This study aims to optimize product locations within a warehouse using turnover rates as an indicator of sales performance. By positioning items based on sales frequency, the approach enhances warehouse layout efficiency and accessibility. Principal Component Analysis (PCA) and k-means clustering were employed to organize products based on their sales patterns. Twenty clusters were formed and used to inform strategic product placement. The results demonstrate the effectiveness of this clustering-based method in improving warehouse operations by reducing expected travel distance as proxy for order-picking times and enhancing operational efficiency. The proposed approach is data-intensive, which may limit its applicability to organizations without sufficient sales data. This study introduces the application of PCA and k-means clustering in optimizing storage location assignments. It highlights the value of using data-driven techniques to solve real world warehouse management challenges. Future studies could investigate incorporating factors such as demand fluctuations to enhance the model's flexibility.
Globally, e-commerce platforms' private labels are becoming increasingly prevalent, posing a significant competitive threat to manufacturers' brands. This study focuses on supply chain scenarios where manufacturers sell products to consumers through platform resale or agency models. Using Stackelberg and bargaining game models, we analyze platform decisions to enter private label markets and both parties' preferences for sales models. Findings reveal that in the Stackelberg game, platforms favor launching private labels when costs and product substitutability are low. In the bargaining game, stronger platform bargaining power increases the likelihood of introducing private labels under the agency model. Regarding sales model selection, both parties prefer the resale model when costs are low and substitutability is high. As bargaining power increases, the range of scenarios where both parties support the resale model expands. Furthermore, the greater the market potential, the broader the regions where both parties opt for the agency model.
Fault diagnosis, as an important part of machinery and equipment health management, plays a vital role in improving the service life of machinery and equipment and in reducing the safety risks of machinery use. Feature extraction directly affects the effectiveness of data-driven fault-diagnosis methods. To improve the accuracy of fault type diagnosis, this study proposes a novel feature extraction method, ensemble empirical mode decomposition-curve quadratic code, by combining ensemble empirical mode decomposition and curve quadratic code to collect rotor vibration signals from rotating machinery where faults occur and uses the method for feature extraction, which can obtain higher-order code with richer feature information. Experiments demonstrate that the proposed feature extraction method maintains an average fault diagnosis rate above 90% across a buffer-coefficient range of 0.05-0.25, with a peak of 92% at 0.15, and effectively improves the correct diagnosis rate of fault types.
Effective supplier selection in e-commerce requires understanding the complex criteria interdependencies that drive company performance. This study addresses this gap by developing a structural framework using a Multiple Criteria Decision Making (MCDM) approach. We integrate the Delphi method with a panel of eight industry experts with Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis to map these critical relationships. The research suggests a seven-level hierarchy that challenges traditional cost-focused evaluations. Foundational criteria like quality, customer service, and manufacturer status are identified as the system's primary drivers, while financial metrics such as price and cost are suggested to be dynamic outcomes. This study proposes a conceptual framework for managers, suggesting a strategic shift from tactical cost-cutting to prioritizing foundational drivers for sustainable business success.
Maintaining continuous operation in high-throughput manufacturing systems with minor failure (MF) problems is challenging to achieve the target production rate by a specific time horizon. The effective use of the dynamic opportunistic maintenance (OM) approach mitigates interference between the continuous schedule production operation and maintenance tasks. The study suggests a simulation-based model combining active and passive maintenance opportunity windows (AMOW, PMOW), Long-Duration Failure Modes (LDFM), and A and B types of downtimes. We aim to determine optimal maintenance policies to enhance system performance and minimize the impact of minor stoppage MF behaviors that take less than 15 minutes. To implement the OM approach, we chose the water bottling factory as a case study, investigated the system's distinctive behavior, and derived appropriate policies for implementing OM actions. The real-time information on machine failure conditions revealed a significant frequency and duration of MF events. Consequently, a discrete-event simulation DES model was developed and validated using the Simio simulation software to achieve the randomness of MF occurrences and derive its impact on the system's performance. The system was exposed to 113 FM modes in various machines and components. To evaluate the solutions to MF's maintenance problem, we compared the production line's performance with the simulation-based optimization of OptQuest. Two functions were defined: (1) to minimize the occurrences of MF for each machine and (2) to maximize the percentage of throughput rate. Finally, the proposed framework was examined against a benchmark of the conceptual model of the current case. The proposed approach was practical as it outperformed the existing benchmark and showed that it can be practically implemented. The experimental results illustrated an improvement in system throughput of up to 10.73% and a reduction in the impact of MF phenomenon frequency of bottlenecks, machine BM and LB machine by 86% and 92%, respectively. The suggested support framework's implementation entails expenses for simulated modeling, continuous evaluation, and optimization programs such as Simio and OptQuest. Cost savings are achieved, however, since it improves productivity, decreases unscheduled interruptions, and minimizes loss of production. Optimization gains, fewer service interruptions, and higher overall equipment effectiveness (OEE) balance the initial expenditure.
