Several health services have used lean healthcare to seek continuous improvement of their processes. Therefore, it is important to investigate the evidence available in the literature about the most used lean tools in the health area to review processes and the main results achieved by the researchers. As an integrative literature review methodology was used, it was conducted in five databases, using the descriptor “quality improvement” and the keyword “Lean Healthcare”. A total of 33 complete articles were selected for analysis. The most recurrent tools were: define, measure, analyze, improve and control (DMAIC); value stream map (VSM); suppliers, inputs, process, outputs, customers analysis (SIPOC), Ishikawa Diagram and 5S. Through the analysis of waste, different interventions were implemented and the main results achieved were reduction in times (processing, waiting, cycle and total), costs, workload and increase in the number of calls. The findings enabled the identification of the main lean tools used in the health area to achieve better results. In particular, we highlight recent studies that have explored the lean six sigma healthcare approach. The results, in addition to contributing to the literature, will also assist managers in choosing the best tool to achieve continuous improvement in hospitals and other health services.

This research aims to understand how sustainable business model innovation (SBMI) can learn from business model innovation. For this, first, we conducted a bibliometric analysis to evaluate the relationships between business model innovation and SBMI literature. After this, we performed a systematic literature review to create a comprehensive framework for managing SBMI. The bibliometric analysis showed that the SBMI stream grew quickly and significantly in recent years, evolving into a separated new research stream, which does not leverage recent business model innovation advancements. Through the performed analyses, we were able to discuss critical gaps in the SBMI literature and shed light on possible pathways to solve these gaps through lessons learned from business model innovation. We depicted five critical gaps for managing SBMI; (1) the need to understand the sustainable business model as a wicked problem, in which SBMI leads to “better than before” solutions calling for systematic SBMI, (2) the poor definition of distinctive dimensions of dynamic capabilities for SBMI, (3) the lack of studies exploring the role of open innovation for improving the SBMI process, (4) the lack of tools supporting SBMI implementation and (5) the need to explore game-changing, competitive advantages of SBMI. The findings of this study contribute to guiding future research on SBMI, which can be a basis for further efforts towards sustainable development.

Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company’s business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.

The digital era is reshaping the competitive landscape, creating a more turbulent environment where digital technologies play a significant role in enabling innovative business models. Companies need to promote business model innovations, readapting their business models, and create new digital business models to thrive in this scenario. The literature emphasizes that dynamic capabilities are the main antecedent to business model innovation. However, the dynamic capabilities construct is poorly operationalized, lacking proper measurements that effectively translate them to practice, remaining a black box. This paper aims to further understand, operationalize and measure the distinctive dimensions of dynamic capabilities for business model innovation. To this end, we follow the design science research methodology, building a tool for dynamic capabilities evaluation through a systematic literature review. We then evaluate the tool based on a three-year, in-depth case study of a software company. Our findings show that the current business model has a central role in shaping dynamic capabilities for business model innovation. The proposed measures encompass activities and practices and business model structure, highlighting the relevance of the co-evolution between business model and dynamic capabilities. Thus, we propose creating what we call a “business model innovation engine” as a function that reshapes the business model to incorporate dynamic capabilities as part of the value creation architecture. We contribute to theory by better translating dynamic capabilities for business model innovation to observable (and measurable) organizational phenomena, linking it to the extant strategic management literature, and elucidating how to measure and guide the build-up of such capabilities. We also add to practice by developing a practical tool for management to use as a means to evaluate their current dynamic capabilities state, therefore guiding for informed strategic action.

In this research, we address the problem of evaluating physical stability (PS) to close tailings dams (TD) from medium-sized Chilean mining using artificial intelligence (AI) algorithms. The PS can be analyzed through the study of critical variables of the TD that allow estimating different potential failure mechanisms (PFM): seismic liquefaction, slope instability, static liquefaction, overtopping, and piping, which may occur in this type of tailings storage facilities in a seismically active country such as Chile. Thus, this article proposes the use of four machine learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural networks (ANN), and extreme gradient boosting (XGBoost), to estimate five possible PFM. In addition, due to the scarcity of data to train the algorithms, the use of generative adversarial networks (GAN) is proposed to create synthetic data and increase the database used. Therefore, the novelty of this article consists in estimating the PFM for TD and generating synthetic data through the GAN. The results show that, when using the GAN, the result obtained by the ML models increases the F1-score metric by 30 percentage points, obtaining results of 97.4%, 96.3%, 96.7%, and 97.3% for RF, SVM, ANN, and XGBoost, respectively.