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The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.

The emission of greenhouse gases has become an increasingly relevant topic in supply chain management. The steel industry is a highly intensive manufacturing industry with significant emission levels, particularly Scope 3 emissions, which are the indirect emissions from suppliers. Since a supply chain is seen as a non-mandatory measurement item within GHG measurement protocols, this article contributes to the literature on assessing the suppliers of a focal company relative to their emissions for complying with Scope 3 (indirect emissions). It adds to the evolving literature on low-carbon supply chains.

This study first conducted a survey with 110 suppliers from a focal transnational buyer company. A cluster analysis was performed, and ANOVA compared constructs relating to public or private ownership and country of origin. Finally, regression tested the relationship between the motivators and governance in the mitigation strategies.

Using cluster analysis, two groups of companies were found that have statistically significant differences. The influence of the country of origin was also found in relation to governance and mitigation strategies, as was the influence of the type of company on governance. Furthermore, the more motivated the suppliers and the more governance measures they adopt, the more companies adopt their own GHG mitigation strategies. These findings are summarized by way of an analytical framework that integrates the constructs with empirical evidence.

The steel industry is a sector that is particularly energy-intensive and produces millions of tons of CO₂ per year. Emissions from its SC (Scope 3) are relevant but still seen as a non-mandatory item for measurement purposes within the GHG measurement protocols, which leads to less attention being paid to the subject. This study contributes by way of its analytical framework that is validated by empirical data that can be tested in further studies.

The case was developed from secondary sources and interviews with a security analyst. The secondary sources include company annual reports, news reports, analyst reports, industry reports, company websites, stock exchange websites and databases such as Bloomberg and CMIE Prowess.

Increasing competition in product and capital markets has put tremendous pressure on managers to become more cost competitive. To address their firms' uncompetitive cost structures, managers may have to consider dramatic restructuring of their businesses. During 2014–2017, Tata Steel Ltd (TSL) UK considered a series of divestitures and a merger plan to nurse the company back to health. The case considers the economics of the restructuring plan. The case is designed to help students analyze a corporate downsizing program undertaken by a large Indian company in the UK and to highlight the dynamic role of the CFO and governance issues in family firms. It introduces students to issues surrounding a typical restructuring and provides students a platform to practice the estimation of value creation in a restructuring exercise. While some cases on corporate restructuring in the context of developed economies are available, there are very few cases written in an emerging market context. This case bridges that gap. TSL presents a unique opportunity to study corporate restructuring necessitated by a failed cross-border acquisition. It illustrates the potential for value loss in large, cross-border acquisitions. It shows how managerial hubris can prompt family firm owners to overbid in acquisitions and create legacy hot spots. In addition, the case can be used to discuss the causes of governance failures such as weak institutional monitoring and poor legal enforcement in emerging markets that could potentially harm minority shareholders.

The case was developed from secondary sources and interviews with a security analyst. The secondary sources include company annual reports, news reports, analyst reports, industry reports, company websites, stock exchange websites and databases such as Bloomberg and CMIE Prowess.

The purpose of the article is to identify relevant criteria for decision support in the implementation of waste-to-energy (WtE)-based systems.

The methodology is a simple case study with a qualitative approach. Five experts involved in the project of a thermoelectric power plant qualitatively evaluated, on a Likert scale, a decision model with 15 indicators derived from recent studies. The research object was the first stage of a project to implement a thermoelectric plant employing municipal solid waste (MSW) in southern Brazil.

The study identified 15 criteria supporting the decision-making process regarding WtE implementation for MSW in a mid-sized city in southern Brazil. The study identified that compliance with MSW legislation, compliance with energy legislation, initial investment and public health impact are the most influential criteria. The study offered two models for decision processes: a simplified one and a complete one, with ten and fifteen indicators, respectively.

The study concerns mid-sized municipalities in southern Brazil.

Municipal public managers have now a methodology based on qualitative evaluation that admits multiple perspectives, such as technical, economic, environmental and social, to support decision-making processes on WtE technologies for MSW.

MSW management initiatives can yield jobs and revenues for vulnerable populations and provide a correct destination for MSW, mainly in developing countries.

The main originality is that now municipal public decision-makers have a structured model based on four constructs (technical, economic, environmental and social) deployed in 15 indicators to support decision-making processes involving WtE and MSW management.

Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.

The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.

The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.

This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

An experimental investigation for developing structure-property correlations of hot-rolled E410 steels with different carbon contents, i.e. 0.04wt.%C and 0.17wt.%C metal active gas (MAG) and cold metal transfer (CMT)-MAG weldments was undertaken.

Mechanical properties and microstructure of MAG and CMT-MAG weldments of two E410 steels with varying content of carbon were compared using standardized mechanical testing procedures, and conventional microscopy.

0.04wt.%C steel had strained ferritic and cementite sub-structures in blocky shape and large dislocation density, while 0.17wt.%C steel consisted of pearlite and polygonal ductile ferrite. This effected yield strength (YS), and microhardness being larger in 0.04wt.%C steel, %elongation being larger in 0.17wt.%C steel. Weldments of both E410 steels obtained with CMT-MAG performed better than MAG in terms of YS, ultimate tensile strength (UTS), %elongation, and toughness. It was due to low heat input of CMT-MAG that resulted in refinement of weld metal, and subzones of heat affected zone (HAZ).

A substantial improvement in YS (∼9%), %elongation (∼38%), and room temperature impact toughness (∼29%) of 0.04wt.%C E410 steel is achieved with CMT-MAG over MAG welding. Almost ∼10, ∼12.5, and ∼16% increment in YS, %elongation, and toughness of 0.17wt.%C E410 steel is observed with CMT-MAG. Relatively low heat input of CMT-MAG leads to development of fine Widmanstätten and acicular ferrite in weld metal and microstructural refinement in HAZ subzones with nearly similar characteristics of base metal.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.