Industry 4.0: The Power of Data

TeamworkTeamwork competence is one of the soft skills. This paper proposes and develops the protocol of a best teaching practice for the evaluation of the transversal competenceCompetence of teamwork, an important soft skill in the future of university students, using a methodological tool used in the professional field of human resources management: the competency-based interviewCompetency-based interview. The proposal of experience detailed in the protocol, includes different steps: (1) a prior analysis by the students of the structure and contents of the competency-based interview, based on material specifically developed for the practice; (2) the conduct of the interview, in which each student carries out a double role, as interviewer and interviewee; (3) the use of a rubric that the interviewer uses as support for the evaluation of the teamwork competence; (4) and a self-evaluation questionnaire to be filled in by the students involved.

TrainingTraining Industry 4.0Industry 4.0 is one of the challenges companies face in the context of the fourth industrial revolution. However, existing training focuses mainly on technology and does not pay much attention to the management implications of implementing Industry 4.0 in organizations. The trainingTraining approach presented in this article and developed through a university-industry cooperation scheme is based on a management perspective supported by an Industry 4.0 Advanced ManagementAdvanced Management Framework, a trainingTraining case based on the IMPULS Industry 4.0 Maturity ModelMaturity model, and a challenge-oriented technology training. The feedback received from the learners reflects that the management approach developed is valuable for the training of senior and middle management, underlining the importance of a goal-oriented strategic approach when implementing Industry 4.0 technologies. The need for more personalized training (more focused on the business problems of the participants) and the need to cope with “remote teaching” are the two training challenges to be addressed in the future.

Simulated annealingSimulated annealing is one of the most popular metaheuristic optimizationOptimisation techniques used in engineering and management to solve combinatorial problems. The algorithm is inspired by the thermodynamic process that occurs in the annealing treatment in metallurgy. Although it is simple to implement, its general operating mechanism and the rationale behind the search strategy are not always that intuitive. In this work, we present a teaching tool implemented in NetLogoNetLogo that illustrates the metaphor of both processes and the effect of annealing cooling schedulesCooling schedules on the quality of the solutions obtained.

The present work is aimed to share the results of one of the initiatives that Universidad Carlos III de Madrid (UC3M) has launched among those oriented to secondary and high school students, more precisely within Tecnocamp activities. It is more than obvious that entrepreneurship offers a feasible and successful way to economic growth and personal fulfillment. In the same way, during the last two decades it has clearly highlighted by most of the researchers that one of the most important things to do in order to develop the entrepreneurial spirit into the citizens is to academically train the citizens from the very early stages at the school and also to develop their business-oriented and entrepreneurial skills.

Blockchain, also known as distributed ledger technology, is a type of transformational technology that is currently regarded as one of the most important instruments of the well-knownIndustry 4.0 Industry 4.0. Blockchain'sBlockchain many properties, including smart contractsSmart Contracts, decentralization, transparency, traceability, data immutability, and data protection, combined with a consensus structure, make it appropriate for application in today's fast-paced global businesses. As a result, businesses should evaluate and compare the value of traditional supply chains with new Blockchain-based systems that add characteristics like transparency to the picture. The purpose of this article is to use a literature review to demonstrate the benefits of BlockchainBlockchain in supply chain management, highlighting key features such as sustainability, decentralization, data immutability, and the usage of smart contractsSmart Contracts. It also seeks to offer experts with positive consequences so that suitable measures may be taken to deploy this technology.

The aim of this paper is to explore and evaluate, using bibliometric analysisBibliometric analysis, the papers published up to January 2021 on the time-driven activity-based costingTime Driven Activity Base Costing (TDABC) system. In recent decades, companies adopting innovations in production management (e.g., lean manufacturing) have had to look for new methods of cost control. For this purpose, the activity-based costing system was developed, among others, with some implementation drawbacks. In 2004, Robert Kaplan, who developed the ABC costing system, developed the TDABC method. The conclusions of the paper show how the TDABCTime Driven Activity Base Costing system has been mainly analyzed in healthcare organizations and little in industrial organizations.

