---

In order to manage a project with integrity, a cohesive communication is needed between its various sections; possible risks, identification of stakeholders, providing the necessary resources on time and managing their availability, focusing on the approved budget and satisfactory quality the project can be successfully done. In the recent year BIM has as new aspects to engineering and architecture, and has become an accepted platform for planning and executing construction projects and contributed to integration of various fields and. also, project management standards, such as PMBOK, have come to aid construction managers. Through the basic capacities of BIM, and questionnaires according to aspects of PMBOK, the present study tries to identify the superior effects of BIM on project management. Moreover, it seeks to recognize the most significant aspects of BIM application on project management. by employing the FANP-AVIKOR decision making method to prioritize the parameters of the collected results, the study’s conclusion will indicate that almost all of PMBOK aspects equally benefit from using BIM; in addition, it will show that 3D BIM capacities, including clash detection, plan correction, are superior in comparison with 6D BIM and 7D BIM capacities.

---

Supply chain management is a set of used methods for the efficient integration of suppliers, manufacturers, warehouses, and sellers to response customer requirements to reduce system costs and to distribute products at the right place and right time. This study aims to identify and rank the supply chain damages using the analytic network process as a practical case in a fast moving consumer goods (FMCG-food industry) company. Firstly the supply chain damages are explored according to literature review. In the next step the most important damages are identified into four cluster of supply include
supply, production, distribution and support. Then, the weight of each identified damages based on its effects on other damages are calculated by using the analytic network process approach. According to results, the most important supply chain damages are logistics, distribution, competition and changing market tastes. The obtained results can provide practical discussion and solutions for similar companies to improve your market share and customer satisfaction.

 

---

The present research aims at investigating feasible improvements by determining optimal number of stations and required workforce using a simulation system, with the ultimate goal of reaching optimal throughput while respecting the problem constraints in an attempt to achieve maximum feasible performance in terms of production rate. For this purpose, similar research works were investigated by reviewing the relevant pieces of the literature on simulation model, car signoff station, and techniques for optimizing the station, and the model of the car signoff unit was designed using data gathering tools, existing documents, and actual observations. Subsequently, the model was validated by means of descriptive statistics and analysis of variance (ANOVA). Furthermore, available data was analyzed using ARENA and SPSS software tools. In a next step, potential improvements of the unit were identified and the model was evaluated accordingly. The results indicated that some 80% of the existing problems could be addressed by appropriately planning for human resources, on-time provision of the required material at reworking units, and minimization of transportation at the stations that contributed the most to the working queue. Thus, the waiting time per station could be minimized while increasing the production rate.

---

This work used two methods for Monitoring and control of autocorrelated processes based on time series modeling. The first method was the simultaneous monitoring of common and assignable causes. This method included applying five steps of data gathering, normality test, autocorrelation test, model selection and control chart selection on all non-stationary process observations. The second method was a novel one for the separate monitoring and control of common and assignable causes. In this method, the process was divided into the parts with and without assignable causes.
The first method was greatly non-stationary due to not separating common and assignable causes. This method also implied that the common causes were hidden in the process. The novel method for the separate monitoring of common and assignable causes could turn the process into a stationary one, leading to identifying, monitoring, and controlling common causes without any interference from the assignable causes. The results showed that, unlike the first method, the second method could be very sensitive to the common causes; it could, therefore, suitably monitor, identify and control both assignable and common causes.
The current work was aimed to use control charts to monitor and control the bootomhole pressure during the drilling operation.
 

---

Regarding population growth and prompt development in developing countries, municipal solid waste management is always a great challenge for governments. Waste to energy conversion is an efficient approach with respect to overcoming not only the challenge of municipal solid waste management but also environmental challenges related to energy consumption like global warming and fossil fuel depletion. One of the substantial problems throughout the implementation of waste to energy approach is process selection. The selected process should be technically feasible and should have a high level of compliance with environmental standards. Owing to an inevitable significance of process selection, this paper focuses on defining the best process by relying on multi-criteria decision-making tools and network analytic process. Considering the effective parameters such as cost, efficiency in material diversity, productivity rate, energy consumption, pollutant emissions, toxic substances, and process time, the result indicates that the physico-chemical process is superior process for pretreatment of material.

