Special Issue "Smart Production Operations Management and Industry 4.0"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editors

Prof. Dr. Giorgio Mossa
Website
Guest Editor
Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Bari, 70126, Italy
Interests: operations management; smart manufacturing; sustainable logistics; environmental management; life cycle assessment; smart city; safety and human factors
Prof. Dr. Fabio De Felice
Website
Guest Editor
Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, 0043, Italy
Interests: digital manufacturing, multi-criteria decision making, safety and human factors, smart manufacturing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to investigate and advance our understanding of the impacts of recent technological developments based on the fourth industrial revolution (also known as Industry 4.0) on the evolution of operations and supply chain management.

In this context, the Special Issue focuses on novel theories, researches, case studies, and literature reviews exploring the changes in business models, strategies, and management modus operandi of firms fostering sustainability principles in the new digital era.

This Special Issue will investigate these emerging challenges across multiple sectors and different industries.

The smart technologies for operations and supply chain management—in both the domains of information technology (IT) and operational technology (OT)—that will be considered in the Special Issue may include:

  • Industrial Internet-of-Things;
  • Industrial analytics;
  • Cyberphysical systems and digital twin;
  • Smart manufacturing;
  • Augmented reality, virtual reality, and mixed reality immersive technologies;
  • Additive manufacturing;
  • Autonomous vehicles and drones.

Topics to be covered include, but are not restricted to, the following aspects of smart and sustainable product lifecycles, smart and sustainable supply chains, and smart and sustainable factories:

  • Challenges, visions, and concepts for Industry 4.0;
  • New business models from smart manufacturing and services;
  • Innovative digital manufacturing/service/supply chain models for Industry 4.0;
  • Industry 4.0 standards for operations and supply chain management;
  • KPIs and performance evaluation of smart production systems;
  • Resource efficiency and sustainability in operations and supply chains with Industry 4.0;
  • Environmental impacts of operations and supply chains from Industry 4.0;
  • Cyberphysical systems for operation of smart production systems;
  • Modeling and simulation of smart production systems;
  • Multi-criteria decision making and decision analysis in smart production systems;
  • Manufacturing data analysis;
  • Real-time diagnostics for quality, reliability, and maintenance;
  • Advanced human–machine interface;
  • Human factors, industrial ergonomics, and safety in smart factories;
  • Integration of additive manufacturing in smart factories.

Prof. Dr. Giorgio Mossa
Prof. Dr. Fabio De Felice
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Industry 4.0
  • Digital transformation of production systems
  • Cyberphysical production systems
  • Smart manufacturing
  • Smart supply chains

Published Papers (16 papers)