In this paper, we focus on two of the sectors with the highest accident rates, the construction sectorConstruction Sector and the manufacturing sectorManufacturing Sector. The aim of this paper is to analyze the common causes and the variables with the greatest influence on the occurrence of occupational accidentsAccidents in Spain in these two sectors. This analysis will allow us to find both similarities and differences that may be of interest in order to take more effective action to prevent accidents in the future. To address the aforementioned objective, a database provided by the Ministry of Labour and Social Economy has been used, which contains all accidentsAccidents registered in the ministry’s Delt@ system from 2009 to 2018. After exploring the database, several variables have been analyzed using the decision tree and clustering data mining technique.

The application of artificial intelligenceArtificial Intelligence (AI) and machine learningMachine Learning (ML) in production environments offers huge potential for the manufacturingManufacturing industry. In order to create added value, ML models must be deployedDeployment into production which means making models available in a specific environment where the results are needed. As an initial task in deployment, called the deploymentDeployment design, decision owners need to define the desired ML system architecture. The goal of this paper is to provide a structured methodology in form of a morphological box containing the available options for the deploymentDeployment design. Through the review of gray literature, the five most relevant parameters are identified as prediction approach, consuming application, model serving, learning method, and hosting solution. Possible values for each parameter are introduced and necessary considerations for the selection of an option are discussed. By means of a case study in the context of predictive qualityPredictive Quality, which describes the use of a MLMachine Learning model to predict the product quality based on production data, the developed concept is applied and validated.

This paper examines the lot-sizingLot-sizing /schedulingScheduling problem for plastic automotive components manufacturing. The scenario in which the problem is tackled refers to a second-tier supplier in the automotive supply chain. Here, the studied second-tier supplier is characterized by transforming plastic granules in injection machines using specific moulds that produce components or finished products. Each mould can be set up on distinct machines to inject one same automobile component, or even two different components or more in the same mould. The same mould is assembled on different injection machines and can have distinct production rates subject to the machine on which it is set up. Our research work puts forward a mixed integer linear programmingMixed Integer Linear Programming (MILP) model to minimize setup, the inventory of raw materialsRaw materials and plastic components, stockout, backorder costs, and machine-mould assignation costs. We demonstrate the usability of this model with randomly generated instances. The results of the experiments show that our MILPMixed Integer Linear Programming converges toward optimal solutions in large instances by reaching efficient solutions in reference to both quality and execution times. The novelty of this model lies in it considering the arrival of materials as raw material for the injection of parts into moulds, the use of raw materialsRaw materials and the availability of containers for packagingPackaging finished products. Moulds can also be set up only during specific time periods in accordance with the quantity of available labor during each time period.

SimulationSimulation is a frequently used tool in engineering fields, in particular its use as a testing mechanism has been one of its longest-lived functions. In the current context, simulation has become an important component in the training of artificial intelligence algorithms. This paper presents an approach to develop a simulationSimulation environment for testing or training novel algorithms to solve the sequencingSequencing problem in jobshopsJob Shop. This environment has the novelty of being as close as possible to the reality of jobshops in terms of variability and dynamism. Specifically, it details the methodology to be followed, the minimum components or features to be included and the considerations to be considered to integrate it with a machine learning algorithm for training and suggests a possible approach to test results for statistical validation.

The main objective of this research is the generation of artificial intelligence algorithms for the solution of the jobshop scheduling problem (JSSP) with real-time adjustments to changing conditions. The advent of technologies such as the Internet of Things (IoT) and data science has boosted the use of advanced machine learning techniques to solve complex problems (NP-Hard) that until recently were impractical using conventional techniques due to their computational times. The ability to have production plants that respond to variations in manufacturing environments has been a requirement of process managers for a long time, having to deal with this problem most of the time through expert judgment, contemplating the effects of variations in the overall performance of the system is a necessity in the face of the high demands of customers and global markets. Therefore, the search for mechanisms that provide solutions to this problem is a very promising line of applied research.

