International Journal of Industrial Engineering: Theory, Applications and Practice
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A Novel Approach to Improve Inventory Management Process of Pharmaceutical Supply Chain
No full textThe pharmaceutical industry relies heavily on efficient inventory management to guarantee the expeditious supply of medications, minimize wastage, and sustain cost control. The dynamic demand for pharmaceuticals and severe regulatory standards present unique inventory management challenges that require systematic and strategic approaches. In Vietnam, a market undergoing accelerated development, the pharmaceutical company must also ensure that its supply chain and inventory administration are efficient. The ineffective operation of the pharmaceutical company’s facility is identified as a problem requiring improvement in this study. By employing statistical data analysis and root cause analysis techniques, the researchers ascertained that an overburdened inventory level is the underlying cause of the inefficiency in the warehouse. Hence, to relieve the pressure on the organization’s warehouse, this study suggests the implementation of a buffer warehouse through the utilization of an innovative integrated Lean Six Sigma approach—DMADV (Define, Measure, Analyze, Design, Verify)—and Multi-criteria decision-making (MCDM) methodology. The design process begins with identifying and analyzing stakeholder requirements and constraints. This is followed by creating Risk Priority Numbers (RPN) and Ishikawa diagrams, which serve as capability indicators for the buffer warehouse. Utilizing the Best-Worst Method (BWM) and the Evaluation Based on Distance from the Average Solution (EDAS) with a Z-number, the study develops a buffer warehouse supplier selection that is optimal for the organization’s supply chain. In conclusion, the research outlines a strategy and a collection of metrics for assessing the efficiency of the buffer warehouse operation. The application of DMADV for analyzing problem-solving is investigated, and a buffer warehouse for inventory is established. Furthermore, the research results provide metrics for buffer warehouse capacity, a methodology for choosing buffer warehouses, and a framework for assessing their efficacy. This results in approximately USD 25,523 in savings or 30 fewer late orders
Optimal Treadmill Attributes Configuration: A Text Mining Approach
Get PDFNew product development aims to create products aligning with consumer preferences, yet discrepancies exist between consumers’ ideal product attributes and manufacturers’ perceptions. Previous studies primarily utilized regression equations or conjoint analysis to determine product attributes’ impact on consumer utility, with limited discussion on optimal attribute combinations. This study adopts a configurational approach using product attribute configuration as the analysis unit. We hypothesize that an ideal configuration exists for treadmill attributes, where products closer to this configuration achieve higher sales. Using text mining to convert online reviews into numerical data, we construct regression equations based on configuration-ideal distance. Results confirm the existence of an ideal treadmill attribute configuration, providing concrete guidance for new product development
A Customer Demand Mining Algorithm Based on Online Comments and Machine Learning
No full textIn the current market environment, the phenomenon of product homogenization is severe. If enterprises cannot deeply understand customer needs and provide differentiated products or services, it is difficult to stand out in the competition. In order to effectively improve overall customer satisfaction and enhance the market competitiveness of enterprises, a customer demand mining algorithm based on online comments and machine learning is proposed. Collect customer demand information data through online comments and process the collected data with redundant information to improve the efficiency and accuracy of demand mining. On this basis, customer demand attribute features have been further extracted, and a customer demand clustering mining model has been constructed using a self-organizing mapping neural network. By training the model, the final clustering mining results can be obtained, thus achieving precise mining of customer needs. This study clearly addresses a key issue in the current field of consumer demand mining: how to efficiently and accurately identify and utilize consumer demand information in online comments. By constructing a clustering mining model based on the Self-Organizing Maps (SOM) neural network, this study fills the literature gap in this field and provides more accurate and practical consumer demand analysis methods for enterprises. The experimental results show that, compared with the three comparison methods, the proposed method has a 98% feasibility of customer demand mining and 92% customer satisfaction. It shows that the proposed method has high feasibility and customer satisfaction for customer demand mining and has a better overall customer demand mining effect. This provides strong support for improving overall customer satisfaction and corporate competitiveness
Optimization of Inventory Replenishment under Asymmetric Stock-Out and Inventory Holding Costs
