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Goal: Objective focuses on development and application of an architecture for measuring the performance of legacy equipment through its connection to the cloud. Methodology: The research was approached by the Design Science Research (DSR) method, which focuses on the development and evaluation of the application of the artifact (legacy equipment performance measurement system) through its technological base. Results: Both in the development and in the evaluation of the application of the system architecture in a real manufacturing system, the results were satisfactory through the accuracy obtained of 86% in the measurement carried out. Regarding equipment performance measurement, the measured index achieved an OEE of 27% efficiency, which is considered low compared to the average of companies with world-class manufacturing. It is concluded that the results made it possible to verify the reliability of the information generated by the architecture of the application system and to measure the performance of the equipment through its applicability in a real manufacturing scenario with a focus on manufacturing management. Search limitations: The development of this research is limited to the application architecture for measuring performance for bread production line. Practical Implications: The cloud architecture contextualizes the use of IIoT technologies (sensors, devices, among others) through cloud application architecture and how this has been transforming the industry and helping in the management of manufacturing operations. Originality / Value: This research underscores the efficacy of a customized integration approach for leveraging existing technologies. While cloud-based Industrial Internet of Things (IIoT) solutions are readily available, this work transcends the limitations of off-the-shelf options by tailoring the architecture to the specific requirements of a real-world production line. This focus on customization enables the architecture to be adapted to a broad spectrum of legacy equipment and production lines across diverse industries, thereby unlocking the full potential of IIoT technologies. Consequently, this research demonstrates the replicability of the proposed methodology for various manufacturing scenarios.

This work concerns the laser wire directed energy deposition (LW-DED) additive manufacturing process. The objectives were two-fold: (1) process mapping – demarcating the process states as a function of the processing parameters; and (2) process monitoring – detecting process anomalies (instabilities) using data acquired from an in-situ meltpool imaging sensor. The LW-DED process enables high-throughput, near-net shape manufacturing. Without rigorous parameter control, however, LW-DED often introduces defects due to stochastic process drifts. To enhance scalability and reliability, it is essential to understand how LW-DED parameters affect processing regimes, and detect deleterious process drifts. In this work, single-track experiments were conducted over 128 combinations of laser power, scanning velocity, and linear mass density. Four process states were observed via high-speed imaging and delineated as stable, dripping, stubbing, and incomplete melting regimes. Physically intuitive meltpool features were used to train simple machine learning models for classifying the process state into one of the four regimes. The approach was benchmarked against computationally intense, black-box deep machine learning models that directly use as-received meltpool images. Using only six intuitive meltpool morphology and intensity signatures, the approach classified the LW-DED process state with statistical fidelity approaching 90 % (F1-score) compared to F1-score 87 % for deep learning models.

In today’s scenario of increasing energy prices, new legislations, and rising consumer concerns regarding environmental issues, industries face an unprecedented challenge of reducing their energy consumption without negatively impacting their profit and productivity. Based on this, companies are focusing on analyzing their energy efficiency, which has various criteria to be considered, and at least three organizational levels. To close this gap, this study developed an Industrial energy efficiency assessment and prioritization model based on energy assessment literature. It utilized multi-criteria analysis for the prioritization of industrial energy efficiency measures. To achieve the goal, a literature review was conducted to map relevant energy efficiency practices from which an industrial energy efficiency assessment tool was developed through the lens of three organizational levels (plant, process, and machine). Subsequently, an energy-efficiency project prioritization tool was proposed using the multi-criteria PROMETHEE II method. The assessment and prioritization model was applied to an energy industry for refinement. It generated an overview of the company's energy efficiency maturity and a ranking of the most recommended measures for the optimal use of energy resources according to established criteria and their weights. Four subcategories (lighting, HVAC systems, compressed air, and motors) were analyzed for the organizational levels, and lighting presented the higher result of a maturity of 2.77 on a scale from 0 to 3, also the maturity of the company was 2.01, which means that is still space for improvement. The improvements were highlighted according to each subcategory studied, pointing to actions that needed to be developed to improve energy efficiency.

Abstract Organisations all over the world are going through the process of digital transformation (DT). Enterprise Architecture (EA) is a method and an organising principle that aligns the business's objectives and strategies with the Information Technology strategy and execution plan. EA provides a guide to direct the evolution and transformation of enterprises with technology. The EA principles are one of the key concepts in the definition of EA; they assist in recognizing the organization vision and validating the outcomes. However, the lack of adequate instruments for assessing the current state and identifying opportunities for EA management procedures improvement often leave organisations unsure of where to begin improving their procedures. The aim of this paper is to help organisations identify these improvement opportunities. To do so, a decision model was developed to evaluate the influence DT technologies have on the EA principles proposed by The Open Group Architecture Framework (TOGAF). A literature review was conducted, and five main DT Technologies applied in the EA scope were identified. With that, a decisional model was created based on two decision‐making methods called Decision‐Making Trial and Evaluation Laboratory and PROMETHEE. The 21 architecture principles proposed by TOGAF were evaluated and the influence the technologies exercised on the principles were identified. As a result, Big Data and Cloud Computing technologies were indicated as having the greatest effect over the analysed principles, therefore concluding that when applied in the EA scope, these technologies can help organisations improve their EA procedures.

