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Purpose: Although Heijunka (production levelling or smoothing) is a lean tool designed to balance changing customer needs and production efficiency, this study aims to analyze the impact of Heijunka and identify various factors which can generally affect its successful implementation. Design/methodology/approach: We adopted an action research (AR) approach to analyze the consequences of implementing Heijunka in a factory that successfully operates in a lean environment but faces demand that is increasingly less predictable, and customers who require ever more individualized products at low prices with extremely demanding delivery timescales. An estimation of costs was carried out to compare three alternative production planning scenarios: (1) make to order (MTO) production, (2) weekly levelled production and (3) production with minimal changeovers. Findings: The analysis of the impact of implementing Heijunka in this factory demonstrated that MTO production planning without Heijunka is the most efficient option for the factory. We explore the reasons why Heijunka is not suitable for this factory even though other lean principles and tools have been successfully implemented. In this way we identify the main factors which might influence the successful implementation of Heijunka, such as changeover time, the number of different product types (and lot sizes) and the capacity that the production lines are running at. Originality/value: Based on close collaboration between researchers and practitioners in the field of Operations Management, this AR study provides important information for decision making in lean environments (applying Heijunka or not), as well for the advancement of the academic debate about successful lean implementation nowadays.

Purpose: Pre-positioning is a crucial choice in pre-disaster humanitarian logistics planning that consists of deciding in advance how much aid and where should it be located to enable effective and prompt operations in the case of an emergency. To support managers making such decisions, we propose four mathematical formulations that, considering the uncertainty on the demand to satisfy, seek to optimize aid prepositioning (before the event) and further distribution (after the event) in order to minimize unmet demand (MUD). The purpose of this paper is to evaluate and compare the performance of these formulations on a real case to discuss when and why should each approach be applied. Design/methodology/approach: The two first formulations adopt the cardinality-constrained (CC) approach to handle uncertainty. These formulations differ in their objective functions, the first formulation’s objective seeks to MUD, whilst the second incorporates equity in the way that demand is satisfied. The two remaining formulations are scenario-based (SB) and as in the previous two formulations, seek to MUD with and without equity considerations, respectively. Findings: Applying our formulations to a case study, we compare the differences between the solutions produced by the proposed formulations and the solutions that would have been produced without uncertainty (perfect information) to have a better understanding of their performance and their behavior. A discussion of the strengths and weaknesses of each model is provided to help managers choose the model that best suits their needs. Originality/value: The formulations are applied to a case study where a food bank is faced with the arrival of a hurricane in Mexico. As far as our knowledge, it is the first work in literature to deal with humanitarian logistics under a cardinality-constrained approach.

Purpose: To enhance enterprise efficiency, this study examines the pivotal role of operations management in optimizing the use of materials, technology, equipment, and personnel, especially within the transformative framework of Industry 4.0. Design/methodology/approach: This article investigates operational preparation in the context of Industry 4.0. Through questionnaires and interviews with stakeholders, problems related to operational preparation were identified. To address these problems, a launching model was developed for production systems. The model considers several parameters important for the production process, including business goals, customer needs, and environmental conditions that impact enterprise performance and profit. Findings: The model determines the importance of jobs to be performed in a certain order to optimize production sequence. The proposed parameters are included in a mathematical-algorithmic launching model based on categorical levels related to the production process, such as profit, delivery times, processing times, total number of technological operations, product types, materials, required quality, product complexity, and resource use. The model was developed based on observations and investigations conducted in Kosovo's enterprises on operations research, and it has the potential to significantly improve production efficiency and profitability in the Industry 4.0 era. Practical implications: The findings of this study have practical implications for operations management within the Industry 4.0 framework, proposing a launching model designed to optimize production processes and enhance efficiency. Originality/value: The originality and value of this research lie in the development of a mathematical-algorithmic launching model that addresses operational preparation in the Industry 4.0 context, taking into account various crucial parameters.

Abstract Maintaining eye contact is a fundamental aspect of non-verbal communication, yet current remote communication tools, such as video calls, struggle to replicate the natural eye contact experienced in face-to-face interactions. This study presents a cutting-edge vision-sharing system for android avatars that enables remote eye contact by synchronizing the eye movements of the operator and the avatar. Our innovative system features an eyeball integrated with a wide-angle lens camera, meticulously designed to mimic human eyes in both appearance and functionality. This technology is seamlessly integrated into android avatars, enabling operators to perceive the avatar’s surroundings as if physically present. It provides a stable and immersive visual experience through a head-mounted display synchronized with the avatar’s gaze direction. Through rigorous experimental evaluations, we demonstrated the system’s ability to faithfully replicate the operator’s perspective through the avatar, making eye contact more intuitive and effortless compared with conventional methods. Subjective assessments further validated the system’s capacity to reduce operational workload and enhance user experience. These compelling findings underscore the potential of our vision-sharing system to elevate the realism and efficacy of remote communication using android avatars.

Abstract This paper explores the application of computer vision and mathematical modeling to analyze the intricate movements involved in weaving a traditional farming tool, the winnowing basket. By utilizing OpenPose algorithms, the study simplifies and visualizes the craftsmen's motions, particularly focusing on wrist movements. Video data of craftsmen in Chiba, Japan, creating Kizumi (place name) winnowing baskets is used as the basis for analysis. The extracted information is used to generate 2D motion trajectories of the wrist, allowing a comparison between beginners who watched parsed videos and those who watched the original videos in terms of skill acquisition and learning time. By visualizing human body behavior and combining statistical results, this study demonstrates the potential of artificial intelligence techniques such as computer vision for observing repetitive human movement and inheriting traditional skills.

The construction industry in New Zealand has undergone significant growth in recent years with an active commercial sector and ongoing infrastructure development projects. However, the industry has faced criticism due to failures, particularly in pipeline projects involving public infrastructure due to its underperformance, unnecessary postponement, and delay. This study, utilizing data from a ten-year plan of the Palmerston North City Council and employing document analysis techniques, examined the issues that have arisen in New Zealand's pipeline projects. The study identified government legislation, workforce, finance, and materials as major factors for the industry's underperformance. To address these issues, the study proposes various strategies and plans to improve the industry’s stability; the proposal has practical implications for government and development agencies seeking the implementation of effective strategies for future projects. Furthermore, findings highlight the need for improved collaboration between the government and the industry’s stakeholders, particularly in developing effective regulatory frameworks and ensuring a skilled workforce. The study provides valuable insights into the construction industry's challenges and opportunities in New Zealand, and underscores the importance of adopting a strategic approach to infrastructure development. The proposed strategies and plans can help to improve the industry's performance, thus contributing to sustainable growth and development in the sector.

