Improve Service Levels Research Articles

Integrating MORS Standards into Tourism Vocational Education: A Case Study from a Chinese Vocational and Technical College

This study delves into the integration of MORS standards in vocational tourism education at Jiangmen Polytechnic. The research indicates that the incorporation of MORS standards significantly enhances both the quality of education and the professional skills of students. Additionally, through modern apprenticeship pilot programs and other school-enterprise cooperative pathways, the college has extended MORS standards to industry practice, improving service levels in the regional tourism industry. Faced with challenges in resource allocation and teacher training, the college adopted strategies such as increased investment, optimized resource allocation, and organized teacher participation in training. Moreover, considering the differences between MORS standards and the actual industry requirements in Guangdong Province, the college selectively introduced certified occupational standards. These practices not only demonstrate the college's capability in educational innovation and industry adaptation but also offer valuable insights to other vocational and technical colleges, especially in the innovation and improvement of tourism vocational education.

중국 인터넷 전자상거래의 물류아웃소싱 협력관계가 물류성과에 미치는 영향 연구

This study focuses on increasingly competitive Chinese e-commerce enterprises and explores the impact of logistics outsourcing partnerships on service expansion and logistics outcomes in supply chain strategy. The research results show that among the determining factors of logistics outsourcing, reducing costs will affect the improvement of service levels in addition to traditional functions, as well as considerations for the use and compatibility of information systems (+). Among the determining factors of logistics outsourcing, the improvement of service level and the utilization and compatibility of information systems have a positive (+) impact on the level of logistics outsourcing. In addition, besides traditional functions, all other functions in the outsourcing level have a meaningful impact on logistics efficiency and effectiveness. In the context of logistics outsourcing decisions, cost reduction has been confirmed as an extremely critical factor. Companies should focus not only on outsourcing traditional logistics functions but also on improving service levels and enhancing the utilization and compatibility of information systems to achieve cost savings. The effective utilization of information systems and compatibility are key elements in elevating the level of outsourcing. Companies need to develop and enhance their ability to effectively use information systems while maintaining compatibility with other systems, which contributes to increased efficiency in logistics management. Furthermore, this underscores the importance of viewing outsourcing as a strategic tool rather than just a means for cost reduction, allowing companies to gain a competitive edge. To achieve the best results, a comprehensive approach that considers both traditional functions and service-related aspects, along with information system functionalities, is necessary. Such an integrated approach is expected to enhance the level of logistics outsourcing and optimize logistics performance.

Resilient supply chain network design under uncertain environment

Modern supply chain operates in a highly uncertain environment caused by natural disasters and market changes. Such a fact has motivated academics and practitioners to pay more attention to the supply chain resilience. This paper reports on designing a resilient supply chain under uncertain environment by an uncertain programming method. The desired resilience level against disruption is achieved with less redundancy by controlling them in the presented models. And parameter uncertainty caused by the limited historical data is also addressed using uncertainty theory. To cope with the problem's complexity, we convert the proposed models into their deterministic formulations, which can easily be solved by cplex solver. The results from sensitivity analysis demonstrate the necessity of including uncertainty during the planning phase of supply chain network design. The performance of resilience constraint and the positive effect of sticker redundancy constraint in improving service level are also investigated fully.

With a Little Help from My Friends? A Longitudinal Network Analysis on Fiscal Stress and Collaboration for Public Service Delivery

Local governments face revenue constraints and increasing demands for public service delivery. Confronted with fiscal pressures, cities, and counties engage in collaborative arrangements to save costs or improve service levels. However, the same pressures can also prevent them from seeking collaboration. Two distinguishable arguments in tension can be identified: fiscal stress as a driver or as a deterrent for collaboration. This study reconciles these contrasting views using longitudinal network analysis to examine how fiscal stress affects the likelihood of collaboration in four critical service areas. Results point toward fiscal stress negatively affecting collaboration in service areas when economies of scale are not achievable, and increasing the likelihood of collaboration in service domains where these scales are possible. Understanding when fiscal stress affects collaboration is particularly relevant given resource disparities at the local level. Such inequality can create reinforcing cycles of fiscal stress and reduced opportunities to collaborate in service delivery.

