Accurate temperature measurements are essential for the proper monitoring and control of industrial furnaces. However, measurement uncertainty is a risk for such a critical parameter. Certain instrumental and environmental errors must be considered when using spectral-band radiation thermometry techniques, such as the uncertainty in the emissivity of the target surface, reflected radiation from surrounding objects, or atmospheric absorption and emission, to name a few. Undesired contributions to measured radiation can be isolated using measurement models, also known as error-correction models. This paper presents a methodology for budgeting significant sources of error and uncertainty during temperature measurements in a petrochemical furnace scenario. A continuous monitoring system is also presented, aided by a deep-learning-based measurement correction model, to allow domain experts to analyze the furnace's operation in real-time. To validate the proposed system's functionality, a real-world application case in a petrochemical plant is presented. The proposed solution demonstrates the viability of precise industrial furnace monitoring, thereby increasing operational security and improving the efficiency of such energy-intensive systems.

Purpose Green Lean tools are aligned with the Circular Economy strategy as they aim at reducing waste, however, they miss the creation of value through waste. Therefore, this paper investigates whether the current implementation of the Green Lean strategy promotes the introduction of the Circular philosophy to achieve sustainable management production processes. It analyses how the resources were managed by implementing the different strategies and tools presented in Green Lean case studies under the lens of Circular Economy and highlights future research paths on the Circular Lean integration. Design/methodology/approach An analysis of the implementation of Green Lean case studies, selected through a Systematic Literature Review, from the manufacturing sector is conducted. Findings The majority of Green Lean implementations were aimed at minimizing waste, which is aligned with the narrowing strategy of Circular Economy and only a few cases studies aimed at closing the production cycles. Even though the main results indicate that being eco-efficient is a good starting point to move towards sustainability, from a Circular Economy point of view, this approach could remain limited. Therefore, the current contribution of Green Lean to the implementation of circular practices is limited. It can be concluded that introducing the circular philosophy in the Green Lean methodology can be achieved through redirecting Green Lean from waste reduction towards a value creation focus. Originality/value The originality of this paper is that it provides a critical review of the literature on the topic of Green Lean integration and Circular Economy.

Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments. This calls for novel strategies when assessing and controlling a building's indoor air quality (IAQ). IAQ can generally be controlled by ventilation and/or policies to regulate human-building-interaction. However, in a building, occupants use rooms in different ways, and it may not be obvious which measure or combination of measures leads to a cost-and energy-effective solution ensuring good IAQ across the entire building. Therefore, in this article, we introduce a novel agent-based simulator, ArchABM, designed to assist in creating new or adapt existing buildings by estimating adequate room sizes, ventilation parameters and testing the effect of policies while taking into account IAQ as a result of complex human-building interaction patterns. A recently published aerosol model was adapted to calculate time-dependent carbon dioxide (CO2) and virus quanta concentrations in each room and inhaled CO2 and virus quanta for each occupant over a day as a measure of physiological response. ArchABM is flexible regarding the aerosol model and the building layout due to its modular architecture, which allows implementing further models, any number and size of rooms, agents, and actions reflecting human-building interaction patterns. We present a use case based on a real floor plan and working schedules adopted in our research center. This study demonstrates how advanced simulation tools can contribute to improving IAQ across a building, thereby ensuring a healthy indoor environment.

The concept of Sustainable Production is evolving with changes triggered by the emergence of new economic and industrial models such as Circular Economy and Industry 4.0. However, most studies that currently link these concepts are based on the principles of Sustainable Production defined 20 years ago. Therefore, the primary aim of this study is to redefine the principles that should govern Sustainable Production operations in the tran-sition towards a Circular Economy and smart industry models. To this end, an initial proposal of 11 principles was shared with 11 world-class experts (academics and practitioners) and a consensus proposal was sought through a Delphi Panel. Ten principles emerged from this study, which were evaluated by experts according to criteria of significance, parsimony, semantic consistency and empirical adequacy. Additionally, to study the rela-tionships between the ten principles, the Interpretative Structural Model (ISM) technique was applied. The ISM technique identified which principles are independent of or dependent on each other and established relation-ships between the principles. The findings suggest that Principle 5 ("Prioritize employees' well-being"), Principle 6 ("Enhance management commitment to sustainability"), Principle 9 ("Measure and optimize sustainable pro-cesses") and Principle 10 ("Boost the use of sustainable technologies") help to establish an ideal context to enhance the development of the rest of the principles that characterize Sustainable Production. The presentation of the ten principles opens new possibilities for researchers while helping managers to better understand sustainability in terms of production and, therefore contribute to achieving SDG 12.

