Literature Review on Big Data Analytics Methods

Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed

Topic Modeling and Classification of Cyberspace Papers Using Text Mining

The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspace is an umbrella term that covers all issues occurring through the interaction of information systems and humans over these networks. Deep evaluation of the scientific articles on the cyberspace domain provides concentrated knowledge and insights about major trends of the field. Text mining tools and techniques enable the practitioners and scholars to discover significant trends in a large set of internationally validated papers. This study utilizes text mining algorithms to extract, validate, and analyze 1860 scientific articles on the cyberspace domain and provides insight over the future scientific directions or cyberspace studies

Big Data Analytics and Its Applications in Supply Chain Management

In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM)

PROMOTION OPTIMIZATION IN COMPETITIVE ENVIRONMENTS BY CONSIDERING THE CANNIBALIZATION EFFECT

This study proposes a new model to optimize sales promotion in competitive markets and examines the impact of competition on sales promotion planning and business performance in retail chains. The model can be used to determine the best promotional discount for different products with a cannibalization effect when competitors are present in the retail market and offer the same products with different discounts. An integer nonlinear programming problem is proposed to model the above issue. To solve the model, it is reformulated as a mixed-integer linear programming problem. Consequently, a MIP solver can be used to solve the model in a reasonable CPU time. Several examples are solved and a sensitivity analysis of the model parameters is performed. The results of our numerical study show interesting findings that considering different competitors is very important in promotion planning and optimization. Failure to take them into account can lead to loss of profits

Development of Market Intelligence Model in the Supply Chain of FMCG(Perishable) Products in Online Retailing