This study evaluates the potential benefits of a labor flexibilityLabor flexibility strategy that simultaneously incorporates: (i) annualized working hours, (ii) multiskilled staff, and (iii) overtimeOvertime; into the annual staffing problem for a retailRetail store. A two-stage stochastic optimization model is proposed to determine: How many staff is required in each store department; what amount of weekly working time (ordinary and overtime hours) is required per employee in an annual planning horizon; how many employees will be multiskilled, and in which departments they will be trained. This formulation considers uncertaintyUncertainty in the staff demand. Using real data from a Chilean retailRetail outlet, the obtained results allow us to design a flexible and cost-effective workforce at a strategic level. That is, the proposed triple strategy reports the lowest total annual cost (a saving of 69%) and also requires a smaller staff size for the store.

Production planningProduction Planning has traditionally been supported by mathematical programming. In recent years, Industry 4.0 and the use of its related techniques have been gaining strength in the production planning field. To develop mathematical programming models integrating Industry 4.0 techniquesIndustry 4.0, it is necessary to migrate from optimizationOptimization software normally used in academia to optimization packages developed in high-level programming languages. This paper aims to propose a conceptual framework to facilitate this migration by transcribing mathematical programming models to the programming languages of the MPLMPL commercial optimizationOptimization software and the optimization package PyomoPyomo developed in Python. This framework can be used by both, academics and practitioners, to translate already implemented models in one software to the other, as well as to implement from scratch a mathematical programming model in any of this software. The proposed conceptual framework is validated through its use for the translation of a production planningProduction Planning model implemented in MPLMPL to Pyomo.

This paper makes a review of the different contributions that the artificial schedulingScheduling in flowshopsFlowshop and jobshopsJobshop using approaches or techniques from the discipline of artificial intelligenceArtificial Intelligence. High quality and growing production is maintained for a promising line of research. The work carries out a literature review following a contrasted methodology that has allowed us to analyze in detail 17 articles published in 2020 and 2021 as a continuation of a previous review. The most outstanding conclusions allow us to see a tendency to improve the use of deep learning techniques in the field of sequencing, both to generate sequences directly and to complement parts of other algorithms.

This work details, in its design and implementation phase, a service that by means of a web application ( http://niotome.cigip.upv.es/ ) allows any user to carry out the execution of a design of experimentsDesign of experiments for the validation of an algorithm, heuristic, or metaheuristic, for the production schedulingProduction scheduling. The work justifies its need and the interest it has for researchers. Likewise, the scope of the problems that can be addressed is defined, and how to use it is shown.

In order to consolidate a study framework on the academic production about digital marketingDigital marketing and artificial intelligenceArtificial Intelligence, this paper aims to provide an overview of the state of research on this a specific topic and to decide on the axes where to dig by using a systematic mapping studySystematic Mapping Study (SMS) methodology. As extended scope research areas both fields require to become less general to face a systematic literature review. For this reason, this study introduces a previous phase in which an initial systematic mapping studySystematic Mapping Study is performed combined with a subsequent text analysis to obtain the most frequent bigrams in the literature and to narrow down more specific and interconnected study areas. As a result, online advertisingOnline advertising and machine learningMachine learning were identified as parameters to perform a final complete systematic mapping studySystematic Mapping Study. The results of this paper allow a framework for all academic production about online advertisingOnline advertising and machine learningMachine learning studied together, by providing a review of this corpus, analyzing annual production rate, sources and cites received.

