Improve Service Levels Research Articles

Revolutionizing Supply Chain : The Synergy of Warehouse Management and Data Analytics

This article examines the transformative impact of integrating data analytics with warehouse management in modern supply chains. It explores how this synergy is revolutionizing inventory management, order fulfillment, and customer service across industries. The article discusses key elements of effective warehouse management, including optimal space utilization, efficient inventory control, and timely order processing. It then delves into how data analytics elevates these functions through inventory optimization, real-time monitoring, and predictive analytics. Case studies from e-commerce and manufacturing sectors illustrate real-world applications and benefits. Finally, the competitive advantages gained through enhanced operational efficiency, cost reduction, and improved service levels are analyzed, highlighting the critical role of this integration in shaping the future of supply chain management.

GRU-IKF scene taxiing trajectory prediction model based on attention mechanism

Aircraft taxi trajectory prediction helps solve operational problems such as airport taxiing conflicts and long waiting times, ensuring airport safety while improving service levels and increasing airport throughput. In view of the fact that the performance of machine learning models depends on good data sets, a method for predicting taxi trajectories of aircraft on the ground based on attention mechanism, fusion of gated recurrent unit (GRU) and improved Kalman filter algorithm (IKF) is proposed. Firstly, three independent gated recurrent unit networks are used to capture the motion state and temporal dependency of aircraft at future moments, and the attention mechanism is introduced to enhance the ability to extract data difference features and learn the mapping relationship from input to output; then, it is fused with the improved extended Kalman filter to integrate the output results of the neural network into the state prediction and update process to improve the accuracy of the predicted trajectory sequence. Finally, the effectiveness of the model was verified using the actual taxiing trajectory of aircraft at Lukou Airport. The simulation results show that the model can effectively and accurately predict the taxiing trajectory of aircraft on the surface, with an overall mean square error of about 0.00128. Compared with the single recurrent neural network (RNN), long short-term memory network (LSTM) and GRU model, the RMSE is reduced by 72.9%and respectively 39.9%, 54.7%and the prediction time is 40 ms. It can accurately and quickly predict the taxiing trajectory, which helps to reduce the operating load of the airport surface management system.

Integrated optimization of logistics routing problem considering chance preference

PurposeThis study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction.Design/methodology/approachThis study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm.FindingsThe study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality.Practical implicationsIn an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level.Originality/valueConsidering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

Re-design methodology for an under-risk multi-business supply network

ABSTRACT Currently, several supply chains of the same business may be managed independently. This is due to different groups of clients or different marketing channels. Parallely, changes in the environment, due to unexpected situations, lead the decision-makers to modify the configuration of supply chains. Therefore, we propose a redesign methodology based on a progression of steps to study holistic synergies. This design analysis is carried out at a static and dynamic level for strategic, tactical and operational decisions. The use of qualitative and quantitative tools allows a set of solutions to be obtained, which take into account inventory levels, processing times, costs and service levels. To compare the preliminary solutions, AHP (Analytic Hierarchical Process) is combined with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). It is applied to an example of pharmaceutical distribution in South America. The results indicate an improvement of service levels and a reduction of costs from risks.

Solving low-carbon last mile delivery problem using discrete marine predators algorithm

With the increasing concentration of greenhouse gases, carbon peaking and carbon neutrality have gradually become a global issue. As the last link of e-commerce logistics, the last mile delivery should not only focus on customer satisfaction and enterprise cost, but also reduce carbon emission and promote green development. In order to solve the last mile delivery problem, this paper builds the last mile delivery model for two common delivery vehicles, considering the impact of transportation cost, carbon emission and customer satisfaction on the last mile delivery path planning. The last mile delivery is an NP-hard problem, a novel Discrete Marine Predators Algorithm(DMPA) is proposed to solve the problem, which combines neighborhood search strategy and RS hybrid operator. Six different test examples are generated based on Solomon for simulation experiments, and the experimental results verify that the proposed algorithm is obviously superior to the marine predators algorithm, simulated annealing algorithm and genetic algorithm. DMPA obtains significantly higher customer satisfaction than other algorithms. The customer satisfaction of C101 and C201 is as high as 0.99, which is close to the optimal value of 1. DMPA can achieve lower transportation costs and less carbon emissions compared with other algorithms. DMPA reduces the transportation cost by at least 61.12$ and the carbon emission by at least 1033.64 kg for C101 by minivan delivery. DMPA reduces the transportation cost by at least 28.89$ and the carbon emission by at least 210.9 kg for C101 by electric tricycle delivery. It can effectively reduce carbon emission, lower transportation cost, improve service level, and promote the integration of the dual carbon target with the internal requirements of national development.

