Forecasting model selection through out-of-sample rolling horizon weighted errors

Demand forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts. © 2011 Elsevier Ltd. All rights reserved.Poler Escoto, R.; Mula, J. (2011). Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Systems with Applications. 38(12):14778-14785. doi:10.1016/j.eswa.2011.05.072S1477814785381

Green supply chain quantitative models for sustainable inventory management: A review

[EN] This paper provides a systematic and up-to-date review and classification of 91 studies on quantitative methods of green supply chains for sustainable inventory management. It particularly identifies the main study areas, findings and quantitative models by setting a point for future research opportunities in sustainable inventory management. It seeks to review the quantitative methods that can better contribute to deal with the environmental impact challenge. More specifically, it focuses on different supply chain designs (green supply chain, sustainable supply chain, reverse logistics, closed-loop supply chain) in a broader application context. It also identifies the most important variables and parameters in inventory modelling from a sustainable perspective. The paper also includes a comparative analysis of the different mathematical programming, simulation and statistical models, and their solution approach, with exact methods, simulation, heuristic or meta-heuristic solution algorithms, the last of which indicate the increasing attention paid by researchers in recent years. The main findings recognise mixed integer linear programming models supported by heuristic and metaheuristic algorithms as the most widely used modelling approach. Minimisation of costs and greenhouse gas emissions are the main objectives of the reviewed approaches, while social aspects are hardly addressed. The main contemplated inventory management parameters are holding costs, quantity to order, safety stock and backorders. Demand is the most frequently shared information. Finally, tactical decisions, as opposed to strategical and operational decisions, are the main ones.The research leading to these results received funding from the Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". It was also funded by the National Agency for Research and Development (ANID) / Scholarship Program/Doctorado Becas en el Extranjero/2020 72210174.Becerra, P.; Mula, J.; Sanchis, R. (2021). Green supply chain quantitative models for sustainable inventory management: A review. Journal of Cleaner Production. 328:1-16. https://doi.org/10.1016/j.jclepro.2021.129544S11632

Optimising location, inventory and transportation in a sustainable closed-loop supply chain

[EN] Operations management researchers and practitioners have shown increasing interest in incorporating sustainability into supply chain (SC) design models. This means that sustainability must be considered in all aspects of the SC, including location, inventory and transportation (LIT) decisions. Hence the aim of this article is to propose an optimisation model that incorporates: (i) LIT decisions in an integrated manner; (ii) the three sustainability (3S) aspects, i.e. economic, environmental and social, into each named decisions; and (iii) a closed-loop supply chain (CLSC) structure. The proposed formulation is a multi-objective mixed integer non-linear programming (MO-MINLP) model whose objectives consider minimisation of economic and social costs (economic aspect) and carbon emissions (environmental aspect), and maximisation of the social impact of SC operations (social aspect). A transformation technique is applied to one of the objective functions, which results in an MO-MILP model solved by the lexicographic method. This article focuses on commodity industries where only one finished product is manufactured. Hence the 3S-LIT model is validated with a randomly generated dataset and against a recently published alternative model applied to the copper mining industryThe research leading to these results received funding from the project "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; from the grant PDC2022-133957-I00 funded by MCIN/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR; and from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101070076 "Optimizing Production and Logistic Resources in the Time-critical Bio Production Industries in Europe" (CLARUS). It was also funded by the National Agency for Research andDevelopment (ANID)/Scholarship Program/Doctorado Becas en el Extranjero/2020 -72210174 and sponsored by the Universidad de Atacama as part of an academic improvement plan.Becerra, P.; Mula, J.; Sanchis, R. (2023). Optimising location, inventory and transportation in a sustainable closed-loop supply chain. International Journal of Production Research. 1-24. https://doi.org/10.1080/00207543.2023.219751512