This study reports a work carried out in a production process of a metallurgical company in the automotive industry. The main objective is to demonstrate that through the reorganization of the layout is possible to significantly reduce the associated wastes. This is an aspect frequently disregarded by companies due to the difficulties associated with the movement of heavy machines, which is a situation identified in the case under study. The dimensions of the space, the machines, and the work areas, as well as the respective restrictions, are factors to be considered in the construction of a logical and organized layout. Initially, the group faced an inadequate dispersion of the machines on the factory floor, causing high distances traveled by the operators in the transportation of the materials. High numbers of intersections resulted in defects as parts were often exchanged in different processing phases. Thus, the idea was to bring together all workstations. However, since the deburring/washing workstation is shared with other productive processes the change could only take place in the workstations that precede this. With the changed layout, such intersections were reduced, as well as the distances traveled by the operators and, consequently, the associated costs. Wastes such as defects and transportation have been reduced with the changes made, which allows the flow of material to be continuous and effective. Hence, with the reduction of 606.51 m covered by workers per day, the company obtains a sales increase of 830.76 € per month.

As at any decision level in operations planning and control (OPC), operational decisions are influenced by the technological advances underpinning the Industry 4.0Industry 4.0 (I4.0) paradigm. In this increasingly digitized environment, schedulingScheduling problems have to cope with stochastic demand, dynamic task allocation flow, routing flexibility, or task rescheduling. The ability to virtually replicate the schedulingScheduling process in an I4.0 environment enables its optimization, simulation, prediction, and automatic analysis in real time. These features are necessary in manufacturing environments with a zero-defect manufacturingZero-defect manufacturing strategy (ZDM) because these factors allow schedulingScheduling problems to be adapted to this strategy’s requirements. Therefore, a scheduling problem in a ZDMZero-defect manufacturing environment driven by a digital twinDigital twin (DT) will favor better production system performance. With this approach, the present article provides an overview of the scientific literature for this combined set of concepts. It presents the academic and research implications of the present research, discusses its results and limitations, and indicates where future research into this theme is to be directed.

This article presents a literature review on the application of lean manufacturingLean manufacturing (LM) techniques under a context of uncertaintyUncertainty. Forty articles have been identified, reviewed, and classified according to the following criteria: keywords, application context, modeling approach, LM techniques/tools, type of LMLean manufacturing waste, type of uncertaintyUncertainty, and software tool. This classification emphasizes the types of uncertainty inherent in lean production planningProduction planning processes and the modeling approaches for optimizationOptimization. The selection of the articles has been based on those scientific journals containing a higher representation of papers within this context. The main findings of this literature review point to the use of three main lean manufacturingLean manufacturing tools; the most used modeling approach, which is interpretive structural modeling; and the main uncertaintyUncertainty studied, which is demand. From the results of this study, it was found that research and experimentation in LMLean manufacturing applications under an uncertaintyUncertainty context only represent 10% of the selected articles, making it an underresearched topic that requires future research efforts.

Additive manufacturingAdditive manufacturing (AM) introduces a set of technology-specific constraints that increase the complexity of production planningProduction planning. As research in the production planning of AM facilities is gaining attention, a literature review revealed a lack of uniformity in the design of efficient approaches. At this stage, it is crucial to bring clarity to the identification of all the problems to solve while focusing on the processing sequence. For this reason, this paper presents a first approach for production planningProduction planning in AM in conjunction with an unambiguous definition of their problems and subproblems.

This research project proposes the development of a digital twinDigital twin (DT) that simulates the behavior of the zero-defect planning system of a supply chain. The research will focus on the incorporation of new zero-defect manufacturingZero-defect manufacturing (ZDM) technologies generated from the DTDigital twin perspective. The production technologies to be proposed will be oriented toward the development of new models and optimization algorithms for the ZDMZero-defect manufacturing planning problem in the new digitalized supply network context. The modeling domain will involve up to the second-tier supplier in the supply chain at the tactical and operational decision levels.

Economic globalization and the increase in consumption by society have created a need for companies to optimize and improve production processes. Thanks to new technologies, it is possible to increase their effectiveness to achieve the required objectives. The degree of automation in factories is already high, so changing the production process does not generate a significant increase in efficiency. Consequently, it is required to insert new tools that allow a more significant increase of the factory resources. This is where the concept of “Industry 4.0Industry 4.0” is born. The aim of this work is to stablish an action protocol to implement the status of a Manufacturing Execution SystemManufacturing Execution System (MES) in a factory. A maturity model will be proposed to analyze the state of implementation of the Manufacturing Execution SystemsManufacturing Execution System of Industry 4.0Industry 4.0 based on three of the three dimensions (technical, operational, and human). The levels of development in each of them are based on the Capability Maturity Model IntegrationCapability Maturity Model Integration ( CMMI).

