Abbas Al-Refaie

Strategic Closed-Loop Facility Location Problem With Carbon Market Trading

The burgeoning environmental regulations are forcing companies to green their supply chains by integrating all of their business value-adding operations so as to minimize the impact on the environment. One dimension of greening the supply chain is extending the forward supply chain to collection and recovery of products in a closed-loop configuration. Remanufacturing is the basis of profit-oriented reverse logistics in which recovered products are restored to a marketable condition in order to be resold to the primary or secondary market. In this paper, we introduce a multiechelon multicommodity facility location problem with a trading price of carbon emissions and a cost of procurement. The company might either incur costs if the carbon cap, normally assigned by regulatory agencies, is lower than the total emissions, or gain profit if the carbon cap is higher than the total emissions. A numerical study is presented which studies the impact of different carbon prices on cost and configuration of supply chains.

Solving the multi-response problem in Taguchi method by benevolent formulation in DEA

The Taguchi method is an efficient approach for optimizing a single quality response. In practice, however, most products/processes have more than one quality response of main interest. Recently, the multi-response problem in the Taguchi method has gained a considerable research attention. This research, therefore, proposes an efficient approach for solving the multi-response problem in the Taguchi method utilizing benevolent formulation in data envelopment analysis (DEA). Each experiment in Taguchi’s orthogonal array (OA) is treated as a decision making unit (DMU) with multiple responses set inputs and/or outputs. Each DMU is evaluated by benevolent formulation. The ordinal value of the DUM’s efficiency is then used to decide the optimal factor levels for multi-response problem. Three frequently-investigated case studies are adopted to illustrate the proposed approach. The computational results showed that the proposed approach provides the largest total anticipated improvement among principal component analysis (PCA), DEA based ranking approach (DEAR) and other techniques in literature. In conclusion, the proposed approach may provide a great assistant to practitioners for solving the multi-response problem in manufacturing applications on the Taguchi method.

Optimizing SMT Performance Using Comparisons of Efficiency Between Different Systems Technique in DEA

Surface-mount-technology (SMT) enables production of reliable printed circuit board at a reduced weight, volume, and cost. Nevertheless, SMT defects increase quality costs and deteriorate performance. In reality, defects are not equally important and their corresponding quality costs vary. In these regards, this research divides SMT defects into three classes; minor, major, and serious. Eight key factors from SMT processes are investigated concurrently with L18(21 times 37) array. Taguchis accumulation analysis method is first applied and it provides incomplete optimal levels for some factor. In this research, each experiment is treated as a decision making unit (DMU) with defect counts set as inputs. Comparisons of efficiency between different systems (CEBDS) technique in data envelopment analysis (DEA) is then adopted to optimized SMT performance. The corresponding actual improvements in minor, major, and serious defect counts are found 0.352, 2.153, and 4.154 dB, respectively. Analysis of variance (ANOVA) is finally performed to determine significant factor effects. It is found that conveyor speed and reflow temperature are the most influential factors on SMT performance. In conclusion, the CEBDS technique turns out to be an efficient approach for optimizing process performance with categorical data.

Optimizing performance with multiple responses using cross-evaluation and aggressive formulation in data envelopment analysis

An efficient optimization procedure is proposed for improving a product/process performance with multiple responses using two Data Envelopment Analysis (DEA) techniques, including the cross-evaluation and aggressive formulation techniques. The experiments generated in a Taguchi orthogonal array are considered Decision-Making Units (DMUs). The multiple responses are set inputs and/or outputs for all DMUs. Cross-evaluation and aggressive formulation techniques are employed to measure a DMU’s performance. The efficiency scores are then adopted to identify the combination of process factor levels that optimizes a product/process performance with multiple responses. Finally, the proposed procedure is illustrated by three case studies previously reported in the literature. The computational results show that the aggressive formulation technique is the most efficient in optimizing performance compared with the cross-efficiency technique, principal components analysis, and genetic algorithm methods. In conclusion, the advantages of the proposed optimization procedure may motivate practitioners to implement it in order to optimize a product/process with multiple responses in a wide range manufacturing applications.

The effects of quality management practices on customer satisfaction and innovation: a perspective from Jordan

This research examines the structural relationships between nine quality management practices and investigates the impact of these practices on customer satisfaction (CS) and innovation for 130 ISO 9001 certified firms in Jordan. The main results include: (1) CS is directly and positively related to customer focus, continuous improvement and human resource (HR) management, while innovation is directly and positively affected by leadership and HR management; (2) leadership has an indirect positive impact on CS, while it positively affects CS; (3) process management (PM) is positively related to supplier management (SM), quality tools and techniques (QTTs) and HR management, while it has an indirect and positive effect on CS; (4) HR management is positively related to leadership and it has a direct positive impact on CS, innovation, PM and QTTs and (5) finally, SM has a direct positive effect on PM. In conclusion, the awareness of Jordanian firms of these relationships may guide them to improve their internal performance as well as strengthen their external competition.