Quantitative modelling approaches for lean manufacturing under uncertainty

[EN] Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the opera-tions that do not add value to the product or processes to increase the value of each performedactivity. Here the main motivation is to study how quantitative modelling approaches can supportLM tools even under system and environment uncertainties. The main contributions of the articleare: (i) providing a systematic literature review of 99 works related to the modelling of uncertaintyin LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifyingLM works under uncertainty; and (iv) identify quantitative models and their solution to deal withuncertainty in LM environments by identifying the main variables involved. Hence this article pro-vides a conceptual framework for future LM quantitative modelling under uncertainty as a guide foracademics, researchers and industrial practitioners. The main findings identify that LM under uncer-tainty has been empirically investigated mainly in the US, India and the UK in the automotive andaerospace manufacturing sectors using analytical and simulation models to minimise time and cost.Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce wastein a context of system uncertainty.The research leading to these results received funding fromthe project 'Industrial Production and Logistics Optimizationin Industry 4.0' (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and grant PDC2022-133957-I00 funded by MCIN/AEI /10.13039/501100011033 and by European Union Next Generation EU/PRTR.Rojas, T.; Mula, J.; Sanchis, R. (2023). Quantitative modelling approaches for lean manufacturing under uncertainty. International Journal of Production Research. 1-27. https://doi.org/10.1080/00207543.2023.229313812

Reporting on Internationalization of Operations, GLOBOP: Design and Management of Global Supply Chains

[EN] The research project entitled Design and Management of Global Supply Chains (GLOBOP) focuses on the specific model configuration stages with productive multi location and new productive implantations, which affect the design of productive and logistic systems, and the associated supplier network design. This research project aims to offer a methodological and technical solution for those companies that have begun an internationalization process in recent years (late movers).This research is funded by a Spanish Ministry of Science and Education Project entitled ‘Operations Design and Management in Global Supply Chains (GLOBOP)’ (Ref. DPI2012-38061-C02-01).Mula Bru, J. (2014). Reporting on Internationalization of Operations, GLOBOP: Design and Management of Global Supply Chains. Industrial Engineering and Management. 3(3):1-3. https://doi.org/10.4172/2169-0316.1000131S133

SIMULATION OPTIMIZATION FOR THE INVENTORY MANAGEMENT OF HEALTHCARE SUPPLIES

[EN] This article covers the inventory management of healthcare supplies problem. Based on the mathematical programming model set out by [1], a causal model and a flow chart were developed to outline the simulation model, which was to be later applied to a highly specialized medical institution that performs high-risk heart surgery, such as catheterizations and angioplasties. With this simulation model, a purchases plan with 21 healthcare supplies was obtained that contemplates all the problem¿s restrictions: purchasing policy (safety stock, available budget); the warehouse¿s physical reality (warehouse capacity); characteristics of supplies (useful life, service level); and suppliers (price, capacity and size of lots or rounding value). Different indicators were also considered, such as service levels, costs of purchases, stockouts costs and inventory maintenance costs. The results obtained with the simulation model came very close to the mathematical programming results, but the computing times were considerably shorter.This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00) and the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 IDIFEDER/2018/025.Buschiazzo, M.; Mula, J.; Campuzano-Bolarin, F. (2020). SIMULATION OPTIMIZATION FOR THE INVENTORY MANAGEMENT OF HEALTHCARE SUPPLIES. International Journal of Simulation Modelling. 19(2):255-266. https://doi.org/10.2507/IJSIMM19-2-514S25526619

Digital twin for supply chain master planning in zero-defect manufacturing

[EN] Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC¿s master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0.The research leading to these results received funding from the EuropeanUnion H2020 Program with grant agreement No. 825631 Zero Defect Manufacturing Platform(ZDMP) and with grant agreement No. 958205 Industrial Data Services for Quality Control inSmart Manufacturing (i4Q) and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 Optimisation of zero-defects productiontechnologies enabling supply chains 4.0 (CADS4.0).Serrano, JC.; Mula, J.; Poler, R. (2021). Digital twin for supply chain master planning in zero-defect manufacturing. IFIP Advances in Information and Communication Technology. 626:102-111. https://doi.org/10.1007/978-3-030-78288-7_1010211162

INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN

[EN] The general objective of this paper is to simulate a supply chain to assess the effects that different inventory management policies and transport capacity systems have on costs (transport) and service levels (stockouts). This paper specifically aimed to facilitate the decision-making process about planning distribution capacities, particularly when contracting a transport fleet in a supply chain under uncertainty with a 1-year time horizon by evaluating different types of scenarios, which vary depending on availability of vehicles and obtaining vehicles. The system dynamics simulation model was applied to a real-world food supply chain and can be adopted by chains related to diversified cropping systems. The results provide the best decision alternative in terms of costs and inventory levels by considering the transport capacity life cycle, the time to acquire additional transport capacity, the reorder point in days of stock and the target inventory.This work was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: from practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003.Freile, A.; Mula, J.; Campuzano Bolarin, F. (2020). INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN. International Journal of Simulation Modelling. 19(3):434-445. https://doi.org/10.2507/IJSIMM19-3-52343444519