The digital transformation of organizations together with the promotion of new technologies in the field of Industry 4.0 is offering new possibilities in the design of production planningProduction planning models. This paper focuses on this new context to facilitate model designers and decision-makers the revision of models to adjust them to these new business contexts. Thus, the design of a model experimentation environment for production planningProduction planning is proposed. The proposal includes three subsystems: Data Modeling, Decision Modeling, and Model Analysis and Investigation: Data Modeling provides quality datasets for the performance of the experiments. Decision Modeling manages the models to be analyzed and to be improved. Finally, Model Analysis and Investigation focus on an experimentation subsystem proposed to analyze the quality and suitability of the models in a concrete experimental context using controlled datasets. Thus, the proposed design facilitates the experimentation of models to later be exploited in business environments.

The construction industry is one of the world’s most important industries, but today it is one of the most inefficient and late adopters of technological advances. Moreover, the development of Building Information ModelingBuilding Information Modeling (BIM) (BIM) technology has proliferated, which has generated a revolution in working and carrying out projects, given the many benefits it offers. This work aims to propose an integrative methodology to complement the current project managementProject Management in construction based on both processes of the Project Management Institute (PMBoK)Project Management Body of Knowledge (PMBoK) Guide and BIM methodology. This implementation aims to unify the processes of both methodologies, eliminating redundancies and simplifying the work of project managers and other professionals in the construction industry (architecture, engineering, and construction [AEC]) to manage their projects simpler and efficiently.

Horizon Europe is the financial tool to deliver the EU contribution to research, development, and innovation actions under the next multiannual financial frameworkMultiannual financial framework (MFF); among them, those related to airspace operations. The budget of Horizon Europe is distributed across areas of intervention which are similar to the characteristics of the “Industry 4.0” concept. To support the design and planning of the future airspace operations research projects, it is relevant to analyze to what extent the use of EU funds in this domain is in line with the distribution of funds expected under the new MFFMultiannual financial framework. The parameters for comparison used are the airspace operations performance objectives, which present some correspondence with both Horizon Europe and Industry 4.0 characteristics. The result of the analysis indicates that the use of funds in the last 20 years follows a distribution similar to the planned one, although EU funds in support of some objectives would be increased at the expense of others to achieve a full alignment with it.

The rapid rise of accommodation platforms such as Airbnb has helped to democratize tourism as a leisure activity, due to the lower prices offered when compared to traditional hotels. However, this rising disruptive industry is affecting the social structures of the hosting cities, which can be understood as an externality of the industry. Prices in the long-term rental market have been stated to rise when Airbnb reaches a city. This is attributed to the switch of part of the properties traditionally offered for long-term rental to the new touristic accommodation market. In this research we focus on how the long-term rental market in the city of Madrid is affected, in terms of prices, by the presence of Airbnb (with a special focus on multilistings) and the distance of the proprieties to the city center. We use artificial neural networks to show that long-term rental prices are directly correlated with the number of properties offered on Airbnb, with the number of multilistings and the distance to the center of the city ranking in second and third position, respectively.

This paper aims to comprehend the human-centered innovations opportunities that might emerge from the Cognitive ErgonomicsCognitive Ergonomics scope within Industry 4.0. The present work can be considered an exploratory study, starting with a literature review to identify the guiding principles and concepts of Industry 4.0Industry 4.0 in order to develop a technological mapping of potential innovationsInnovation areas and analyze the potential implementation challenges. Industry 4.0 networked approach rethinks persons’ working environments and daily work processes. Persons in the smart factory: workers on the level of operators and managers face in this automatized and digitalized system, a variety of challenges. The Cognitive ErgonomicsCognitive Ergonomics seems to be the next leading paradigm in safety management of Industry 4.0Industry 4.0, closely related to the embedded technologies use and workforce well-being to ensure optimal levels of productivity and efficiency.