Can dockless and docked bike-sharing substitute each other? Evidence from Nanjing, China

Bike-sharing systems are promoted worldwide due to their low carbon environmental and social benefits. However, the substitution relationship between dockless and docked bike-sharing systems is barely studied. This paper uses an online survey in Nanjing, China to reveal the factors influencing the substitution relationship between dockless and docked bike-sharing systems using a hybrid choice model. Results show that male prefer to substitute dockless bike-sharing with docked bike-sharing. It is more likely that dockless bike-sharing provides substitution services for docked bike-sharing in the scenario of transferring with bus/subway. Satisfaction to service quality of both bike-sharing systems influence the substitution relationship between the two bike-sharing systems. This paper offers policy implications to improve service level and promote coordinated services of the two bike-sharing systems.

Optimizing Last-Mile Delivery Operations: Leveraging Predictive Analytics, Technology Integration, and Sustainable Practices

In the contemporary landscape of e-commerce and retail industries, the optimization of last-mile delivery operations emerges as a pivotal element for sustaining customer satisfaction and operational efficiency. This study delves into a comprehensive exploration of various strategies aimed at refining the last-mile delivery process, a segment known for its significant challenges, including high costs, inefficiencies, and environmental concerns. By integrating predictive analytics, this research goes beyond traditional logistics methods to forecast delivery needs and driver performance accurately, allowing for pre-emptive adjustments that enhance reliability and efficiency. Furthermore, the investigation into technology integration sheds light on how advanced software solutions, such as route optimization algorithms and real-time tracking systems, can drastically reduce delivery times and operational costs while simultaneously increasing customer satisfaction levels. Additionally, the paper emphasizes sustainable practices within last-mile delivery operations, exploring eco-friendly approaches that not only mitigate environmental impact but also potentially lower delivery costs through fuel savings and efficiency gains. Through the analysis of real-world data and illustrative case studies, this research articulates a holistic framework aimed at improving last-mile delivery. Such a framework is indispensable for companies seeking to maintain a competitive edge in the fast-paced and ever-evolving retail sector. The findings of this study underscore the importance of embracing technological advancements and sustainable practices to address the multifaceted challenges of last-mile delivery, ultimately leading to improved service levels and operational excellence.

Determining the Appropriate Transportation Option with the Decision Tree

Due to the fact that the population density in Turkey is concentrated in the Marmara region, many businesses are positioning their facilities in this region. For this reason, logistics companies, also called 3PL, serving these businesses are located in the same region. Logistics companies determine various transportation strategies according to the shipment volumes of the customers they serve. The main objectives of these strategies are based on cost minimization, productivity increase and improvement of customer service levels. In this publication, a decision tree was used to determine the transportation strategy of a customer of the logistics company whose case study was conducted. The costs of alternative options, including the current transportation option, were calculated and the transportation option with the lowest cost was preferred. In the study, real data was used and a case study was carried out in a logistics company serving in our country. The reason why the decision tree method is preferred is that it is easy to implement and can answer many basic logistics problems.

Reducing the bullwhip effect to enhance maritime service resilience: The power of blockchain technology for cutting down the market uncertainty in the maritime supply chain

In order to enhance the maritime service resilience due to the uncertain environment, our study use the game-theoretical model to find the optimal scheme based on the hypothesis of rational participants, involves a port and a carrier. The results show three aspects. Specifically, firstly, through using the technical superiority of blockchain and changing the influence rate of market disturbance, the negative effects of market uncertainty and bidirectional bullwhip effect can be reduced under the different conditions. Secondly, the ability improvement of maritime service can improve the maritime resilience. Moreover, the blockchain can empower the traditional service in order to improve the traditional ability of the maritime supply chain. Finally, we reveal the relationship between the bidirectional bullwhip effect and product quantity by investigating the degree of market fluctuation of heterogeneous supply and demand. It needs to be highlighted that if you pay little attention to the fluctuation of the market demand, it's more likely to produce a bidirectional bullwhip effect. Based on these results, the conclusion shows that the stakeholder can enhance maritime service resilience by reducing the bullwhip effect, such as improving service level or adopting the blockchain technology based on the specific conditions.