Sustaining continuous improvement activities is one of the biggest challenges that companies face. In order to foster operator participation in continuous improvement activities, this paper aims to present and validate a scale for measuring middle manager trustworthiness. Firstly, we combine the available literature with two concept mappings, one with operators and another with supervisors, and an expert panel (consisting of experienced academics, practitioners and consultants) to identify the specific middle manager behaviors that are essential to convey trustworthiness. Secondly, an empirical analysis with 211 operators from three different companies located in three different countries (i.e., Peru, Honduras and Spain) served to define, debug and test the reliability and the convergent, discriminant, and nomological validity of the scale. The validated scale presents middle manager trustworthiness as a reflective-reflective second-order construct formed by four critical dimensions: human qualities; training and development; technical and managerial competencies; team building. To the best of our knowledge, this paper is the first to present a valid and reliable scale for measuring middle manager trustworthiness in continuous improvement activities. The proposed scale can be used as a guideline for companies which aim to foster operators' trust and participation in continuous improvement activities and for those activities to be sustainable over time.

Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have.This framework can be used by other researchers as a roadmap for the design of newdata science methodologies or the updating of exitin

Purpose The purpose of this study is to critically evaluate the current status of Design of experiments (DoE) in the service industry or non-manufacturing processes in the manufacturing sector. This involves the identification of critical success factors and challenges in the use of DoE in the context of the service industry as well as the key skills needed for its successful applications. Design/methodology/approach In order to identify the critical success factors and challenges, an online survey instrument was developed and distributed across Six Sigma Black Belts (SSBB), Six Sigma Master Black Belts (SSMBB), process improvement champions, continuous improvement professionals, DoE experts and specialists as well as academics who are involved in DoE research and applications. Findings A total of 109 participants from over 20 countries responded to the study. One of the first findings of the study is that over 40% of the process improvement professionals in the service sector do not have any hands-on experience with the application of DoE. Further, this study highlights the following critical factors for successful DoE applications: dedicating adequate resources in terms of time, budget and people for the experiment; getting a clear understanding of the problem at hand; securing a reliable and validated measurement system for measuring the response or quality characteristics of interest; and identifying input independent variables, which influence the quality characteristics during the brainstorming stage. Some respondents also mention the importance of having training or education in DoE as a critical success factor. At the same time, the lack of adequate planning to experimentation, mathematical knowledge and statistical reasoning remains as challenge to be solved. Research limitations/implications As data were collected at an individual level, inter-reliability, which measures the consistency of judgements on a particular survey item, could not be gauged. Moreover, due to low sample size across some continents, an intercontinental comparative study on the extent of the application of DoE across various continents could not be performed. Originality/value To the best of the authors' knowledge, this is the first global survey carried out to understand the status of DoE in the service industry. The results of the global study can be used to encourage quality, continuous improvement and process improvement professionals in the service sector to drive wider acceptance of one of the most powerful tools in the Six Sigma toolkit for problem-solving scenarios. Moreover, senior managers in the service industry can take away some of the key benefits, challenges and success factors in the implementation of DoE.

Purpose This paper unifies previous research literature on employee participation in continuous improvement (CI) activities through the development and validation of a conceptual model. The purpose of this model is to illustrate how organizational drivers foster organizational and individual enablers which, in turn, strengthen employee participation in CI. The article also discusses the results and managerial implications. Design/methodology/approach Firstly, the article introduces the main variables affecting employee participation in CI, looking at the different possible relationships proposed in existing literature. In accordance with the Kaye and Anderson (1999) framework, these variables are categorized into organizational drivers, organizational and individual enablers and individual outcomes. Based on these categories, a model was put forward and empirically validated using data collected from three Spanish companies (n = 483) and using partial least squares structural equations modelling (PLS-SEM). Findings A model was put forward, proposing PIRK systems (power, information, rewards, knowledge) as the main organizational driver of employee participation in CI activities. PIRK impacts positively on social influence (organizational enabler), self-efficacy and job satisfaction (individual enablers). These enablers, together with employee intention of participating, help determine employee participation in CI activities. Practical implications Organizations...