No full textPerishable products are an essential part of commerce. Shelf-life characteristics are usually not modeled in traditional inventory models. This study proposes an inventory replenishment model for perishable products with an asymmetric cost structure for holding and stock-out costs. The modeling phase involves the shelf-life characteristics of products. Shelf life is essential due to sustainability concerns, costs, and service levels due to perished products. In contrast to classical safety stock models, where stock-out costs increase linearly, the proposed model utilizes incrementally increased fixed costs for holding costs in a conflicting cost structure. It incorporates the shelf-life of the products, calculates the probability of perishing, and formulates accurate waste and total costs using an asymmetrical cost structure. The model is applied to a real dataset to assess the performance and compare it with the traditional approach. The performance of the proposed model is better, with a total cost reduction of 45.33%. Additionally, the model demonstrated a 17.21% increase in service level. The sensitivity analysis further underlined the robustness of the proposed model across various demand scenarios and shelf-life conditions. The main research gap addressed by this study is the lack of consideration for shelf-life characteristics and asymmetric cost structures in traditional inventory models. By integrating these factors, this research provides a more accurate and cost-effective approach to inventory management for perishable products, enhancing sustainability and service levels. This study's findings can help businesses optimize inventory strategies, reduce waste, and improve operational efficiency
An Optimization Model for Risk Minimization in Stock Portfolio Selection
No full textStock portfolio selection is a challenging task for investors due to its multidimensional aspects. Investors always aim to maximize the expected return on their investments while maintaining a reasonable level of risk. Therefore, the appropriate choice of stocks is vital to achieving the desired return with minimal risk in financial markets. The objective of this study is to help answer the question of how to invest a specific amount of money across a range of assets in the safest and most effective way to achieve the maximum desired return with the minimum risk. A linear optimization model was developed to minimize overall investment risk, subject to realistic constraints such as the expected total return and investment ratios among different asset categories. To validate the model, a group of well-established securities was selected from various industries in the United Arab Emirates. Historical data spanning five years were collected for each stock category. In this study, stocks were grouped into five categories: construction, transportation, logistics, telecommunications, and banking. The optimization model was solved using Excel Solver with the simplex method. The results showed that the investment proportions satisfied all constraints, the objective function was optimized, and the best stock portfolio was determined. The model can help investors identify optimal portfolio allocations that achieve a target return while minimizing risk
Unfolding The User Experience: A Comprehensive User Experience Framework For Foldable Smartphones
Get PDFThis study presents a comprehensive framework for understanding User Experience (UX) in foldable smartphones, a rapidly emerging mobile technology category. Through product research, user studies, and expert evaluations, this research identifies three core elements: the hinge, the user, and the folding-state interface. The framework emphasizes the hinge's crucial role in maintaining screen continuity, enhancing portability, and ensuring user comfort during folding operations. Ergonomic compatibility and psychological acceptability are identified as essential user factors, while state consistency, intuitive compatibility, proper feedback, and exclusive features constitute vital interface elements. An exploratory case study examining the impact of hand size on UX revealed significant differences in pressure application and fatigue levels between users with different hand sizes, highlighting the importance of ergonomic considerations in design. This framework provides a foundation for future development in foldable smartphones, aiming to improve product attractiveness by systematically addressing specific user requirements and experiences
Iterative Associative Method of Dynamic Consequents (IAMDC) for Mamdani Fuzzy Systems Type I as an Attention Mechanism