A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network

Supply chain management and distribution network design has attracted the attention of many researchers in recent years. The timely satisfaction of customer demands leads to reducing costs, improving service levels, and increasing customer satisfaction. For this purpose, in this research, the mathematical programming models for a two-level distribution network including central warehouses, regional warehouses, and customers are designed so that several products with definite demands in multiple periods are distributed from central warehouses to customers. In this problem, two objective functions are considered. The first objective function seeks to minimize the costs of establishment, transportation, inventory, and shortage, and the second objective function attempts to maximize the satisfaction level corresponding with the supply rate of different goods for numerous customers. The presented models include the basic model, inventory-based model, multi-period inventory-based model, and multi-period inventory-based reverse logistics model. The validation and applicability of the proposed models were demonstrated by implementation in a real case study of the automobile industry. The LINGO software 20.0 was used to solve the models. The results show that incorporating the inventory management policies into the basic model and converting from a single-period to a multi-period reverse logistics model will significantly increase company profitability and customer satisfaction.

Outsourcing Supply Logistics for Health Commodities in Africa

Abstract: Outsourcing of health-commodity supplies to third-party logistics providers is one of the ways of reducing costs and/or improving service levels in the public sector. This paper evaluates three forms of outsourcing: full outsourcing of inventory management, partial outsourcing of specific activities, and contingent partial outsourcing of specific logistics activities. It notes that, in Africa, contracting-out supply logistics has mostly taken the form of partial outsourcing of transportation only or storage and transportation of specific health commodities to all or selected geographical regions. Partial outsourcing offers limited benefits since it cannot provide adequate pressures on public-sector logistics to be efficient or maintain uninterrupted supplies in times of catastrophes. Improvements in logistics performance achieved through partial outsourcing should not mask the need for more expansive arrangements that support not only partial outsourcing but also full outsourcing in situations of inefficiency and in times of supply-disruption catastrophe.

Human in the Loop Automation: Ride-Hailing with Remote (Tele-)Drivers

Tele-driving refers to a novel concept by which drivers can remotely operate vehicles (without being physically in the vehicle). By putting the human back in the loop, tele-driving has emerged recently as a more viable alternative to fully automated vehicles with ride-hailing (and other on-demand transportation-enabled services) being an important application. Because remote drivers can be operated as a shared resource (any driver can be assigned to any customer regardless of trip origin or destination), it may be possible for such services to deploy fewer drivers than vehicles without significantly reducing service quality. In this paper, we examine the extent to which this is possible. Using a spatial queueing model that captures the dynamics of both pickup and trip times, we show that the impact of reducing the number of drivers depends crucially on system workload relative to the number of vehicles. In particular, when workload is sufficiently high relative to the number of vehicles, we show that, perhaps surprisingly, reducing the number of drivers relative to the number of vehicles can actually improve service level (e.g., as measured by the amount of demand fulfilled in the case of impatient customers). Having fewer drivers than vehicles ensures that there are always idle vehicles; the fewer the drivers, the likelier it is for there to be more idle vehicles. Consequently, the fewer the drivers, the likelier it is for the pickup times to be shorter (making overall shorter service times likelier). The impact of shorter service time is particularly significant when the workload is high, and in this case, it is enough to overcome the loss in driver capacity. When workload is sufficiently low relative to the number of vehicles, we show that it is possible to significantly reduce the number of drivers without significantly reducing service level. In systems in which customers are patient and willing to queue up for the service, we show that reducing the number of drivers can also reduce delay, including stabilizing a system that may otherwise be unstable. In general, relative to a system in which the number of vehicles equals the number of drivers (as in a system with in-vehicle drivers), a system with remote drivers can result in savings in the number of drivers either without significantly degrading performance or actually improving performance. We discuss how these results can, in part, be explained by the interplay of two counteracting forces: (1) having fewer drivers increasing service rate and (2) having fewer drivers reducing the number of servers with the relative strength of these forces depending on system workload. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: This work was supported by the US National Science Foundation [Grant SCC-1831140], and the Guangdong (China) Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [2023B1212010001]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01687 .