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Open AccessArticle
Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break
Sustainability 2020, 12(14), 5543; https://doi.org/10.3390/su12145543 - 09 Jul 2020
Abstract
Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the [...] Read more.
Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don’t target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
A Door-to-Door Waste Collection System Case Study: A Survey on its Sustainability and Effectiveness
Sustainability 2020, 12(14), 5520; https://doi.org/10.3390/su12145520 - 08 Jul 2020
Abstract
Municipal waste management is a relevant topic these days, in its relation to sustainable and environmental concerns. Sorting waste fractions at home for a door-to-door collection system proves to positively affect the environmental impacts of waste management strategies both by reducing the amounts [...] Read more.
Municipal waste management is a relevant topic these days, in its relation to sustainable and environmental concerns. Sorting waste fractions at home for a door-to-door collection system proves to positively affect the environmental impacts of waste management strategies both by reducing the amounts of the waste landfilled and by originating new circular economies. However, the environmental impact caused by both waste collection and transport, together with waste quality, should be carefully evaluated to assess the sustainability of such a collection system. In order to evaluate the logistic and environmental effectiveness of a newly implemented door-to-door collection system in Altamura, a mid-sized town in Southern Italy, a survey was designed and submitted to a sample of citizens. The results obtained from the 385 completed surveys show that the door-to-door collection of glass waste is inefficient since most of the designated bins remain partially filled and less frequently delivered; citizens are more motivated to adequately collect sorted waste fractions upon receiving information about the subsequent environmental benefits and outcomes of the fractions collected; a high percentage of people still use disposable items in their daily life. Possible changes to the weekly bins collection schedule have been proposed in order to have a more proficient and environmentally sustainable waste collection service in the town. The survey is part of a project aiming at developing a smart device to support users in home waste management. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
An Integrated Key Performance Measurement for Manufacturing Operations Management
Sustainability 2020, 12(13), 5260; https://doi.org/10.3390/su12135260 - 29 Jun 2020
Abstract
This paper proposes a comprehensive production performance measurement framework and illustrates the method to evaluate the performance and guide practitioners to make further improvement. The development comprises four steps. (1) Performance indicators derived from business excellence models are enumerated to provide the performance [...] Read more.
This paper proposes a comprehensive production performance measurement framework and illustrates the method to evaluate the performance and guide practitioners to make further improvement. The development comprises four steps. (1) Performance indicators derived from business excellence models are enumerated to provide the performance model: 74 indicators, which can be classified in terms of their characteristics, are identified in six criteria. (2) A multiple criteria decision-making approach based on the analytic hierarchical and network processes, which determine the weights of the criteria and indicators, is applied. In addition, this study introduced additional formulas to derive the final performance values. (3) A performance measurement framework that integrates the measurement and result analysis processes is implemented. (4) The proposed framework is verified through a case study. The results of the case study show that the proposed framework identifies the gaps and discrepancies among the management levels, enabling the determination of means for continuous improvement. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
An Economic Order Quantity Stochastic Dynamic Optimization Model in a Logistic 4.0 Environment
Sustainability 2020, 12(10), 4075; https://doi.org/10.3390/su12104075 - 15 May 2020
Abstract
This paper proposes a stock dynamic sizing optimization under the Logistic 4.0 environment. The safety stock is conceived to fill up the demand variability, providing continuous stock availability. Logistic 4.0 and the smart factory topics are considered. It focuses on vertical integration to [...] Read more.
This paper proposes a stock dynamic sizing optimization under the Logistic 4.0 environment. The safety stock is conceived to fill up the demand variability, providing continuous stock availability. Logistic 4.0 and the smart factory topics are considered. It focuses on vertical integration to implement flexible and reconfigurable smart production systems using the information system integration in order to optimize material flow in a 4.0 full-service approach. The proposed methodology aims to reduce the occurring stock-out events through a link among the wear-out items rate and the downstream logistic demand. The failure rate items trend is obtained through life-cycle state detection by a curve fitting technique. Therefore, the optimal safety stock size is calculated and then validated by an auto-tuning iterative modified algorithm. In this study, the reorder time has been optimized. The case study refers to the material management of a very high-speed train. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
The Model of Diffusion of Knowledge on Industry 4.0 in Marshallian Clusters
