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The purpose of this paper is to examine the mediating effect of learning organization dimensions on the relationship between the implementation of total quality management practices and companies’ operational performance improvement.

The authors carried out a cross-sector survey with 135 Brazilian manufacturing companies that have been implementing total quality management as an organizational strategy for at least five years. Collected data were analyzed using multivariate data analysis techniques.

The findings provide guidelines for manufacturers to increase their learning capability by reinforcing the implementation of total quality management practices, whose synergistic effects may be currently neglected. Results show that an enhanced organizational learning capability can significantly impact the improvement level of operational performance through the application of total quality management practices.

Several authors have investigated the relationship between total quality management implementation and learning organization aspects. However, most studies examined their relationship from a narrow perspective or under specific contexts, lacking empirical validation of their concurrent effect on operational performance improvement. The study aims at bridging this gap.

This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days.

In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor.

Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression.

The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications.

To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

This study aims to explore the relationship between digital transformation and resilience development in the Australian food supply chain (FSC), and identify the contribution of digital technologies to it using the dynamic capabilities theory as theoretical lens.

For that, a mixed-method approach was used. It combines both quantitative and qualitative data to identify trends and details of the phenomenon, yielding more robust findings. We firstly collected and analyzing quantitative data obtained from food industry practitioners and, then, qualitative data gathered through semi-structured interviews with experts.

The study findings suggest that the relationship between digital transformation and resilience varies among tiers of the FSC and that digital technologies adoption affects resilience development differently across tiers. This highlights the potential cost savings of developing strategies that jointly address digital transformation and resilience development, improving performance outcomes and determining the extent to which digital technologies enhance or inhibit certain aspects of resilience in the FSC.

The study frames the relationship between digital technologies and resilience within the dynamic capabilities theory and suggests that digitalization can enhance resilience by enabling organizations to sense, seize, and transform strategies. We also provide insights for managers to develop strategies that simultaneously enhance digitalization and resilience, resulting in improved performance during disruptive events.

This study aims at identifying the contribution of Industry 4.0 (I4.0) integration into supply chains (SCs) to the enhancement of SC resilience.

A scoping review was conducted so that the relevant literature on SC resilience, and I4.0 integrated into SC management was examined.

The authors summarize the main findings from existing research and propose three research directions: (1) empirical validation of the contribution of I4.0 ICTs to SC resilience; (2) explore the role of processing-actuation technologies in enhancing restorative capacity; and (3) integration between I4.0 ICTs and omni-channel strategy as a means to resilience development at consumer and retail levels. The literature on the design of resilient smart SCs is far outnumbered by works reporting applications of I4.0 ICTs at different SC tier levels. However, the authors’ scoping review organizes the information available on these themes, setting the ground for the development of new theoretical propositions.

The integration of digital technologies from I4.0 can fundamentally change the SC management, acting as enablers of a more effective response to disruptions. However, the digital transformation of SCs is still incipient, and literature is particularly sparse when considering the contribution of I4.0 to the resilience of SCs.

Healthcare supply chains (HSCs) have been adopting Industry 4.0 (I4.0) as a means to boost their resilience. The first objective of this study is to identify the effect of contextual variables of HSCs on resilience development and I4.0 adoption. Second, the paper examines the pervasiveness of the relationship between resilience and I4.0 across different contextual characteristics.

179 organizations from the HSC in Brazil and India were surveyed. Responses were analyzed using multivariate data techniques.

Large HSC agents are more likely to develop resilience abilities and adopt I4.0 technologies when these factors are analyzed independently. However, the joint analysis of resilience and I4.0 displayed a large number of significant correlations among small organizations.

Findings provide managers of HSC arguments to enhance resilience through the digitalization. HSC organizations can identify HSC organizations' context to tailor initiatives on resilience and digitalization.

This paper aims to examine the moderating effect of Industry 4.0 (I4.0) technologies on the relationship between socio-technical (ST) practices and workers' health, quality and productivity performance.

In this paper, 192 practitioners from different manufacturing firms adopting I4.0 technologies were surveyed, analyzed the collected data using multivariate techniques and discussed the results in light of ST theory.

Findings indicate that I4.0 moderates the relationship between ST practices and performance, to an extent and direction that varied according to the focus of the technologies and practices adopted.

The I4.0 movement has triggered changes in the work organization at unprecedented rates, impacting firms' social and technical aspects. This study bridges a gap in the literature concerning the integration of I4.0 technologies into manufacturing firms adopting ST practices, enabling the verification of the moderating effects on workers' performance. Although previous studies have investigated that relationship, the moderating effect of I4.0 on performance is still underexplored, characterizing an important contribution of this research.