Inventory Management Optimization: Balancing Cost and Service Levels

Efficient management of inventory plays a role, in the success of businesses across sectors. Finding the equilibrium between keeping inventory costs low and meeting customer needs is a challenge that requires constant exploration and creativity. This study delves into the complexities of optimizing inventory management focusing on strategies that effectively handle the trade offs between minimizing expenses and fulfilling customer expectations. By delving into an analysis of existing literature this study consolidates approaches, models and best practices implemented by companies to enhance the efficiency of inventory management. It thoroughly examines aspects such as improving demand prediction accuracy reducing lead times optimizing safety stock levels and integrating supply chains. Additionally it explores how advanced technologies like intelligence data analytics and automation are transforming inventory management processes.Moreover this research delves into how industry landscapes, market dynamics and business goals influence inventory management tactics. Through real world examples and empirical data analysis it offers insights, on how organizations can tailor their strategies to meet needs while balancing the objectives of cost control and service level improvement. In the end this study adds to what we know about improving inventory management by providing advice and tips for companies to gain lasting edges in todays ever changing market. KEYWORDS: Research Paper, Blockchain, Management, Risk Evaluation, Empirical Analysis

Post Utilization of the Venues of the 31st World University Summer Games

This study explores the post-game utilization of venues at the 31st Summer Universiade and assesses respondents’ satisfaction with venue services. The study found that although large-scale sports events such as the Summer Universiade are held in China, the issue of venue utilization after the games has become an urgent problem that needs to be solved. Although a lot of manpower and material resources have been invested in the construction of modern sports venues, they often only focus on the requirements during the event and ignore the operation and utilization of the venues after the game. In this context, through a survey and analysis of 278 respondents, this study found that venue infrastructure and recreational and leisure facilities scored higher, but other aspects such as venue facilities, accessibility, service personnel, catering services, etc. scored lower Low. Respondent satisfaction is positively correlated with gender, but not significantly correlated with other demographic characteristics such as age, education, occupation, etc. In social services, product appearance is positively evaluated, but functionality and reliability are not. Finally, the study found that only reliability and transportation accessibility were positively related to satisfaction, emphasizing the important impact of transportation on venue reliability. In summary, this study provides important reference value for solving the post-match utilization issues of large-scale sports event venues. It is recommended that while improving service levels and facility quality, strengthen the function and reliability of social services and pay attention to transportation accessibility, to improve the satisfaction of using the venue after the game.

A Survey Study on the Satisfaction of Rural Tourists in Nanchong City, Sichuan Province

This study employs field survey methods to investigate the satisfaction of tourists visiting rural areas in Nanchong City. After conducting reliability and validity tests on the collected data, the results showed that the questionnaire data met the standards for reliability and structural fit. The hypotheses set earlier were then verified, finding that individual differences among tourists have a certain impact on tourist satisfaction. Furthermore, the positive effect of perceived value on tourist satisfaction was also validated. Therefore, the factors affecting tourist satisfaction mainly stem from the inadequacies in the tourism development of Nanchong City, including the need for optimization of infrastructure and environment, relatively small development scale, single development mode, and the need for improvement in service levels.

A hybrid machine learning with process analytics for predicting customer experience in online insurance services industry

It is essential to innovate and improve service levels by predicting possible process outcomes due to the growth of online service providers. This study estimates the customer satisfaction level based on customer experience analysis. In doing so, we answer recent calls for research about a more thorough exploration of customer behavior using predictive process monitoring techniques. In particular, a hybrid framework of supervised/unsupervised machine learning methods is proposed to predict the outcomes of customers’ experiences while dealing with the problem of high intra-class variance. This problem occurs due to the large dispersion of traces identified in the customer journeys. In this regard, customer journeys are first matched with the event log format aiming to implement a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique based on the similarity between the customer journeys. After summarizing the journeys by removing low-value activities, the multi-class decision tree classification method is applied, and the level of customer satisfaction is predicted. Due to the imbalanced nature of the data, the oversampling for imbalanced classification is applied to achieve good results in accuracy indicators such as recall, precision, and F1-score. Finally, the proposed approach has been evaluated on a real-life event log, BPI Challenge 2016, to investigate unsatisfied customers. The results of the machine learning models on the test data show a high degree of accuracy in predicting customer dissatisfaction.

A machine learning-based hybrid recommender framework for smart medical systems.

This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy. The second method implements the term frequency and inverse document frequency (TF-IDF) algorithm to construct a model based on the patient's symptom vector space, incorporating score weighting, modified cosine similarity, and K-means clustering. Then, the alternating least squares (ALS) matrix decomposition and user collaborative filtering algorithm are applied to calculate patients' predicted scores for doctors and recommend top-performing doctors. Experimental results show significant improvements in metrics called precision and recall rates compared to conventional methods, making the proposed approach a practical solution for department triage and doctor recommendation in medical appointment platforms.