Data envelopment analysis approaches for solving the multiresponse problem in the taguchi method

Abstract This research proposes a procedure for solving the multiresponse problem in the Taguchi method utilizing two data envelopment analysis (DEA) approaches, including comparisons of efficiency between different systems (CEBDS) and bilateral comparisons. In this procedure, each experiment in Taguchis orthogonal array (OA) is treated as a decision-making unit (DMU) with the multiresponses as the inputs and outputs for all DMUs. For each factor of OA, the DMUs are divided into groups, each at the same factor level. Then, DMUs efficiency is separately evaluated by the CEBDS approach and the bilateral comparisons approach for each factor. The level efficiency, or the average of the efficiencies obtained by the CEBDS and the bilateral comparisons approaches for that factor level, is then used to determine the optimal factor levels for multiresponses. Three case studies are provided for illustration; in all, the proposed procedure provides the largest total anticipated improvements. Hence, it should be considered the most effective among all approaches applied in the case studies, including principal component analysis, DEA-based ranking approach, and others. In addition, the proposed procedure is more effective and requires less computational effort when the DMUs efficiency is evaluated by the bilateral comparisons approach instead of the CEBDS approach. In conclusion, the proposed procedure will provide great assistance to practitioners for solving the multiresponse problems in manufacturing applications on the Taguchi method.

Optimising correlated QCHs in robust design using principal components analysis and DEA techniques

Taguchi method is found efficient for optimising process performance with a single quality characteristic (QCH) of a product or process. In practice, however, customers are concerned about multiple QCHs, which are usually correlated. This research proposes and implements an approach using principal components analysis (PCA) and two data envelopment analysis (DEA) models, including CCR and super efficiency, for optimising multiple correlated QCHs in robust design. The PCA is first utilised to obtain multiple uncorrelated linear combinations of principal components, which are the same number of QCHs and hence avoid the loss of information by ignoring some principal components. Then, these components are utilised in two DEA models to decide optimal factor levels. Three real case studies are provided for illustration; in all of which the proposed approach is found more efficient than some other techniques in literature, including engineering judgement, PCA, PCA and grey analysis, and utility concept. In conclusion, the proposed approach shall provide a great assistance to process/product engineers for obtaining robust design with multiple correlated QCHs.

Factors influencing the repurchase intention and customer satisfaction: a case of Jordanian telecom companies

Todays harsh competition has forced telecom companies to continually enhance customer satisfaction, which leads to repurchases intention and consequently larger market share. Determination the key success factors in gaining competitive advantage within telecom industry has received significant research attention in different countries. This research examines the relationships between service quality, customer satisfaction, corporate image, perceived value, perceived price, loyalty, trust and checking their effects on repurchase intentions in Jordanian telecom companies using structural equation modelling. A structural model of 20 hypotheses is built. The data was collected from 550 users of telecom service. Results reveal that customer satisfaction is positively affected by service quality, perceived value, perceived price and image. Moreover, repurchases intention is positively affected by customer satisfaction, loyalty and perceived value. In conclusion, customer satisfaction is the key factor to be considered if telecom companies are willing to survive in todays harsh competition.

Influences of TPM, TQM, Six Sigma practices on firms performance in Jordan

In todays sharp competitions, firms are continuously looking for strategies to improve firm performance (FP) and gain competitive advantage. Some of the most prominent programmes are total productive maintenance (TPM), total quality management (TQM) and Six Sigma. This research aims at investigating the relationship between TPM, TQM and Six Sigma practices. It also examines their effects on FP in Jordan. Data were collected form 47 manufacturing firms. Structural equation modelling (SEM) is then used for analysing the proposed structural model. The obtained results showed significant positive effects of TPM and TQM on FP. In addition, there is a significant positive relationship between TPM and TQM. On the other hand, it is found that Six Sigma partially affects both FP and TQM. Further, three sub-models related to TPM, TQM, and Six Sigma practices are proposed then tested to verify their influences on each corresponding programme. In conclusions, Jordanian firms successfully implemented and integrated TQM and TPM practices to improve FP. But they are still deficient in utilising Six Sigma practices to improve FP, as well as, in integrating it with TQM. Managerial efforts should be directed toward fully understanding SSs implementation and integration with other improvement programmes.

Super-Efficiency DEA Approach for Optimizing Multiple Quality Characteristics in Parameter Design

This paper proposes an efficient approach for optimizing the multiple quality characteristics (QCHs) in manufacturing applications on the Taguchi method using the super efficiency technique in data envelopment analysis (DEA). Each experiment in Taguchis orthogonal array (OA) is treated as a decision making unit (DMU) with multiple QCHs set as inputs or outputs. DMUs efficiency is measured then adopted as a performance measure to identify the combination of optimal factor levels. Three real case studies were employed for illustration in which the proposed approach provided the largest total anticipated improvements in multiple QCHs among other techniques such as principal component analysis (PCA) and DEA based ranking (DEAR) approach. Analysis of variance is finally employed to decide significant factor effects and to predict performance.