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Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation.

To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome.

The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment.

In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention.

A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.

The purpose of this paper is to explore how companies approach the management of cyber and information risks in their supply chain, what initiatives they adopt to this aim, and to what extent along the supply chain. In fact, the increasing level of connectivity is transforming supply chains, and it creates new opportunities but also new risks in the cyber space. Hence, cyber supply chain risk management (CSCRM) is emerging as a new management construct. The ultimate aim is to help organizations in understanding and improving the CSCRM process and cyber resilience in their supply chains.

This research relied on a qualitative approach based on a comparative case study analysis involving five large multinational companies with headquarters, or branches, in the UK.

Results highlight the importance for CSCRM to shift the viewpoint from the traditional focus on companies’ internal information technology (IT) infrastructure, able to “firewall themselves” only, to the whole supply chain with a cross-functional approach; initiatives for CSCRM are mainly adopted to “respond” and “recover” without a well-rounded approach to supply chain resilience for a long-term capacity to adapt to changes according to an evolutionary approach. Initiatives are adopted at a firm/dyadic level, and a network perspective is missing.

This paper extends the current theory on cyber and information risks in supply chains, as a combination of supply chain risk management and resilience, and information risk management. It provides an analysis and classification of cyber and information risks, sources of risks and initiatives to managing them according to a supply chain perspective, along with an investigation of their adoption across the supply chain. It also studies how the concept of resilience has been deployed in the CSCRM process by companies. By laying the first empirical foundations of the subject, this study stimulates further research on the challenges and drivers of initiatives and coordination mechanisms for CSCRM at a supply chain network level.

Results invite companies to break the “silos” of their activities in CSCRM, embracing the whole supply chain network for better resilience. The adoption of IT security initiatives should be combined with organisational ones and extended beyond the dyad. Where applicable, initiatives should be bi-directional to involve supply chain partners, remove the typical isolation in the CSCRM process and leverage the value of information. Decisions on investments in CSCRM should involve also supply chain managers according to a holistic approach.

A supply chain perspective in the existing scientific contributions is missing in the management of cyber and information risk. This is one of the first empirical studies dealing with this interdisciplinary subject, focusing on risks that are now very high in the companies’ agenda, but still overlooked. It contributes to theory on information risk because it addresses cyber and information risks in massively connected supply chains through a holistic approach that includes technology, people and processes at an extended level that goes beyond the dyad.

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

This paper aims to identify the unidimensionality and reliability of 84 factors that influence the performance of construction projects and develop a confirmatory factor analysis (CFA) model.

The study adopted a deductive research approach and started by identifying the positive factors that influence construction project performance. This was followed by the modification of the identified factors. After that, a questionnaire was developed out of the factors for data collection. Exploratory factor analysis was used to establish the factor structure of the positive factors, and this was verified using CFA afterwards. A model fit analysis was performed to determine the goodness of fit of the hypothesised model, followed by the development of the confirmatory model.

The study demonstrated substantial correlation in the data, sufficient unidimensionality and internal reliability. In addition, the estimated fit indices suggested that the postulated model adequately described the sample data.

The paper revealed that performance can be enhanced if stakeholders identify and leverage the positive factors influencing performance. The paper suggests that project stakeholders, particularly government, project owners, consultants and construction firms, can improve project performance by critically examining economic and financial systems (EFS), regulation and policy-making systems (RPS), effective management practices (EMP) and project implementation strategies (PIS).

The contribution of this paper to the present literature is identifying the positive factors and developing the confirmatory factor model. The model comprised 42 positive variables under four indicators: EMP, RPS, PIS and EFS.

Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers.

In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC.

A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors.

The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

Reputation risk onsets in focal firm whenever any entity of its supply chain (SC) faces risk-crisis event. A framework for modeling and predicting holistic SC reputation risk is proposed by integrating operational risk (OR) drivers originating from upstream and downstream partners and focal firm. A fuzzy cognitive map (FCM) is then developed to predict and quantify Pharmaceutical SC reputation risk.

Using event study methodology, SC reputation risk framework with 13 input OR drivers was developed. Based on pharmaceutical supply chain experts’ opinion, the correlation between reputation risk and its input drivers was estimated. The developed FCM tool was validated using nine real-life instances. A series of “what-if” scenario analyses were performed to demonstrate effectiveness of proactive and reactive mitigation strategies against reputation risk.

Quality and unethical governance risks significantly impacted reputation in Pharmaceutical SC and a firm should prefer “risk avoidance” against these risks. The upstream risks significantly affect reputation in a Pharmaceutical SC as compared to the downstream risks. Proactive mitigation strategies and assertive crisis communication are suggested for upstream risks while diminishment/ bolstering/rebuilding reactive crisis communication is recommended for downstream risks.

Reputation risk is often overlooked in SC literature. This work develops a model to quantify the reputation risk considering the indirect consequences of the ORs that originates at any point in a SC. The proposed FCM tool aids SC manager to focus on higher attribution risk events and devise an optimal combination of proactive and reactive mitigation strategies to avoid/minimize the economic loss due to reputation crisis.

A huge number of events can affect the operations of a long and complicated supply chain. This paper deals with the development of a supply chain risk mitigation index (SCRMI) based on a risk mitigation maturity framework. A comprehensive list of supply chain risks has been ascertained and segregated into risks faced at various supply chain echelons through a detailed literature review.

This paper is based on an extensive literature review and questionnaire to identify risks. order of magnitude analytic hierarchy process (OM-AHP) was used as the methodology to assess the prioritization of supply chain risks under two clusters, viz., Probability and severity leading to risk were tested in a distillery.

SCRMI was determined and used to categorize their maturity level in facing supply chain risks. Thus, organization can focus on improvements for their specific needs.

The model was tested in the distillery industry. It should be tested in other contexts with other methods to provide generalizability.

This research provides direction to managers for choosing risk mitigation strategies based on the global supply chain environment. SCRMI can be a performance metric for the supply chain managers.

The manufacturer's readiness to take action in the face of disruptions in the supply chain is a critical challenge in today's complex business environment and SCRMI framework is instrumental in such business environment.