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Phosphonic acids are good complexing agents. However, they are not good as inhibitors except for a very few. Synergistic inhibition is offered in the presence of metal cations like Ca²⁺, Mg²⁺, Zn²⁺ and others in neutral media. The zinc ion is an ideal choice. The part of zinc ions are now replaced by polymers, azoles to prepare eco‐friendly inhibitor formulations. They are also used as corrosion inhibitors in concrete, coatings, rubber blends, acid cleaners, anti‐freeze coolants, etc. Discusses the various applications of phosphonic acids and their action mechanisms.

This paper aims to improve some properties of poly (methyl methacrylate) by copolymerization with butyl acrylate (BA) monomer along with the incorporation of the stable and economical synthesized silicone-containing monomer hexamethyldisilazanomethacryloxyphenyl ketone (HDMK) into the copolymer matrix.

For this target solution copolymerization of methyl methacrylate (MMA), BA and HDMK were carried out using a 250 mL four-necked round-bottom flask. Before solution polymerization start-up, the reaction vessel was first charged with 34.8 mL toluene and heated to 170 °C with stirring and reflux cooling. A monomer mixture of 25.86 g (260 mmol) MMA, 26.40 g (200 mmol) BA, 3.00 g (8.60 mmol) HDMK and 0.45 g (2.00 mmol) dibenzoyl peroxide was added continuously from the dropping funnel over a period of 4 h.

The HDMK was successfully synthesized and the water resistance of acrylic resins was improved because of the existence of HDMK.

The materials that were used in this research paper had a reasonably low cost. Also, the procedures for synthesis of monomers and polymers were extremely easy because there was no need for high pressure or temperature and no dangerous solvents were used.

The acrylic resin that contained HDMK was used to synthesis a white architectural paint for exterior coating. Examining the paint characteristics has shown acceptable washing and abrasion resistance, good brushing, excellent storage stability and great surface coating.

HDMK was synthesized for the first time.

The purpose of this paper is to assess the current state of performance measurement (PM) in academic literature and in the automotive supply chain (SC). The research question is to what extent PM systems (PMS) in academic literature and in industry are applicable in automotive logistics.

The authors use a combination of a systematic literature review (SLR) and an industry survey. The SLR is used to identify literature on design aspects for PMS and current PMS reported in academic literature. The survey adds an industry perspective and is used to validate the design aspects found in literature. The synthesis aims to deliver both, current research gaps and needs in industry.

The findings from SLR indicate, there is a broad base of literature regarding design aspects for PMS. Literature on PMS in context of automotive logistics is nevertheless rare. The industry survey leads to the conclusion that conformity with design aspects from literature increases added value of PMS. Nevertheless, current PMS show improvement needs in automotive context. Academic literature lacks detailed process orientation, transparency and is seen as too high level to be helpful for practitioners. For systems in industry, the SC perspective can be improved. In addition, applicability for continuous improvement is identified as research need in industry and literature.

Due to the selected survey approach, additional research is required to obtain more generalizable results. To increase reliability and validity, a selection of the findings should be further analyzed, e.g. by focusing on regions outside Western Europe or a more in depth analysis of selected aspects. In addition insights into selected research topics highlighted in the paper should be assessed in detail by applying different methodologies (e.g. case studies) to validate the findings.

The findings add to the research on PMS. Latest academic literature is reviewed. The findings are combined with an industry perspective. The combination of SLR and a broad survey enables us to draw conclusions which are relevant for researchers and practitioners alike. Where academic research should focus more on the practical applicability, industry can benefit from the findings by integrating a more holistic perspective into their PMS, especially with a SC wide evaluation.

The purpose of this paper is to algorithmically and objectively investigate the previous literature on supply chain resilience (SCR) and advance theory by synthesizing new research domains.

A two-staged analysis approach, integrating systematic literature review (SLR) with VOSviewer co-occurrence analysis, was applied to the articles published between 2003 and 2018.

The authors find exponential growth in the literature on SCR over the last decade; however, there is still a gap for empirical research on numerous drivers, barriers, theories, moderators, mediators and research methods intertwined in building SCR.

The review identifies major clusters in which SCR research is conducted and devises a future research agenda based on the findings of co-occurrence analysis.

The findings provide managers with a broad spectrum of factors that are indispensable to build resilience and inform business policy.