Pricing strategy research in the dual-channel pharmaceutical supply chain considering service.

In the context of developing a new era, the pharmaceutical supply chain market has gradually transformed from a seller's market to a buyer's market. The closer the consumers are, the greater the market pricing power, so the pharmaceutical market power of manufacturers and retailers has also changed. This study considers the effect of service on the pricing strategy of the pharmaceutical platform supply chain. The study aimed to coordinate optimization, and the coordination strategy of the pharmaceutical platform supply chain of complementary products is discussed mainly by researching the price and service factors. Various situations are studied by hypothesis and model solving. This study uses Stackelberg game decision-making. Manufacturers are at the forefront of platform supply chain decisions. The research found that the price was lower under centralized decision-making than under decentralized decision-making. Coordination between price and service levels needs attention in the pharmaceutical platform supply chain of complementary products, and the service level should be controlled within a certain range. Only by improving the service level can enterprises maximize profits, providing a theoretical basis for pharmaceutical supply chain pricing strategy research. Supply chain members must strive to improve service levels to improve medical supply consumers' (patients) psychological satisfaction level. Service levels do not fully mitigate channel conflict. Therefore, pharmaceutical complements have become a way to alleviate the conflicts in the pharmaceutical platform supply chain.

Understanding the impact of chronic diseases on COVID-19 vaccine hesitancy using propensity score matching: Internet-based cross-sectional study.

To investigate whether chronic diseases are associated with higher COVID-19 vaccine hesitancy and explore factors that influence COVID-19 vaccine hesitancy in patients with chronic diseases. Vaccine hesitancy has been acknowledged as one of the greatest hazards to public health. However, little information is available about COVID-19 vaccine hesitancy among patients with chronic diseases who may be more susceptible to COVID-19 infection, severe disease or death. From 6 to 9 August 2021, we performed an internet-based cross-sectional survey with 22,954 participants (14.78% participants with chronic diseases). Propensity score matching with 1:1 nearest neighbourhood was used to reduce confounding factors between patients with chronic diseases and the general population. Using a multivariable logistic regression model, the factors impacting COVID-19 vaccine hesitancy were identified among patients with chronic diseases. Both before and after propensity score matching, patients with chronic diseases had higher COVID-19 vaccine hesitancy than the general population. In addition, self-reported poor health, multiple chronic diseases, lower sociodemographic backgrounds and lower trust in nurses and doctors were associated with COVID-19 vaccine hesitancy among patients with chronic diseases. Patients with chronic diseases were more hesitant about the COVID-19 vaccine. Nurses should focus on patients with chronic diseases with poor health conditions, low socioeconomic backgrounds and low trust in the healthcare system. Clinical nurses are recommended to not only pay more attention to the health status and sociodemographic characteristics of patients with chronic diseases but also build trust between nurses and patients by improving service levels and professional capabilities in clinical practice. Patients or the public were not involved in setting the research question, the outcome measures, or the design or implementation of the study. However, all participants were invited to complete the digital informed consent and questionnaires.

Communication support in care homes for older adults: Views and reported practices of speech and language therapists and care home activities staff in the UK.

Speech and language therapists (SLTs) and care home activities staff play key roles in managing and supporting the communication needs of older residents in care homes. However, the current practice and perspectives of these two professions in the United Kingdom has not been examined. To investigate the practice patterns and views of SLTs and activities staff working in UK care homes for older adults in relation to residents' communication needs. Two online surveys, with 63 questions (SLT survey) and 46 questions (activities staff survey) in total, were created using the online platform Qualtrics. Participants were asked to consider their routine practice before COVID-19. Results were analysed using descriptive statistics and qualitative content analysis. A total of 116 valid responses were received from SLTs and 29 valid responses from activities staff. A high level of communication needs in care homes was reported by both participant groups, as was insufficient time and resources and lack of managerial encouragement in this area. SLTs reported that the majority of referrals to their service from care homes was for swallowing needs (70%). Cognitive communication difficulty was the most commonly reported communication need by SLTs (65%). Most SLTs (73%-87%) provided some level of communication intervention and considered management of residents' communication needs to be both part of the SLT role and a good investment of their time. Lack of confidence setting goals and providing direct intervention for communication needs was reported, with 25% feeling stressed at the thought of this. The main themes from free text responses about SLT service improvement were increased staff training, funding (of resources and specialist posts) and changes to service provision (referral criteria and accessibility/awareness of SLT service). Hearing impairment was the communication need most commonly reported by activities staff (43%). Participants demonstrated relatively high awareness of communication difficulty in residents and reported high levels of knowledge and confidence identifying and supporting residents' communication. Most (79%-89%) considered identifying and supporting the communication needs of residents to be part of their role and expressed interest in receiving further training in communication support. The reported activities staff data set may be positively biased. SLTs and activities staff were highly motivated to support the communication needs of care home residents. Increased training, time and resources dedicated to managing the communication needs of residents emerged as opportunities for service improvement across both data sets. What is already known on the subject There is a high level of communication need amongst older care home residents. Social interaction and relationships are important factors contributing to quality of life in this population and rely on successful communication. Speech and language therapists (SLTs) and activities staff play key roles in managing and supporting the communication needs of this client group, but the current practice and perspectives of these professions in the United Kingdom has not been examined. What this study adds A high level of communication need in care home residents was identified by both SLT and activities staff and both participant groups were motivated to address, identify and manage this need. However, insufficient time and resources, as well as a perceived lack of encouragement from managers to provide communication support/intervention, were reported by both groups. SLT practice was constrained by referral criteria and care pathways, which differed between services. Suggestions for SLT service improvement are reported. Clinical implications of this study Targeted, ongoing staff training is required in care homes to improve the communication environment and develop care home staff capacity to support residents' communication needs. There is also a call for service level improvements to increase the range of SLT practice in care homes, including a greater focus on communication needs and more specialist (e.g., dementia) SLT roles.