Opportunities to improve students’ civil engineering skills for Industry 4.0 and train them for potential challenges in their professional careers go beyond industry sponsorship of capstone design projects. Students and faculty can benefit from industry mentors’ involvement who can use their expertise in addressing design problems and are enthusiastic about sharing their knowledge with students. Practicing engineers have a relevant, realistic, real-world viewpoint on their subject and reinforce their relevance to professional engineering practices. Students benefit tremendously from working on real-world problems of interest to industry, business, company-specific project management, product creation processes, and experience with financial, legal, and regulatory design constraints. This paper offers an overview of the multidisciplinary capstone design course in the civil engineering curriculum in Jordan and highlights relevant practices. It provides valuable guidance for incorporating Industry 4.0 skills into the teaching of capstone design courses to deal with challenges related to the context of digital transformation. The paper concludes that industry integration in engineering curriculum can generate multi-skilled and competent engineers with essential cognitive skills such as creativity, complex information processing, and lifelong learning.

Due to the construction industry’s rapid growth, concrete is now the standard building material used in new construction. In recent days, the development of the construction industry has focused heavily on how to maintain and identify the concrete structure of some older buildings. However, conventional concrete damage identification lacks precision and accuracy. Therefore, this work suggests an ultrasonic rebound enhancement approach based on deep learning. In order to detect concrete damage, the new algorithmic model measures the concrete data using the ultrasonic rebound technique and then analyses it using deep learning. By utilizing the improved Genetic Particle Swarm Optimization algorithm (GA-PSO) combined with the Back Propagation Neural Network, an improved GA-PSO-BP long-term concrete ultrasonic rebound comprehensive strength measurement model was established. The experimental results show that the improved GA-PSO-BP algorithm has a lower root mean square error than the conventional algorithm, which is lower than the Back Propagation neural network 0.008 and lower than the Genetic Algorithm-Back Propagation algorithm 0.001, and a higher accuracy rate than the Back Propagation algorithm model 0.07 and higher than the Genetic Algorithm-Back Propagation algorithm model 0.02. As a result, the improved GA-PSO-BP algorithm performs more precisely and accurately than the conventional approach. This study has practical applications for practitioners in the construction industry. The high accuracy and precision of the new algorithm render it an effective tool for identifying structural damage in old concrete buildings, which provides more reliable data support for maintenance efforts. This not only generates novel methods for enhancing concrete damage detection but also contributes to the sustainable development of the construction industry.

The growth of advanced technologies has been rising in recent years, which affects business operations throughout the supply chain. The smart warehouse is one of many technologies that increase warehouse management efficiency, for instance, increasing accuracy and reducing operating costs and processing times. Smart warehouse technologies are expected to be critical factors for effectively entering the fourth industrial revolution in Thailand. Currently, the utilization of smart warehouses in Thailand is not widespread; however, many organizations view such technologies as integral parts that will change the entire industry in Thailand. This research project explores and analyzes innovative warehouse technologies and their requirements for various industry sectors in Thailand. In addition, both qualitative and quantitative methods are employed to gather data. In-depth interviews of executives and warehouse managers are conducted, together with self-administered questionnaire surveys, to both smart warehouse users and providers. TOWS Analysis is applied to identify appropriate strategic options. Policy recommendations to effectively improve the unitization of smart and innovative warehouse technologies are also discussed.

With the development of scientific research and engineering practice, the amount of refractory waste is increasing gradually, and algorithms for the logistics management of waste have gained attention. Because there are a lot of obstacles in the city, solid waste will increase the field distance during transportation, thus producing a lot of Oxocarbon, which will promote the greenhouse effect. In order to reduce carbon production in the process of urban solid waste logistics management, this research improves the brainstorming algorithm based on low-carbon economy and discussion. The study first proposes a large number of different ideas based on the low-carbon economy. Then, through fitness comparison, the idea of clustering centers is further optimized. For similar ideas that arise during the thinking process, research has added mutation factors to facilitate ideas jumping out of local optima. Finally, the proposed fusion algorithm will be tested on the Logis dataset to calculate the carbon emissions of solid waste transportation devices within a day and compared with three models, such as brainstorming based on discussion. The motion distances of the four model design paths are 22 km, 23 km, 26 km, and 27 km, respectively, with corresponding carbon emissions of 3.9 t, 6.0 t, 6.8 t, and 8.9 t, respectively. The experimental results showed that the proposed model not only had the shortest design path but also had the lowest carbon emissions, which was in line with the current low-carbon economy environment. The application of this model in the rational transportation of urban solid waste will be useful. It is a novelty to integrate the idea of a low-carbon economy into the DMBSO algorithm, which can effectively deal with the carbon growth of the environment.

Rapid advances in deep learning and computer vision enable traditional cloud-based decision-making through edge computing with the Artificial Intelligent Internet of Things (AIoT) image sensors (AIoT-IS), thus improving the timeliness and security of image recognition. This study is indented to investigate the potential costs and benefits of AIoT-IS applications. This study summarizes AIoT-IS application scenarios for construction safety monitoring and proposes a cost/benefit analysis method for AIoT-IS implementation projects. According to the case study results, AIoT-IS achieves significant benefits, with a Net Present Value Index (NPVI) of 19.17% and a Benefit/Cost Ratio (BCR) of 4.65 as applied to construction site safety monitoring. Interviews with domain experts also provided qualitative feedback, pointing to the directions for future research. The proposed method is applicable for the decision-making of AIoT-IS adoption and the feasibility assessment of other innovative construction technologies.

There is an urgent need for the development of cutting-edge port infrastructure monitoring solutions that exploit different multimodal data towards timely optimal port management strategies and decision-making. Advanced monitoring applications allow for optimising maintenance, rehabilitation, and upgrade actions by assessing the structural integrity of port structures that is affected by a vast variety of stressors such as aging, imposed loads, inadequate maintenance treatments, human-induced factors, natural hazards, and the ever-changing climate. The purpose of this research is to enhance Remote Sensing (RS) port monitoring practices by investigating the potential of combining different types of RS methods to record and assess infrastructure condition. Two RS types of data, a) satellite imagery and b) aerial imagery from Unmanned Aerial Vehicle (UAV), were considered for structural monitoring at Lavrio port, located in northeastern Attica, Greece. In particular, the applied monitoring program was focused on its windward rubble mound structure where temporal changes in the armour layer were detected. Significant parameters regarding spatial resolution and UAV flight characteristics were further investigated aiming at ensuring high-quality data. The overall research indicated that RS synergies proved to be a promising practice for acquiring advanced spatial and temporal information on port infrastructure condition.