Recent studies have shown that the application of business model patternsBusiness model patterns facilitates business model innovationBusiness Model Innovation. However, the literature is still quite fragmented, and there is no commonly accepted approach to characterize business modelsBusiness Model based on patterns. To fill this gap, this paper conducts a systematic literature review, aiming to address three research questions: (1) How are BM patterns defined and operationalized? (2) What are the application domains in which BM patterns are explored? and (3) What methodological approaches are followed in defining BM patterns? The results of the review show that there is a lack of clarity between the concepts of business model archetypesBusiness model archetypes and business model patterns. Furthermore, few studies address business model patternsBusiness model patterns from a generic point of view; there is a lack of integrative approaches that include business model patterns based on different domains; and some overlap of patterns from one study to another has also been identified. Finally, in terms of methodological approach, the use of morphological analysis and the BM pattern generation methodology suggested by (Amshoff et al. in Int J Innov Manag 19:1540002, 2015) is recommended for the analysis and identification of business model patternsBusiness model patterns.

This paper aims to determine the relations between Industry 4.0Industry 4.0. (I4.0) technologies and lean manufacturingLean manufacturing (LM) practices to provide a lean supply chain managementSupply chain management 4.0 (LSCM 4.0) framework. First, a systematic review of the scientific literature on LSCM 4.0 is presented to examine its content and degree of contextualization. Next a general LSCM 4.0 construct is proposed, based on identified theoretical evidence. Ten waste types that impact the performance of today’s companies are indicated. The use of lean tools supports the change in the organizational culture toward a flexible resilient organization. I4.0 technologies, such as IoT, cloud computing, artificial intelligence and simulation, among others, are fundamental for the digital transformation of supply chains (SCs) and well support the implementation of LM tools like Kanban and just-in-time. For SC users and researchers, the results contribute a decision-making approach in a digitization context and, at the same time, to reduce waste, even when facing possible disruptions.

The objective of this article is to study the main factors that enable transformation toward Industry 4.0Industry 4.0 (I4.0), along with the barriersBarriers involved in its implementation. It is a first step toward a more wide and empirical study on I4.0 development in selected Spanish companies. Here a detailed analysis of 23 antecedent works is carried out. It then proposes a classification of the general dimensions to which both enablersEnablers and barriers refer: strategical, managerial and organizational, technological and sustainabilitySustainability. This study contributes to the literature analysis about implementing I4.0 by presenting both the main barriers and enablersEnablers to implement this structure.

BlockchainBlockchain has become more relevant in the efficient and secure management of the supply chainSupply chain management, due to the grown importance that companies give to ensuring the transparency and traceability of all the movements made in each of the links of the supply chain. In this article, a study of the blockchainBlockchain practices carried out by different companies from various sectors, come to infer that traceability, security, and cost reduction are the main objectives when companies decide to tackle projects of this nature. Another issue that can be deduced is that companies generally look for partners specialized in this sector to help them implement these projects, thus sharing both the knowledge generated and the development costs of the projects.

Given the demands and social awareness of the environment, today it is important to link operations in a company with processes that allow sustainabilitySustainability circles to be created. The main research objective of this article is to propose a methodology for the design and management of operations strategiesOperations strategy in globalGlobal supply chainsSupply chain with a circular economyCircular economy and sustainabilitySustainability approach. To fulfill this objective, a constructive research methodology is proposed in which each specific objective is fulfilled with the sequential activities and tasks set out in the time planning scheme. The future results to be obtained by the proposed methodology would be for industrial companies belonging to globalGlobal supply chains to optimize their operations strategiesOperations strategy in cost, quality, delivery, and flexibility terms in a sustainable manner.

