Top

The International Journal of Advanced Manufacturing Technology

Published in:

02-07-2021 | ORIGINAL ARTICLE

Decision-making in a fast fashion company in the Industry 4.0 era: a Digital Twin proposal to support operational planning

Authors: Carlos Henrique dos Santos, Gustavo Teodoro Gabriel, João Victor Soares do Amaral, José Arnaldo Barra Montevechi, José Antônio de Queiroz

Published in: The International Journal of Advanced Manufacturing Technology | Issue 5-6/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This work explores the improvement of operational decision-making in a Fast Fashion manufacturing company, considering the Industry 4.0 era. The segment requires agile and flexible decision-making techniques to guarantee the companies survival in a high variety environment of products and demand. The proposed approach was based on three stages. First, we suggested changes and improvements in the system to adapt it to the Industry 4.0 principles. Then, we proposed a Digital Twin (DT) focused on operational resource planning (physical and human). The DT was composed of a Discrete Event Simulation model, an Artificial Intelligence model, and a decision dashboard that provides a user-friendly interface for the decision-maker. Finally, the last stage corresponds to cyclical and constant DT-based decision-making. The DT-based decisions helped to decrease the number of operators in the line reducing their idleness and, at the same time, the total lead time became shorter. Therefore, we highlight that the concepts and solutions of Industry 4.0 might be consistent with small companies without major structural changes, contributing to the evolution of the manufacturing systems.

previous article Vortex formation mechanism and experimental research of abrasive flow precision machining special inner curved surface based on large eddy simulation

next article Mechanical performance and microstructure of near-unthinned die-less spinning using ball-crown-shape roller

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Watson MZ, Yan R-N (2013) An exploratory study of the decision processes of fast versus slow fashion consumers. J Fash Mark Manag 17:141–159. https://doi.org/10.1108/JFMM-02-2011-0045CrossRef

Mehrjoo M, Pasek ZJ (2015) Risk assessment for the supply chain of fast fashion apparel industry: a system dynamics framework. Int J Prod Res 54:28–48. https://doi.org/10.1080/00207543.2014.997405CrossRef

Fares N, Lebbar M, Sbihi N (2018) Quick response in fast fashion retail: an optimization supply chain responsiveness model. In: Proceedings of the 2018 International Conference on Optimization and Applications. ICOA 2018, Mohammedia, pp 1–5

Caro F, Gallien J (2012) Clearance pricing optimization for a fast-fashion retailer. Oper Res 60:1404–1422. https://doi.org/10.1287/opre.1120.1102MathSciNetCrossRefMATH

Al-Zubaidi H, Tyler D (2004) A simulation model of quick response replenishment of seasonal clothing. Int J Retail Distrib Manag 32:320–327. https://doi.org/10.1108/09590550410538024CrossRef

Sardar S, Lee YH, Memon MS (2016) Multi-objective outsourcing strategies for functional and fast fashion products in textile supply chain. Int J Eng Technol 8:870–886

Wan J, Cai H, Zhou K (2015) Industrie 4.0: enabling technologies. In: Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. ICIT 2015, Harbin, pp 135–140CrossRef

Da Xu L, Xu EL, Li L (2018) Industry 4.0: state of the art and future trends. Int J Prod Res 56:2941–2962. https://doi.org/10.1080/00207543.2018.1444806CrossRef

Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94:3563–3576. https://doi.org/10.1007/s00170-017-0233-1CrossRef

10.

Tao F, Zhang M (2017) Digital Twin Shop-Floor : A New Shop-Floor Paradigm Towards Smart Manufacturing. IEEE Access 5:20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069CrossRef

11.

Cachon GP, Swinney R (2011) The value of fast fashion: quick response, enhanced design, and strategic consumer behavior. Manag Sci 57:778–795. https://doi.org/10.1287/mnsc.1100.1303CrossRefMATH

12.

Passariello C (2008) Logistics Are in Vogue With Designers. In: Wall Str. J. https://www.wsj.com/articles/SB121451654414108561. Accessed 3 Jan 2021

13.

Rohwedder C, Johnson K (2008) Pace-Setting Zara Seeks More Speed To Fight Its Rising Cheap-Chic Rivals. In: Wall Str. J. https://www.wsj.com/articles/SB120345929019578183#:~:text=Permalink-,Pace-Setting Zara Seeks More Speed To,Its Rising Cheap-Chic Rivals&text=ARTEIXO%2C Spain -- Zara stores,As rivals catch up%2C Mr. Accessed 3 Jan 2021

14.