The development of the shipping industry has put forward new requirements and challenges for its pilotage and services. To promote navigation development and ensure safe and efficient ship navigation, experiments have been conducted to combine geographical information systems (GIS) big data with neural systems, resulting in a proposed intelligent ship pilotage service method. This paper presents a novel approach to building a ship pilot system using GIS technology. The system is enhanced with the introduction of the Faster-RCNN model to improve its positioning function, and Gaussian distribution is employed to optimize the loss function. Finally, the system’s ship pilot service set time parameters are solved based on ship entry and exit scheduling to achieve intelligent navigation and services for ship piloting. The data showed that on the MarineT dataset, the research method (GIS big data positioning-neural network) achieved its maximum fitness value at 36 iterations of the system, with a value of 99.78. At the same time, when the system ran 66 times, the average absolute percentage error obtained by the research method infinitely approached 0. In addition, based on the AIS dataset, when the recall rate of the four algorithms was 0.800, the accuracy of the research method was 0.873, with the highest numerical value. Practical applications have shown that when the system iterated 51 times, the total waiting time for ship piloting in and out of the port quickly decreased to 177.92 hours, which is significantly better than manual scheduling time. The aforementioned findings suggest that the implemented system has the capability to considerably decrease piloting time, deliver cutting-edge technical aid for the current expansion of the shipping sector, and establish a stable basis for future intelligent shipping technology.

This paper presents an automated tool for comparative estimates of earthmoving productivity, focusing on excavation operations. The developed system includes three modules (computational, data storage, operational) and a series of forms that estimate construction productivity for three types of equipment (excavator, loader, truck) based on fourteen estimation methodologies. To the authors’ best knowledge, it is the first research attempt at developing an automated tool based on the comparative evaluation of such a diverse set of estimation methodologies. All system components have been developed in a Microsoft (MS) environment, thus taking advantage of their user-friendly nature. A numerical example corroborated the tool’s validity. Future research may optimize the module integrated into the developed application to assist construction estimators in reaching informed decisions when deploying critical project resources.

Abstract Polymerase chain reaction (PCR) is an effective method for diagnosing infectious diseases and has been the primary method throughout the novel coronavirus disease (COVID-19) pandemic. PCR tests (from specimen collection to result acquisition) involve sample pretreatment, nucleic acid extraction, and PCR procedure. Automating the pretreatment process is crucial to mitigate the risk of infection for workers and to reduce the likelihood of sample contamination-triggered misdiagnosis, particularly when handling centrifuge tubes, cryopreservation tubes, and microtubes. Robotic systems have been engineered to automate cell culture and PCR-based diagnosis, predominantly designed for use with screw-capped containers. However, this leaves a notable gap in automation solutions for microtubes equipped with press-type caps. To address this gap, we developed a versatile microtube capper/decapper system. On the other hand, many tasks of manual operation using microtubes, which are routinely conducted in clinical tests and biological experiments, are performed. Compared to screw-type caps for centrifuge and cryopreservation tubes, press-type caps for microtubes present a considerably higher risk of the worker's fingers contacting the inside of the cap and/or generating airborne droplets. Despite the risks of contamination and infection derived from the manual handling of microtube caps, which can compromise diagnosis/experiment accuracy and worker safety, devices for manually opening and closing microtube caps without direct contact remain lacking. Therefore, leveraging the technology from the developed versatile microtube capper/decapper system for laboratory automation, we created a manually operated microtube equipped with an automatic capper/decapper system tailored for personnel in clinical and biological laboratories. In this study, we first examined the required specifications and prerequisites for a manual microtube capper/decapper and clarified the operating methods, operating procedures, operation environment, device size, accompanying functions, etc. Based on the required specifications and preconditions, we proceeded with the mechanical and control design of the conceptual model, manufactured a prototype, and confirmed its basic functions and performance. The compliant to the required specifications and preconditions and the usefulness of the proposed manual microtube capper/decapper were validated through various experiments and demonstrations. Using the proposed microtube capper/decapper, even small-scale operations, which are challenging to streamline, can be performed nearly as efficiently as full manual operations. Although operation time was not reduced, the ability to open and close microtubes without manual contact is crucial for improving diagnostic and experimental accuracy and for reducing the burden on and enhancing the safety of laboratory personnel. Because microtubes are used in various clinical tests and biological experiments, we believe that the proposed system can markedly reduce the workload for personnel across numerous clinical and biological laboratories.

Abstract This study aimed to develop daily living support robots for patients with hemiplegia and the elderly. To support the daily living activities using robots in ordinary households without imposing physical and mental burdens on users, the system must detect the actions of the user and move appropriately according to their motions. We propose a reaching-position prediction scheme that targets the motion of lifting the upper arm, which is burdensome for patients with hemiplegia and the elderly in daily living activities. For this motion, it is difficult to obtain effective features to create a prediction model in environments where large-scale sensor system installation is not feasible and the motion time is short. We performed motion-collection experiments, revealed the features of the target motion and built a prediction model using the multimodal motion features and deep learning. The proposed model achieved an accuracy of 93% macro average and F1-score of 0.69 for a 9-class classification prediction at 35% of the motion completion.

Abstract The manipulation of deformable linear objects (DLOs) such as ropes, cables, and hoses by robots has promising applications in various fields such as product assembly and surgical suturing. However, DLOs are more difficult to manipulate than rigid objects because their shape changes during manipulation. Furthermore, preventing a DLO from colliding with the environment is important to prevent it from becoming entangled and causing shape control to fail. In this paper, we proposed an obstacle avoidance and shape control scheme for DLOs based on differentiable simulation that does not require prior data or a specialized controller. First, we established a dynamic model of the DLO that allows for both forward dynamics transfer and error backpropagation to obtain gradients. Then, we employed model predictive control to optimize the embedded neural network for predicting the actions that would manipulate the DLO. Finally, the control scheme was made applicable to DLOs with different material properties by allowing online adaptation of the model parameters essential to deformation during manipulation. Simulations and real-world experiments demonstrate that the proposed control scheme could manipulate the DLO stably and accurately to avoid obstacles and achieve the goal state. In addition, the online adaptation of parameters helped mitigate the sim-to-real gap.