While some SLRs exist in the current literature of SCR, the authors undertake a unique analytical perspective, resulting in an idiosyncratic set of research domains for further investigation in the area.

The purpose of this paper is threefold: first, to investigate the role of big data and predictive analytics (BDPA) and social capital on the performance of humanitarian supply chains (HSCs); second, to explore the different performance measurement frameworks and develop a conceptual model for an HSC context that can be used by humanitarian organizations; and third, to provide insights for future research direction.

After a detailed review of relevant literature, grounded in resource-based view and social capital theory, the paper proposes a conceptual model that depicts the influence of BDPA and social capital on the performance of an HSC.

The study deliberates that BDPA as a capability improves the effectiveness of humanitarian missions to achieve its goals. It uncovers the fact that social capital binds people, organization or a country to form a network and has a critical role in the form of monetary or non-monetary support in disaster management. Further, it argues that social capital combined with BDPA capability can result in a better HSC performance.

The proposed model integrating BDPA and social capital for HSC performance is conceptual and it needs to be empirically validated.

Organizations and practitioners may use this framework by mobilizing social capital, BDPA to enhance their abilities to help victims of calamities.

Findings from study can help improve coordination among different stakeholders in HSC, effectiveness of humanitarian operations, which means lives saved and faster reconstruction process after disaster. Second, by implementing performance measurements framework recommended by study, donors and other stakeholders will get much desired transparency at each stage of HSCs.

The findings contribute to the missing link of social capital and BDPA to the existing performance of HSC literature, finally leading to a better HSC performance.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

This study aims to show that management with big data analytics capability can achieve more advantages of the global sourcing process. Furthermore, this study using its conceptual attitude model aims to show that big data analytics management capability leads to an increase in firm performance by the mediating role of integration.

Using an online questionnaire, 158 managers from 13 Iranian companies taking advantage of the global sourcing process were surveyed. The validity of the hypotheses was evaluated using partial least squares based on structural equation modeling (PLS method).

The results of the study showed that big data analytics management capability has a positive impact on global sourcing and firm performance directly, and by the mediating role of integration.

Previous studies have carefully addressed the role of big data and big data analytics in firms. However, this is among a few studies addressing the role of big data analytics capability, especially management capability, in improving firms’ performance. The results of this study shed light on the fact that how global sourcing takes the best advantage of big data analytics management capability for better accomplishment of organizations’ duties. The results of this study also disclose how big data analytics management capability helps organizations with their performance and bring benefits to their units.

The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage.

The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire.

The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage.

The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

This paper aims to go some way towards addressing the dearth of research into performance measurement systems and metrics of supply chains by critically reviewing the contemporary literature and suggesting possible avenues for future research.

The article provides a taxonomy of performance measures followed by a critical evaluation of measurement systems designed to evaluate the performance of supply chains.

The paper argues that despite considerable advances in the literature in recent years, a number of important problems have not yet received adequate attention, including: the factors influencing the successful implementation of performance measurement systems for supply chains; the forces shaping their evolution over time; and, the problem of their ongoing maintenance.

The paper provides a taxonomy of measures and outlines specific implications for future research.

The purpose of this paper is to recognise the current state of big data analytics (BDA) on different organisational and supply chain management (SCM) levels in Brazilian firms. Specifically, the paper focuses on understanding BDA awareness in Brazilian firms and proposes a framework to analyse firms’ maturity in implementing BDA projects in logistics/SCM.

A survey on SCM levels of 1,000 firms was conducted via questionnaires. Of the 272 questionnaires received, 155 were considered valid, representing a 15.5 per cent response rate.

The knowledge of Brazilian firms regarding BDA, the difficulties and barriers to BDA project adoption, and the relationship between supply chain levels and BDA knowledge were identified. A framework was proposed for the adoption of BDA projects in SCM.

This study does not offer external validity due to restrictions for the generalisation of the results even in the Brazilian context, which stems from the conducted sampling. Future studies should improve the comprehension in this research field and focus on the impact of big data on supply chains or networks in emerging world regions, such as Latin America.

This paper provides insights for practitioners to develop activities involving big data and SCM, and proposes functional and consistent guidance through the BDA-SCM triangle framework as an additional tool in the implementation of BDA projects in the SCM context.

This study is the first to analyse BDA on different organisational and SCM levels in emerging countries, offering instrumentalisation for BDA-SCM projects.