SEBRAE (2014) FAST FASHION NO VAREJO. In: SEBRAE. http://www.bibliotecas.sebrae.com.br/chronus/ARQUIVOS_CHRONUS/bds/bds.nsf/84f335cf0a92c75d356512b9ec15f0ec/$File/2014_08_22_RT_Julho_Varejo_FastFashion_pdf.pdf. Accessed 16 Apr 2021

15.

Bonilla MDR, del Olmo Arriaga JL, Andreu D (2019) The interaction of Instagram followers in the fast fashion sector: the case of Hennes and Mauritz (H&M). J Glob Fash Market 10:342–357. https://doi.org/10.1080/20932685.2019.1649168CrossRef

16.

Majeed MAA, Rupasinghe TD (2017) Internet of things (IoT) embedded future supply chains for industry 4.0: an assessment from an ERP-based fashion apparel and footwear industry. Int J Supply Chain Manag 6:25–40

17.

Nicoletti L, Padovano A (2019) Human factors in occupational health and safety 4.0: a cross-sectional correlation study of workload, stress and outcomes of an industrial emergency response. Int J Simul Process Model 14:178–195. https://doi.org/10.1504/IJSPM.2019.099912CrossRef

18.

Winkelhaus S, Grosse EH (2020) Logistics 4.0: a systematic review towards a new logistics system. Int J Prod Res 58:18–43. https://doi.org/10.1080/00207543.2019.1612964CrossRef

19.

Princes E (2020) Facing disruptive challenges in supply chain 4.0. Int J Supply Chain Manag 9:52–57

20.

Sony M (2018) Industry 4.0 and lean management: a proposed integration model and research propositions. Prod Manuf Res 6:416–432. https://doi.org/10.1080/21693277.2018.1540949CrossRef

21.

Sanders A, Elangeswaran C, Wulfsberg J (2016) Industry 4 . 0 Implies Lean Manufacturing : Research Activities in Industry 4 . 0 Function as Enablers for Lean Manufacturing. J Ind Eng Manag 9:811–833. https://doi.org/10.3926/jiem.194CrossRef

22.

Zhong RY, Xu X, Klotz E, Newman ST (2017) Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 3:616–630. https://doi.org/10.1016/J.ENG.2017.05.015CrossRef

23.

Moeuf A, Pellerin R, Lamouri S, Tamayo-Giraldo S, Barbaray R (2018) The industrial management of SMEs in the era of Industry 4.0. Int J Prod Res 56:1118–1136. https://doi.org/10.1080/00207543.2017.1372647CrossRef

24.

Longo F, Nicoletti L, Padovano A (2017) Smart operators in industry 4.0: a human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput Ind Eng 113:144–159. https://doi.org/10.1016/j.cie.2017.09.016CrossRef

25.

Alfieri A, De Marco A, Pastore E (2019) Last mile logistics in Fast Fashion supply chains: a case study. IFAC-PapersOnLine 52:1693–1698. https://doi.org/10.1016/j.ifacol.2019.11.444CrossRef

26.

Backs S, Jahnke H, Lüpke L, Stücken M, Stummer C (2020) Traditional versus fast fashion supply chains in the apparel industry : an agent-based simulation approach. Ann Oper Res XX:1–27. https://doi.org/10.1007/s10479-020-03703-8CrossRef

27.

Cagliano AC, Demarco A, Rafele C, Volpe S (2011) Using system dynamics in warehouse management : a fast-fashion case study. J Manuf Technol Manag 22:171–188. https://doi.org/10.1108/17410381111102207CrossRef

28.

Fares N, Lebbar M, Sbihi N, Mamoun AEBE (2018) Data Mining Dynamic Hybrid Model for Logistic Supplying Chain : Assortment Setting in Fast Fashion Retail. In: Proceedings of the 2018 Advanced Intelligent Systems for Sustainable Development, pp 1–7. https://doi.org/10.1007/978-3-030-11928-7

29.

Fares N, Lebbar M, Sbihi N (2018) A Customer Profiling’ Machine Learning Approach, for In-store Sales in Fast Fashion. In: Proceedings of the 2018 Advanced Intelligent Systems for Sustainable Development, pp 1–6. https://doi.org/10.1007/978-3-030-11928-7

30.

Kaczorowska-Spychalska D (2018) Shaping consumer behaviour in the fashion industry by interactive communication forms. Fibres Text East Eur 26:13–19. https://doi.org/10.5604/01.3001.0012.1307CrossRef

31.

Kotouza MT, Tsarouchis S, Kyprianidis A et al (2020) Towards Fashion Recommendation: an AI System for Clothing Data Retrieval and Analysis. In: Proceedings of the 2020 International Conference on Artificial Intelligence Applications and Innovations, pp 1–12. https://doi.org/10.1007/978-3-030-49186-4

32.