This research focused on intervening in the optimization algorithm used by the Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) tool to analyze small-angle scattering (SAS) profiles using the Rigid-Body model. CREASE uses the genetic algorithm (GA) with dynamic adaptation as its optimization algorithm. The aim is to evaluate the performance of CREASE by replacing the GA with a Harmony Search (HS)-based metaheuristic, specifically the Nobel Global Harmony Search (NGHS), in the analysis of SAS profiles of low-concentration solutions vesicles-assembled amphiphilic macromolecules. Results showed that NGHS achieved similar accuracy to GA but with higher efficiency, achieving similar quality solutions with only one-sixth, and in some cases one-tenth, the number of fitness function evaluations used by GA. Besides, CREASE-NGHS achieved SAS profile analysis convergence with less than half the number of fitness function evaluations, saving computational resources and facilitating a more complete analysis. In addition, NGHS addressed some shortcomings of the GA optimization process and facilitated its use and adaptation to distinct types of samples for users with little experience in optimization.

Evolving market trends, characterized by an increasing demand for personalized products with short life cycles and variable demands, pose a significant challenge to the industry. One of the industry's strategies is to adopt robotic assembly systems to improve productivity and increase system flexibility. The widespread adoption of robots in assembly processes is evident; however, success is not guaranteed with implementation alone. Equally critical is addressing assembly planning and scheduling problems in robotic systems. To facilitate understanding, this review offers, in Section 2, a classification of robotic assembly systems, with an emphasis on a new layout termed the robotic matrix-structure assembly system. Section 3 classifies the planning and scheduling problems applied to the robotic assembly systems. In Section 4, we discuss the approaches and techniques used to formulate and solve the planning and programming challenges. Finally, statistical data are presented to illustrate current research trends and identify gaps for future research.

Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required to reduce scrap. In this study, the multi-objective flexible job shop scheduling problem (MO-FJSP) that considers energy efficiency is discussed. The created model aims to minimize the energy consumption, total machine workload and makespan. In this study, new job arrivals are considered as dynamic events. Another dynamic event added to the model is the addition of rework processes between operations to reduce the scrap rate when a scrap decision is made during the production stages. The enhanced NSGA II algorithm was applied to solve this problem. The enhanced NSGA II algorithm was applied to test instances and its performance was compared using some of the multi-objective performance indicators. These experimental results prove the effectiveness of the proposed solution method.

As an integral part of production planning control, order release management is critical to enhance the competitiveness and production efficiency of companies. Previous literature shows limited application of optimization-based models in assembly job shops, primarily due to the intricate nature of product structures and assembly operations. Therefore, based on the idea of the allocated clearing function (ACF) model, we introduce material flow constraints and complex assembly structure constraints during the assembly stage, proposing the assembly job shop allocated clearing function (AACF) model. The performance of the AACF model and the rule-based mechanisms in terms of cost and timing measures are compared through experiments containing 6 factors and 96 scenarios. The results show that the AACF model performs better in terms of cost management, service level and order due date deviation. In addition, a sensitivity analysis of the objective function parameters is performed to confirm the robustness of the AACF model. Finally, a case application in a real assembly shop illustrates the feasibility and validity of the proposed AACF model.

Assembly buildings, in the context of the low-carbon transformation of the construction industry, achieve superior outcomes in terms of carbon emission reduction, enhancement of building uniformity, and optimization of resource utilization as compared to traditional buildings. However, the supply chain for assembly building is marked by a significant susceptibility to risk and a need for timely fulfillment of requirements. This paper examines the risk of disruption and capacity limitations in the assembly building supply chain resulting from supply disruptions. It establishes a three-tier supply chain for assembly buildings, including primary component suppliers, backup suppliers, assembly manufacturers, and retailers. The study compares the optimal decision-making and coordination strategies of the supply chain members under centralized, decentralized, and joint agreements. The supply chain dual-source procurement decision coordination model is constructed by incorporating capacity constraints and analyzing the effects of supply disruption probability, repurchase coefficient, revenue sharing coefficient, cost, and other parameters on the expected profits of the supply chain members using arithmetic simulation. Research has indicated that when the likelihood of a disturbance occurring rises, the anticipated financial gain for the main provider decreases, while the predicted financial gain for the secondary supplier increases. The implementation of a collaborative agreement between the assembly maker and the parts backup provider would result in much greater anticipated profits compared to the decentralized decision-making approach. The impact of the revenue sharing coefficient on the predicted earnings of retailers and assembly manufacturers is more significant compared to the repurchase coefficient. The selection bias between NA and NB techniques under capacity constraints mostly arises from the assertiveness of the wholesale asking prices of inexpensive component suppliers, leading assembly manufacturers to increasingly prefer the NA option. This paper's research successfully achieves the contractual coordination of the assembly building supply chain, enhances the resilience of the assembly building supply chain, and promotes the long-term sustainable development of the assembly building supply chain.

With the organic integration of the Internet and remanufacturing industry, traditional manufacturers can recycle used products and sell products (including new and remanufactured products) through e-commerce retail platforms. A recycling and remanufacturing supply chain with three members (manufacturer, e-commerce retail platform, third-party recycler) is constructed in this paper. Manufacturer has remanufacturing capabilities, and the e-commerce retail platform can provide logistics service. In response to the organic integration of the Internet and remanufacturing industry, we mainly consider the pricing decisions of consumer preferences for online sales models and recycled products’ quality. Based on the impact of consumer used product recycling promotion activities on supply chain, a pricing game model was constructed for three recycling channels: manufacturer, e-commerce retail platform, and third-party recycler. Optimal pricing decision, logistics service level, used product recycling promotion intensity index, and recycling rate were obtained. Research has shown that consumer preferences can significantly improve supply chain logistics service level, pricing, market demand, and profits; Strengthening consumer awareness of remanufacturing used products and improving recycled products’ quality can not only lower consumer purchase prices and expand consumer demand, but also increase profits of supply chain members.

The stochastic contextual bandit problem, recognized for its effectiveness in navigating the classic exploration-exploitation dilemma through ongoing player-environment interactions, has found broad applications across various industries. This utility largely stems from the algorithms’ ability to accurately forecast reward functions and maintain an optimal balance between exploration and exploitation, contingent upon the precise selection and calibration of hyperparameters. However, the inherently dynamic and real-time nature of bandit environments significantly complicates hyperparameter tuning, rendering traditional offline methods inadequate. While specialized methods have been developed to overcome these challenges, they often face three primary issues: difficulty in adaptively learning hyperparameters in ever-changing environments, inability to simultaneously optimize multiple hyperparameters for complex models, and inefficiencies in data utilization and knowledge transfer from analogous tasks. To tackle these hurdles, this paper introduces an innovative transfer learning-based approach designed to harness past task knowledge for accelerated optimization and dynamically optimize multiple hyperparameters, making it well-suited for fluctuating environments. The method employs a dual Gaussian meta-model strategy—one for transfer learning and the other for assessing hyperparameters’ performance within the current task —enabling it to leverage insights from previous tasks while quickly adapting to new environmental changes. Furthermore, the framework’s meta-model-centric architecture enables simultaneous optimization of multiple hyperparameters. Experimental evaluations demonstrate that this approach markedly outperforms competing methods in scenarios with perturbations and exhibits superior performance in 70% of stationary cases while matching performance in the remaining 30%. This superiority in performance, coupled with its computational efficiency on par with existing alternatives, positions it as a superior and practical solution for optimizing hyperparameters in contextual bandit settings.