Sustainability 2020, 12(9), 3815; https://doi.org/10.3390/su12093815 - 07 May 2020
Abstract
Industry 4.0 is perceived as the innovative approach to manufacturing management, thanks to which enterprises gain efficiency and improve competitiveness. The research on Industry 4.0 carried and published refer to the scope of solutions recognized as Industry 4.0 and the level of recognition [...] Read more.
Industry 4.0 is perceived as the innovative approach to manufacturing management, thanks to which enterprises gain efficiency and improve competitiveness. The research on Industry 4.0 carried and published refer to the scope of solutions recognized as Industry 4.0 and the level of recognition and implementation of solutions within Industry 4.0. The conclusion from the latter is that enterprises, though striving for innovation and improvement, have no knowledge on solutions available. Hence, the research goal of the paper was to identify the level of knowledge on Industry 4.0 among enterprises and analyze the mechanism of knowledge diffusion. The subjects of research were enterprises in Marshallian clusters, as they are linked, which may contribute to knowledge diffusion and Industry 4.0 solutions dissemination. Research methodology implemented included three stages, namely knowledge level recognition, Industry 4.0 knowledge diffusion model development, and validation of the model with case-based simulation. The conclusions, based on simulation results, refer to mechanism and the most important parameters of knowledge on Industry 4.0 diffusion. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications
Sustainability 2020, 12(9), 3791; https://doi.org/10.3390/su12093791 - 07 May 2020
Cited by 1
Abstract
Many companies are struggling to manage their production systems due to increasing market uncertainty. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing have been touted as having the potential to transform production management, the realities of [...] Read more.
Many companies are struggling to manage their production systems due to increasing market uncertainty. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing have been touted as having the potential to transform production management, the realities of their adoption and use have been much more challenging than anticipated. In this paper, we explore these challenges and present a conceptual model, a use-case matrix and a product–process framework for a smart production planning and control (smart PPC) system and illustrate the use of these artefacts through four case companies. The presented model adopts an incremental approach that companies with limited resources could employ in improving their PPC process in the context of industry 4.0 and sustainability. The results reveal that while make-to-order companies are more likely to derive greater benefits from a smart product strategy, make-to-stock companies are more likely to derive the most benefit from pursuing a smart process strategy, and consequently a smart PPC solution. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles
Sustainability 2020, 12(9), 3604; https://doi.org/10.3390/su12093604 - 29 Apr 2020
Abstract
Due to the increasing demand for water supply of urban areas, treatment and supply plants are becoming important to ensure availability and quality of this essential resource for human health. Enabling technologies of Industry 4.0 have the potential to improve performances of treatment [...] Read more.
Due to the increasing demand for water supply of urban areas, treatment and supply plants are becoming important to ensure availability and quality of this essential resource for human health. Enabling technologies of Industry 4.0 have the potential to improve performances of treatment plants. In this paper, after reviewing contributions in scientific literature on I4.0 technologies in dam operations, a study carried out on a Brazilian dam is presented and discussed. The main purpose of the study is to evaluate the economic, environmental, and social advantages achieved through the adoption of Artificial Intelligence (AI) in dam operations. Unlike automation that just respond to commands, AI uses a large amount of data training to make computers able to take the best decision. The current study involved a company that managed six reservoirs for treatment systems supplying water to almost ten million people at the metropolitan area of São Paulo City. Results of the study show that AI adoption could lead to economic gain in figures around US$ 51,000.00 per year, as well as less trips between sites and less overtime extra costs on the main operations. Increasing gates maneuvers agility result in significant environmental gains with savings of about 4.32 billion L of water per year, enough to supply 73,000 people. Also, decreasing operational vehicle utilization results in less emissions. Finally, the AI implementation improved the safety of dam operations, resulting in social benefits such as the flood risk mitigation in cities and the health and safety of operators. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
Impact of Advanced Manufacturing Technologies on Green Innovation
Sustainability 2020, 12(8), 3499; https://doi.org/10.3390/su12083499 - 24 Apr 2020
Abstract
The main aim of this paper is to evaluate if manufacturing firms can boost their performance through green innovations. The literature on this topic shows contradictory findings. We have concentrated on the effect of advanced manufacturing technologies (AMT) on green innovations. To the [...] Read more.