Get PDFThe description of a fuzzy inference system (FIS) is qualitative, known as the expert-driven approach. Generally, antecedents are determined combinatorially, while consequents are defined based on expert knowledge, which often involves issues of high interpretability and imprecision. Therefore, this article presents an Iterative Associative Method of Dynamic Consequents (IAMDC) as an Attention Mechanism. The k-means algorithm is used to group premises based on the distance-similarity of their fuzzy values, in numerical and objective form, thereby eliminating the imprecision of experts in defining the consequents' categories, making a significant contribution to the development of FIS. Furthermore, based on this distance, the consequents of each rule are classified. A normalization phase is proposed for the distances obtained in each rule to identify the most significant probability of occurrence, and based on this, estimate the parameters of the consequences in each rule using the corresponding product fuzzy operator, which is another significant contribution of this research. The novel prototype is validated through a simulation model based on the case of an automotive manufacturing company, in which supplier evaluation is developed using four evaluation criteria. Five possible combinations—Prod-Max, Min-Max, Max-Min, Max-Max, and Max-Prod — were used as inference rules for the proposed associative fuzzy inference system (AFIS) and compared with the present evaluation method in the company and with a conventional fuzzy inference system. The results of the proposed system were more accurate and reliable, with lower mean-squared error values
Determination of The Location Layout of Electricity Agriculture Tractors In Open Landscape Conditions with The P-Median Chance Constrained Mixed Integer Mathematics Model
Get PDFAgriculture has continuously evolved in terms of technology and economics throughout human history. From ancient times to the present, it has consistently embraced technological innovations to produce more efficient and higher-quality products. Over the past two decades, the rise of electric vehicles has emphasized the importance of electric tractors in agriculture. The widespread adoption of Agricultural Electric Tractors can lead to more sustainable and ecologically friendly farming practices. Although agricultural electric tractors are more environmentally friendly compared to traditional internal combustion tractors, the limited availability of solar-powered charging stations poses a significant barrier to their widespread use. This study aims to develop a mixed-integer mathematical model to determine the optimal locations for solar-powered charging stations in open-field agricultural areas for Agricultural Electric Tractors. The Chance-Constrained optimization model is compared with a deterministic model to evaluate the performance of the proposed mathematical model. Given that solving the deterministic model is an NP-hard problem, a Binary Genetic Algorithm was employed as a solution approach. This comparison focuses on assessing the effectiveness of the Chance-Constrained model in handling uncertainty, highlighting differences in solution quality, computational efficiency, and robustness. Additionally, the study identifies the locations for off-grid (solar-powered) charging stations for electric vehicles in agricultural settings. The results obtained from the mathematical model, where only one solar-powered charging station provides service, have been tested using the Arena simulation program and subsequently interpreted
The Combined Effect of Ergonomic Factors on Work and Cognitive Performance in The Automotive Industry
No full textThe study aimed to explore the impact of combined ergonomic factors on the work and cognitive performance of final inspection workers (n=18) in the automotive industry. Three levels of ergonomic factors were studied: posture, mental workload, and work shift. The Poison Test and Digit Symbol Substitution Test were used to assess the work and cognitive performance of final inspectors. Results found that posture and mental workload significantly impact work performance (15.22%) and cognitive performance (10.21%), while mental workload and work shift significant-ly impact both (2.98%) and (1.81%). Work shift and posture also substantially impact both (6.61%) and (4.96%). The combined effects of posture, workload, and Work shift significantly impacted both work performance (5.24%) and cognitive performance (7.88%). The study reveals that posture, mental workload, and work shift significantly impact the work and cognitive performance of final inspection workers in the automotive industry
Capacitated Location-Routing Problem for a Combined Manned-Unmanned Teaming System using Lagrangian Relaxation and Location-based Heuristic
No full textThe integration of unmanned systems into modern military operations has heightened the importance of manned-unmanned teaming (MUM-T), particularly in maritime surveillance missions. This study addresses a critical challenge in MUM-T operations: Optimally locating manned surface vessels and assigning reconnaissance routes for unmanned surface vehicles (USVs) under capacity constraints. We formulate this model as a capacitated Location-Routing Problem and propose a two-phase solution framework. The first phase solves three variants of the facility location problem—the uncapacitated facility location problem, p-median problem, and capacitated facility location problem—using Lagrangian relaxation. The second phase applies a location-based heuristic to generate efficient routing plans for USVs. Computational experiments using benchmark datasets demonstrate the effectiveness of the proposed approach, particularly highlighting efficiency in the uncapacitated case. This study contributes to logistics planning for hybrid manned-unmanned systems by adapting classical optimization tools to military reconnaissance scenarios