consider four realistic instances for a grocery retailer characterized by products of different shelf life and variance of demand. Experiments show that best results are achieved by a base stock policy that discounts products of different ages based on a threshold: If the quantity of the inventory of a given age is greater than a threshold it applies a discount, otherwise no discount is proposed. In the presented configurations, this policy increases the average reward compared to policies that do not discount.

Piling and discounting perishable products: optimal replenishment and discounting policy for perishable products

Piling and discounting perishable products: optimal replenishment and discounting policy for perishable products

Retailer’s optimal pricing and replenishment policy for new product and buy back policy for used product with price and time dependent demand and shortages

The new age consumer, manufacturers and government is aware about the hazardous impact of manufacturing on the environment and limited assets are factors which are considered while buying a product. As an outcome, consumers have demonstrated their enthusiasm to purchase utilized or restored items. We looked at a scenario in this article where a retailer collects and resells old products to customers in addition to selling new products. Detailed formulation and discussion are given to the optimum price and ordering strategies for new products as well as the optimum buyback strategy for used products. The demand of the products is the time and price sensitive. Additionally, Shortages are permitted and there is a partial backlog. Maximizing a retailer’s total profit per time unit while determining the best selling price and quantity to place an order for new products and the best amount to buy back for used products is the objective. The numerical example demonstrates the framework and sensitivity evaluations are carried out on the significant parameters.

Government Subsidies and Policies for Mask Production Under COVID-19: Is It Wise to Control Less?

Under the COVID-19 pandemic, governments worldwide have subsidized manufacturers or consumers on the production or purchase of masks. However, the impacts of these subsidies on the mask supply chain (MSC) operations are unclear. Motivated by our interview with a mask manufacturer as well as the observed real-world practices, we establish consumer utility-based stylized models to analytically examine government subsidies and policies in the MSC. We utilize the infection transmission model to capture the social health risk during the COVID-19 outbreak. The government aims to maximize social welfare, which includes the manufacturer's profit, consumer surplus, social health risk, and government subsidy expenditure. Results indicate that when the price is not controlled (i.e., the manufacturer decides it), the manufacturer and consumer subsidy programs are equally efficient in enhancing consumer surplus as well as reducing harm to social health risk under COVID-19. Thus, the government can conduct a subsidy scheme that is easier to implement in practice. However, we surprisingly find that the government's excessive intervention will cause disequilibrium in the MSC. When the price or the manufacturer's dishonest behavior is fully controlled by the government, subsidizing the MSC is not always advisable. Besides, our findings are consistent with the public interest theory; that is, the proper implementation of dishonesty prevention and pricing control policies can improve social welfare but sacrifice consumer surplus. Our results contribute to healthcare operations management and generate managerial insights for MSC management during COVID-19 with industrial validation.