---

Cognitive based (Chatbot) blood bank provides the communication platform among the stakeholders of blood bank. In the past the blood recipient will have to contact the blood bank and the blood donors individually, which is a time consuming process.  To address this issue in this paper we propose a Secure Dynamic Interactive Blood Bank based on Cognitive Computing which can fulfill the blood request of the needy with less hardship. Hence the proposed work aims to overcome this problem by requesting the recipient to simply send a message to a chatbot.  The motivated individuals who are willing to donate blood can register their name by interacting with the chatbot. If the requested blood group is available at the blood bank / registered donor then the recipient will get contact details of the blood bank / registered donors available at that instant. Donor data will be maintained in Cloud database. The proposed system is a cognitive chatbot, which acts as a communication platform among the stakeholders such as blood bank, blood donor and the needy. This system is built using cognitive technology of Google; it makes conversations using chatbots very similar to human conversations, thereby making the proposed system more efficient compared to the existing ones.

---

The Today’s interconnected world generates huge digital data, while millions of users share their opinions, feelings on various topics through popular applications such as social media, different micro blogging sites, and various review sites on every day. Nowadays Sentiment Analysis on Twitter Data which is considered as a very important problem particularly for various organizations or companies who want to know the customers feelings and opinions about their products and services. Because of the data nature, variety and enormous size, it is very practical for several applications, range from choice and decision creation to product assessment. Tweets are being used to convey the sentiment of a tweeter on a specific topic. Those companies keeping survey millions of tweets on some kind of subjects to evaluate actual opinion and to know the customer feelings. This paper major goal would be to significantly collect, recognize, filter, reduce and analyze all such relevant opinions, emotions, and feelings of people on different product or service could be categorized into positive, negative or neutral because such categorization improves sales growth about a company's products or films, etc. We initiate that the Naïve Bayes classifier be the mainly utilized machine learning method for mining feelings from large data like twitter and popular social network because of its more accuracy rates. In this paper, we scrutinize sentiment polarity analysis on Twitter data in a distributed environment, known as Apache Spark.

---

In today’s world, manufacturing companies are required to integrate their sources with manufacturing systems and use novel technologies in order to survive in the competitive world market. In this context, computer integrated manufacturing (CIM) and its related technologies are taken as novel and efficient schemes; therefore, selecting the best technology among them has been a challenging issue. Such an investment decision is, in nature, a multi-attribute problem. In fact, manufacturing technologies have various advantages and disadvantages which need to be considered in order to choose the best one. In this paper, we briefly study the structure and goals of computer integrated manufacturing systems, the role of different sectors in traditional and modern manufacturing systems, and the effect of information communication on them. Then, various options regarding the implementation of an integrated computer manufacturing technology are introduced and a  combined model of the fuzzy analytical hierarchy process and fuzzy TOPSIS is proposed to handle the above-mentioned multiple criteria decision making problem. Finally, the considered options for manufacturing technologies are ranked using a numerical example.
 

---

The Today’s digital world computations are tremendously difficult and always demands for essential requirements to significantly process and store enormous size of datasets for wide variety of applications. Since the volume of digital world data is enormous, this is mostly generated unstructured data with more velocity at beyond the limits and double day by day. In last decade, many organizations have been facing major problems to handling and process massive chunks of data, which could not be processed efficiently due to lack of enhancements on existing and conventional technologies. In this paper address, how to overcome these problems as efficiently by using the most recent and world primary powerful data processing tool, which is hadoop clean open source and one of the core component called Map Reduce, but which has few performance issues. This paper main goal is  address and overcome the limitations and weaknesses of Map Reduce with Apache Spark.

---

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.

---

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

---

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)

---

In some statistical processes monitoring (SPM) applications, relationship between two or more ordinal factors is shown by an ordinal contingency table (OCT) and it is described by the ordinal Log-linear model (OLLM). Newton-Raphson algorithm methods have also been used to estimate the parameters of the log-linear model. In this paper, the OLLM based processes is monitored using MR and likelihood ratio test (LRT) approaches in Phase I. Some simulation studies are applied to performance evaluation of the proposed approaches in terms of probability of signal under step shifts, drifts and the presence of outliers. Results show that, by imposing the small and moderate shifts in the ordinal log-linear model parameters, the MR statistic has better performance than LRT. In addition, a real case study in dissolution testing in pharmaceutical industry is employed to show the application of the proposed control charts in Phase I.  

