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The purpose of this technical paper is to explore the application of analytics and Six Sigma in the manufacturing processes for iron foundries. This study aims to establish a causal relationship between chemical composition and the quality of the iron casting to achieve the global benchmark quality level.

The case study-based exploratory research design is used in this study. The problem discovery is done through the literature survey and Delphi method-based expert opinions. The prediction model is built and deployed in 11 cases to validate the research hypothesis. The analytics helps in achieving the statistically significant business goals. The design includes Six Sigma DMAIC (Define – Measure – Analyze – Improve and Control) approach, benchmarking, historical data analysis, literature survey and experiments for the data collection. The data analysis is done through stratification and process capability analysis. The logistic regression-based analytics helps in prediction model building and simulations.

The application of prediction model helped in quick root cause analysis and reduction of rejection by over 99 per cent saving over INR6.6m per year. This has also enhanced the reliability of the production line and supply chain with on-time delivery of 99.78 per cent, which earlier was 80 per cent. The analytics with Six Sigma DMAIC approach can quickly and easily be applied in manufacturing domain as well.

The limitation of the present analytics model is that it provides the point estimates. The model can further be enhanced incorporating range estimates through Monte Carlo simulation.

The increasing use of prediction model in the near future is likely to enhance predictability and efficiencies of the various manufacturing process with sensors and Internet of Things.

The researchers have used design of experiments, artificial neural network and the technical simulations to optimise either chemical composition or mould properties or melt shop parameters. However, this work is based on comprehensive historical data-based analytics. It considers multiple human and temporal factors, sand and mould properties and melt shop parameters along with their relative weight, which is unique. The prediction model is useful to the practitioners for parameter simulation and quality enhancements. The researchers can use similar analytics models with structured Six Sigma DMAIC approach in other manufacturing processes for the simulation and optimisations.

The objective of this paper is to investigate how firms react to cybersecurity information sharing environment where government organizations disseminate cybersecurity threat information gathered by individual firms to the private entities. The overall impact of information sharing on firms' cybersecurity investment decision has only been game-theoretically explored, not giving practical implication. The authors therefore leverage the Cybersecurity Information Sharing Act of 2015 (CISA) to observe firms' attitudinal changes toward investing in cybersecurity.

The authors design a quasi-experiment where they set US cybersecurity firms as an experimental group (a proxy for total investment in cybersecurity) and nonsecurity firms as a control group to measure the net effect of CISA on overall cybersecurity investment. To enhance the robustness of the authors’ difference-in-difference estimation, the authors employed propensity score matched sample test and reduced sample test as well.

For the full sample, the authors’ empirical findings suggest that US security firms' overall performance (i.e. Tobin's Q) improved following the legislation, which indicates that more investment in cybersecurity was followed by the formation of information sharing environment. Interestingly, big cybersecurity firms are beneficiaries of the CISA when the full samples are divided into small and large group. Both Tobin's Q and sales growth rate increased for big firms after CISA.

The authors’ findings shed more light on the research stream of cybersecurity and information sharing, a research area only explored by game-theoretical approaches. Given that the US government has tried to enforce cybersecurity defensive measures by building cooperative architecture such as CISA 2015, the policy implication of this study is far-reaching.

The authors’ study contributes to the research on the economic benefits of sharing cybersecurity information by finding the missing link (i.e. empirical evidence) between “sharing” and “economic impact.” This paper confirms that CISA affects the cybersecurity industry unevenly by firm size, a previously unidentified relationship.