IntroductionToday, the strategic importance of information is obvious to all businesses. In addition, the competitive environment of each company is constantly changing. The Spring 2020 event was a testament to this fact. Due to the health and economic crisis caused by the emergence and spread of an unknown virus, various teams found it difficult to convey their advertising messages, campaigns and services. They could no longer rely on their assumptions about what customers buy and why and how they buy it (Johnson, 2020). Access to rich information for businesses that operate both in the field of e-commerce and in the retail sector of perishables is crucial. These products have a short life cycle and should be consumed faster. If the market intelligence model is properly designed for such businesses based on the supply chain of perishables, then managers will be able to correctly identify their customers, competitors and the business environment and run their business more successfully and grow as a result. In Iran, not much research has been conducted to provide a model that simultaneously addresses the aspects related to supply chain, market intelligence and online retail of fast-moving (perishable) products. and each of the models or patterns in the literature address one aspect of the issue. If market intelligence is at the service of the supply chain, it can create opportunities to reduce costs and increase customer satisfaction through collaborative decisions. Based on what was presented in the introduction, the main question of the research is extracted as follows.Research QuestionRQ1: what are dimensions and components of Market Intelligence model in the supply chain of FMCG (perishables) products in online retailing.Literature ReviewThe concept of market intelligence has attracted a lot of attention in recent years. Various experts have defined market intelligence in some way: market intelligence is formed through detailed and accurate information about business environment in general, consumer needs and preferences, technology and changes in the business environment that can affect buyers. (Hedin,2014). Market intelligence enables small businesses to identify market attractiveness and create value and drive innovation (Del Vecchio, 2018).   2.1. Supply Market IntelligenceThe relationship between market intelligence and supply chain can be found under concept of supply market intelligence. (SMI). Market intelligence is a process for gaining competitive advantage and reducing risk by increasing knowledge about market dynamics and includes market intelligence, process and price benchmarking to evaluate sourcing performance, competitive sourcing identifying strategic opportunities in markets that lead to lower prices ,emerging supply channels and markets (Hanfield,,2010).   2.2. Organization Information Processing Theory (OIPT)One of the theories which is the basis for market intelligence and business intelligence is organization information processing theory (OIPT), which was introduced by Galbraith in late 1973. According to Galbraith, when uncertainty is low, organizations can be managed by relying on rules and programs and hierarchical referrals but in situations where the organization is facing high uncertainty, the need for information processing increases and there are two general solutions in this regard: organizations must either reduce the need for information processing or increase information processing capabilities by investing in information systems (Galbraith, 1974).2.3. Market OrientationThe root of market intelligence can also be traced to a concept called market orientation. The concept of market orientation has been developed from two perspectives: behavioral perspective and market intelligence perspective. According to Kohli and Jaworski, market orientation is a set of behaviors or activities related to market intelligence, dissemination of market intelligence among different units of the organization and responsiveness based on it (Kohli & Jaworski, 1990). According to Narver and Slater, Market Intelligence has three main components: customer orientation, competitiveness, and cross-sectoral coordination. In short, market orientation states that customer orientation helps companies to understand the needs and wants of their customers and take basic steps to meet them. Competitiveness will enable companies to create more value for customers than competitors and thus achieve a sustainable competitive advantage.  The role of market intelligence is in collecting, analyzing and disseminating this information (Narver & Slater, 1990). MethodologyIn this study mixed method approach has been adopted. First, in order to achieve the research objectives and identify the indicators of market intelligence in the supply chain of perishable products (fruits and vegetables), the seven-step approach of Sandelowski and  Barroso’s (2003) meta-synthesis method was used. The statistical population covers the research published in 3 databases of ProQuest, Science Direct and Google Scholar during the period time of 2010-2021 for keywords of market intelligence and supply market intelligence. For other keywords, different period time was applied. In the second part, to obtain additional indicators, semi-structured interviews were conducted by an exploratory approach. In this regard, interviews were conducted with experts in the field of retail of fast-moving and perishable products, service providers of fruits in Iran’s e-commerce environment. ResultsIn order to achieve the most relevant research to enter the meta-synthesis process, criteria for inclusion and exclusion of research were considered.. A total of 1654 studies were reviewed, of which 276 studies had related topics, and with elimination of duplicated studies, There were 202 researches available, of which 113 had abstracts, 48 ​​had content and 31 had appropriate quality and analysis method. In order to combine the findings of the research, the approach of Sandelowski and Barroso has been followed, in the sense that after careful study of studies and articles, codes have been identified from their texts and the researcher has formed a classification based on it and Similar classifications were placed on the topic that best described it. The sample of Codes, concepts and category identified in meta-synthesis phase is illustrated in table 1.Table 1. An example of coding in meta-synthesis processCodesConceptCategoryCustomer Demographic InformationCustomer InsightCustomer & Market Insight Customer personalizationCustomer interests and NeedsFocus group sessions with customersCustomer EngagementCall Center interaction with customerCustomers surveysThe coding process at the meta-synthesis stage led to the identification of 5 categories (supply chain intelligence, market and customer insight, business intelligence, social business intelligence and competitive intelligence), 23 concepts and 5 categories.In the second phase of the research, the new items identified in the theme analysis of semi-structured interviews with experts which included Order, Co-Branding, Customer Club, and Financial Issues. By comparing and combining the dimensions and components obtained in the two qualitative stages of the research, the market intelligence model for perishable products in the field of online retail was presented in the form of the model presented in Figure 1. Figure 1. Supply market intelligence (research model) In order to validate the model, the conditions for establishing reliability and validity (convergent and divergent validity) and fit indices must be met according to Table 2.  