Maturity models enhance the performance of companies by prescribing a trajectory through stages of increasing capability. However, a recent review of maturity models concludes that current maturity models hardly meet the design principles required for prescriptive use. To address this deficiency, we conducted semistructured interviews and a Group Model Building study with industrial companies in Spain in which we studied the progression toward a Leading Green Company as the highest maturity stage of environmental management. The findings from the study were tested using surveys with enterprises in Spain, Italy, and the United Kingdom, semistructured interviews in the United Kingdom and case studies in Spain. Using these data sources, we develop a causal model that captures an idealized environmental management maturity dynamic progression though stages. By mapping maturity stages to feedback loops connected to actions to improve those maturity levels, system dynamics can help companies articulate policies for transitioning toward higher maturity stages.

Water has become a critical resource due to increased manufacturing activities. However, there is a lack of detailed information on water management and consumption by industries. In the recent bibliography, lean-green was established as a good approach for achieving sustainability in manufacturing industries, but few studies have aimed to achieve both operational and environmental improvements in water consumption. In this paper, we present a multi-case study in the agri-food industry in which water consumption in company activities is monitored, allowing them to improve their industrial processes based on lean-green practices, leading to a zero-waste strategy for this critical resource. The aim of this paper is to demonstrate the importance of having detailed knowledge regarding water consumption in order to discover, in a lean-green context, new improvement opportunities which could remain hidden by the current way of analysing consumption.

This book aims to introduce the Circular Economy to companies, in order to understand its origin, principles and benefits. The book presents a methodology to approach the Circular Economy in a systematic way. The guide is structured in four chapters that show the steps to help organizations on their way to the Circular Economy. The chapters are: 01. Transition to the Circular Economy, 02. Fields of Action in the Circular Economy, 03. Implementation methodology, 04. Tools for developing the Circular Economy. The methodology follows the improvement cycle, based on 1. Diagnosis, 2. Opportunity analysis, 3. Implementation plan, 4. Evaluation and feedback. The last chapter also presents a set of tools to help companies in their way to the Circular Economy

This book presents the proceedings of the XXII International Conference on Industrial Engineering and Operations Management, International IIE Conference 2016, and International AIM Conference 2016. This joint conference is a result of an agreement between ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización), ABEPRO (Associação Brasileira de Engenharia de Produção), AIM (European Academy for Industrial Management) and the IIE (Institute of Industrial Engineers), and took place at TECNUN-School of Engineering (San Sebastián, Spain) from July 13th to 15th, 2016. The book includes the latest research advances and cutting-edge analyses of real case studies in Industrial Engineering and Operations Management from diverse international contexts, while also identifying concrete business applications for the latest findings and innovations in operations management and the decisions sciences.

Vivimos en un maravilloso planeta, en el que hemos conseguido la tecnología necesaria para alcanzar altos niveles de bienestar y alargar la esperanza de vida humana. Este avance tecnológico ha cambiado radicalmente nuestra forma de vivir y relacionarnos con el planeta. En concreto, el consumo de energía a partir de combustibles fósiles ha ocasionado una serie de cambios ambientales con consecuencias potenciales como inundaciones, tormentas, sequías, aumento del nivel del mar y extinción masiva de especies. Pero éste no es el único aspecto en el nuestra actividad industrial y social está influyendo. Ya conocemos muchos datos que nos alertan de las consecuencias de nuestro desarrollo.

A failure detection system is the first step towards predictive maintenance strategies. A popular data-driven method to detect incipient failures and anomalies is the training of normal behaviour models by applying a machine learning technique like feed-forward neural networks (FFNN) or extreme learning machines (ELM). However, the performance of any of these modelling techniques can be deteriorated by the unexpected rise of non-stationarities in the dynamic environment in which industrial assets operate. This unpredictable statistical change in the measured variable is known as concept drift. In this article a wind turbine maintenance case is presented, where non-stationarities of various kinds can happen unexpectedly. Such concept drift events are desired to be detected by means of statistical detectors and window-based approaches. However, in real complex systems, concept drifts are not as clear and evident as in artificially generated datasets. In order to evaluate the effectiveness of current drift detectors and also to design an appropriate novel technique for this specific industrial application, it is essential to dispose beforehand of a characterization of the existent drifts. Under the lack of information in this regard, a methodology for labelling concept drift events in the lifetime of wind turbines is proposed. This methodology will facilitate the creation of a drift database that will serve both as a training ground for concept drift detectors and as a valuable information to enhance the knowledge about maintenance of complex systems.

