International Journal of Industrial Engineering & Production Research

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The present study is focused to investigate the effect of the various machining input parameters such as cutting speed (v_c), feed rate (f), depth of cut, and nose radius (r) on output i.e. surface roughness (R_a and R_q) and metal removal rate (MRR) of the C40 steel by application of an artificial neural network (ANN) method.  ANN is a soft computing tool, widely used to predict, optimize the process parameters. In the ANN tool, with the help of MATLAB, the training of the neural networks has been done to gain the optimum solution. A model was established between the computer numerical control (CNC) turning parameters and experimentally obtained data using ANN and it was observed from the result that the predicted data and measured data are moderately closer, which reveals that the developed model can be successfully applied to predict the surface roughness and material removal rate (MRR) in the turning operation of a C40 steel bar and it was also observed that lower the value of surface roughness (R_a and R_q) is achieved at the cutting speed of 800 rpm with a feed rate of 0.1 mm/rev, a depth of cut of 2 mm and a nose radius of 0.4 mm.

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One of the challenges that banks are faced with is recognition and differentiation of customers and providing customized services to them. Recognizing valuable customers based on their field of business is one of the key objectives and competitive advantages of banks. To determine guild patterns of the valuable customers based on their transactions and value of each guild for the bank, the banking tools on which the customer’s transactions take place need to be surveyed. Using deeper insights into the value of each guild, banks can provide customized services to ensure satisfaction and loyalty of their customers. Study population was comprised of the holders of point of sale (POS) devices in different guilds and the transactions done through the devices in an 18-months period. Datamining methods were employed on the set of data and the results were analyzed. Data preparation and analysis were done though online analytical processing (OLAP) method and to find guild patterns of the bank customers, value of each customer was determined using recency, frequency, monetary (RFM) method and clustered based on K-means algorithm. Finally, specifications of customers in the most valuable cluster were analyzed based on their guilds and the rules were extracted from the model developed using C5 decision tree algorithm.

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One of the goals of the manufacturing industry in the globalisation era is to reduce defects. Due to a variety of factors, the products manufactured in the industry may not be defect-free. Six Sigma is one of the most effective methods for reducing defects. This paper focuses on implementing Six Sigma in the automobile industry's stator motor shaft assembly. The high decibel noise produced by the stator motor is regarded as a rejected piece. Six Sigma focuses on continuous improvement and aids in process optimization by identifying the source of the defect. In the Six Sigma process, the problem is measured and analysed using various tools and techniques. Before beginning this case study, its impact on the company in terms of internal and external customer cost savings is assessed. This case study was discovered to be in a high-impact area. The issue was discovered during the Core and Shaft pressing process. Further research leads to dimensional tolerance, which reduces the defect percentage from 16.5 percent to 0.5 percent.

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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.

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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.

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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

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Organizations have used Customer Lifetime Value (CLV) as an appropriate pattern to classify their customers. Data mining techniques have enabled organizations to analyze their customers’ behaviors more quantitatively. This research has been carried out to cluster customers based on factors of CLV model including length, recency, frequency, and monetary (LRFM) through data mining. Based on LRFM, transaction data of 1865 customers in a software company has been analyzed through Crisp-DM method and the research roadmap. Four CLV factors have been developed based on feature selection algorithm. They also have been prepared for clustering using quintile method. To determine the optimum number of clusters, silhouette and SSE indexes have been evaluated. Additionally, k-means algorithm has been applied to cluster the customers. Then, CLV amounts have been evaluated and the clusters have been ranked. The results show that customers have been clustered in 4 groups namely high value loyal customers, uncertain lost customers, uncertain new customers, and high consumption cost customers. The first cluster customers with the highest number and the highest CLV are the most valuable customers and the fourth, third, and second cluster customers are in the second, third, and fourth positions respectively. The attributes of customers in each cluster have been analyzed and the marketing strategies have been proposed for each group.

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This paper designs a Scenario analysis approach to determine the joint production policy for two products under possible substitution. The Scenario analysis is designed to improve decision making by considering possible outcomes and their implications. The traditional multi-products production models assume that there is no possible substitution between products. However, in real-world cases, there are many substitutable products where substitution may occur in the event of a product stock-out. The proposed model optimizes production quantities for two products under substitution with the aim of minimizing the total cost of inventory system, including setup and holding costs, subject to a resource constraint. To analyze the problem, four special Scenarios are derived and discussed in detail. Furthermore, the total cost functions are derived for each Scenario separately, and then a solution procedure is suggested based on the Scenarios developed. The numerical examples are implemented, and the results are discussed in detail.

