International Journal of Industrial Engineering: Theory, Applications and Practice
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MANUFACTURING CAPABILITY ASSURANCE FOR PRODUCT WITH MULTIPLE CHARCTERISICS: A CASE STUDY APPLIED TO LOW DROPOUT VOLTAGE REGULATOR
No full textHuang et a1. (2002) proposed a multi-process capability analysis chart (MPCAC) based on process capability indices S pk ' C pi' and Cpu to evaluate the integrated process capability for the entire product with the nominal-the-best, the larger-thebetter, and the smaller-the-better characteristics. The MPCAC conveys critical information of multiple characteristics of the entire product regarding the process accuracy, the process precision, and the process capability zones from one single chart, which is an effective tool for evaluating product quality. However, from a practical perspective, the MPCAC chart did not consider sampling errors hence the capability information provided from this chart is often unreliable and misleading. In this paper, a manufacturing application involving multiple characteristics of low dropout voltage regulator (LDOVR) is investigated. We consider the sampling error by obtaining the lower confidence bounds (LCBs) of S pk ' Cpl ' and Cpu' In order to exactly measure the degree of process centering, the LCB of the accuracy index Cn is also considered in this MPCAC. The LCB presents the minimum true capability of the process, which is essential to product capability assurance. We employ the LCBs to MPCAC to provide more reasonably reliable capability assurance for the product with mUltiple characteristics
OPTIMAL INBOUND/OUTBOUND PRICING MODEL FOR REMANUFACTURING IN A CLOSED-LOOP SUPPLY CHAIN
Get PDFThe paper presents a model for optimizing inbound and outbound pricing for closed-loop supply chains that remanufacture reusable products. Remanufacturers create reusable products from returned used products and sell the products “as new” to manufacturers or consumers. By implementing a return subsidy, remanufacturers can encourage the consumer to return used products. Demand for the as-new components often depends on the selling price and inventory. The available inventory increases as the subsidy increases and as the price decreases. Our model can determine the optimal subsidy and selling price for used and remanufactured products, respectively. Our model uses the Karush–Kuhn–Tucker conditions to solve its nonlinear problem. Sensitivity analysis reveals how different parameters affect profit under model-optimized conditions
AN INDUSTRIAL APPLICATION OF IMPROVED PARTICLE SWARM OPTIMIZATION: AVAILABILITY ASSESSMENT OF ELECTROSTATIC PRECIPITATOR
Get PDFThe Electrostatic Precipitator (ESP) is common equipment used in thermal power plants and industrial mining plants such as steel, copper, and cement. ESP is installed to capture the dust in the exhaust gas of boilers or furnaces. The availability of ESP is vital for plants since any interruption in this device causes serious process problems and environmental pollution. As a result, the availability of ESP is crucial, and a comprehensive study in this area must be performed for maintenance activities. This paper presents a novel method for assessing complex equipment availability, such as ESP, based on improved dynamic particle swarm optimization (IDPSO). To evaluate the availability of ESP, all related systems, sub-systems, and all components of ESP must be considered. Availability assessment of ESP, consisting of many series-parallel sections and components, can be challenging and time-consuming. An IDPSO is used to search for the most probable states among numerous possible states. In addition, IDPSO overcomes shortcomings of standard PSO, such as falling into local optimums. The proposed method is applied to the actual data of an ESP installed at a copper factory. The results show the proposed method achieved an accuracy of 99.54 % in availability assessment
Time Standards and Disability: A Work Measurement Perspective
No full textTraditionally, work measurement has been concerned with establishing consistent data on activity and job completion times for comparative, control, and/or remuneration purposes. The application of accurate work standards is not only crucial from the employees’ point of view but is also very important for the employers as it gives a measure of the productivity, and also standardizes the time it takes for businesses to produce its products or services. With changing demographics and growing need to include special population into the work force, it is necessary to analyze, modify, and implement the tried and tested approaches to productivity and performance measurement and improvement. This paper investigates the need to revise work standards in order to accurately determine levels of productivity and job completion times for the disabled. Specifically, the objectives of this paper are: a) to help practitioners understand the concept of a fair day’s work when it comes to the special disabled population; and b) to scrutinize the relation between published research on disability, work standards, and the need to customize them for the disabled population
Efficacy of Lean Metrics in Evaluating the Performance of Manufacturing Systems
Get PDFThe efficacy measure is an important indicator in lean performance measure. The research is to develop a quantitative analysis framework and a simulation methodology to evaluate the efficacy of lean metrics in the production systems. A procedure for quantitative analysis of lean metrics is developed and the evaluation of the lean effectiveness in predicting the performance is presented. Lean metrics are embedded into simulation model so that the simulator is able to provide automatically lean metrics for the systems without any extra effort. The embedded lean simulation is used to investigate the significance of various improvement opportunities. This efficacy of these metrics for the performance measurement is a leading indicator for a manufacturing system. A validation is done to show the effectiveness of the proposed systems
Capacity Planning In a Telecommunications Network: A Case Study
No full textSatisfying the demand in a timely fashion is a critical task in any company. In a service sector like telecommunications where sold information transmission units cannot be stored in inventory or cannot be backordered, having appropriate capacity to fulfill all demand becomes crucial. In order to achieve this goal, not only a well designed planning system but a sound capacity expansion strategy is necessary. In this paper, capacity planning in a telecommunications network is studied through the novel application of inventory control techniques aiming to meet the demand to a certain service level. In addition, a capacity expansion plan via equipment purchase is carried out through a mathematical programming model. The use of inventory control techniques in the telecommunications industry is the principal contribution of this work. The use of inventory control techniques in the telecommunications industry is the principal contribution of this work.