A general outline of a sustainable supply chain 4.0

[EN] This article presents a literature review to identify the current knowledge of supply chains 4.0 from the sustainability perspective. Reviewed papers were classified in terms of objectives, results, and sustainability approaches. Additionally, a critical discussion with the main results and recommendations for further research was carried out. Manufacturing supply chains have been contemplated but agri-food supply chains and chains related to diversified cropping systems have been also considered. In this way, 54 articles were identified and revised, and were classified according to the three main aspects of sustainability: economic, social, and environmental. The classification of articles indicated that more attention has been paid to the environmental aspect in the industry 4.0 (I4.0) context in the literature, while the social aspect has been paid less attention. Finally, reference frameworks were identified, along with the I4.0 models, algorithms, heuristics, metaheuristics, and technologies, which have enabled sustainability in supply chains.This research was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: From practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003; and the Spanish Ministry of Science, Innovation, and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00).Cañas, H.; Mula, J.; Campuzano-Bolarín, F. (2020). A general outline of a sustainable supply chain 4.0. Sustainability. 12(19):1-17. https://doi.org/10.3390/su121979781171219Design Principles for Industrie 4.0 Scenarios https://ieeexplore.ieee.org/document/7427673Liao, Y., Deschamps, F., Loures, E. de F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609-3629. doi:10.1080/00207543.2017.1308576Tseng, M.-L., Zhu, Q., Sarkis, J., & Chiu, A. S. F. (2018). Responsible consumption and production (RCP) in corporate decision-making models using soft computation. Industrial Management & Data Systems, 118(2), 322-329. doi:10.1108/imds-11-2017-0507Ghadimi, P., Wang, C., Lim, M. K., & Heavey, C. (2019). Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains. Computers & Industrial Engineering, 127, 588-600. doi:10.1016/j.cie.2018.10.050Wang, C., Ghadimi, P., Lim, M. K., & Tseng, M.-L. (2019). A literature review of sustainable consumption and production: A comparative analysis in developed and developing economies. Journal of Cleaner Production, 206, 741-754. doi:10.1016/j.jclepro.2018.09.172Exploring Linkages between Lean and Green Supply Chain and the Industry 4.0 https://link.springer.com/chapter/10.1007/978-3-319-59280-0_103Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168-179. doi:10.1016/j.psep.2018.04.018Lin, K., Shyu, J., & Ding, K. (2017). A Cross-Strait Comparison of Innovation Policy under Industry 4.0 and Sustainability Development Transition. Sustainability, 9(5), 786. doi:10.3390/su9050786Man, J. C. de, & Strandhagen, J. O. (2017). An Industry 4.0 Research Agenda for Sustainable Business Models. Procedia CIRP, 63, 721-726. doi:10.1016/j.procir.2017.03.315KIEL, D., MÜLLER, J. M., ARNOLD, C., & VOIGT, K.-I. (2017). SUSTAINABLE INDUSTRIAL VALUE CREATION: BENEFITS AND CHALLENGES OF INDUSTRY 4.0. International Journal of Innovation Management, 21(08), 1740015. doi:10.1142/s1363919617400151Waibel, M. W., Steenkamp, L. P., Moloko, N., & Oosthuizen, G. A. (2017). Investigating the Effects of Smart Production Systems on Sustainability Elements. Procedia Manufacturing, 8, 731-737. doi:10.1016/j.promfg.2017.02.094Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering, 127, 925-953. doi:10.1016/j.cie.2018.11.030Ding, B. (2018). Pharma Industry 4.0: Literature review and research opportunities in sustainable pharmaceutical supply chains. Process Safety and Environmental Protection, 119, 115-130. doi:10.1016/j.psep.2018.06.031Bag, S., Telukdarie, A., Pretorius, J. H. C., & Gupta, S. (2018). Industry 4.0 and supply chain sustainability: framework and future research directions. Benchmarking: An International Journal. doi:10.1108/bij-03-2018-0056Ghafoorpoor Yazdi, P., Azizi, A., & Hashemipour, M. (2018). An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach. Sustainability, 10(9), 3031. doi:10.3390/su10093031Braccini, A., & Margherita, E. (2018). Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company. Sustainability, 11(1), 36. doi:10.3390/su11010036Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215-225. doi:10.1016/j.jmsy.2018.10.006Paravizo, E., Chaim, O. C., Braatz, D., Muschard, B., & Rozenfeld, H. (2018). Exploring gamification to support manufacturing education on industry 4.0 as an enabler for innovation and sustainability. Procedia Manufacturing, 21, 438-445. doi:10.1016/j.promfg.2018.02.142Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, 10(1), 247. doi:10.3390/su10010247Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. doi:10.1016/j.psep.2018.05.009Hidayatno, A., Destyanto, A. R., & Hulu, C. A. (2019). Industry 4.0 Technology Implementation Impact to Industrial Sustainable Energy in Indonesia: A Model Conceptualization. Energy Procedia, 156, 227-233. doi:10.1016/j.egypro.2018.11.133Sustainable Value Stream Mapping and Technologies of Industry 4.0 in Manufacturing Process Reconfiguration: A Case Study in an Apparel Company https://ieeexplore.ieee.org/document/8476750Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of Cleaner Production, 204, 643-659. doi:10.1016/j.jclepro.2018.08.327Wiśniewska-Sałek, A. (2018). Sustainable Development in Accordance With the Concept of Industry 4.0 on the Example of the Furniture Industry. MATEC Web of Conferences, 183, 04005. doi:10.1051/matecconf/201818304005Müller, J. M., & Voigt, K.-I. (2018). Sustainable Industrial Value Creation in SMEs: A Comparison between Industry 4.0 and Made in China 2025. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(5), 659-670. doi:10.1007/s40684-018-0056-zTsai, W.-H., & Lu, Y.-H. (2018). A Framework of Production Planning and Control with Carbon Tax under Industry 4.0. Sustainability, 10(9), 3221. doi:10.3390/su10093221Birkel, H., Veile, J., Müller, J., Hartmann, E., & Voigt, K.-I. (2019). Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers. Sustainability, 11(2), 384. doi:10.3390/su11020384Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., & Ruiz, M. C. (2018). OperaBLE: An IoT-Based Wearable to Improve Efficiency and Smart Worker Care Services in Industry 4.0. Journal of Sensors, 2018, 1-12. doi:10.1155/2018/6272793Ardanza, A., Moreno, A., Segura, Á., de la Cruz, M., & Aguinaga, D. (2019). Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm. International Journal of Production Research, 57(12), 4045-4059. doi:10.1080/00207543.2019.1572932Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7(1), 36. doi:10.3390/pr7010036Belaud, J.-P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50. doi:10.1016/j.compind.2019.06.006Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to Industry 4.0. British Food Journal, 121(8), 1730-1743. doi:10.1108/bfj-11-2018-0747Miranda, J., Ponce, P., Molina, A., & Wright, P. (2019). Sensing, smart and sustainable technologies for Agri-Food 4.0. Computers in Industry, 108, 21-36. doi:10.1016/j.compind.2019.02.002Stock, T., Obenaus, M., Kunz, S., & Kohl, H. (2018). Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254-267. doi:10.1016/j.psep.2018.06.026Chaim, O., Muschard, B., Cazarini, E., & Rozenfeld, H. (2018). Insertion of sustainability performance indicators in an industry 4.0 virtual learning environment. Procedia Manufacturing, 21, 446-453. doi:10.1016/j.promfg.2018.02.143Smart Factories in Industry 4.0: A Review of the Concept and of Energy Management Approached in Production Based on the Internet of Things Paradigm https://ieeexplore.ieee.org/document/7058728Bonilla, S., Silva, H., Terra da Silva, M., Franco Gonçalves, R., & Sacomano, J. (2018). Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges. Sustainability, 10(10), 3740. doi:10.3390/su10103740De Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018). When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18-25. doi:10.1016/j.techfore.2018.01.017Meng, Y., Yang, Y., Chung, H., Lee, P.