Kumar P, Ashok D (2018) Artificial neural network based geometric error correction model for enhancing positioning accuracy of a robotic sewing manipulator. Procedia Comput Sci 133:1048–1055. https://doi.org/10.1016/j.procs.2018.07.069CrossRef

33.

Lee S, Lim Y, Lee W et al (2020) A Store Management System for the Improvement of Shopping Process of Omni-shoppers of Fast Fashion Brand. In: Proceedings of the 2020 International Conference on E-Business and Applications, pp 138–144. https://doi.org/10.1145/3387263.3387280

34.

Papachristou E, Chrysopoulos A, Bilalis N (2020) Machine learning for clothing manufacture as a mean to respond quicker and better to the demands of clothing brands : a Greek case study. Int J Adv Manuf Technol:1–12. https://doi.org/10.1007/s00170-020-06157-1

35.

See-to EWK, Ngai EWT (2018) Customer reviews for demand distribution and sales nowcasting: a big data approach. Ann Oper Res 270:415–431. https://doi.org/10.1007/s10479-016-2296-zCrossRefMATH

36.

dos Santos CH, De Queiroz JA, Leal F, Montevechi JAB (2020) Use of simulation in the industry 4.0 context : creation of a Digital Twin to optimise decision making on non-automated process. J Simul 14:1–14. https://doi.org/10.1080/17477778.2020.1811172CrossRef

37.

Shafto M, Conroy M, Doyle R et al (2010) DRAFT Modeling , Simulation, Information Technology & Processing Roadmap. In: Technology Area 11 - National Aeronautics and Space Administration (NASA), pp 1–27

38.

Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 96:1149–1163. https://doi.org/10.1007/s00170-018-1617-6CrossRef

39.

Yildiz E, Møller C, Bilberg A (2021) Demonstration and evaluation of a digital twin-based virtual factory. Int J Adv Manuf Technol 114:185–203. https://doi.org/10.1007/s00170-021-06825-wCrossRef

40.

dos Santos CH, Montevechi JAB, de Queiroz JA, de Carvalho Miranda R, Leal F (2021) Decision support in productive processes through DES and ABS in the Digital Twin era : a systematic literature review. Int J Prod Res 59:1–20. https://doi.org/10.1080/00207543.2021.1898691CrossRef

41.

Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ (2021) A review of vibration-based damage detection in civil structures: from traditional methods to Machine Learning and Deep Learning applications. Mech Syst Signal Process 147:1–45. https://doi.org/10.1016/j.ymssp.2020.107077CrossRef

42.

Shi Z, Yao W, Li Z, Zeng L, Zhao Y, Zhang R, Tang Y, Wen J (2020) Artificial intelligence techniques for stability analysis and control in smart grids: methodologies, applications, challenges and future directions. Appl Energy 278:1–25. https://doi.org/10.1016/j.apenergy.2020.115733CrossRef

43.

Russels S, Norvig P (2020) Artificial Intelligence: a Modern Approach, 4th edn. Pearson

44.

Lee J, Davari H, Singh J, Pandhare V (2018) Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manuf Lett 18:20–23. https://doi.org/10.1016/j.mfglet.2018.09.002CrossRef

45.

Raschka S, Julian D, Hearty J (2016) Python: Deeper Insights into Machine Learning: Leverage benefits of machine learning techniques using Python, 1st edn. Packt Publishing

46.

Altan A, Karasu S (2019) The effect of kernel values in support vector machine to forecasting performance of financial time series and cognitive decision making. J Cogn Syst 4:17–21

47.

Lall U, Moon Y, Kwon H, Bosworth K (2006) Locally weighted polynomial regression : parameter choice and application to forecasts of the Great Salt Lake. Water Resour Res 42:1–11. https://doi.org/10.1029/2004WR003782CrossRef

48.

Liu C, Hu Z, Li Y, Liu S (2017) Forecasting copper prices by decision tree learning. Res Policy 52:427–434. https://doi.org/10.1016/j.resourpol.2017.05.007CrossRef

49.

Mishra N, Soni HK, Sharma S, Upadhyay AK (2018) Development and analysis of Artificial Neural Network models for rainfall prediction by using time-series data. Int J Intell Syst Appl 10:16–23. https://doi.org/10.5815/ijisa.2018.01.03CrossRef

50.

Khorram M, Faria P, Abrishambaf O et al (2019) CO2 Concentration Forecasting in an Office Using Artificial Neural Network. In: Proceeding of the 2019 International Conference on Intelligent System Application to Power Systems, pp 1–6. https://doi.org/10.1109/ISAP48318.2019.9065944

51.