The purpose of this paper is to determine leadership styles at each hierarchic level that best support the LM implementation process in a given company.

To achieve that, the authors propose a method that combines information from two sources in three major steps. First, using opinions from experts on lean implementation from an activity sector of interest the authors determine leadership styles that best suit each phase of the LM roadmap for that sector. Next, the authors analyze a specific company within the activity sector to determine: at which lean roadmap phase the company is at the moment; and the style of its current leaderships at each hierarchical level. Finally, the authors combine information from previous steps to diagnose the suitability of company’s leaderships to its lean implementation needs.

The method points at improvement alternatives that may be developed simultaneously at different leadership hierarchical levels in companies. Further, using the matrix of ideal leadership styles, companies may be able to identify implementation phases in the lean process that are poorly served by current leadership styles, anticipating problems and developing HRM practices to mitigate them. That is quite relevant, since changes in leadership behaviors and expectations may take longer time to be implemented; therefore, it is important to understand these opportunities and have a clear vision of current gaps within the company.

The identification of leaderships’ attributes and behaviors in companies at different phases of the lean implementation roadmap contributes to the existing body of knowledge on lean manufacturing. The method is intended as a supporting tool for lean implementation, as it enables the assessment of gaps in leadership behaviors in the organization, and directs to improvements according to the phase of lean implementation. The goal is to complement existing lean roadmaps by driving improvements in leadership-related aspects of the implementation process.

In this paper, the authors examine the impact of Industry 4.0 (I4.0) technologies on the relationship between total productive maintenance (TPM) practices and maintenance performance.

Data collection was carried out through a multinational survey with 318 respondents from different manufacturing companies located in 15 countries. Multivariate data techniques were applied to analyze the collected data. Diffusion of innovations theory (DIT) was the adopted theoretical lens for our research.

The authors’ findings indicate that I4.0 technologies that aim to process information to support decision-making and action-taking directly affect maintenance performance. Technologies oriented to sensing and communicating data among machines, people, and products seem to moderate the relationship between TPM practices and maintenance performance. However, the extent of such moderation varies according to the practices involved, sometimes leading to negative effects.

With the advances of I4.0, there is an expectation that several maintenance practices and performance may be affected. Our study provides empirical evidence of these relationships, unveiling the role of I4.0 for maintenance performance improvement.

This paper aims to propose a method to determine which mass customization (MC) characteristics should be prioritized in mass-customized service design.

Looking at manufacturing MC systems and conducting a literature review, it is not possible to observe a methodological step to define customized service design as the one we propose in this work. Results show a systematic classification of MC characteristics based on MC enablers and service enablers. These enablers are related by a quality function deployment (QFD) matrix and rewritten using a reverse QFD procedure.

In the end, it was possible to determine which characteristics should be prioritized in mass-customized services.

Two case studies were performed: one with an electric power supplier and another one with a university.

It shows that despite easy customization, organization is not always interest in service features customization. The explanation in these two cases is customization cost, which compared to the benefit does not seem advantageous for the organization.

This paper creates a methodology to design a first phase in customized services in Latin American services and that is the original contribution.

This paper aims to propose an approach based upon the principal component analysis (PCA) to define a contribution rate for each variable and then select the main variables as inputs to a neural network for energy load forecasting in the region southeastern Brazil.

The proposed approach defines a contribution rate of each variable as a weighted sum of the inner product between the variable and each principal component. So, the contribution rate is used for selecting the most important features of 27 variables and 6,815 electricity data for a multilayer perceptron network backpropagation prediction model. Several tests, starting from the most significant variable as input, and adding the next most significant variable and so on, are accomplished to predict energy load (GWh). The Kaiser–Meyer–Olkin and Bartlett sphericity tests were used to verify the overall consistency of the data for factor analysis.

Although energy load forecasting is an area for which databases with tens or hundreds of variables are available, the approach could select only six variables that contribute more than 85% for the model. While the contribution rates of the variables of the plants, plus energy exchange added, have only 14.14% of contribution, the variable the stored energy has a contribution rate of 26.31% being fundamental for the prediction accuracy.

Besides improving the forecasting accuracy and providing a faster predictor, the proposed PCA-based approach for calculating the contribution rate of input variables providing a better understanding of the underlying process that generated the data, which is fundamental to the Brazilian reality due to the accentuated climatic and economic variations.