International Journal of Research in Industrial Engineering
Volume:10 Issue: 1, Winter 2021

Six Sigma has gained wide acceptance as an improvement methodology to enhance the organization competitiveness in market. For SMEs building a competitive advantage is a difficult task. Changes in the economic environment affect the way such entities perceive factors which could help them not only survive on the market but shape their competitiveness. In the period of significant economic turbulence, the factors that play a major role in shaping the competitive market position are company image (product brand) and lower product price. This study uses Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) approach as a framework to identify, quantify, and eliminate sources of variation in Meseret Gabi Machinery Metal Works Enterprise in Dessie (Ethiopia). This helps to improve the competitiveness of the enterprise in the market place by addressing the complaints and requirements of the customer continuously.

Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset.

The green smart supply chain is a phenomenon that has emerged as a result of the development of sustainable and smart business and information technology trends. Sustainable and green supply chains are an innovative phenomenon that uses information technology to improve the quality of activities in operating areas. In order to ensure that activities are adapted to social and environmental needs. In this regard, the Internet of Things is one of the most important components of technology infrastructure for smart. For this purpose, in this research, a framework for implementing a green IoT-based supply chain is presented. This framework is based on the four-stage architecture of the Internet of Things and has been created by emphasizing the literature and the interaction and review of the opinions of active experts in this field. This framework illustrates the direct relationship between data generation and how it interacts with the sectors affected by environmental sustainability and outlines a clear pathway for sustainable and green decision-making in the supply chain. This framework has been endorsed by experts in the supply chain field and can pave the way for effective implementation of the green supply chain with an emphasis on technology in manufacturing organizations.

The aim of this study is to examine customers’ satisfaction with the level of service in Murtala Muhammed International Airport (MMIA), Lagos, Nigeria; so as to identify the service attributes requiring managerial attention in the airport. The study evaluated twenty eight service attributes in order to assess passengers’ expectations and satisfaction. Survey was conducted and four hundred copies of questionnaire were administered to the study population. The data was analyzed using descriptive statistics, GAP analysis and t-test statistics. The results revealed that the twenty eight service attributes showed a significant difference (significant level ≤ 0.05) between male and female respondents’ perceptions. Although, the expectations of these service attributes were high yet their satisfaction level was low. This suggests that customers’ satisfaction with MMIA, Lagos decreased. Thus, the service attributes that are of high expectation to passengers using the airport but performing poorly with low satisfaction level include “Speed of bags delivery service”, “Flight are screens”, “Comfort of waiting” and “Phone/Internet/IT facilities.” The aforementioned service attributes are those that require managerial attention in the airport. Hence, major improvement is needed here so as to enhance customers’ satisfaction.

The continuous growth of the population causes an increased demand for our healthcare services. Insufficient hospitals face challenges to serve the patient within a preferable duration. Long lines in front of counters increase the processing time of a patient. From the entry to the completion, plenty of time waste just for unscheduled hospital management system. Job shop scheduling is an optimization process in which jobs are assigned with maintain a particular sequence. In this paper, we proposed flexible job shop scheduling to solve this type of problem by considering patients as job and test counter as machine for the optimization of the processing time and increase the efficiency of a hospital or a clinic. Genetic Algorithm was used to analyze the processing time for multiple counter of a hospital for a stable and effective scheduling. The results showed that an optimized make-span was generated and patients could fulfill their needs much quickly after applying flexible job shop scheduling.

The use of assembly lines is one of the important approaches in mass production of industrial products. Imbalance of assembly lines increases cycle time and idle times, resulting in reduced production rates, line efficiency, and increased system costs, which ultimately lead to low productivity. A hybrid model assembly line is a type of production line on which various models of products are assembled. These assembly lines are increasingly accepted in the industry in order to overcome the diversity of customer demand. The hybrid model assembly line is able to respond quickly to sudden changes in demand for different models of a product without maintaining a large inventory.The purpose of this paper is to present a multi-objective integer linear mathematical programming model for balancing assembly lines, which is solved using the general criteria method. The three objective functions considered in this model are: (1) Minimizing cycle time (2) Minimize the idle time of each station and (3) increase the efficiency of the assembly line. In order to investigate the model, Iran-Shargh Neishabour Company has been considered as a case study. After implementing the proposed model of the paper, the results show the optimal performance of the proposed model and the studied parameters in line balancing have been significantly improved.

Data Envelopment Analysis (DEA) is one of the non-parametric methods for evaluating each unit's efficiency. Limited resources in the healthcare system are the main reason for measuring the efficiency of hospitals. Because Operating Rooms (OR) are the most vital part of any hospital, we determine the factors affecting operating rooms' efficiency and evaluate the performance and ranking of operating rooms in 10 of Tehran's largest hospitals. This model's inputs include accuracy in scheduling surgeries, average turnover time, number of successful surgeries and live patients, number of canceled surgeries, number of surgical errors, and number of emergency surgery. Also, outputs consist of the number of operating rooms and equipment, the average number of beds, the number of employees, and the patient satisfaction rate. First, we determine the weight of inputs and outputs by Group Analytic Hierarchy Process (GAHP) with considering experts' ideas in 10 hospitals; then, we utilize three types of DEA model which are input-oriented CCR (CCR-I), output-oriented CCR (CCR-O), input-output oriented CCR (CCR_IO) and AP models to estimate the efficiency of ORs and rank them.