Dey A (2016) Machine Learning Algorithms: a Review. Int J Comput Sci Inf Technol 7:1174–1179

52.

Choi TM, Hui CL, Liu N, Ng SF, Yu Y (2014) Fast fashion sales forecasting with limited data and time. Decis Support Syst 59:84–92. https://doi.org/10.1016/j.dss.2013.10.008CrossRef

53.

Loureiro ALD, Miguéis VL, da Silva LFM (2018) Exploring the use of deep neural networks for sales forecasting in fashion retail. Decis Support Syst 114:81–93. https://doi.org/10.1016/j.dss.2018.08.010CrossRef

54.

Liu P, Ming W, Hu B (2020) Sales forecasting in rapid market changes using a minimum description length neural network. Neural Comput Applic 8:1–12. https://doi.org/10.1007/s00521-020-05294-8CrossRef

55.

Buer S, Strandhagen JO, Chan FTS (2018) The link between Industry 4.0 and lean manufacturing : mapping current research and establishing a research agenda. Int J Prod Res 7543:1–17. https://doi.org/10.1080/00207543.2018.1442945CrossRef

56.

Tortorella GL, Fettermann D (2018) Implementation of industry 4.0 and lean production in brazilian manufacturing companies. Int J Prod Res 56:2975–2987. https://doi.org/10.1080/00207543.2017.1391420CrossRef

57.

Montevechi JAB, Santos CH, Gabriel GT et al (2020) A Method Proposal for Conducting Simulation Projects in Industry 4.0: a Cyber-Physical System in an Aeronautical Industry. In: Proceeding of the 2020 Winter Simulation Conference. Orlando, USA, pp 2731–2742. https://doi.org/10.1109/WSC48552.2020.9384041

58.

Gabriel GT, Campos AT, Leal F, Montevechi JAB (2020) Good practices and deficiencies in conceptual modelling: a systematic literature review. J Simul XX:1–17. https://doi.org/10.1080/17477778.2020.1764875CrossRef

59.

Sargent RG (2013) Verification and validation of simulation models. J Simul 7:12–24. https://doi.org/10.1057/jos.2012.20CrossRef

60.

Ren S, Chan HL, Ram P (2017) A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty. Ann Oper Res 257:335–355. https://doi.org/10.1007/s10479-016-2204-6MathSciNetCrossRef

61.

Riedmiller M (1994) Advanced supervised learning in multi-layer perceptrons - From backpropagation to adaptive learning algorithms. Comput Stand Inter 16:265–278. https://doi.org/10.1016/0920-5489(94)90017-5CrossRef

62.

Braga PL, Oliveira ALI, Meira SRL (2007) Software Effort Estimation using Machine Learning Techniques with Robust Confidence Intervals. In: Proceeding of the 2007 International Conference on Hybrid Intelligent Systems Software, pp 352–357. https://doi.org/10.1109/his.2007.56

63.

Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org/10.5194/gmd-7-1247-2014CrossRef

64.

Wright L, Davidson S (2020) How to tell the difference between a model and a digital twin. Adv Model Simul Eng Sci 7:1–13. https://doi.org/10.1186/s40323-020-00147-4CrossRef

65.

dos Santos CH, Lima RDC, Leal F et al (2020) A decision support tool for operational planning : a Digital Twin using simulation and forecasting methods. Production 30:1–17. https://doi.org/10.1590/0103-6513.20200018CrossRef

66.

Alam KM, EL Saddik A (2017) C2PS : a Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems. IEEE Access 5:2050–2062. https://doi.org/10.1109/ACCESS.2017.2657006CrossRef

Title: Decision-making in a fast fashion company in the Industry 4.0 era: a Digital Twin proposal to support operational planning
Authors: Carlos Henrique dos Santos
Gustavo Teodoro Gabriel
João Victor Soares do Amaral
José Arnaldo Barra Montevechi
José Antônio de Queiroz
Publication date: 02-07-2021
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 5-6/2021
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-07543-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 5-6/2021

Thermal error prediction of ball screws based on PSO-LSTM

Operational vibration of a waterjet focuser as means for monitoring its wear progression

A novel polishing technology for leading and trailing edges of aero-engine blade

Correction to: Optimization of CVC shifting mode for hot strip mill based on the proposed LightGBM prediction model of roll shifting

Deformation analysis and processing parameter optimization of roller clinching

Investigation of surface quality in Cost of Goods Manufactured (COGM) method of μ-Al2O3 Powder-Mixed-EDM process on machining of Ti-6Al-4V

Premium Partners