Increasing customer apprehensions regarding the security and nutritional value of agricultural goods are compelling governments and industries to implement traceable, transparent, and reputable logistics management systems. Blockchain-based agricultural logistics management systems guarantee the permanence of data once it is uploaded but cannot cope with the risk of data being falsified before uploading to the blockchain. In this work, we developed a collaborative game model between government bodies and agricultural enterprises based on the evolutionary game theory and explored the influencing factors of enterprises following the rules to share the real traceability information through numerical simulation using MATLAB. The findings show that government incentives and penalties promote positive behavior, and consumer and media supervision contribute to supply chain transparency, but firms tend to share truthful information only when it benefits them. This study builds upon existing research on the impact of social variables on both members' decision-making behavior. It highlights the positive roles of consumers and the media in the supervision of agricultural product traceability, which can help to raise public awareness of social responsibility and thus promote positive interaction in the market.

The Covid-19 pandemic has significantly impacted consumer behavior and commerce, prompting a shift towards online goods and services. The surge in demand has led to inefficiencies and disruptions, especially in the last-mile delivery (LMD) process. Because of the LMD, the final stage of the supply chain, plays a crucial role in transporting goods from businesses to consumers, challenges such as the cost inefficiencies of direct home delivery have underscored the need for innovative solutions. In this study, the collection delivery points (CDPs) approach was adopted instead of direct home delivery. It focuses on addressing these challenges by adopting service points as dynamic CDPs and handling the problem as a dynamic location routing problem (DLRP). Two solutions approaches are proposed, to select candidate depots strategically and determine efficient route configurations, to aim to minimize travel distance. One of them is a two-phased hierarchical method that starts with clustering and continues with an Ant Colony Optimization (ACO) based-hybrid algorithm, and the other one is based solely on an ACO-based hybrid algorithm. The performance of these approaches is evaluated on modified benchmark instances from the literature. It has been observed that the ACO based-hybrid algorithm is more successful in terms of total travel distance, and if an evaluation is made in terms of the number of routes, it is recommended that the results of the two-phased hierarchical method should also be considered. Furthermore, a real word case study was conducted with the proposed methods and the results were compared from different perspectives. The results corroborate the findings regarding benchmark instances, thereby providing additional validation to the results obtained.

The competitiveness of the companies depends on continuous progress, and the allocation of the continuous improvement is crucial in today’s market competition. The main approaches proposed in the literature are distributed and centralized policies. This research proposes a novel model for resource allocation that surpasses existing methods. It leverages throughput rate evaluation as the core principle. This model considers key improvement parameters and utilizes a mathematical framework to allocate improvement resources to each parameter. The experiments conducted concern failure parameters (Mean Time Between Failures and Mean Time To Repair), setup time, defective rate, bottleneck numbers and the increasing processing time of the bottleneck. The original contribution is twofold: the evaluation considers a wider range of conditions and their impact on flow line’s sustainability. The model emphasizes sustainability by focusing on minimizing the idle time of workstations within the flow line. Numerical analysis of the proposed model reveals a substantial reduction in total idle time improving the related energy consumption. Furthermore, the model achieves a throughput rate that closely aligns with the centralized policy, traditionally considered the most effective approach.

This study introduces a novel framework for classifying mesolevel industrial ecosystems, focusing on their roles in reindustrialization and proactive import substitution. The research aims to address the gap in existing literature by offering a detailed categorization of these ecosystems, considering various dimensions like sectoral composition, geographic scope, and nature of linkages among participants. The study employs an expert survey methodology, targeting 180 specialists across different sectors to identify industrial ecosystems with the highest potential for fostering economic transformation. The hierarchical classification methodology used is meticulously detailed, and the rationale behind the selection of survey par- ticipants and questions is explained. The survey findings revealed that inter-sectoral and open ecosystems are particularly effective in promoting reindustrialization and proactive import substitution. The results also emphasized the significance of network-structured ecosystems and highlighted the preference for digital and strategic networks business models, alongside half-open innovation models. The study concludes that mesolevel industrial ecosystems play a pivotal role in economic transformation, particularly in the context of reindustrialization and import substitution. Openness, networking, and inter-sectoral collaboration are identified as key traits of ecosystems that significantly contribute to these goals. The paper also suggests potential areas for future research, such as the integration of digital technologies and environmental sustainability considerations in ecosystem classifications.

This study proposes a Structural Equation Model (SEM) to explore the relationship between the behavior from the driver, perceived service atmosphere, perceived value, attitude and loyalty intention of customers from using taxi and car-hailing services. Meanwhile, the relationship of safety, green value, privacy, risk, sensitivity, and security as sustainability measures and customer attitude to the two services are also included in our model. Results suggest that behavior from drivers, service atmosphere, together with multiple sustainability measures, would have direct and indirect positive effects on loyalty intention, regardless of the user groups.

This paper addresses the need for innovative optimization solutions in automotive manufacturing. Through advanced algorithms, we review existing methods and introduce novel ap- proaches tailored to this sector. Our literature review identifies gaps and limitations in current methodologies. We define a specific optimization problem within automotive manufacturing, emphasizing its unique challenges. Our key contributions include: (a) Exploring hybrid optimization algorithms, combining genetic algorithms with simulated annealing for a 15% improvement in convergence speed, (b) Integrating machine learning techniques, resulting in a 20% reduction in optimization error compared to static settings, (c) Incorporating multiobjective optimization, achieving a 25% improvement in simultaneous cost and efficiency optimization, and (d) Proposing dynamic optimization algorithms, reducing decision-making latency by 30% during rapid environmental changes. Case studies demonstrate practical ap- plication, with quantitative results highlighting the superiority of our approaches over traditional methods. Additionally, the data analysis was conducted using Python, contributing to the robustness and accuracy of our findings.