Table 2. Conditions for establishing Reliability & ValidityindicatorsAllowable ValidityReliabilityComposite Reliability > 0.7 and Cronbach's alpha>0.6Convergent validityLoading Factor >0.5CR>AVEAVE>0/5Rho_A>0/6Discriminate validityAVE>MSVFit Indices‌GOF>0/36SRMR0/9Descriptive statistics and central indicators such as mean, standard deviation, skewness and kurtosis for each of the components and dimensions and indicators are reported in the above table 3.Table 3. Sample of Descriptive indicators and first-order confirmatory factor analysis The reliability index was evaluated by measuring the factor loads and the reliability of the latent variables was evaluated by the compositional reliability . Cronbach's alpha results, compositional  reliability and are shown in Table 4.Table 4. Sample of Cronbach's alpha results, composite reliability and convergent validityDimensionComponentsCronbach’s AlphaCA>0/6rho_A>06Composite ReliabilityCR>0/7Average variance extractedAVE>0/5Supply chain intelligenceSuppliers club & insight0/6920/7150/8650/762Service Provider Portal0/9250/9260/9380/656Competitive intelligenceResponse to Competition0/8440/8480/8950/682Tactical competition0/8910/8940/9330/822Customer & Market InsightCustomer Engagement0/9000/9000/9380/834Social Business IntelligenceCompetitive insight in social network0/7160/7160/8760/779Social Customer Interaction0/8450/8450/9280/866According to Table4, the Cronbach's alpha value for all variables is greater than the appropriate limit of 0.6 . Also the value of the compositional reliability coefficient for each variable is more than the desired limit of 0.7. In this model, the convergent validity of the model variables is all higher than 0.5, all of which are at an appropriate level.     ConclusionIn this study, the aim was to develop a market intelligence model in the supply chain of perishable products in the field of online retailing. Handfield (Handfield, 2006), introduced the supply market intelligence concept and considered business intelligence and market intelligence as the information drivers of  supply chain processes. According to the meta-synthesis of literature and analysis of semi-structured interviews with 14 experts, the components of each of the proposed dimensions were identified and social business intelligence and supply chain intelligence were identified as new dimensions of supply market intelligence model. In fact, a complete and optimal supply chain should include those activities that the customers value ​​and are willing to pay for the resulting services or products. Therefore, understanding customer behavior is very important. What is very important in the supply chain is that supply is aligned with demand across the supply chain, so a better understanding of suppliers and end customers is the best way to reduce costs in the supply chain., As a summary, the identified dimensions and the importance and role of each in the supply market intelligence model is discussed. - Supply chain intelligence. In this dimension, the components related to the to the links that make up the chain (logistics, sourcing, service provider gateway ...) should be considered to ensure that these links work efficiently. In e-commerce, logistics and service provider portals (such as website or mobile App) are very important because they are the connection point with customers and if the delivery is not done properly, especially for perishable products, in addition to customer dissatisfaction will cause product waste. Also, the service provider portals should have appropriate features such as speed, graphics, user friendliness, user experience, security, providing complementary services, ease of payment and other important features to make users and customers will revisit the website.- Market and customer insights. In this dimension, 4p components and customers are defined. It is crucial to identify market trends as well as the position that the business has with its customers. In fact, depending on the type of product and service that customers are willing to pay for, supply chain processes can be restructured. - Competitive intelligence. The way competitors market their products and services and the scanning of the external business environment are crucial in shaping the business supply chain. According to the resource-based view theory, a service should be defined in the supply chain that cannot be easily copied or provided by competitors and brings a competitive advantage to the firm, and this requires knowledge of the technologies adopted by competitors and the type of service and price offered by them.- Business intelligence. One of the important dimensions of the supply market intelligence model is business intelligence. In fact, the revenue model, sales volume, statistics and financial information and value that the retailer has created for itself, and the and the evaluation of incentives provided in the form of discount plans, provide insight to managers to focus on those products and services in the supply chain that they bring better and more to the business, and according to these factors, the company's revenue model can be defined.- Social business intelligence. Social networks have had a significant impact in the last decade. Social customers are able to share information with countless members of these networks, so analyzing social customer relationships and current trends in these networks and analyzing the performance of competitors in these networks is very important. In fact, these networks have created a new potential market for businesses and require their own sourcing and marketing.Based on what was covered in this study, it can be concluded that those businesses that operate in the field of online retailing, always need to find themselves in the path of information flow, which is an attempt to reduce uncertainty