This paper investigates investment and cooperation strategies concerning blockchain technology within a platform supply chain comprising two suppliers under network externality. We develop a game-theoretic model of a platform supply chain that includes two directly competing suppliers and a platform. The study concludes that when only one supplier adopts blockchain technology, that is a scenario of partial cooperation, the other supplier may engage in free-riding by increasing its product price. The extent of this free-riding depends on the intensity of product competition, consumer trust in blockchain technology, and the strength of network externality. In the case of tripartite cooperation, where both suppliers adopt the technology, they may engage in bidirectional free-riding, which enhances their respective pricing power and profits. These findings suggest that the platform supply chain managers can leverage suppliers' free-riding behavior to refine their blockchain investment and cooperation strategies.
This article proposes the Transmuted Ailamujia Distribution (TAD), a generalization of the Ailamujia distribution with an extra shape parameter (gamma) to account for different behaviors of reliability data. The paper provides the essential statistical inferences of TAD, such as moments, generating functions, and entropy. Parameters are estimated using maximum likelihood estimation for both full and right-censored data. A simulation study using Monte Carlo simulation (1000 runs, n = 30-500) is conducted to compare the performance of the estimators. The simulation results show that gamma is estimable even when the data is censored. The application of TAD to vinyl chloride concentration data shows that TAD is superior to Weibull, Gamma, and other alternative distributions (CVM = 0.3456, AIC = 119.87). The article also gives the explicit reliability function: mean time to failure (MTTF = 3.907 units), hazard rates to indicate wear-out, stress-strength reliability P(X > 2.0) = 0.734, and process control limits (95th percentile = 5.82 units).
The study experimentally investigates the Electrical Discharge Machining (EDM) performance of AISI-D2 tool steel using a powder metallurgy (PM) fabricated WC/Cu electrode compared to a conventional Cu electrode. EDM parameters such as discharge current (Id), pulse-on time (Pon), and pulse-off time (Poff) were optimized using response surface methodology (RSM) to evaluate material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra). The WC/Cu PM electrode demonstrated a 33% improvement in MRR, 40.8% reduction in TWR, and 12% improvement in Ra compared to the Cu electrode. SEM analysis revealed enhanced surface integrity, including fine craters, thinner recast layers and fewer microcracks, attributed to the enhanced thermal stability and spark energy concentration of the PM electrode. Furthermore, SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis highlighted the strategic strengths of WC/Cu PM electrode, emphasizing increased machining efficiency, extended electrode life, and alignment with sustainable manufacturing practices, underscoring their industrial applicability.
Traditional supply chains face significant challenges due to suppliers with concealed and uncontrolled relationships. This causes unexpected disruptions and delays, leading to financial losses. Current risk management approaches cannot see evolving relationships, track causes, or provide real-time insights. To address this, an explainable and adaptable risk prediction framework has been proposed, combining Temporal Graph Neural Networks (TGNN), Neural Causal Discovery using the Peter-Clark (PC) algorithm, and an Adaptive Hawk-Moth Optimization (AHMO) method. The framework constructs supply chain graphs that evolve while utilizing Multi-Layer Perceptron (MLP) technology with skip connections to identify supplier relationships and assess risk. Causal SHAP enhances the model with additional explanation capabilities by offering understandable risk score explanations. Results demonstrate that the model achieves strong real-world performance through its power and flexibility, evidenced by experimental results with an MAE of 0.0112, MSE of 0.0019, RMSE of 0.0439, MSLE of 0.0046, and RMSLE of 0.0141.
The digital transformation (DT) of firms can enhance competitiveness and operational performance. However, few cases exist regarding the successful DT of small and medium-sized retailers (SMRs). So how can DT be accelerated within these enterprises? To address this question, an integrated evaluation model that combined two Multi-Criteria Decision Making (MCDM) methods was used to find the critical factors (CFs) driving DT in SMRs. Using the Technology-Organization-Environment (TOE) framework, we collected relevant extant literature to establish a three-level hierarchical structure of factors that enterprises have considered in DT. We next obtained information through a survey of top management of SMRs in Taiwan and applied the Fuzzy Analytic Hierarchy Process (FAHP) to determine the weight of each factor. Then, the concept of acceptable advantages of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was utilized to objectively determine the eight CFs. These CFs are: establishment of differentiation capabilities, support from senior executives, pressure from peer competition, digital infrastructure, organizational resources, industry characteristics, laws and regulations, and customer requests. This paper found that the environmental context is the driving factor of DT for SMRs rather than the technological context. Based on its other findings, this paper offers practical solutions for SMRs that are seeking to accelerate the DT process. All of these findings have implications for DT solution providers and relevant government entities who have a vested interest in growing their SMR clients/constituents.