Design of a Framework for Demand Forecasting Using Time Series Decomposition-Based Approach

In recent years, artificial intelligence (AI) has made highly accurate demand forecasting possible. However, improving forecast accuracy does not necessarily mean reducing inventory costs or improving service levels in supply chain and inventory management, which are closely related to demand forecasting. Workers require not only high accuracy but also a basis for making decisions a based on forecasts. Autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) are demand forecasting methods with high accuracy and interpretability. However, these methods cannot provide evidence for demand fluctuations such as trends and seasonality, although they exhibit an autoregressive time-series structure. In this study, a framework for demand forecasting with high accuracy and interpretability was designed using time series decomposition and ARIMA to support decision makers in demand forecasting. The Seasonal-trend decomposition using locally estimated scatterplot smoothing (STL) is used to decompose a time series into three components trend, seasonality, and residual to provide decision makers with an easily understandable basis for demand changes. In addition, the ARIMA model is used for trends and residuals to achieve highly accurate forecasts. Comparing the prediction accuracies of the proposed STL-ARIMA and SARIMA models shows that STL-ARIMA has higher interpretability than the SARIMA model and the same accuracy.

Smart services and privacy, can we have both?

Smart services’ data-intensive nature means their convenience for users comes with privacy concerns. This poses challenges for the supply of smart services. This paper analyses the impact of user-differentiated privacy costs on smart service supply strategies. It also discusses the effects of privacy protections and data utilisation capabilities on the improvement of smart service levels. We find that providers tend to segment the market based on user privacy sensitivity to capture the maximum consumer surplus. Strict privacy protection requirements may cause providers to sacrifice service quality. Privacy protection is conducive to improving the level of smart services only when users fully recognise these services’ value. Interestingly, our study finds improvements in service levels by providers are driven by two factors. On the one hand, providers choose to improve their service levels to obtain more data and create more profits. On the other hand, to reduce the opportunity costs of early investments to develop services, providers need to improve their capabilities to utilise the value of data, ultimately leading to improved service levels. This study brings a new perspective to the supply of smart services and offers suggestions for how to resolve the contradiction between smart service use and privacy preservation.

Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions

Supply chains operate in a highly distuptive environment where a SC master plan should be updated in line with disruptions to ensure that a high service level is provided to customers while total cost is minimised. There is an absence of knowledge of how a SC master plan should be updated to cope with disruptions using hybrid modelling. To fill this gap, we present a hybrid modelling framework to update a SC master plan in presence of disruptions. The proposed framework, which is a precursor to a SC digital twin, integrates simulation, machine learning, and optimisation to identify the production, storage, and distribution values that maximise SC service level while minimising total cost under disruptions. This approach proves effective in a SC disrupted by demand increase and lead time extension. Results show that employing hybrid modelling leads to a noticeable improvement in service level and total cost. The outcome of the new knowledge on using hybrid modelling for managing disruptions provides essential learning for the extension of modelling through a digital twin for SC master planning. We observe that in the presence of disruptions it is more economical to keep higher inventory at downstream SC members than the upstream SC members.

Server Routing-Scheduling Problem in Distributed Queueing System with Time-Varying Demand and Queue Length Control

We study a server routing-scheduling problem in a distributed queueing system, where the system consists of multiple queues at different locations. In a distributed queueing system, servers are shared among multiple queues, and they travel between queues in response to stochastic and time-varying demands. Although server traveling can improve service levels and shorten queue lengths, server routing and scheduling is complicated. We propose a dynamic programming model to solve this special routing-scheduling problem with time-varying demand, stochastic travel time, and queue-length constraints. In order to tackle large-scale practical instances, we design a dynamic programming-based rollout heuristic algorithm. Experiments on large-scale airports and scenic spots show that our approach reduces the total working periods of servers/employees without violating queue-length constraints. Furthermore, we demonstrate that our algorithm outperforms existing benchmark methods and the practical schedules of a scenic spot. Funding: Financial support from the National Natural Science Foundation of China [Grant 71972133] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0099 .