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Tourists are getting more aware of the environment. To determine the effectiveness of eco-tourism infrastructure management, the motivation and segmentation of demand for eco-tourism have been analysed using functional theory as a guide. The empirical analysis was conducted in the Dnipro, Zaporizhzhia, and Kherson regions. 382 surveys were obtained by random sampling. To make the data analysis, factor analysis and non-hierarchical segmentation were performed. The results indicate that there are several eco-tourism motivational aspects including self-development, interpersonal relationships, defence functions, building personal relationships, reward, and appreciation from nature. Three different segments of eco-tourists were also identified based on their motives related to nature, reward, and escape. Characteristics of different segments were also specified. This study will help government agencies and private companies improve their travel content and develop more effective marketing plans.
The research has shown that in most cases, the success of any project is in cooperation between NGOs, locals, authorities, and the private sector. The optimal level of local participation is determined by the specifics and scale of each project, which may focus on individual villages or several communities that experience any impact of tourism.
The economic essence of the concept of tourism motivation has been improved, which is defined as a set of needs that affect a person in the process of participation in tourism activities and are a central factor in the decision-making process. Studying the most important motivations of eco-tourists in the region, three groups of motives have been identified: cultural and educational activities, proximity to nature, health and rehabilitation measures.

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The Project Management Body of Knowledge (PMBOK) is a widely used model of project management based on prior experience. This standard does not distinguish between small and large projects, but small projects, with their limited schedules and budgets, face challenges using the extensive structure proposed by this standard. It has been suggested that the standard can be adapted to each project within its specifications; however, the tailoring procedures are complex, time-consuming, and at times impossible to apply to small projects. The present study examined whether or not the PMBOK is an appropriate model for small projects. To address this issue, a questionnaire was prepared and sent to 134 professional project managers. Analysis of the data confirmed that the assumption that PMBOK is a challenge to small projects was not contradicted. Most participants agreed that the procedure should be tailored to prioritize the standard tools and guiding techniques, in addition to the knowledge areas, for small projects.

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This study seeks to introduce the influential factors in controlling and dealing with uncertainty in intermittent demand. Hybrid forecasting and Grey Theory, due to their potential in facing complex nature, insufficient data, have been used simultaneously. Different modeling, unbiased weighting results have been used in estimating the safety stock(SS) by both theoretical and experimental methods. In other words, this work deals with the less studied feature of various modeling errors and their effect on SS determination and recommends its use to address the uncertainty of intermittent demand as a criterion for introducing a superior model in the field of inventory.

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The health insurance system can play an effective role to control health expenditures. The purpose of this study is to provide a model for estimating the physician visit tariffs. To achieve this goal, a hybrid model was used. fuzzy logic is the most appropriate tool for controlling systems and deriving rules for the relationship between inputs and outputs. So, the output of the data mining techniques enter the fuzzy logic as an input variable. The data were collected from the Health Insurance Organization of Iran in two sections including the physicians' costs and physicians' deductions. Owing to the techniques used in this model, NN had the least error, as compared to other data mining techniques (0.0034 and 0.0013, respectively). After defining the variables, membership functions and fuzzy logic rules, the accuracy of the whole control model was confirmed by random data. This research has dealt with the domains of health insurance , their connections and defining effective variables better and more extensively than the other studies in the field.

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In today's world, COVID-19 pandemic has affected many organizations. Pandemic issues have created financial and social problems for businesses. Crisis and risk management have a significant impact on reducing consequences of pandemics. Rapid response to risk enhances the performance of organizations in times of crisis. Therefore, a framework to provide risk treatment in a pandemic crisis seems essential. To do this, this paper presents a framework to identify risk factors posed by pandemics. In this regard comprehensive risk factors by considering sustainability concept are illustrated for university. Then, identified risk factors are evaluated by best–worst methodology (BWM) and then the important risks are recognized. Using the importance of risk and the strengths and weaknesses of the business, solutions to reduce the impact of risk are suggested to managers. The results of this paper can be used in order to enhance resiliency of the organization in front of the pandemics from social and financial viewpoints.
 

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Integrated treatment planning for cancer patients has high importance in intensity modulated radiation therapy (IMRT). Direct aperture optimization (DAO) is one of the prominent approaches used in recent years to attain this goal. Considering a set of beam directions, DAO is an integrated approach to optimize the intensity and leaf position of apertures in each direction. In this paper, first, a mixed integer-nonlinear mathematical formulation for the DAO problem in IMRT treatment planning is presented. Regarding the complexity of the problem, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE), are utilized to solve the model. The parameters of both algorithms are calibrated using the Taguchi method. The performance of two proposed algorithms is evaluated by 10 real patients with liver cancer disease. The statistical analysis of results using paired samples t-test demonstrates the outperformance of the PSO algorithm compared to differential evolution, in terms of both the treatment plan quality and the computational time. Finally, a sensitivity analysis is performed to provide more insights about the performance of algorithms and the results revealed that increasing the number of beam angles and allowable apertures improve the treatment quality with a computational cost.
 