The term Industry 4.0Industry 4.0 refers to a new industrial model concept based on the digitization and monitoring of the certain operational processes included in the business model. The objective of this study is to deepen the knowledge of real business application of Industry 4.0Industry 4.0 tools in a sample of large worldwide companies within the pharma sectorPharma sector. From the analysis carried out, it can be inferred that companies in the pharma sectorPharma sector are already far away from applying different Industry 4.0 technologies as a way to increase process efficiency and traceability, especially “Internet of Things” and “additive manufacturing”.

This paper presents a conceptual framework to apply blockchainBlockchain technology to two implementation areas in a supply chainSupply chain. It specifically intends to provide product tracking information to all stakeholders in the product development, assembly and subsequent delivery phases, manage information of supplied components to perform the assemblies where it is included, and track injected components. Information flow and exchange are supported by blockchainBlockchain. This article contributes to this emerging technology by providing an overview of blockchainBlockchain and its application to an industrial supply chainSupply chain by presenting the real problems encountered in the chain, examines the implications of information centralization, traceability, and transparency, and identifies some potential challenges in the work performed by collaborating companies in quality, receipt, and shipment of goods terms, among others.

This work proposes a mathematical programmingMathematical programming model to model the collaborative replenishment process between competitive supply chainsSupply chain. The basic objective is to reduce the costs associated with replenishment, production, inventories and customer services by putting to good use volume discounts in a production setting with variable capacity, along with the deferred demand possibility. The collaborative mathematical model is compared to a non-collaborative model, and the cost savings made with collaboration are analyzed. The proposed models are applied to a case study based on real data acquired from three supply chainsSupply chain in the footwear sector in Colombia. The collaborative model’s results indicate cost savings for the participating collaborative enterprises regardless of them being small or large.

A new idea is emerging more and more strongly in the European UnionEuropean Union (EU), of the so-called Physical InternetPhysical internet, a model that aims to handle physical goods in their storage and transport processes in a parallel way to how the Internet handles data, which involves efficiencyEfficiency and sustainabilitySustainability. Some steps and considerations will be required, and in the background, we perceive an appreciable necessary transformation in the volumes and dimensions of the goods to be transported and therefore in the industrial and consumer products.

Bike sharing services are a reality that is developing more and more every day, contributing to reduce private car use. A bike sharing system is not limited to the fleet and the stations, but requires important support of internal office services, cyclical maintenance of the bikes, and their continuous redistribution. The various supporting services should be organized according to the number of circulating bicycles, and thus accurate demand previsions can provide considerable help in optimizing the costs bear by the service provider. The use of bicycles follows a cyclical pattern, but it also depends highly on the weather conditions. This work aims to adapt and apply different machine learningMachine Learning algorithms to predict this demand. It uses a real database, containing data on two years of bicycle rentals in London. The results obtained validate the methodology.

The use of LeanLean methodology today has expanded from the industrial sector to other very diverse sectors. A key tool for optimizing processes is Value Stream MappingValue Stream Mapping (VSM) as it allows for identifying activities that do not add value to production. Due to its great applicability, the VSM could consider not only industrial aspects to generate a global vision with a social, economic, and environmental focus of the activities of the industry. This work focuses on the environmental approach of the VSM, conducting a bibliographic review on this topic. When conducting the analysis, it is identified that few cases consider these aspects mentioned. There are companies that carry out a current VSMValue Stream Mapping analyzing their energy systems, present improvement solutions, and propose a future VSM to avoid waste. There are companies that also include social and economic variables to take greater advantage of the benefits of Energy Value Stream MappingEnergy Value Stream Mapping (EVSM) (EVSM). It was concluded that VSM with an environmental focus can and should be implemented to improve the sustainability of productive activities. In addition, by using performance indicators, activities that are measurable can be established and constantly propose improvement actions. For future research, the concept of the sustainable circular economy could be introduced, emphasizing the reduction to the minimum of waste in the sanitary process and giving value to the current resources, materials and products used, proposing significant improvements.