Expanding Service Capabilities Through an On-Demand Workforce

Call Center Staffing: On-Demand Is in Demand Traditional call centers face challenges in quickly adapting their service capacity to meet fluctuations in demand, even with short staffing periods. In their research article “Expanding Service Capabilities Through an On-Demand Workforce,” Sun and Liu propose a solution for call centers to improve service levels and reduce operating expenses. They develop a two-stage decision model that determines the optimal combination of permanent and on-demand staff, along with an optimal on-demand staffing and call scheduling policy that minimizes costs. By utilizing diffusion approximation, they derive approximate solutions for the second-stage problem. The optimal staffing rule employs switching boundaries to determine when to bring in or dismiss on-demand agents, while the scheduling rule employs a nested threshold rule that prioritizes customer urgency. Interestingly, the call scheduling rule exhibits an intriguing pattern arising from the interaction between on-demand staffing and call scheduling decisions. Their research findings highlight significant cost savings achievable through the implementation of an on-demand workforce.

Artificial intelligence in supply chain and operations management: a multiple case study research

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies’ competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.

Power user portrait model based on random forest

AbstractWith the vigorous development of the energy Internet, all kinds of user information data are increasing day by day. How to comprehensively and deeply mine the effective information of users, develop a model to predict the behaviour characteristics of big data users, distinguish customer relationships, and provide an accurate basis for the next behaviour of users for various platforms have become one of the research hotspots of big data analysis of user behaviour. The data is sampled according to the feature vector of power user. The portrait mining of power user is conducted, and the user screening and analysis are conducted by using the measure of decision tree node purity in the model. The decision tree variable of the up–down stopping rule is generated. Then the results of the model and the Logistics model are tested and analysed, which can effectively predict the behaviour of power user. The proposed user strategy based on the characteristics of power consumption behaviour is analysed to verify the effectiveness of the scheme. The example shows that the model has a strong ability to distinguish and good stability than the traditional Logistics model, which can effectively predict the user's behaviour in advance, reduce user complaints, and help enterprises and users to form a long‐term mechanism of mutual benefit and reciprocity, which has a strong practical significance. This paper analyses the panorama of users through power big data technology and proposes a maturity model to evaluate the priority of users' electricity consumption. It emphasises the use of resources and methods provided in the power big data technology package to solve the practical problems of users' electricity consumption, and helps power companies to avoid market risks and improve service levels, which has strong practical significance.

Research on China’s tourism marketing model in the data perspective: based on ecological perspective

Abstract With the continuous improvement of technology and residents’ living standards. Should actively adapt to the opportunities and challenges brought by the Internet to the tourism marketing model, For the sustainable and rapid development of tourism on the wings of takeoff. In this paper, based on a brief explanation of the basic theory of database marketing, to achieve sustainable development of tourism marketing. Adopted the development of ecotourism boutique routes, Implementing multi-faceted marketing channels and means. Improvement of ecotourism infrastructure and service levels and other development measures. Create a tourism marketing model that responds to the trend of the times. Innovative marketing with big data technology can greatly stimulate the tourist crowd. Two times more efficient with dual cores. Get 56 times the execution efficiency with 64 cores. Get the efficiency of linear growth. The most direct way to increase the share of tourism companies in the consumer market. Maximize the value of marketing economic inputs and outputs. The results show that the improved PSO algorithm is more effective in database stability and dispersion. This makes it important for tourism marketing.