The low recycling rates of express packaging are worsened by the rise of live-streaming e-commerce. This study investigates the effects of stream pushing by platforms, recycling rewards, and merchants' freight insurance on consumer recycling behavior. A tripartite evolutionary game model involving platforms, merchants, and consumers was developed, incorporating stochastic differential equations. Results show that active stream pushing by platforms significantly boosts consumer recycling participation. Merchants who offer return freight insurance encourage higher recycling rates, creating a win-win situation with consumers. Additionally, moderate recycling rewards can enhance recycling rates, but excessively high rewards may reduce platforms' incentive for stream pushing, ultimately hindering recycling efforts.
Statistical process control charts play a vital role in industrial manufacturing and service operations. While most existing control charts are designed for monitoring continuous data, methods for discrete data-particularly categorical data-remain relatively underdeveloped. In this paper, a sign-based EWMA multinomial control chart is proposed for monitoring multinomial processes. The properties and detection performance of the proposed chart are systematically compared with those of existing exact EWMA and CUSUM multinomial control charts. Numerical studies, together with a real data application, demonstrate that the exact EWMA multinomial control chart consistently exhibits the best and most robust detection performance, whereas the proposed sign-based EWMA multinomial control chart performs the worst among the charts considered.
Products sold to the market typically go through the introductory and maturity stages. The increase in general market demand during the maturity stage leads retailers to adopt a price increase strategy to boost profits. However, when the price is higher than the psychologically acceptable reference point of the consumers, they may perceive the transaction as unfair, leading them to abandon the transaction and thereby reduce demand. At this time, if retailers consider the reference price effect in advance and adopt the discount strategy, consumers will take the first-stage price as a reference point, which will instead have a psychologically positive effect and increase demand. To investigate the impact of these two strategies on supply chain profits, we construct models for price increase and discount strategies that consider the reference price effect and compare the profit differences of manufacturers, retailers, and the supply chain in centralized and decentralized scenarios. Results reveal that, in centralized decision-making, a threshold exists for the retailer adopting either the price increase or decrease strategy. However, in decentralized decision-making, retailers will always adopt the price increase strategy. In addition, in centralized decision-making, the presence of consumers sensitive to price fairness will always reduce the overall profit of the supply chain. However, it is counterintuitive that under certain parameter constraints in decentralized decision-making, the presence of such consumers may increase the overall profit of the supply chain.
This study investigates a two-echelon low-carbon supply chain comprising one manufacturer and one retailer, considering distinct green subsidy and carbon quota allocation modes. Four Stackelberg game models are developed, including the GT (grandfathering rule combined with green subsidies based on emission abatement cost), BT (benchmarking rule combined with green subsidies based on emission abatement cost), GA (grandfathering rule combined with green subsidies based on emission abatement amount) and BA (benchmarking rule combined with green subsidies based on emission abatement amount) game models. The joint impacts of carbon-related and subsidy-related parameters on optimal solutions, profitability, environmental benefits, and social welfare of the proposed models are analyzed. A comparative analysis is proposed in terms of optimal equilibrium solutions and model performance. Moreover, numerical simulations are conducted to verify the research results and to seek more managerial insights. The results reveal that a high degree of consumers' low-carbon preference is conducive to enhancing performance for the entire supply chain. The carbon trading price has different impacts on the chain members' profits. Moreover, the AB game model is the best option to improve the profitability of the retailer and the social welfare of the entire supply chain, and the manufacturer prefers the GA game model from a profitability perspective. The research findings not only serve to optimize business strategies and performance of enterprises but also provide a theoretical foundation and practical guidance for the effective implementation of low-carbon policies for policymakers.
This study proposes a predictive maintenance model for slurry pumps used in the mold flux drying process by integrating the ISO 10816-3 vibration standard with AI-based machine learning algorithms. Real-world sensor data on vibration, current, pressure, revolutions per minute (RPM), and temperature are collected and processed for training and evaluating AI models based on eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Random Forest (RF) algorithms. The vibration zones defined by the ISO 10816-3 standard are employed as ground truth labels to enhance the interpretability and reliability of the predictive maintenance models. Among the AI models, the XGBoost-based model demonstrates the best predictive performance. A hybrid diagnostic system is developed by integrating the ISO-defined vibration thresholds with the XGBoost algorithm, which provides effective early warning alerts for potential failures in real time.
