Sustainable Competitive Advantage Driven by Big Data Analytics and Innovation

Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience

This article provides a theoretical framework for comprehending the connections between dynamic data analytics capability (DDAC), innovation capabilities (IC), supply chain resilience (RES), and sustainable supply chain performance (SSCP). Since this is the first empirical investigation of the sequential mediation effect between DDAC and SSCP through IC and RES, it fills a critical need in the supply chain literature. A quantitative methodology was used, involving a survey questionnaire distributed to 259 large Pakistani manufacturing firms. We used PLS-SEM to test for the expected associations. Findings show that using DDAC has a beneficial effect on both innovative and resilient capabilities, which in turn leads to better SSCP. The research illuminates the sequential mediating roles of product, process, and resilience, underlining the need of combining data-driven innovation with resilience in order to achieve sustainable supply chain performance. These results provide useful guidance for businesses that want to boost their sustainability results by taking a more all-encompassing approach to data-driven innovation and resilience.

IT Capabilities, Strategic Flexibility and Organizational Resilience in SMEs Post-COVID-19: A Mediating and Moderating Role of Big Data Analytics Capabilities

This research provides a novel progression to the existing research about big data analytics capabilities (BDAC) by investigating and measuring its influence on organizational resilience and strategic flexibility. Toward that end, 400 different SMEs in Saudi Arabia were approached. Data were collected via questionnaire. Results confirm that the ability to handle big data analytics totally mediates the relationship between IT capabilities and strategic flexibility. Big data infrastructure flexibility has a negative effect on strategic flexibility. Big data personal expertise not only negatively affects the relationship between IT capabilities and strategic flexibility but also stimulates and reinforces the relationship between strategic flexibility and organizational resilience. The critical pathway developed and tested the trend to make the organization as an immune system able to make the best of the worst. This implies the urgent need for policymakers and managers to adopt and comprehend the concept of BDAC instead of IT capabilities to define oriented plans specifically formulated for stimulating strategic flexibility and organizational resilience. By adopting the proposed model, SMEs can interact more effectively internally and externally.

Digital transformation, smart technologies, and eco-innovation are paving the way toward sustainable supply chain performance

The world has limited resources and resource depletion has been a serious concern for many years. To ensure that ecological balance is maintained, the United Nations has developed 17 Sustainable Development Goals (SDGs). This study attempts to meet the 12th SDG of "responsible production and consumption" and provide a guideline to manufacturing organizations in Pakistan. Many scholars have examined the role of different technologies on sustainable performance. However, research using eco-innovation (EI), digital transformation (DT), and smart technologies (ST) is still scarce. The current study develops the conceptual model based on the resource-based view (RBV) and technology, organization, and environment (TOE) theories, and using a simple random sampling technique, valid data from 375 professionals in the manufacturing industry is collected to test the relationship between sustainable development strategy (SDS), EI, DT, ST, and sustainable supply chain performance (SSCP). The results indicate strong positive relationships between SDS, EI, DT, ST, and SSCP. Results indicate that even if organizations are executing SDS and EI, without DT and ST, organizations cannot achieve SSCP. Therefore, it is recommended to the managers that they must apply DT and ST to guarantee their pursuit of achieving sustainable performance and contribution toward SDGs.

Big Data Capability and Sustainable Competitive Advantage: The Mediating Role of Ambidextrous Innovation Strategy

After the rapid expansion of data variety, velocity and volume, human civilization experienced rapid changes in the period between the “IT” and “Data” ages. Researchers view big data and data capability as new sustainable competitive advantages that enhance the sustainability of organizational development. This paper aims to develop and empirically test a framework that will investigate how big data capability is achieved through exploitative, explorative and ambidextrous modes of innovation strategies, and will also explore how they can, in turn, build firms’ sustainable competitive advantage. Using data from surveys of 229 respondents working in Chinese manufacturing firms, we test the framework using regression and bootstrapped mediation analyses. It also shows how big data capability will make firms more inclined to implement exploitative innovation strategy and construct sustainable competitive advantage, as opposed to explorative innovation strategy; when viewed from an ambidextrous perspective, combined dimension of ambidextrous innovation strategy is found to partially mediate between big data capability and sustainable competitive advantage while balanced dimension of ambidextrous innovation strategy does not. The conclusions are of great significance because they will help firms to deal with challenges that arise in big data applications and digital transformation. The findings offer new insight into the strategic choices of organizational innovations.

Big Data and AI for Process Innovation in the Industry 4.0 Era

The fourth industrial revolution or what can be referred to as Industry 4 [...]

Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector

The study explores the crucial big data analytics capabilities (BDAC) for healthcare in Bangladesh. After a rigorous and extensive literature review, we list a wide range of BDAC and empirically examine their applicability in Bangladesh’s healthcare sector by consulting 51 experts with ample domain knowledge. The study adopted the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Findings highlighted 11 key BDAC, such as using advanced analytical techniques that could be critical in managing big data in the healthcare sector. The paper ends with a summary and puts forward suggestions for future studies.

Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis

The Industry 4.0 (I4.0) concept paves the way for the circular economy (CE) as advanced digital technologies enable sustainability initiatives. Hence, I4.0-driven CE-oriented supply chains (SCs) have improved sustainable performance, flexibility and interoperability. In order to smoothly embrace circular practices in digitally enabled SCs, quantitative techniques have been identified as crucial. Therefore, the intersection of I4.0, CE, supply chain management (SCM) and quantitative techniques is an emerging research arena worthy of investigation. This article presents a bibliometric analysis to identify the established and evolving research clusters in the topological analysis by identifying collaboration patterns, interrelations and the studies that significantly dominate the intersection of the analysed fields. Further, this study investigates the current research trends and presents potential directions for future research. The bibliometric analysis highlights that additive manufacturing (AM), big data analytics (BDA) and the Internet of Things (IoT) are the most researched technologies within the intersection of CE and sustainable SCM. Evaluation of intellectual, conceptual and social structures revealed that I4.0-driven sustainable operations and manufacturing are emerging research fields. This study provides research directions to guide scholars in the further investigation of these four identified fields while exploring the potential quantitative methods and techniques that can be applied in I4.0-enabled SCs in the CE context.

The Impact of Integration of Industry 4.0 and Internal Organizational Forces on Sustaining Competitive Advantages and Achieving Strategic Objectives

Adopting and implementing the Industry 4.0 strategy to increase the overall performance of the organization became one of the main aims of organizations. However, ignoring the linkages between implementing strategic decisions and organizational internal factors/forces can endanger and shrink its performance, competitive advantages, and thus its strategic success. In this context, many companies failed to achieve the expected benefits of adopting the Industry 4.0 strategy. Therefore, the gained advantages of adopting the Industry 4.0 strategy should be sustained through perfect and comprehensive integration between Industry 4.0 concepts and the accompanying upgrades and changes in the organizational internal factors/forces. This will capitalize on organizations’ internal strengths and avoid weaknesses or turn them into strengths. In this paper, a conceptual model is proposed to investigate the relation between Industry 4.0 and internal organizational forces and examine their impacts on the sustainable competitive advantages of the organization. In the hypothesized model, three innovation capabilities (i.e., technological, economic, and commercial innovation) have been used to mediate the relation between the internal forces and the sustainable competitive advantages in parallel with Industry 4.0 adoption. The model and the proposed hypotheses have been simulated and tested using partial least squares structural equations modeling software called SmartPLS. The sample size used is 125 responses from different manufacturing fields. The results demonstrate the significant role that the internal organizational forces play in maintaining and sustaining the organization’s competitive advantages in combination with Industry 4.0.

A Comparative Analysis of Selected Predictive Algorithms in Control of Machine Processes

The paper presents a comparative analysis of selected algorithms for prediction and data analysis. The research was based on data taken from a computerized numerical control (CNC) milling machine. Methods of knowledge extraction from very large datasets, characteristics of classical analytical methods used in datasets and knowledge discovery in database (KDD) processes were also described. The aim of the study is a comparative analysis of selected algorithms for prediction and data analysis to determine the time and degree of tool usage in order to react early enough and avoid unwanted incidents affecting production effectiveness. The research was based on K-nearest neighbor, decision tree and linear regression algorithms. The influence of the rate of learning and testing set sizes were evaluated, which may have an important impact on the optimization of the time and quality of computation. It was shown that precision decreases with the increase of the K value of the average group, while the percentage of the number of classes in a given set (recall) increases. The harmonic mean for the group mean also increases with increasing K, while a significant decrease in these values was observed for the standard deviations of the group. The numerical value of accuracy decreases with increasing K.

Using Big Data for Sustainability in Supply Chain Management

In the literature, several frameworks have been proposed to help sustainability management in supply chains. Nevertheless, they present a number of shortcomings. With the aim of overcoming these shortcomings, this paper proposes a framework for sustainable supply chain management composed of six dimensions: methodology, organization, stakeholders, maturity model, human resources, and technology. The main innovations of the framework are that (1) it includes a methodology that acts as a guide to sustainability management and improvement in a holistic way by using a balanced scorecard for any type of supply chain and covering the whole project life cycle; (2) it combines quantitative and qualitative methods for sustainability assessment; (3) it describes the techniques and technology to be used in each task of the methodology; and (4) it identifies the past impact of SC sustainability, as well as predicting its future impact, using Big Data analytics. The practical utility, completeness, and level of detail of the framework were validated through questionnaires answered by both five academics and three professionals. In addition, the framework was applied to a case study to (1) validate its usefulness and (2) to improve it with the feedback obtained.