A METHOD FOR SELECTING THE OPTIMAL PORTFOLIO OF PERFORMANCE IMPROVEMENT PROJECTS IN A MANUFACTURING SYSTEM
No full textThis paper proposes a method for selecting the optimal portfolio of performance improvement projects in a manufacturing system. The proposed method consists of evaluation process and selection process for selecting the optimal portfolio. In the evaluation process, the strategic value of a project portfolio is evaluated by considering both the strategy and the performance improvement of a system. In the selection process, the objective is to maximize the strategic value of a project portfolio without violating constraints and a genetic algorithm is used to select the optimal project portfolio. The strategic value of a project portfolio obtained in the project portfolio evaluation process is used as the fitness function of the genetic algorithm. An example of a project portfolio selection in a manufacturing system is discussed in order to illustrate the proposed project portfolio selection metho
MOSS SOFTWARE: A NEW TOOL FOR MULTI-OBJECTIVE GREEN SUPPLIER SELECTION
No full textCompetition between companies is getting more intense by the day. Corporations need to decrease costs while improving the quality and reliability of their deliveries. Meanwhile, the demand for environmentally sustainable products and operations is increasing. These issues are all compounded by the supplier relations between companies and their need to develop better economic and environmental supply chain contracts. This research thus aims to propose a tool, i.e., the MOSS Software, concerning the total cost, rate of defected materials, rate of late deliveries, and environmental improvement potential for suppliers. MOSS is a two-stage software. First, the objectives are included in a multi-objective evolutionary mathematical model, and the model is solved using the NSGA-III algorithm. In the second stage, as a post-Pareto analysis approach, the k-means algorithm is used for selecting representative solutions among Pareto sets by comparing silhouette values for different k values. An application of the MOSS software is also presented in this paper
A FUZZY GRA-BASED DECISION-MAKING APPROACH ON THE SELECTION OF LEAN TOOLS: A CASE STUDY OF INDIAN APPAREL INDUSTRY
No full textMost manufacturing industries find many Non-Value-Added (NVA) components compared to value-added components from their shop floor. Identifying the best Lean Tools (LTs) from many available tools for their present manufacturing environment considering many wastes remains a multi-criteria decision-making problem. The problem becomes complicated when decision-makers' preference for wastes and LTs is uncertain. Grey theory is the most predominant mathematical method to address this issue. This paper proposed a framework that incorporates value stream mapping (VSM) and grey relational analysis coupled with principal component analysis under fuzzy environment for LTs selection problem. The VSM of the current and future state are compared to visualize the improvement. The framework developed in this work is illustrated in a case study from the Indian apparel industry. The results show a significant reduction in production lead time (62.5%), cycle time (2.3%), NVA time (62.6%), and the number of workers (16.6%)
PREDICTION OF DEMAND FOR RED BLOOD CELLS USING RIDGE REGRESSION, ARTIFICIAL NEURAL NETWORK, AND INTEGRATED TAGUCHI-ARTIFICIAL NEURAL NETWORK APPROACH
No full textAccording to their need, regional blood centers collect donated blood and distribute processed blood to the blood transfusion centers. The prediction of blood components to be demanded by transfusion centers becomes of more importance, especially these days when the impact of COVID-19 is increasing. Since donors are afraid to go to blood donation centers, blood component stocks rapidly decrease. This study aims to predict the blood transfusion centers' demand for quantities of red blood cells, an important blood component, from a regional blood center using the artificial neural network method. The method's parameters values affect the prediction performance of the method. Therefore, the Taguchi method is integrated with artificial neural network methods to optimize the parameters. The prediction results of the integrated Taguchi-artificial neural network approach, artificial neural network method, and ridge regression method are each compared with the actual demand of regional blood centers. It is determined that the integrated Taguchi-artificial neural network approach predicts actual demand more accurately