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

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.

[1]  Jay Lee,et al.  Industrial Artificial Intelligence for industry 4.0-based manufacturing systems , 2018, Manufacturing Letters.

[2]  Mahsa Khorram,et al.  CO2 Concentration Forecasting in an Office Using Artificial Neural Network , 2019, 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP).

[3]  José Arnaldo Barra Montevechi,et al.  Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimise decision making on non-automated process , 2020, J. Simulation.

[4]  Amilcare Porporato,et al.  Impact of hydroclimatic fluctuations on the soil water balance , 2006 .

[5]  MarianaMartínez Bonilla,et al.  The interaction of Instagram followers in the fast fashion sector: The case of Hennes and Mauritz (H&M) , 2019, Journal of Global Fashion Marketing.

[6]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[7]  Pericles A. Mitkas,et al.  Towards Fashion Recommendation: An AI System for Clothing Data Retrieval and Analysis , 2020, AIAI.

[8]  José Arnaldo Barra Montevechi,et al.  Decision support in productive processes through DES and ABS in the Digital Twin era: a systematic literature review , 2021, Int. J. Prod. Res..

[9]  Hermann Jahnke,et al.  Traditional versus fast fashion supply chains in the apparel industry: an agent-based simulation approach , 2020, Annals of Operations Research.

[10]  J. Framiñan,et al.  Proportional order-up-to policies for closed-loop supply chains: the dynamic effects of inventory controllers , 2021, Int. J. Prod. Res..

[11]  A. Alfieri,et al.  Last mile logistics in Fast Fashion supply chains: a case study , 2019, IFAC-PapersOnLine.

[12]  Hyun-Han Kwon,et al.  Locally weighted polynomial regression: Parameter choice and application to forecasts of the Great Salt Lake , 2006 .

[13]  F. Chan,et al.  The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda , 2018, Int. J. Prod. Res..

[14]  Nello Cristianini,et al.  On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms , 2020, ArXiv.

[15]  Sanjiv Sharma,et al.  Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data , 2018 .

[16]  Yong Tang,et al.  Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions , 2020, Applied Energy.

[17]  Antonio Padovano,et al.  Human factors in occupational health and safety 4.0: a cross-sectional correlation study of workload, stress and outcomes of an industrial emergency response , 2019, Int. J. Simul. Process. Model..

[18]  R. Yan,et al.  An exploratory study of the decision processes of fast versus slow fashion consumers , 2013 .

[19]  Zbigniew J. Pasek,et al.  Risk assessment for the supply chain of fast fashion apparel industry: a system dynamics framework , 2016 .

[20]  Abdulmotaleb El Saddik,et al.  C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems , 2017, IEEE Access.

[21]  José Arnaldo Barra Montevechi,et al.  Good practices and deficiencies in conceptual modelling: A systematic literature review , 2020, J. Simulation.

[22]  Robert G. Sargent,et al.  Verification and validation of simulation models , 2013, Proceedings of Winter Simulation Conference.

[23]  Louise Wright,et al.  How to tell the difference between a model and a digital twin , 2020, Advanced Modeling and Simulation in Engineering Sciences.

[24]  Jaeyoung Choi,et al.  A Store Management System for the Improvement of Shopping Process of Omni-shoppers of Fast Fashion Brand , 2020 .

[25]  Naila Fares,et al.  Data Mining Dynamic Hybrid Model for Logistic Supplying Chain: Assortment Setting in Fast Fashion Retail , 2018 .

[26]  R. Pellerin,et al.  The industrial management of SMEs in the era of Industry 4.0 , 2018, Int. J. Prod. Res..

[27]  Martin A. Riedmiller,et al.  Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms , 1994 .

[28]  David J. Tyler,et al.  A simulation model of Quick Response replenishment of seasonal clothing , 2004 .

[29]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[30]  Antonio Padovano,et al.  Human factors in occupational health and safety 4.0: a cross-sectional correlation study of workload, stress and outcomes of an industrial emergency response , 2019, International Journal of Simulation and Process Modelling.

