Maintenance Maturity Level Identification using MABAC Method: An Adaptation of TPM Pillars in a Public Service Sector

914-917Having systematic maintenance practices does sustain the lifecycle of the facilities. Hence, engineers have always been seeking for practical approaches toward providing better maintenance scheme. Such attempts have resulted in the appearance of numerous maintenance management models including the most commonly accepted one, that is, the total productive maintenance (TPM). Although the concept of TPM and its corresponding pillars have extensively been investigated in the recent literature of Maintenance Engineering and Management, the majority of the previous research attempts, including the multi-criteria decision making (MCDM) applications, have handled them within the context of the manufacturing sector; and almost none of them have been applied in the services sector. This paper proposes the multi-attributive border approximation area comparison (MABAC) method, a newly developed MCDM technique, as a tool upon which eight TPM pillars are evaluated in order to identify the maintenance maturity level in a public service sector. The investigations indicate that the proposed model equips maintenance engineers with an insight into the mechanism upon which TPM pillars can operate effectively. The results of the proposed model indicate that the investigated institution is generally not matured enough to be up to the desired level of TPM performance

Maintenance Maturity Level Identification using MABAC Method: An Adaptation of TPM Pillars in a Public Service Sector

Having systematic maintenance practices does sustain the lifecycle of the facilities. Hence, engineers have always been seeking for practical approaches toward providing better maintenance scheme. Such attempts have resulted in the appearance of numerous maintenance management models including the most commonly accepted one, that is, the total productive maintenance (TPM). Although the concept of TPM and its corresponding pillars have extensively been investigated in the recent literature of Maintenance Engineering and Management, the majority of the previous research attempts, including the multi-criteria decision making (MCDM) applications, have handled them within the context of the manufacturing sector; and almost none of them have been applied in the services sector. This paper proposes the multi-attributive border approximation area comparison (MABAC) method, a newly developed MCDM technique, as a tool upon which eight TPM pillars are evaluated in order to identify the maintenance maturity level in a public service sector. The investigations indicate that the proposed model equips maintenance engineers with an insight into the mechanism upon which TPM pillars can operate effectively. The results of the proposed model indicate that the investigated institution is generally not matured enough to be up to the desired level of TPM performance

Monitoring the performance of petrochemical organizations in Saudi Arabia using data envelopment analysis

The petrochemical industry plays a crucial role in the economy of the Kingdom of Saudi Arabia. Therefore, the effectiveness and efficiency of this industry is of high importance. Data envelopment analysis (DEA) is found to be more acceptable in measuring the effectiveness of various industries when used in conjunction with non-parametric methods such as multiple regression, analytical hierarchy process (AHP), multidimensional scaling (MDS), and other multiple criteria decision making (MCDM) approaches. In this study, ten petrochemical companies in the Kingdom of Saudi Arabia are evaluated using Banker, Charnes and Cooper (BCC)/Charnes, Cooper, and Rhodes (CCR) models of DEA to compute the technical and super-efficiencies for ranking according to their relative performances. Data were collected from the Saudi Stock Exchange on key financial performance measures, five of which were chosen as inputs and five as outputs. Five DEA models were developed using different input–output combinations. The efficiency plots obtained from DEA were compared with the Euclidean distance scatter plot obtained from MDS. The dimensionality of MDS plots was derived from the DEA output. It was found that the two-dimensional positioning of the companies was congruent in both plots, thus validating the DEA results

Elicitation of the factors affecting electricity distribution efficiency using the fuzzy AHP method

Efficient and uninterrupted energy supply plays a crucial role in the quality of modern daily life, while it is obvious that the efficiency and performance of energy supply companies has a significant impact on energy supply itself and on determining and finetuning the future roadmap of the sector. In this study, the performance and efficiency of energy supply companies with respect to productivity is investigated with reference to a case study of an electricity distribution company in Turkey. The factors affecting the company’s performance and their corresponding weight have been determined and obtained using the analytical hierarchy process (AHP) and the Fuzzy AHP methods, two well-known multi-criteria decision-making methods, which are widely used in the literature. The results help demonstrate that the criteria obtained to evaluate the company’s energy supply performance play a crucial role in developing strategies, policies and action plans to achieve continuous improvement and consistent development

DEA-Based PROMETHEE II Distribution-Center Productivity Model: Evaluation and Location Strategies Formulation

