International Journal of Industrial Engineering and Productional Research
Volume:32 Issue: 4, Dec 2021

Preventive maintenance (PM) of machines has the critical role in a factory or enterprise. It decreases number of failures, increases reliability, as well as minimizes costs of production systems.  The managers’ duty of maintenance section is to prioritize machines and then, implement PM programs for them. Since machines have the different measures with respect to the maintenance costs, reliability, mean time between failures (MTBF), availability of spare parts, etc., the machines evaluation problem can be considered as a multiple criteria decision-making (MCDM) problem. Accordingly, the MCDM techniques can be applied to solve them. The aim of this paper is to extend the ELECTRE III (eLimination et choix traduisant la realite´– elimination and choice translation reality) method to interval type-2 fuzzy sets (IT2FSs) using curved (such as Gaussian) membership functions (MFs). The extended ELECTRE III methodology is then utilized to a maintenance group MCDM (GMCDM) matrix including the quantitative and qualitative criteria. In the proposed approach, the criteria weights, the assessment of alternatives with respect to criteria, and the thresholds are stated with Gaussian interval type-2 fuzzy sets (GIT2FSs). In order to show the effectiveness and applicability of the proposed approach, a case study and an illustrative example are exhibited using real decision-making problems. Due to the high correlation coefficients between our method and the others, as well as the results obtained by the proposed method, it can be taken into account as a valid and reliable approach to prioritize machines for PM.

Overall Equipment Effectiveness (OEE) is a very powerful indicator for the performance evaluation of a manufacturing organization. However, determining the OEE target remains subjective and it’s usually based on the decision of concerned managers. In this paper, we tested the OEE target determination model based on the measured OEE. Such a method is based on the fuzzy logic principle and on two other decision-making support methods. To do this, we began with a literature review on OEE and its constituents. Then, a detailed description of the research methodology and the proposed model is provided. Thus, a case study in a manufacturing agri-food organization was conducted to test the proposed model and validate the obtained results.

The article highlights the development of a Non-Gaussian Process Monitoring Strategy for a Steel Billet Manufacturing Unit (SBMU). The non-Gaussian monitoring strategy being proposed is based on Modified Independent Component Analysis (ICA) which is a variant of the widely employed conventional ICA. The Independent Components(IC) being extracted by modified ICA technique are ordered as per the variance explained akin to that of Principle Component Analysis (PCA). Whereas in conventional ICA the variance explained by the ICs are not known and thereby causes hindrance in the selection of influential ICs for eventual building of the nominal model for the ensuing monitoring strategy. Hotelling T2 control chart based on modified ICA scores was used for detection of fault(s) whose control limit was estimated via Bootstrap procedure owing to the non-Gaussian distribution of the underlying data. The Diagnosis of the Detected Fault(s) was carried out by employment of Fault Diagnostic Statistic. The Diagnosis of the Fault(s) involved determination of the contribution of the responsible Process and Feedstock characteristics. The non-Gaussian strategy thus devised was able to correctly detect and satisfactory diagnose the detected fault(s)

The study investigates the impact of work environment on organizational performance in Ethiopiachr('39')s Arjo Dedessa Sugar Factory (ADSF) and Finchaa Sugar Factory (FSF), as well as the relationship between physical work environment, work-related risk and injuries, psychological work environment, and social work environment. The total number of people employed in two industries is 867 and 2824. To examine the work-related environment condition, a stratified random sampling technique was used to select a sample of 266 and 338 employees. A 60% response rate has been achieved. Statistical software SPSS V 23.0 was used to analyze and determine the relationship between dependent and independent variables using Pearson correlation and linear regression analyses. The findings show that employees in ADSF have a moderate social work environment compared to those in FSF, but both organizationschr('39') physical work environments are the least conducive. Both ADSF and FSFchr('39')s physical work environments had statistically significant effects on their performance. The suggestion was made to improve the social environment in order to improve the employeeschr('39') psychological health.

In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided

Innovation is an essential tool for the supply chain to gain its competitive advantage and improve its performance. Many researchers have remarked that supply chain innovation is a vital tool for improving the performance of a supply chain and can be very productive. This research attempts to identify and analyze the effective factors on the innovation supply chain in the health sector. The effective factors on the innovative supply chain were extracted by reviewing the literature, similar studies, and experts’ surveys. In this regard, 49 criteria were determined in eight dimensions. Intuitive fuzzy DEMATEL (IFD) and AHP methods were used to determine the weight and the relationships between them. The results indicated organizational innovation as the most important dimension, government support innovation as the most effective dimension, and process innovation as the most affected dimension. Some researchers believe that this period guarantees the survival and success of service organizations in this competition. However, the logistics and communication network of a business is required as a new and innovative landscape to use the competitive advantage opportunities to perceive the global era.

The aims of this research is to analyze effect to perceived organization support, symbolic incentives meaning, and knowledge sharing quality on innovative work behavior at e-commerce in Indonesia. The number of samples in this research that was examined were 160 samples of business owner or manager who seller their product in e-commerce, originating from the provinces of Special Region Yogyakarta, Central Java, East Java, and West Nusa Tenggara, Indonesia. We use purposive sampling technique for the data collection method. The result of this research indicate that perceived organization support has a significant positive effect on symbolic incentives meaning. Perceived organization support has a significant positive effect on knowledge sharing quality. Perceived organization support has a insignificant positive effect on innovative work behavior. Symbolic incentives meaning has a insignificant positive effect on innovative work behavior. Knowledge sharing quality has a insignificant positive effect on innovative work behavior.

Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a  mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.

In the field of logistics, there is a daily need for decision making, i.e. the need to solve business problems by selecting an appropriate solution. During the implementation of decision-making processes, it is necessary to find an optimal solution that will best meet the needs of companies. The selection of an optimal solution is crucial for the profitability, cost-effectiveness and long-term development of companies. The decision-making process in logistics is facilitated by applying various tools such as multi-criteria decision-making methods. In this paper, an integrated SWARA (Step-wise Weight Assessment Ratio Analysis) – MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) model was developed and applied in order to classify products. Fifty alternatives, i.e. products were evaluated based on three criteria. The first criterion is the quantity of purchased products, the second criterion is the unit price of products and the third criterion is the annual value of purchase. The SWARA method was applied to determine the significance of the criteria, while the classification of products was performed using the MARCOS method. According to the results of the originally created MCDM model, the products were grouped into three categories A, B, and C. Then, a sensitivity analysis was performed using a model involving the integration of SWARA method and ABC analysis. Using this model, the classification of products into three groups was performed on the basis of the aforementioned criteria, and then a comparative analysis was conducted.

Productivity growth and efficiency improvements are the major sources of economic development. Pure efficiency, scale efficiency, and technology are basic factors, and rules and regulations and balance are recently known factors affecting the Malmquist productivity index. In this paper, we focus on the role of physical space facilities of bank branches as a factor affecting the decomposition of Malmquist productivity index. First, we propose a new model applying weight restrictions in basic DEA models for constant returns to scale technologies. The weight restrictions increase the discrimination power of basic DEA models. Then the new model is used to develop an extended Malmquist index, which gives a novel decomposition describing the roll of bank branch facilities on productivity growth or decline. The validity of proposed method is confirmed with a real data of 74 commercial bank branches in two time periods 2017 and 2018 and the results for both traditional and extended Malmquist index are analyzed.