Abstract Industry 4.0 driven by the internet of things (IoT) is changing the way of producing and has been offering smart manufacturing systems with support technologies for the digital transformation of manufacturing plants seeking improvements in productivity, in control over the process, and customisation of production, among others. Due to these technological developments, small and medium‐sized industries have been identified as a weak link in adapting their processes and resources, where they are usually the biggest victims in the transition to industry 4.0. The evidence points out that the excess data inserted in the databases of the manufacturing system of the industries influences the decision‐making process of managers, making the process more complex and dynamic. This research focuses on a systematic literature review to assess how data‐based performance measurements for machines are being handled in the context of industry 4.0. The methodological approach follows the application of the PROKNOW‐C (Knowledge Development Process‐Constructivist) method used to build a Bibliographic Portfolio in a structured way in line with the research theme. The results presented in the Bibliometric Analysis enabled the construction of a performance measurement model based on the sources of the researched articles.

This research is based on the need to reduce the consumption of potable water due to the current water scenario allied to the company's socio-environmental situation. The metallurgical industry is the third industrial group with the highest consumption of potable water in the world. This study can be used in other metallurgical industry segments besides residences, public and private works. Currently, the water usage scenario of Curitiba and the metropolitan region is classified as ‘quantitative critical’, which can cause companies to accelerate the process of water collection through alternative sources. Through surveying potential sources for water collection, three sources of capture and one point for press water were classified. For validation of these potentials, chemical-physical tests were performed on the water samples. Subsequently, reservoir sizing calculations were performed based on the ABNT NBR 15527 standard. Thus, the first idea of reducing potable water consumption was up to 84%, even in the month with the lowest volume of rain. HIGHLIGHTS The case study was in a metallurgical multinational company located in an industrial park area. This region has large growth with a critical hydric scenario.; It counts with different types of water (climatizer, drainage pit, drainage groove, rainwater) storage, also the using of these different types of waters.; Positive social impact.; Positive environmental impact.; Positive economic impact.; Reduction of potable water consumption for non-potable purposes.;

Governments and companies in all affected countries have taken measures to prevent, on the one hand, the rapid spread of the virus among the population and, on the other hand, to alleviate the economic and commercial consequences of the crisis that is already changing the daily functioning of the population and the companies. The COVID-19 pandemic has radically and unexpectedly transformed the global economic landscape. The objective of this work is to demonstrate a set of information that can meet the expectations of the internal and external stakeholders of an organization concerned with constructively relating to its stakeholders–aiming at the possibility of carrying out the work and serving customers in the context of the pandemic. The research method is the study based on the literature review process focusing on information and research systems, books, and a manufacturer's case study. It also has practical data–information collected from audits carried out. Stakeholders' expectations and interests are connected to this issue and can be aligned through an Audit of Contagion and Transmission Protocols of SARS-COV-2.

Goal: The present paper aims to investigate what are the main implications of Industry 4.0 technologies for production management. Design / Methodology / Approach: The study was carried out through a survey with companies of the metal-mechanic and automotive industry of the Curitiba Metropolitan Region (Brazil) and of the Upper Bavaria Region (Germany) in order to determine the overall implementation level of technologies commonly associated with Industry 4.0 and their respective impacts to operations performance objectives. Results: The results reveal that the overall implementation and improvement levels of the Industry 4.0 technologies is higher in Germany than in Brazil. Regarding the technologies, the Internet of Things presented one of the highest overall improvement levels for both regions, showing that the adoption of these technology could bring significant improvements for all performance objectives. Limitations of the investigation: The results obtained with the present research refer only to the scenario of Curitiba and Metropolitan Region and the Upper Bavaria Region, more specifically from the companies of the metal-mechanic and automotive sectors and may differ from other sectors or regions. Practical implications: From this research, companies that are willing to invest in new technologies can find a starting point to understand the benefits that they can possibly achieve based on results obtained from companies that have already adopted the technologies in Brazil and Germany. Originality / Value: Studies that formally discuss the implications of the Industry 4.0 technologies for production management are lacking. Most studies focus excessively on the technologies without approaching their impact on management systems.

Purpose: Performance measurement systems (PMS) in Non-profit Organizations (NPOs) are more complex than in for-profit organizations. NPOs have an orientation toward social mission and values, and they consider not only organizational efficiency and viability, but also the social impact of the organization. This research provides a comprehensive synthesis of PMSs in NPOs.Design/Methodology/Approach: Using a literature review, supported by bibliometric and network analyses. A paper set of 240 articles related to this research field is examined. Topics that are the most prevalent in this research area and their interrelationships are identified, presenting an outline of current efforts.Findings: Despite the descriptive analyses for the paper set, a framework is proposed for organizing the design-implementation factors of PMSs in non-profit organizations, identifying the main requirements for their successful development.Originality/Value: Investigation on performance measurement in non-profit organizations is still in its early stages of development with many opportunities to further develop the field. Conceptual frameworks and models, as well as specific theories, are being generated for this field of research, and the process of adapting models from the general field of performance measurement is taking place. The meta-framework that organizes the main research topics of PMS in non-profit organizations and the framework that consolidates factors that influence the design-implementation of PMSs in non-profit organizations developed represents this paper contribution.

Recent research on management control and performance measurement and management (PMM) points towards a concern to provide suitable systems in nonprofit organizations (NPOs). However, few attempts have been made to understand these organizations and how their peculiarities influence this process. This research empirically discusses NPOs’ features through the lens of performance measurement and how these features influence performance measurement system’ design, the first step for an iterative PMM. A case study with two NPOs in the United States of America and Brazil provides valuable insights into the design factors. Results indicate that various factors related to purpose, stakeholders, and management influence the design of the performance-measurement system. Their unique organizational characteristics impact the usability and viability of the application of performance-measurement systems.