Drop Trailer Forecasting in Volatile Networks

This paper delves into the critical aspect of demand forecasting within the broader context of optimizing drop trailer management in volatile networks, with a specific focus on a large pallet manufacturer’s supply chain operation. The study underscores the importance of accurate demand forecasting as a foundational element for informing subsequent optimization models. The main objective is to enhance our sponsor company’s supply chain efficiency by accurately predicting future trailer requirements, which is crucial for the subsequent development of an effective inventory control and optimization model. This research utilizes forecasting methods like Gradient Boosted Trees and highlights the challenges of traditional forecasting methods in the context of our sponsor company’s complex and dynamic supply chain network. The demand forecast is meant to inform the development of optimization models crucial for ensuring the effective allocation and management of trailer assets, ultimately leading to cost reductions and improved service levels within our sponsor company’s network. The study contributes significantly to supply chain management literature by showcasing the application of sophisticated forecasting techniques in a real-world context and setting the stage for the development of robust optimization models in the domain of drop trailer management.

Analysis of the impact of rural tourism culture on the improvement of the level of public services

Abstract This paper first evaluates and analyzes the level of public service development from the dimensions of public information service, public transportation service, and infrastructure service using the entropy value method. Then, it studies the correlation between rural tourism culture and public service level from a qualitative perspective and then uses the threshold regression model and dynamic panel model to test the impact of rural tourism and culture industry and rural tourism and culture resources on the high-quality development of public services. After testing, the regression coefficient value of rural tourism and culture industry on the high-quality development of public services is 0.786, and the impact of rural tourism and culture resources on the improvement of public service level is significant at the level of 10%. The specific impact of rural tourism and culture on the efficiency of public services is fully explored in this study, which provides a theoretical basis for optimizing the efficiency of public services.

Pricing Decision of Closed-Loop Supply Chain to Improve Service Level under Patent Protection

Pricing Decision of Closed-Loop Supply Chain to Improve Service Level under Patent Protection

Research on the Network Equilibrium of Fresh Agricultural Product Supply Chain with Third Party Logistics Participation in Decision Making

Introducing Third-party logistics into the supply chain of fresh agricultural products can reduce the logistics cost of the supply chain and improve the freshness and quality of fresh agricultural products. Taking the fresh agricultural product supply chain network, which includes three decision-making entities: fresh agricultural product suppliers, 3PL service providers, and retailers, as the research object, a fresh agricultural product supply chain network equilibrium model is constructed using variational inequality theory. The impact of 3PL service providers' logistics service level on their equilibrium transaction volume and overall profit is studied. The research results show that when Third-party logistics is involved in the supply chain of fresh agricultural products, considering the logistics service level of 3PL service providers, with the improvement of service level, the overall profit of the supply chain of fresh agricultural products increases. It is a beneficial attempt to realize the profit of the supply chain of fresh agricultural products to improve the freshness and quality of fresh agricultural products through Third-party logistics; When the logistics service level rises to a certain level, and the service level continues to improve, the overall profit growth of the supply chain decreases. It is necessary to improve the impact of logistics service level on the freshness and quality of fresh agricultural products through other forms.