[31]  Peipei Liu,et al.  Sales forecasting in rapid market changes using a minimum description length neural network , 2020, Neural Computing and Applications.

[32]  Mona Liza Moura de Oliveira,et al.  A Method Proposal for Conducting Simulation Projects in Industry 4.0: A Cyber-Physical System in an Aeronautical Industry , 2020, 2020 Winter Simulation Conference (WSC).

[33]  Jens P. Wulfsberg,et al.  Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing , 2016 .

[34]  Na Liu,et al.  Fast fashion sales forecasting with limited data and time , 2014, Decis. Support Syst..

[35]  Gérard P. Cachon,et al.  The Value of Fast Fashion: Quick Response, Enhanced Design, and Strategic Consumer Behavior , 2011, Manag. Sci..

[36]  M PraveenKumar,et al.  Artificial neural network based geometric error correction model for enhancing positioning accuracy of a robotic sewing manipulator , 2018 .

[37]  Dominika Kaczorowska-Spychalska.  Shaping Consumer Behavior in Fashion Industry by the Interactive Communication Forms , 2018, Fibres and Textiles in Eastern Europe.

[38]  Michael Sony,et al.  Industry 4.0 and lean management: a proposed integration model and research propositions , 2018 .

[39]  Aabid Abdul Majeed,et al.  Internet of Things (IoT) Embedded Future Supply Chains for Industry 4.0: An Assessment from an ERP-based Fashion Apparel and Footwear Industry , 2017 .

[40]  Jianhua Liu,et al.  Digital twin-based smart production management and control framework for the complex product assembly shop-floor , 2018, The International Journal of Advanced Manufacturing Technology.

[41]  Aytaç Altan,et al.  THE EFFECT OF KERNEL VALUES IN SUPPORT VECTOR MACHINE TO FORECASTING PERFORMANCE OF FINANCIAL TIME SERIES , 2019 .

[42]  Eric W. T. Ngai,et al.  Customer reviews for demand distribution and sales nowcasting: a big data approach , 2016, Annals of Operations Research.

[43]  Ray Y. Zhong,et al.  Intelligent Manufacturing in the Context of Industry 4.0: A Review , 2017 .

[44]  Hau-Ling Chan,et al.  A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty , 2017, Ann. Oper. Res..

[45]  Nikolaos Bilalis,et al.  Machine learning for clothing manufacture as a mean to respond quicker and better to the demands of clothing brands: a Greek case study , 2020, The International Journal of Advanced Manufacturing Technology.

[46]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[47]  Antonio Padovano,et al.  Smart operators in industry 4.0: A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context , 2017, Comput. Ind. Eng..

[48]  Felipe Caro,et al.  Clearance Pricing Optimization for a Fast-Fashion Retailer , 2010, Oper. Res..

[49]  Naila Fares,et al.  Quick response in fast fashion retail: An optimization supply chain responsiveness model , 2018, 2018 4th International Conference on Optimization and Applications (ICOA).

[50]  Anna Corinna Cagliano,et al.  Using system dynamics in warehouse management: a fast‐fashion case study , 2011 .

[51]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[52]  Yan Li,et al.  Forecasting copper prices by decision tree learning , 2017 .

[53]  Meng Zhang,et al.  Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing , 2017, IEEE Access.

[54]  Mostafa Ezziyyani,et al.  Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) , 2019, Advances in Intelligent Systems and Computing.

[55]  G. Tortorella,et al.  Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies , 2018, Int. J. Prod. Res..

[56]  José Arnaldo Barra Montevechi,et al.  A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods , 2020, Production.

[57]  Jiafu Wan,et al.  Industrie 4.0: Enabling technologies , 2015, Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things.

[58]  Lucas F. M. da Silva,et al.  Exploring the use of deep neural networks for sales forecasting in fashion retail , 2018, Decis. Support Syst..

[59]  E. H. Grosse,et al.  Logistics 4.0: a systematic review towards a new logistics system , 2019, Int. J. Prod. Res..