The increase in environmental and social awareness has triggered numerous green initiatives at companies. Traditionally, companies¿ training related to environmental management focuses on technical aspects and theoretical concepts. In addition to these theoretical contents, it is necessary to carry out practical activities to impart the desired behaviors and skills. Serious games have been found to improve employees understanding of practical environmental sustainability challenges, by offering opportunities to obtain first-hand experiences that may otherwise be too costly and difficult to reproduce in reality. This work presents Pizz@green, a serious game which simulates the productive and environmental efficiencies generated by the improvements of processes in a pizza shop. The purpose of the game is the practical learning of the environmental impact caused by the production processes of a company.

In the turbulent global environment in which organizations now compete, having reliable, accurate and readily available information is a key factor. Information is a strategic company resource. To the extent that TQM is an information-intensive management model, it is not unreasonable to assume that companies committed to TQM models should be efficient when managing information. This study has two main objectives. First, we aim to explore whether the information managed by companies committed to TQM models (in particular a quality management system based on the ISO 9000 international quality standards series) meets the dimensions required for information quality. Second, we aim to explore how information quality dimensions influence information consumers¿ overall feeling of being well informed in those companies. Using a quantitative approach, we used a questionnaire survey to capture the perceptions of managers in companies committed to a quality management system based on the ISO 9000 international quality standards series with respect to the information they managed. More specifically we applied a nonparametric test and performed a multiple regression analysis to address the objectives of the study. The analysis shows evidence that the information managed already meets many dimensions of information quality among the companies analysed.

The main objective of this paper is to present the logical framework approach (LFA) as a tool that prepares companies and workers to implement continuous improvement programs. This methodology encourages worker participation in different steps in order to reach consensus in the organization. In addition, the application of LFA improves the capabilities of workers in areas such as participatory analysis, problem analysis and objectives analysis. These capabilities are necessary in any continuous improvement program. The paper also presents the results of applying LFA in two different companies.

Manufacturers have the challenge to increase productivity given complex manufacturing environments. A source that provides substantial levels of productivity is the overall equipment effectiveness (OEE) metric, which is an indicator to improve not only equipment utilization; but also the overall manufacturing operations, because of the valuable information that comes from the availability, performance and quality rates. Although information technologies have been introduced, companies use manually recorder data and have complicated measurement procedures. As a consequence, inaccurate information is generated and opportunities to improve productivity are missed. This paper presents a continuous improvement framework based on Lean manufacturing philosophy, operated by a system of wireless devices to support the real time equipment performance metrics. In order to validate the framework, results of a case study are exposed.

Order picking is the process of retrieving products from storage in response to a specific customer order. Although this process¿ design has significant impact on warehouse global performance, a practical-oriented design procedure is still lacking in literature. This paper presents a novel order picking system design methodology based on Design for Six Sigma approach, that aims to assist designers making decisions by suggesting the most effective operational and organizational policies and the equipment needed in order to fulfill customer¿s orders. A case research in a home appliances manufacturer is presented in order to demonstrate the effectiveness of this approach.

Manufacturers continue facing the never ending challenge of finding the best way to increase productivity levels in order to remain competitive. Significant level of productivity are given by the efficient utilization of production equipment, thus manufacturing companies use the overall equipment effectiveness (OEE) rate as an indicator to control equipment utilization related to the maintenance activity; however they do not take into account other valuable OEE-information to improve their entire manufacturing operations. This paper presents a continuous improvement model Plug&Lean-CiMo, whose aims are the accurate calculation of the OEE indicators, the appropriate classification of losses, and the systematic integration of lean manufacturing philosophy tools in an improvement methodology. The model has the advantage of using a portable wireless system to support the automated collection, of data to make the continuous improvement process easier. A case study is presented to illustrate the validation of the model.