-H., & Shao, C. (2018). Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability, 10(12), 4779. doi:10.3390/su10124779Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107-119. doi:10.1016/j.compind.2018.06.004Huh, J.-H., & Lee, H.-G. (2018). Simulation and Test Bed of a Low-Power Digital Excitation System for Industry 4.0. Processes, 6(9), 145. doi:10.3390/pr6090145Fritzsche, K., Niehoff, S., & Beier, G. (2018). Industry 4.0 and Climate Change—Exploring the Science-Policy Gap. Sustainability, 10(12), 4511. doi:10.3390/su10124511IoT Solution for Energy Optimization in Industry 4.0: Issues of a Real-life Implementation https://ieeexplore.ieee.org/document/8534537Towards a System-of-Systems for Improved Road Construction Efficiency Using Lean and Industry 4.0 https://ieeexplore.ieee.org/document/8428698HERNANDEZ LUNA, M., ROBLEDO FAVA, R., FERNANDEZ DE CORDOBA CASTELLA, P., PAREDES, A., MICHINEL ALVAREZ, H., & ZARAGOZA FERNANDEZ, S. (2018). USE OF STATISTICAL CORRELATION FOR ENERGY MANAGEMENT IN OFFICE PREMISES ADOPTING TECHNIQUES OF THE INDUSTRY 4.0. DYNA, 93(1), 602-607. doi:10.6036/8844Energy Management in Industry 4.0 Ecosystem: A Review on Possibilities and Concerns https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2018/097.pdfWang, X. V., & Wang, L. (2018). Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0. International Journal of Production Research, 57(12), 3892-3902. doi:10.1080/00207543.2018.1497819Tsai, W.-H. (2018). Green Production Planning and Control for the Textile Industry by Using Mathematical Programming and Industry 4.0 Techniques. Energies, 11(8), 2072. doi:10.3390/en11082072Sherazi, H. H. R., Imran, M. A., Boggia, G., & Grieco, L. A. (2018). Energy Harvesting in LoRaWAN: A Cost Analysis for the Industry 4.0. IEEE Communications Letters, 22(11), 2358-2361. doi:10.1109/lcomm.2018.2869404Tsai, W.-H., Chu, P.-Y., & Lee, H.-L. (2019). Green Activity-Based Costing Production Planning and Scenario Analysis for the Aluminum-Alloy Wheel Industry under Industry 4.0. Sustainability, 11(3), 756. doi:10.3390/su11030756Analysis of the Variables That Affect the Intention to Adopt Precision Agriculture for Smart Water Management in Agriculture 4.0 Context https://ieeexplore.ieee.org/document/8766384Franciosi, C., Iung, B., Miranda, S., & Riemma, S. (2018). Maintenance for Sustainability in the Industry 4.0 context: a Scoping Literature Review. IFAC-PapersOnLine, 51(11), 903-908. doi:10.1016/j.ifacol.2018.08.459DE LAS HERAS GARCIA DE VINUESA, A., AGUAYO GONZALEZ, F., & CORDOBA ROLDAN, A. (2018). PROPOSAL OF A FRAMEWORK FOR THE EVALUATION OF THE SUSTAINABILITY OF PRODUCTS FROM THE PARADIGM OF THE CIRCULAR ECONOMY BASED ON INDUSTRY 4.0 (1ST PART). DYNA, 93(1), 360-364. doi:10.6036/8631DE LAS HERAS GARCIA DE VINUESA, A., AGUAYO GONZALEZ, F., & CORDOBA ROLDAN, A. (2018). PROPOSAL OF A FRAMEWORK FOR THE EVALUATION OF THE SUSTAINABILITY OF PRODUCT SUSTAINABILITY FROM THE PARADIGM OF THE CIRCULAR ECONOMY BASED ON INDUSTRY 4.0. (Part 2). DYNA, 93(1), 488-496. doi:10.6036/8718Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Rocha-Lona, L., & Tortorella, G. (2019). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. Journal of Manufacturing Technology Management, 30(3), 607-627. doi:10.1108/jmtm-03-2018-0071Joung, C. B., Carrell, J., Sarkar, P., & Feng, S. C. (2013). Categorization of indicators for sustainable manufacturing. Ecological Indicators, 24, 148-157. doi:10.1016/j.ecolind.2012.05.030Campuzano-Bolarín, Marín-García, Moreno-Nicolás, Bogataj, & Bogataj. (2019). Supply Chain Risk of Obsolescence at Simultaneous Robust Perturbations. Sustainability, 11(19), 5484. doi:10.3390/su11195484Campuzano-Bolarín, F., Mula, J., Díaz-Madroñero, M., & Legaz-Aparicio, Á.-G. (2019). A rolling horizon simulation approach for managing demand with lead time variability. International Journal of Production Research, 58(12), 3800-3820. doi:10.1080/00207543.2019.163484