The main aim of this paper is to evaluate if manufacturing firms can boost their performance through green innovations. The literature on this topic shows contradictory findings. We have concentrated on the effect of advanced manufacturing technologies (AMT) on green innovations. To the authors’ best knowledge, this research is the first to examine the impact of a firm’s own AMT on green innovation and the firm’s performance at the same time. Green innovation in our research relates to green product innovation. The data analysis is performed through three-step OLS regression analysis and two evaluation models. One model looks at AMT and how they affect green innovation, and the second model looks at how AMT and green innovations affect performance. Our findings suggest that AMT contribute to both the firm’s performance and green innovation. We found that technology is a moderator for green innovations. While the majority of research emphasizes that firms will not eco-innovate unless they receive subsidies or severe restrictions are imposed, we show that out of all innovations, 66% are green innovations. Restrictions such as having ISO 14000 certification do not contribute to green innovation, but rather the age of the firm does. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
Digital Facility Layout Planning
Sustainability 2020, 12(8), 3349; https://doi.org/10.3390/su12083349 - 20 Apr 2020
Abstract
In recent years, companies have increased their focus on sustainability to achieve environmental-friendly improvements, to manage pressures from society and regulations, and to attract customers that appreciate sustainability efforts. While companies have mainly aimed short-term/operational improvements, long-term improvements are difficult to reach. One [...] Read more.
In recent years, companies have increased their focus on sustainability to achieve environmental-friendly improvements, to manage pressures from society and regulations, and to attract customers that appreciate sustainability efforts. While companies have mainly aimed short-term/operational improvements, long-term improvements are difficult to reach. One of the fundamental, strategical decision-making processes for a company is facility layout planning. The layout of a facility can have a significant impact on daily operations. Aiming for the goal of sustainability, a dynamic layout decision-making process can support in achieving it. However, the technologies used currently enable only the design of a static layout due to the time-consuming operations involved. In this paper, the introduction of emerging technologies such as 3D mapping, Indoor Positioning System (IPS), Motion Capture System (MoCap), and Immersive Reality (IR) for dynamic layout planning are assessed and discussed. The results obtained clearly demonstrate that the usage of these technologies favor a reconfigurable layout, positively affecting all the three pillars constituting the sustainability concept: the costs involved are reduced, social aspects are improved, and the environment is safeguarded. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model
Sustainability 2020, 12(8), 3111; https://doi.org/10.3390/su12083111 - 13 Apr 2020
Abstract
In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The [...] Read more.
In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses’ energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
New Business Models for Sustainable Spare Parts Logistics: A Case Study
Sustainability 2020, 12(8), 3071; https://doi.org/10.3390/su12083071 - 11 Apr 2020
Abstract
Additive manufacturing of spare parts significantly impacts industrial, social, and environmental aspects. However, a literature review shows that: (i) academic papers on the adoption of additive manufacturing have focused mainly on large companies; (ii) the methods required by SMEs to adopt new technologies [...] Read more.
Additive manufacturing of spare parts significantly impacts industrial, social, and environmental aspects. However, a literature review shows that: (i) academic papers on the adoption of additive manufacturing have focused mainly on large companies; (ii) the methods required by SMEs to adopt new technologies differ from those employed by large companies; and (iii) recent studies suggest that a suitable way to help small- and medium-sized enterprises (SMEs) to adopt new additive manufacturing technologies from the academic world is by presenting case studies in which SMEs are involved. Given the increasing number of global SMEs (i.e., SMEs that manufacture locally and sell globally), we claim that these companies need to be assisted in adopting spare-parts additive manufacturing for the sake of resource and environmental sustainability. To bridge this gap, the purpose of this article is to present a case study approach that shows how a digital supply chain for spare parts has the potential to bring about changes in business models with significant benefits for both global SMEs (more effective logistic management), customers (response time), and the environment (reduced energy, emissions, raw materials, and waste). Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
Digitalization: An Opportunity for Contributing to Sustainability From Knowledge Creation
Sustainability 2020, 12(4), 1460; https://doi.org/10.3390/su12041460 - 15 Feb 2020
Cited by 3
Abstract
This paper aims at exploring the perspective of sustainability when digital transformation is adopted by one organization, although it was not the first goal targeted. Two different cases are analyzed, covering manufacturing and service industries. In those cases different factors will be analyzed, [...] Read more.