Optimizing Container CNI Metrics for Improved Service LevelIndicators and Objectives in Container Networking

Container networking is a critical component of cloud computing, providing the infrastructure for the deployment andscaling of containerized applications. However, ensuring high-performance and reliable container networking can bechallenging due to the complexity of container network architectures and associated metrics. This paper proposes a newapproach to optimize container CNI (Container Network Interface) metrics to improve service level indicators (SLIs)and objectives (SLOs) in container networking. The approach involves analyzing key CNI metrics, such as networklatency, bandwidth, and packet loss, and fine-tuning them to optimize network performance and availability. This studyconducted experiments on Kubernetes clusters to validate the approach using different CNI plugins and workloads. Theresults show that optimizing CNI metrics can significantly improve network performance, reduce network downtime,and enhance the overall service level of container networking. The proposed approach provides a practical and effectivesolution for managing container network performance and availability and has the potential to benefit a wide range ofcloud-based applications.

Significance of Linear Programming for Optimization

Linear programming (LP) is a powerful mathematical technique that has revolutionized optimization problems across various fields. This abstract highlights the significance of linear programming in optimization and its profound impact on decision-making, resource allocation, and efficiency enhancement. The primary objective of linear programming is to maximize or minimize a linear objective function, subject to a set of linear constraints. LP provides a systematic framework for modeling and solving complex optimization problems, enabling decision-makers to make informed choices and allocate resources optimally. The significance of linear programming lies in its ability to address a wide range of real-world challenges efficiently and effectively. In the realm of operations research, linear programming has found extensive applications in supply chain management, production planning, scheduling, and logistics optimization. By formulating decision problems into mathematical models, LP allows organizations to streamline their processes, minimize costs, optimize resource utilization, and maximize overall productivity. Linear programming has also made significant contributions in the field of economics. It has enabled economists to analyze and optimize economic systems, such as determining the optimal production levels for different goods, optimizing resource allocation in a market, and solving complex pricing problems. LP provides a quantitative framework for economic decision-making, facilitating the identification of optimal solutions and supporting policy development. Furthermore, linear programming plays a crucial role in the field of transportation and network optimization. It aids in solving problems related to route optimization, facility location, network design, and capacity planning. By efficiently allocating resources and optimizing transportation networks, LP helps reduce costs, improve service levels, and enhance overall efficiency in the transportation sector. In summary, the significance of linear programming for optimization is evident in its wide-ranging applications and its ability to tackle complex decision problems in various domains. By employing mathematical modeling and optimization techniques, LP allows decision-makers to make informed choices, allocate resources optimally, and achieve desired objectives. The ongoing advancements in LP algorithms and computing technologies further enhance its applicability and make it an indispensable tool for optimization in today's dynamic and competitive world.

Complaint management: comparison between traditional and digital banks and the benefits of using management systems for improvement

PurposeThis paper compares traditional and digital banks in nine categories of complaints and provides insights to improve complaint management performance.Design/methodology/approachA sample of the major Brazilian banks was defined, with four traditional and four digital banks. The grey relational analysis (GRA) method was applied as an analytical tool to compare the most frequent complaints of traditional and digital banks. The most critical complaints identified were considered to discuss potential improvements in complaint management using quality and service management system concepts.FindingsThe GRA method enabled the development of a ranking of nine complaint categories, considering the uncertainty involved in the data and differentiating between traditional and digital banks. The most critical complaint categories, regardless of business model, were “unauthorized charges” and “poor service,” which were ranked first and second in the frequency rankings. Traditional and digital banks differed the most in the complaint category “unfair charge,” ranking third and eighth in the rankings, respectively.Practical implicationsManagers from traditional and digital banks can improve complaint management performance by applying ISO 9001 and ISO 20000 concepts such as incident, problem, change, service level, availability, capacity, information technology service continuity and financial management.Social implicationsThe study's findings can help bank managers improve service levels in the face of technological competition. Improving these organizations is an important factor for developing countries such as Brazil.Originality/valueThis paper reveals the differences between two business models regarding complaint management. It also considers a methodological approach to include the uncertainty related to customers' perception and subjectivity inherent to complaints.

A hybrid metaheuristic approach for solving a bi-objective capacitated electric vehicle routing problem with time windows and partial recharging

PurposeThe vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.Design/methodology/approachIn this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).FindingsThe results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.Originality/valueIn this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.