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The main objective of this study is to determine factors influencing the adoption and impact of online social networks use in terms of performance among students within public universities in Abu Dhabi. Although various limitations exist, the findings have been encouraging, as it has managed to shed some lights on new variables affecting the use of online social networks. This study proposed an extended model of the Unified Theory of Acceptance & use of Technology (UTAUT) and found that five variables play an important role to determine the performance impact of online social networks namely performance expectancy, effort expectancy, social influence, facilitating conditions, and actual usage, in addition to the significant moderation role that service quality plays in the model which was significant on two relationships and insignificant in the remaining two. The findings of this study can provide policymakers with important insights on how to more successfully incorporate online social networks to improve students’ performance and public university services, and how to encourage the management to ensure that students are more likely to utilize new technologies and thereby enabling  better learning outcome, wider reach of services, gives students more control over their daily tasks and enhances their performance.

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Previous studies have reported that trust is the main issue that needs to be resolving. (McKnight & Chervany, 2001). Trust proficiently leads people or organizations to acquire maximized benefits and potentially gives an organization a competitive advantage in markets, communities, and hierarchies (Robbins (2016), Semuel & Chandra (2014). The extent of this study revolves around develop consumer trust in the quality of cosmetic product scope.  Researchers have shown an increased interest in the cosmetics field as the average annual growth in the last twenty years is 4.5% and the rate of growth presume to continue over 3%.   The objectives of this research are to (1) understand factor involves in the process of build consumers’ confidence and trust virtually in offline and online business, (2) to determine the prominent information need to be an underline in marketing strategy, and (3) to understand how trust can affect consumer preference on cosmetics product. This study underlined cosmetic price, cosmetic brand name, and cosmetic country of origin are the prominent information that needs to underline in marketing strategy. Important issues were addressed and recommendations were made for prospect research.
 

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Brand loyalty is driven by share, comments, online review, like, and dislike on the social media platform of specific brands. The study empirically assessed with the influence of the dimensions of brand's personality as a moderator on SMBC and brand loyalty among customers’ popular fashion brands. The Aaker Brand Personality Scale used to measure the personality of fashion brands. Online brand personality can exist in the same way as offline brands. This means that social media has brand personalities, and these can influence consumer perceptions in different ways. This research utilized a quantitative approach in which questionnaires was distributed to SMBC users as the research population. The research was performed Structural Equation Modeling using IBM SPSS Statistics 23 software and Smart PLS 3.2.9 to analyze the data. The findings were help brands to make marketing plans to influence any type of unsatisfactory situations.
 

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Dubai has witnessed the growth of numbers in population and global visitors, which makes it necessary for the city to have an excellent police department to secure all citizens, residents and visitors.  This is necessary for improving Dubai's security and financial condition and cementing the city's importance in the world.  The main objective of this study is to examine the relationship between the delegation of authority, organizational functionality and decision-making process among the employees in Dubai police department UAE.  A total of 380 employees were selected as the study sample using a multistage sampling method. Questionnaires were used in data collection and responses were analysed using partial least squares structural equation modelling (PLS-SEM)  for data analysis. The results showed that the delegation of authority affects decision-making among the target population. Moreover, delegation of authority helps the organisation in achieving the objectives with accordance to the imperative’s factors of organizational functionality of the organisation. The results of this research contributed substantially to the current body of knowledge in the domain of delegation of authority in Arab context.  The novelty of this study stem from the reality that the issues and problems of power delegation in Dubai police department was assessed in terms of decision-making process. From these results some recommendations are also suggested which are quite helpful especially, with regards to the latest global models of contemporary leadership and the latest approaches and methods of modern decision-making.

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This study intends to evaluate the mediating role of employees performance in the relationship between delegation of authority, organizational functionality, and the decision making process among the employees of police department at Dubai. The study has collected data in the various police stations at Dubai from 380 employees through questionnaires based survey using random sampling technique. The study noted that employees performance has mediating role between the delegation of authority, organizational functionality, and the decision making process among the police employees at Dubai. The results of this research contributed substantially to the current body of knowledge in the domain of delegation of authority in Arab context. The novelty of this study stem from the reality that the issues and problems of power delegation in Dubai police department was assessed in terms of decision-making process.

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Venture capital (VC) financing is associated with the challenges of double-sided moral hazard, and uncertainty, which leads to the difficulty in estimating the venture's value accurately and consequently the impossibility of determining the optimal equity sharing between the entrepreneur and investor. Traditionally, convertible preferred equity mechanisms used to be implemented as an incentive to decline moral hazard. However, despite the emphasis on investor risk-taking, such mechanisms transfer the investor risk to the entrepreneur and do not mitigate the incentive of opportunistic behaviors. Furthermore, according to the literature review, and to the best of the authors’ knowledge, there has not been developed any practical mechanism for equity sharing in VC financing up to now. This paper proposes a fair equity sharing mechanism, which alleviates the above-mentioned deficiencies. It adjusts both parties' share during the equity dilution in each stage of financing, regarding the difference between the venture's ex-ante and ex-post values. Moreover, it manages uncertainty by applying staged financing and the option of abandonment at the end of each stage. The proposed mechanism has been verified by using the mathematical tools and drawing its curves for a case study.