A Review of Uncertain Decision-Making Methods in Energy Management Using Text Mining and Data Analytics

The managerial and environmental studies conducted in the energy research area reflect its substantial importance, particularly when optimizing and modifying consumption patterns, transitioning to renewable sources away from fossil ones, and designing plans and systems. The aim of this study is to provide a systematic review of the literature allowing us to identify which research subjects have been prioritized in the fields of energy and sustainability in recent years, determine the potential reasons explaining these trends, and categorize the techniques applied to analyze the uncertainty faced by decision-makers. We review articles published in highly ranked journals through the period 2003–2020 and apply text analytics to cluster their main characteristics; that is, we rely on pre-processing and text mining techniques. We analyze the title, abstract, keywords, and research methodology of the articles through clustering and topic modeling and illustrate what methods and fields constitute the main focus of researchers. We demonstrate the substantial importance of fuzzy-related methods and decision-making techniques such as the Analytical Hierarchy Process and Technique for Order Preferences by Similarity to Ideal Solutions (TOPSIS). We also show that subjects such as renewable energy, energy planning, sustainable energy, energy policy, and wind energy have gained relevance among researchers in recent years

Development of fuzzy Artificial Intelligence and Multi-Objective planning Model to Optimize the Portfolio of Investment Companies

Proper management and optimal allocation of financial resources will increase gross national product and growth, create jobs and increase public welfare. The purpose of this study is to present an investment strategy that has tried to pave the way for the development of the investing company in the financial markets. Therefore, the forthcoming research can be considered as applied in terms of purpose. Also, considering that in the present research, mathematical modeling, modeling, artificial intelligence, etc. are used and the optimization of the investor company's portfolio is evaluated with the proposed model, so it is a quantitative and descriptive research. This study evaluated the performance of the proposed model in three modes: prudent, moderate and risky investor company. The results showed that for all three cases, the proposed strategy performs significantly better than the market index and other previous strategies. At the end of the investment period, the risky portfolio was more valuable than other portfolios. On the other hand, a prudent portfolio has achieved a more stable and stable return. These results revealed that the proposed fuzzy programming is able to reflect the characteristics and desires of the investor company in the portfolio composition

Designing a Predictive Analytics for the Formulation of Intelligent Decision Making Policies for VIP Customers Investing in the Bank

Special, privileged or VIP customers are of great significance to the banks since they continuously and broadly invest in deposits and remain loyal to the banks. This loyalty is dependent on the broad and specific services they receive, deposit interests, and the tuned regulatory actions that banks take for according to the grade of special customers and their propensity to risk. In the current research, a dataset of two thousand ordinary and special privileged customers were collected according to their demographics, accounts information, and level of investment in the bank. The grade of special customer and their propensity to taking risks are also determined by the experts of the bank. Afterwards, a range of learning algorithms are applied for designing and validating classification and prediction methods on special customers’ grades and their propensity to risk. Final results are then analyzed and prepared as a set of intelligent and improvable rules that assist the bank managers in formulating interactive and predictive decision making policies from the initiation of the customer relationship with the bank

A Model for Learners Segmentation and Educational Performance Improvement Using Data Mining Algorithms

Educational performance measurement through the identification and analysis of data extracted from learners’ activities can effectively result in the improvement of educational performance. In this Article, data of international learners was analyzed based on design science methodology and using data mining methods. In this regard, domestic and international research has been reviewed over the past decade and the academic and non-academic data of students were clustered into three categories: family, supportive, and academic behavior. After the validation of algorithms outputs and determining the number of optimal clusters in each category, clusters were labeled and analyzed. Analysis of labels presents the experience of success or failure of students and roots of effective performance in each cluster, and the labeling method proposed is a new and applicable method in most of the learning centers for segmenting and formulating the educational performance

A Comprehensive Evaluation of the Impact of Most Applied Services on News Websites Quality

Websites have been considered as one of the most important means of communication among different stakeholders. Regarding this fact, the news websites have been taken into account as one of the most important types of websites since they provide the users with significant information providing capabilities and they also play a major role in creating and disseminating knowledge in the society. The design and utilization of such websites have been considered a significant challenge in the modern business environment. Making use of various services in building the news websites has a great impact in improving the quality and value of such websites and consequently, it causes a meaningful increase in the rate of website visits and returns. The main goal of this paper is to provide an appropriate solution for designing and analyzing the news websites with the use of the best combination of the effective, qualitative, and value-generating services. For this purpose and for increasing the quality of news websites, the most important services have been extracted and categorized. This categorization is based on the broad and deep investigation of the most utilized services of the top 50 news websites in the world. Consequently, the services and their role in creating added value have been evaluated and validated by the experts in the field. The presented model can be used by the news websites managers and administrators for designing more effective and efficient websites in order to better satisfy the demands of customers