The fifth industrial revolution emphasizes human-robot collaboration to enhance transformation processes. While collaborative robots (cobots) are increasingly attracting interest in various industries, integrating them into assembly lines remains challenging. This paper aims to investigate whether cobots are replacing traditional robots or if a synergy between both technologies is essential in the era of Industry 5.0 to maximize benefits. The real case study presented provides a comparison between robots and cobots in an automotive factory using the Fuzzy AHP methodology. The results indicate that cobots are advantageous in produc- tion units with low-volume and high-variability tasks. However, they fall short of the reli- ability, precision, and productivity that traditional robots offer for repetitive tasks. Finally, a framework is proposed to guide decision makers in adopting the suitable solution to their needs while ensuring optimal performance as well as workers wellbeing.

The implementation of TQM to improve product quality does not only focus on systems and technology but also requires competent human resources such as leadership, technical competence, and organizational culture as users and controllers of systems and technology. This will have an impact on the company's operational performance which is expected to continue to increase. This research aims to analyze and test the influence of leadership, technical competence, and organizational culture on the implementation of Total Quality Management (TQM) and its impact on operational performance. The research method used is a descriptive statistical method with an explanatory type. The sampling technique was carried out in a proportional cluster, randomly sampling all ceramic workers in 4 different companies with a total of 368 respondents. The analysis technique used is Covariance- based Structural Equation Modeling (SEM) analysis, and the application used is Lisrel. The research results show that leadership directly influences the implementation of TQM to run well as 33,80%. Technical competition directly influences TQM at 21,10%. Organizational culture has a direct influence on TQM at 16,70%. Meanwhile, the impact of TQM is very influential and has a direct effect on the operational performance of the ceramic industry, so it can increase company profits.

Purpose: This study aims to enhance production quality by applying Quality Control (QC) principles through acceptance sampling, specifically analyzing the efficacy of attribute acceptance sampling plans in final lot receptions. Design/methodology/approach: Through a comprehensive review and critical evaluation of various sampling methodologies found in literature, this paper assesses their efficiency under distinct administrative and operational conditions. It emphasizes the comparison of different attribute acceptance sampling plans by examining variations in parameters and key performance indicators, such as Average Outgoing Quality (AOQ) and inspection time allocation percentage. Furthermore, it proposes a model for Continuous Sampling Plans (CSP) to evaluate these plans' performance in response to operational characteristic variations. Findings: The analysis reveals that the selected methodologies significantly aid in decision-making processes for lot acceptance, utilizing non-conforming rates depicted by the Average Quality Level (AQL). This provides a robust framework for improving Quality Control strategies, demonstrating the potential of these methodologies to optimize production quality through strategic lot acceptance. Practical implications: This paper outlines a practical approach for industry practitioners to enhance decision-making in lot acceptance, offering a method to balance quality control with operational efficiency effectively. Originality/value: By comparing a wide range of attribute acceptance sampling plans and introducing a novel CSP model, this research contributes valuable insights into the optimization of QC strategies. It offers a unique perspective on enhancing production quality, marking a significant advancement in the field of Quality Control and management.

Purpose: The purpose of this paper is to propose an opportunistic group maintenance model for a multi-component series system considering reducing CO2 emission and increasing system efficiency, which establishes a maintenance policy to minimize the cost rate of the system life cycle. Design/methodology/approach: Structural dependence and economic dependence between the components in a multi-component series system are analyzed to make a condition-based maintenance policy. To minimize the cost rate of the system life cycle, clustering theory and two decision variables, which include the preventative maintenance cycle multiplier and basic preventive maintenance interval of each component, is utilized to make an opportunistic policy for system optimal maintenance under the background of increasing energy efficiency and reducing CO2 emissions. Findings: It can be concluded that the government imposes fines on excessive CO2 emissions and energy consumption indicators, which can influence the maintenance decisions of enterprises, promote enterprises to shorten the cycle of preventive maintenance, and avoid excessive CO2 emissions of various components and exceed the indicators as much as possible, so as to enable enterprises to actively save energy, reduce emissions and control carbon emissions. Furthermore, when the single preparation cost of repair maintenance increases, enterprises need to shorten the maintenance period to avoid frequent component failures. As the cost of a single prep for preventive maintenance rises, organizations need to extend maintenance cycles and avoid frequent parts downtime - minimizing their own repair costs. Practical implications: Considering the high maintenance cost and low energy efficiency of multi-component systems, this model assists production managers to have better maintenance of these systems. Originality/value: 1.Model innovation: comprehensive consideration of two variables in the selection of decision variables for preventive maintenance or opportunistic maintenance; The selection and synthesis of ecological factors when making ecologically conscious maintenance decisions for multi-component systems make up for the gaps of single variables and one-sided indicators in previous studies and models. 2. Methodological innovation: In the identification of opportunity maintenance, the idea of clustering is first combined; In the simulation analysis, genetic algorithm is used to obtain the optimal parameters quickly and accurately. A blend of management, statistics, biology and computer science.

Abstract This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance.

Several mobile telecommunication companies (MTCs), such as AT&T, Verizon, and China Mobile, are investing in content services to drive continued growth as the telecom industry has become saturated. In this study, we investigate the competition between two MTCs, one of which (MTC 1) provides both data and content services, while the other (MTC 2) provides only data services. We propose a two-stage game-theoretic model to analyze the two MTCs' optimal pricing strategies for data service and content service as well as the investment strategy for the content service. Our analysis reveals that a higher value of value-added service offered by MTC 1 will incentivize it to raise its subscription fee for the content service and induce both MTCs to reduce the prices of their data services, which results in an increased demand for the content service and a decreased demand for MTC 2's data service. As MTC 1 invests further in the quality of the content service, it would benefit from increasing the subscription fee for the content service, and the two MTCs should increase the subscription fee for the data service. However, compared to the decisions in the scenario where MTC 1 does not provide content service, the provision of this service by MTC 1 leads to a lower subscription fee for the two MTCs' data services, an increase in the demand for MTC 1's data service, and a decrease in the demand for MTC 2's data service. These findings reveal that regularly monitoring market competition, as well as adjusting pricing strategies based on market scenarios and the value of services, can enhance competitiveness and long-term profitability for MTCs.

More and more marketplace platforms choose to use the data gathered from consumers (e.g., customer search terms, demographics) to provide a data analytics service for third-party sellers, both encouraging innovations and improving the operations of the latter. Through adopting the data analytics service, a seller can enhance its competitiveness by improving the quality of its products when there is more than one seller offering substitutes. This study develops a game-theoretic model to characterize a scenario with a marketplace platform and two competing sellers that sell substitutable products and decide whether to adopt the data analytics service provided by the platform. Then, we find that a seller's decision of whether or not to purchase the service depends on the competitor's decision and the two sellers' absorptive capacities of knowledge. Furthermore, a seller can benefit from adopting the data analytics service only if the seller has a high absorptive capacity, which can help increase product quality. When only one seller adopts, the platform and competing sellers can benefit from the adoption if this seller's absorptive capacity is high and the other's is moderate. The sellers' interests and social welfare can be aligned unless both sellers' absorptive capacities are low or high.