This paper aims at exploring the perspective of sustainability when digital transformation is adopted by one organization, although it was not the first goal targeted. Two different cases are analyzed, covering manufacturing and service industries. In those cases different factors will be analyzed, mainly focused on the positive effects of knowledge creation facilitated by direct or indirect application of digitalization. Specific analysis of different cases were carried out to identify different initiatives and the impact on environmental performance. The positive effects of the institutional dimension were also assessed. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants
Sustainability 2020, 12(3), 1088; https://doi.org/10.3390/su12031088 - 04 Feb 2020
Cited by 2
Abstract
In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital [...] Read more.
In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research
Sustainability 2020, 12(1), 86; https://doi.org/10.3390/su12010086 - 20 Dec 2019
Cited by 6
Abstract
The adoption of Industry 4.0 technologies has become particularly important nowadays for companies in order to optimize their production processes and organizational structures. However, companies sometimes find it difficult to develop a strategic plan that innovates their current business model and develops an [...] Read more.
The adoption of Industry 4.0 technologies has become particularly important nowadays for companies in order to optimize their production processes and organizational structures. However, companies sometimes find it difficult to develop a strategic plan that innovates their current business model and develops an Industry 4.0 vision. To overcome the growing uncertainty and dissatisfaction in implementing Industry 4.0, new methods and tools that specifically address dedicated companies’ areas, such as logistics, supply chain management, and manufacturing processes, were developed to provide guidance and support to align companies’ business strategies and operations. In particular, this paper develops and presents the application of a maturity model for Logistics 4.0, focusing on the specific applications of Industry 4.0 in the area of logistics. To do so, extant maturity models, linked to the context of Industry 4.0 implementation in logistics processes, were examined in the main scientific research. Afterward, two companies have been investigated through a survey, built around three fundamental macro-aspects, named (i) the propensity of the company towards Industry 4.0 and Logistics 4.0, (ii) the current use of technologies in the logistics process, and (iii) the investments’ level towards Industry 4.0 technologies for a Logistics 4.0 transition. By doing so, a maturity model for Logistics 4.0 emerged as the main result of our research, able to identify the level of maturity of companies in implementing the Industry 4.0 technologies in their logistics processes. Moreover, the model highlighted the strengths and weaknesses of the two investigated companies with respect to the transition towards Logistics 4.0. On the basis of the obtained results, a roadmap for enhancing the digitalization of logistics processes, according to the principles of the fourth industrial revolution, was finally proposed. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Open AccessArticle
R&D Expenditure for New Technology in Livestock Farming: Impact on GHG Reduction in Developing Countries
Sustainability 2019, 11(24), 7129; https://doi.org/10.3390/su11247129 - 12 Dec 2019
Abstract
The achievement of the objectives of reducing greenhouse gas (GHG) emissions has increasingly received attention and support from decision makers and research by scholars. The livestock sector has always been one of the major sources of GHG emissions, especially in developing countries that [...] Read more.
The achievement of the objectives of reducing greenhouse gas (GHG) emissions has increasingly received attention and support from decision makers and research by scholars. The livestock sector has always been one of the major sources of GHG emissions, especially in developing countries that do not have green technologies to improve the management of livestock waste. In order to achieve an absolute reduction in emissions, developed countries have applied a wide range of mitigation options; however, there are few studies from the developing world, although greenhouse gas emissions in developing countries have registered a rapid growth. Therefore, this research aims to assess and understand whether public R&D investments can affect emissions deriving from the livestock sector in developing countries. We made use of the FAOSTAT data (FAO Statistical Databases United Nations) and ASTI data set (Agricultural Science and Technology Indicators), collecting data from 29 Africa countries, in 2014 (latest data available). The data were analyzed by means of a Generalized Propensity Scores (GPS) approach, an increasingly widespread technique that is more robust than regression models, especially in small datasets. Our analysis suggests that the livestock sector in these countries shows an improvement in its relationships with the environment and GHG emission levels when the level of public R&D (Research and Development) investment on agriculture is greater. Therefore, reducing greenhouse gas emissions by investing in research and development can lead to more efficient and sustainable resource management for developing countries. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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Review

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Open AccessReview
Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions
Sustainability 2020, 12(2), 492; https://doi.org/10.3390/su12020492 - 08 Jan 2020
Cited by 5
Abstract
Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the [...] Read more.
Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0. Full article
(This article belongs to the Special Issue Smart Production Operations Management and Industry 4.0)
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