The current era of industrial economics necessitates warehouse and logistic distribution centers (DCs) to contribute productively toward an organization’s success. Playing such a critical productive role implies that logistics activities must be practiced effectively and efficiently. However, the indistinguishability between effectiveness and efficiency leads to a somewhat shallow interpretation, and consequently, a diluted evaluation. Hence, this paper aims to develop a productivity evaluation model for nine DCs belonging to an international automotive vehicles and spare parts company. The developed model was set up based on two multi-criteria decision making (MCDM) approaches: the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) and data envelopment analysis (DEA). PROMETHEE II was employed to evaluate the effectiveness, while the DEA was utilized in order to measure the efficiency of the investigated DCs. The resulting hybrid model collectively creates what can conceptually and practically be considered a productivity evaluation model. The results also provide six different strategies through which distribution center locations can be evaluated in order to implement potential future initiatives

An Innovative Job Evaluation Approach Using the VIKOR Algorithm

Fairness is a key issue that requires the attention of human resource management practitioners. Having a robust methodical procedure for identifying the value of job positions in an enterprise is essential. Consequently, there is a need for a job evaluation system that ensures fair compensation for each position. A poorly defined job evaluation system creates the dilemma of mismatches between employees and their competencies for their responsibilities and, accordingly, their wages. This results in employee dissatisfaction, which ultimately exacerbates attrition, which is costly because of the loss of talented employees. This paper proposes a VIKOR algorithm as an innovative approach to job evaluations. Engineering-related positions in an international aviation company were analyzed to illustrate the appropriateness of the proposed approach for managing the job evaluation dilemma. The results indicate that 29 job grades would be appropriate for this firm. In addition, the proposed algorithm was found to be superior to other multiple-criteria decision-making techniques at managing the job evaluation dilemma

Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers

The Development of an Efficiency-Based Global Green Manufacturing Innovation Index: An Input-Oriented DEA Approach

Innovation-based economic growth is considered to be a vital strategic aim for all economies, but environmentally friendly concepts and sustainable development (SD) must also be considered. The literature on the Global Innovation Index (GII) shows various investigations relevant to innovation, yet the lack of comprehensive consideration within the GII of environmental concerns represents a critical challenge. This paper aims to provide a holistic-perspective evaluation model for the top 15 manufacturing countries worldwide in order to resolve this. The efficiency-based Global Green Manufacturing Innovation Index (GGMII) was developed by formulating an input-oriented data envelopment analysis model. Criteria such as the value added to the gross domestic product (GDP), corresponding CO2 emissions, and unemployment rates were examined in order to represent the economic, environmental, and social dimensions of SD, respectively. Other scientific and technological dimensions were also considered. The data corresponding to all ten of the criteria were collected from World Bank Open Data. The results show a mismatch between the original GII and the proposed GGMII for the top eight manufacturing countries (the United States, the United Kingdom, Germany, Korea, France, China, Japan, and Canada), while the remaining countries (Italy, Spain, Russia, India, Mexico, Brazil, and Indonesia) occupied the same rank in both indices, but showed a sizable diminution in their original GII scores. The proposed GGMII might be utilized as a benchmarking instrument for all countries worldwide in the future

Performance Evaluation and Improvement Using Balanced Scorecards and Analytic Hierarchy Process: The case

Copyright: © 2013 Said Ali ElQuliti, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The purpose of this paper is to present a hybrid model for performance evaluation using Balanced Scorecards and Analytic Hierarchy Process (AHP) to evaluate the performance of the Western Substation Projects Division (WSPD) in the Saudi Electricity Company. Actually, the current measurement system in WSPD is subjectively depending on individuals and lacking of a scientific procedure to evaluate its performance. While the literature offers plentiful stories about successful applications of the Balanced Scorecard and the AHP, few of them are reported in details. This article provides a detailed logical model for evaluation and improving the performance. The proposed model identifies different strategic objectives related to the four Balanced Scorecard perspectives, which have also been prioritized using AHP. Moreover, the hybrid model also succeeded in identifying the strategic objectives that play the most important role in overall performance measurement. The overall outcomes of this study offer a useful tool to the decision makers to prioritize/ranking different BSC perspectives and their strategic objectives. The final results offer a useful tool for the decision makers to evaluat