Most of the literature on reviewer assignment problem (RAP) considers fitness or efficiency as the evaluation criterion of an assignment, e.g., the degree of matching between reviewers and proposals. In this study, we consider fairness of an assignment and establish two multi-objective linear integer programming models for proposal assignment. We introduce a definition of fairness based on the closeness of social relationships between scholars. The closeness of social relationships is quantified based on social networks among reviewers and serves as part of objective functions. An improved space-partitioning algorithm is designed to solve the multi-objective models with integer objectives. When the problem size is large, two meta-heuristic algorithms are applied. Experiments on real and randomly generated cases are provided to illustrate the effectiveness of the proposed models and algorithms.

This paper examines the influence of digital finance (DF) on the green level of transportation companies (GLTC, n = 112) listed on the Chinese A-share market from 2011 to 2021 through moderation and mediation models. According to the conclusions, first, a significantly positive correlation exists between DF and GLTC. Second, DF indirectly promotes GLTC by enhancing corporate value and alleviating financing constraints. Both enterprise value and financing constraints have a chain mediation effect. Third, the government's digital preference can enhance the role of DF in promoting GLTC and alleviating financing constraints. In addition, governmental digital preference can moderate the impact of financing constraints on GLTC. Fourth, the effect of DF on GLTC exhibits regional, scale, and ownership heterogeneities. Drawing on the conclusions of the empirical analysis, several pertinent recommendations are proposed from micro and macro perspectives.

We construct correlation-based networks linking 86 assets (stock indices, bond indices, foreign exchange rates, commodity futures, and cryptocurrencies) and analyze the impact of asset selection on portfolio optimization using different centrality measures (including degree, eigenvector, eccentricity, betweenness, PageRank, and hybrid centralities). In times of a global crisis, peripheral assets located in cross-market networks are more suitable for investment. By comparing portfolio performance based on different centrality measures, we find that (i) hybrid, eigenvector, and PageRank centralities can best improve portfolio performance; (ii) degree centrality is suitable for larger portfolios; and (iii) eccentricity and betweenness centralities are unsuitable for network optimization portfolios. In response, we explain them based on the construction principle of centrality measures. Additionally, our optimal portfolios suggest that investors pay more attention to the role of emerging countries, which are less exposed to external shocks and whose financial markets are more likely to remain stable.

Complex equipment refers to special equipment that differs from general equipment. The collaborative development work of complex equipment in the military-civilian integration context involves numerous suppliers. We consider a two-tier supply network composed of different suppliers that participate in the development work to assemble complex equipment that cooperate with a main-manufacturer regarding spare parts. However, in terms of spare parts, a substitution relationship exists in assembly work for complex equipment. Hence, selecting a suitable supplier from the matching process between suppliers and spare parts under a military-civilian integration background is essential. This study considers three main analyses to obtain a suitable supplier for the development work of complex equipment. First, we construct a two-stage model to acquire the necessary evaluation dimension for subsequent processes. Second, we examine the evaluated attributes for the matching process based on entropy-group-DEMATEL analysis. Third, we perform information aggregation for the uncertain preference information between spare parts and suppliers using a Bonferroni mean operator. Finally, an illustrative example is presented to demonstrate the whole efficiency. Through the aforementioned analysis, we can select a suitable supplier that could participate in complex equipment military-civilian collaborative development work.

The quality of logistics services affects the performance and competitiveness of express logistics companies. Conventional evaluation methods of service quality are based on constructing a rating index system and then comprehensively evaluating the associated questionnaires and interviews. Such methods are often subjective, time-consuming, and include a limited sample size (data). On the other hand, customers' opinions concerning logistics services can now be deciphered using online customer reviews. Therefore, this study proposes a method combining the models of latent Dirichlet allocation and long-short term memory (LDA-LSTM) to overcome the limitations of conventional evaluation methods. The main contributions include four aspects. First, the LDA-LSTM model can extract comprehensive aspects and opinions to develop evaluation indexes and scores for logistics service quality, and the reviews of a logistics firm can be examined to verify the effectiveness of this method. Second, the LDA-LSTM model can handle a situation in which several aspects and opinions express one topic or sentiment, and it outperforms the joint sentiment topic model (JST) and naive Bayes classification (NB). Third, positive and negative ratings can reflect a firm's overall service quality, with an excellent rating highlighting the best service quality, which can provide a multi-dimensional evaluation. Fourth, we also identify the indicators of logistics service quality on which customers focus, and we compare the service quality among express enterprises in China.

This study examines service investment decisions in the context of two store brand introduction modes and the mode selection of a retailer. The research focuses on a supply chain comprised of a national brand manufacturer (NBM), an external store brand supplier (SBS), and a retailer. A national brand product (NB) is distributed to the market through both direct and retail channels. The retailer consistently offers service efforts within the retail channel and has two options for launching a store brand product (SB): producing the SB or procuring it from an external source. The findings indicate that, when the retailer has a low fixed cost and a substantial market size for one-stop consumers, the retailer internally producing its own SB is more profitable. However, when the market size for one-stop consumers and the fixed cost of producing the SB are both low, or when the retailer possesses a relatively high fixed cost, it is advisable for the retailer to procure a lower-quality SB from the SBS. Alternatively, the retailer could establish its own production facilities to launch a higher-quality SB. Furthermore, it is noteworthy that the NBM consistently favors the retailer outsourcing the SB production to an external SBS. This implies that both the NBM and the retailer can achieve a “win-win” outcome when the retailer procures the SB.

Purpose: This article focuses on the problem of deciding the annual investment that a company should allocate to the rehabilitation of its water distribution and sanitation networks. The objective is to find the investment amount necessary to maintain an adequate quality and sustainability of the infrastructure. It is not a simple decision, as there are different criteria that may be of interest to the managing company. In this paper, we consider four criteria related to the reliability of individual pipes and the complete network. These indicators are the infrastructure value index, the average age of network pipes, the risk index and the probability of failure. Design/methodology/approach: A methodology is proposed to estimate the best annual investment by analysing the evolution of these indicators. Concretely, two strategies are tested. The first one is a minimax-based approach that seeks a balanced solution for all the indicators. The second one is named as minimal deviation strategy and seeks to minimise the deviation of all the indicators in the last year of the time horizon compared to the initial year. Findings: In order to obtain a realistic sample of the performance of both strategies, 201 scenarios, i.e. 201 different annual investments have been tested. According to the first strategy, an annual investment of 55.5 M€ is the best option, while the minimal deviation strategy presents an annual investment of 39.5 M€ as the best decision. The study reveals that different evaluation functions lead to completely different annual investment. Concretely, the minimax evaluation function is more conservative than the minimal deviation strategy. Originality/value: This study proposes an original approach to address the decision problem of investments in asset management. To the best of the authors’ knowledge, it is the first attempt to treat that problem using this kind of evaluation functions. However, it is still a simple proposal and there are many options to continue this line of research.