---

This paper considers a customer order scheduling (COS) problem in which each customer requests a variety of products processed in a two-machine flow shop. A sequence-independent attached setup for each machine is needed before processing each product lot. We assume that customer orders are satisfied by the job-based processing approach in which the same products from different customer orders form a product lot (job). Each customer order for a product is processed as a sublot (a batch of identical items) of the product lot by applying the lot streaming (LS) idea in scheduling. We assume that all sublots of the same product must be processed together by the same machine without intermingling the sublots of other products. The completion time of a customer order is the completion time of the product processed as the last product in that order. All products in a customer order are delivered in a single shipment to the customer when the processing of all the products in that customer order is completed. We aim to find an optimal schedule with a product lots sequence and the sequence of the sublots in each job to minimize the sum of completion times of the customer orders. We have developed a mixed-integer linear programming (MILP) model and a multi-phase heuristic algorithm for solving the problem. The results of our computational experiments show that our model can solve the small-sized problem instances optimally. However, our heuristic algorithm finds optimal or near-optimal solutions for the medium- and large-sized problem instances in a short time.

---

Nowadays, major challenges in the cold chain of perishable products, such as dairy products, are that these products do not reach customers on time. Answering the question of how to make the cold supply chain of perishable products more agile, the possibility of more control over this issue can be increased. This study tries to investigate the factors affecting the agility of the cold supply chain and after identifying the effective factors, rank them using the GRAY-DEMATEL-AHP. To data gathering, the literature of the subject and the opinions of experts and stakeholders who have sufficient experience in the cold chain have been used and the identified factors have been confirmed after several revisions by the Delphi through snowball sampling. Also, in order to take advantage of both the GRAY and DEMATEL approaches, this paper uses a combination of these two methods to examine causal relationships among the factors affecting the agility of the cold supply chain. The results show that Among the sourcing sub-factor, government decision-making and policies with a weight of 0.212 has gained the first rank and in the sub-factor of distribution, loading time and speed of action in distribution, with a weight of 0.188, has gained the first rank. Also, among the sub-factor of production, accurate planning and speed of action in order production, with a weight of 0.342, has gained the first rank. This paper adds valuable knowledge to the study of the dairy industry cold supply chain agility.

---

In a system, prediction of remaining useful lifetime (RUL) of servicing before reaching to a specified breakdown threshold is a very important practical issue, and research in this field is still regarded as an appreciated research gap. Operational environment of an equipment is not constant and changes regarding to stresses and shocks. These random environmental factors accelerate system deterioration by affecting on the level or rate of degradation path. The present study focuses on providing a practical operational guideline to estimate the RUL of a system with general degradation path after receiving a shock which only affects on the degradation level. Due to exact estimation of the shock arrival times and measuring the magnitudes of future shocks to investigate shock effects on RUL is almost impossible in the real world and laborious in practice, in this research a new procedure based on total defect size monitored in the constant inspection periods and Accelerated Factor (AF) is proposed to analyze RUL of the system. A Micro-Electro-Mechanical system (MEMS) is used as an example and the results show the applicability of the proposed approach.

---

Purpose: Non-cancerous prostate lesions such as prostate calcification, prostate enlargement, and prostate inflammation cause too many problems for men’s health. This research proposes a novel approach, a combination of image processing techniques and deep learning methods for classification and segmentation of the prostate in CT-scan images by considering the experienced physicians’ reports.
Methodology: Due to the various symptoms and nature of these lesions, a three-phases innovative approach has been implemented. In the first phase, using Mask R-CNN, in the second phase, considering the age of each patient and comparison with the standard size of the prostate gland, and finally, using the morphology features, the presence of three common non-cancerous lesions in the prostate gland has investigated.
Findings: A hierarchical multitask approach is introduced and the final amount of classification, localization, and segmentation loss is 1%, 1%, and 7%, respectively. Eventually, the overall loss ratio of the model is about 9%.
Originality: In this study, a medical assistant approach is introduced to increase diagnosis process accuracy and reduce error using a real dataset of abdominal and pelvics’ CT scans and the physicians’ reports for each image. A multi-tasks convolutional neural network; also presented to perform localization, classification, and segmentation of the prostate gland in CT scans at the same time.

---

Forging Industry Supply Chain involves various actors and acts as Industry Intermediate providing various products for downstream industrial customers. This study aims to analyze supply chain performance and recommend improvement strategy at forging Industry. This study applied supply chain operation reference (SCOR) and Analytical Hierarchy Process (AHP) to analyze supply chain performance. A SWOT analysis assisted to improve supply chain performance. The data was validated at PT ABC and PT XYZ as two focal company in supply chain operations of forging industry. The results show that the supply chain performance at PT. ABC 99.42% and 99.05% in 2019 and 2020, respectively.  PT. XYZ showed supply chain performance score as 96.60% and 97.52% in 2019 and 2020, respectively. This study has succeeded in formulating efforts to improve the supply chain performance, namely: producing quality goods according to domestic market specifications, maintaining good relations with suppliers or outsourcing, improving services using high technology.
 

---

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.

---

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.