The current research aims to develop a multi-objective model for the flexible job shop scheduling problem (FJSP) by incorporating flexibility in sequencing and activity constraints under conditions of uncertainty. To achieve this, a comprehensive study was conducted to identify research gaps. Based on these gaps, the assumptions and research objectives were formulated. The proposed multi-objective mathematical programming model (MOMPM) focuses on three primary goals: minimizing makespan, minimizing labor workload, and minimizing weighted delays. The contributions of this study include flexibility in sequencing, activity constraints, uncertainty in processing times, and sequence-dependent setup times. The model is solved using a multi-objective genetic algorithm (MOGA), and the results indicate that flexibility in activities does not lead to suboptimal solutions, nor do activity constraints. However, uncertainty in processing times can adversely affect the optimal solutions across all objectives.
Understanding the kinematics and dynamics of an industrial robotic arm is vital for optimizing its movements, ensuring safety, planning efficient paths, and developing advanced control strategies. This study provides valuable insights into the behavior of the six degrees of freedom robotic manipulator, considering the variations in joint angles and the corresponding range of motion in relation to the nature of the traversed path. Both the forward and inverse kinematic problems are addressed, and a numerical approach is utilized. Using a weaving motion with the welding torch to deposit filler metal across the joint is considered a method for analysis. Throughout different welding trajectories featuring sections containing curved and oblique trajectories, the analysis of the robotic arm is conducted. The proposed method is evaluated for different criteria through three different approaches related to the Jacobian Matrix, specifically pseudo-inverse, damped least squares inverse and weighted damped pseudo-inverse, for different configurations of the robotic manipulator. This study can contribute to effectively developing a welding robotic system in various manufacturing settings and can lead to improvements in quality by refining precision and reducing errors while also driving cost reductions through increased efficiency.
Make-to-order (MTO) systems require flexible production schedules and inventory management. The outcome of this study is a hybrid MTO Closed Loop Supply Chain (CLSC) model that combines raw/recycled materials for cost minimization. In this study, a mixed-integer programming (MIP) model and two solution approaches have been developed: a simplified mixed-integer linear programming (MILP) for computational efficiency and a genetic algorithm to solve full mixed-integer nonlinear programming MINLP. Monte Carlo simulation was used to account for variability in demand, return rates, and production processes. The contributions of this work include: CLSC model tailored to MTO with dual MIP formulations with comparative analysis of MILP and MINLP performance, practical insights into implementing cost-efficient hybrid CLSC. There is a research gap, which most studies assume deterministic conditions in MTO CLSC. The presented work covers this gap and models the complexity of MTO where demand, lead times, and return rates are uncertain. The model in this work is developed based on shaft manufacturing industrial setups. However, the proposed methodology can be applied in other contexts with needed customization and changes to accommodate the specifications of other context which are characterized by high-value component recovery.
This study aimed to investigate the influence of sitting posture on the distraction levels during prolonged reading sessions among college students. The environmental variables examined were confined to adjustments in classroom seating, specifically seat depth and backrest height. The participants are university students with normal vision and no history of musculoskeletal disorders. The findings revealed no significant interaction effect between seat depth and backrest height on attention during extended reading. Nevertheless, participants seated on the front third of the chair or without backrest support exhibited higher levels of distraction. Based on the statistical findings, sitting in the front two-thirds of the seat, with support above the lower back, has proven more effective during extended periods of reading. As a result, schools should consider purchasing chairs with adjustable backrests or mandate the use of lumbar pads. These measures allow students to adjust seat depth, promoting better posture and enhancing reading performance during prolonged classroom sessions.
Enhancing epidemic monitoring capabilities to support policymaking has become a global health priority. This study proposes a layered evolution-based expert system that integrates feature extraction, feature importance analysis, and robustness-oriented design for epidemic trend monitoring. The proposed framework combines genetic algorithm (GA)-based evolutionary mechanisms with Taguchi method-based optimal operating condition design to enhance analytical stability under varying configurations. Using publicly available COVID-19 data from Japan, the proposed system monitors epidemic dynamics across four mortality levels while simultaneously identifying representative features and estimating their relative importance. The results demonstrate that the system reduces the feature set by approximately 27% while maintaining stable predictive performance under more granular severity definitions. Robustness is further evaluated through statistical validation under varying dataset compositions and scales. Overall, the proposed expert system provides an interpretable and robust framework for epidemic trend monitoring that complements existing prediction-oriented approaches and may support informed public health decision processes through feature-level insights.