Purpose: Automatic grouping of data according to certain characteristics is made possible by clustering algorithms, which makes them an essential tool when working with large datasets. However, although they are unsupervised tools, they generally require the specification of the number of clusters to be formed, k, a task that may be simple for a human, but quite complex to automate. Despite the most commonly used k-value selection techniques offer acceptable results, they are not without shortcomings, suggesting that there is ample room for improvement. This paper briefly introduces clustering techniques, discusses the main shortcomings of conventional k-value selection techniques and examines the advantages and limitations of nine promising alternatives presented in recent years. Design/methodology/approach: An evaluation of the main shortcomings of classic k-value estimation techniques has been carried out, and the newest proposals have been explained and compared. Findings: New k-value estimation indices and methodologies proposed by authors guarantee better results, extending the use of these techniques to large volumes of data, and complex shapes and structures. However, no generical methodology able to overcome all the described shortcomings has still been developed. Research limitations/implications: This research is limited to the newest developed techniques for k-value estimation, including proposals published since 2019. Older proposals have not been considered, as the newest ones overcome the former’s shortcomings. A k-value estimation techniques review published in 2019 is cited in the test as a base reference. Practical implications: Although the examples listed in the text apply to industry, the techniques described and discussed in this review are applicable to any area of science that can benefit from the use of clustering techniques. Originality/value: To date, there has been no paper comparing the new k-value estimation techniques. Although there are literature reviews comparing the classical methods, these methods are nowadays nearly obsolete due to the complexity of the data usually faced.

Purpose: This research develops a model integrating inventory with order pick-up that considers both the vendor’s capacities and the pick-up time for the multi-vendor single-buyer (MVSB) system. Design/methodology/approach: A model of integrating inventory with pick-up of order considering both the vendor’s capacities and the pick-up time for the MVSB’s system is formulated as a mixed integer non-linear programming, and a heuristic-metaheuristic algorithm is proposed to solve the problem. This model applies a decomposition approach to minimize total relevant costs, which consist of the buyer’s ordering, vendor’s set-up, vendor’s and buyer’s holding, and pick-up. Findings: This research proposes an inventory model integrated with the pick-up of orders, considering both the capacity of the vendor and the pick-up time for the MVSB system. This research also proposes a hybrid heuristic-metaheuristic algorithm that can obtain a solution in realistic computational time. Originality/value: This paper proposes a mathematical model for the inbound inventory routing problem, considering both the capacities of vendors and the pick-up time for the MVSB system.

Abstract The amount of labelled data in industrial use cases is limited because the annotation process is time-consuming and costly. As in research, self-supervised pretraining such as MAE resulted in training segmentation models with fewer labels, this is also an interesting direction for industry. The reduction of required labels is achieved with large amounts of unlabelled images for the pretraining that aims to learn image features. This paper analyses the influence of MAE pretraining on the efficiency of label usage for semantic segmentation with UNETR. This is investigated for the use case of log-yard cranes. Additionally, two transfer learning cases with respect to crane type and perspective are considered in the context of label-efficiency. The results show that MAE is successfully applicable to the use case. With respect to the segmentation, an IoU improvement of 3.26% is reached while using 2000 labels. The strongest positive influence is found for all experiments in the lower label amounts. The highest effect is achieved with transfer learning regarding cranes, where IoU and Recall increase about 4.31% and 8.58%, respectively. Further analyses show that improvements result from a better distinction between the background and the segmented crane objects.

The paper discusses the concept of Kaizen, a Japanese philosophy of continuous improvement, and highlights its historical significance and widespread adoption in various industries worldwide. This paper is based on literature review of 98 research articles published related kaizen's evolution, industry application, and limitations during 2000-2022. The study employed a comprehensive search strategy across multiple databases like Scopus, Google Scholar, and others to gather relevant systematic reviews on kaizen. It references established guidelines like PRISMA and it mentions the planned thematic analysis using NVivo software, highlighting its applicability in identifying patterns and themes across diverse data sources. The review showed that Kaizen adoption is a useful tool for organizations looking to achieve continuous performance improvement and sustainability over time.

The semiconductor industry faces the dual challenge of risk assessment and process improvement. This paper introduces a framework that integrates Failure Mode and Effects Analysis (FMEA) and Plan-Do-Check-Act (PDCA) cycle to address these challenges effectively. FMEA serves as the initial step to identify potential risks within the system, followed by applying the PDCA cycle to systematically address and enhance the identified risks. The comparison between the initial Risk Priority Number (RPN) value, determined through FMEA, and the post-RPN in PDCA value gauges the success of the framework. Implementing the system in a semiconductor assembly line yielded a significant 51% improvement in RPN, with additional Lean tools incorporated into PDCA, such as SMART goals, 6M, and multi-voting. This integrated framework amplifies risk management, fosters continuous improvement, optimizes resource utilization, and empowers data-driven decisions, ultimately bolstering organizational growth. However, the study acknowledges limitations such as its single-case focus and potential RPN calculation subjectivity.

This research investigates the synergistic relationship between Lean Manufacturing and Industry 4.0, crucial paradigms in modern industry, through a literature review of 60 scientific articles published from January 2013 to November 2023. The study reveals a tripartite relationship: Industry 4.0 technologies amplify Lean Manufacturing’s efficiency; Lean Manufacturing principles facilitate Industry 4.0 adoption; and their interaction fosters mutual enhancement, indicating a dynamic interplay. The results show three main trends: combining Industry 4.0 technologies, especially the Internet of Things, with Lean Manufacturing principles to make things more efficient and productive; highlighting the important role of Lean Manufacturing and Industry 4.0 in tackling sustainability issues and encouraging eco-friendly practices; and combining Lean principles with Industry 4.0 as a step toward Industry 5.0. Challenges include the need for a cultural transformation to align Lean’s simplicity with Industry 4.0’s complexity, resolve compatibility issues between legacy systems and new technologies, enhance data management and cybersecurity, address skill deficiencies through staff training, and ensure supplier and partner alignment. This research enriches academic and industrial discourse by presenting a novel outlook on the convergence of lean manufacturing and Industry 4.0, highlighting its significance for future works.