Machine learning–assisted efficient demand forecasting using endogenous and exogenous indicators for the textile industry

Yasir, Muhammad; Ansari, Yasmeen; Latif, Khalid; Maqsood, Haider; Habib, Adnan; Moon, Jihoon; Rho, Seungmin

doi:10.1080/13675567.2022.2100334

Cited by 13 publications

(8 citation statements)

References 44 publications

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“…Besides, LSTM outperformed autoregression techniques, enabling managers to get a relatively straightforward image of the possibilities in the future (Soltani et al, 2022). Moreover, (Yasir et al, 2022) proved that employing LSTM decreased demand forecasting errors. Furthermore, BiLSTM has been applied in recent research.…”

Section: Ai Methodsmentioning

confidence: 93%

Applications of Artificial Intelligence for Demand Forecasting

Nguyen

2023

OSCM: An Int. Journal

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Artificial Intelligence (AI) has significantly contributed to the growth of various sectors and industries. One of the most significant contributions of AI applications is in forecasting. Consumer demand fluctuates faster than ever as a result of economic growth, advances in technology, and higher customer expectations, making forecasting future demand more difficult. Demand forecasting is a vital operation of supply chain management that aids in the best matching of supply and demand. Thus, improving demand forecasting accuracy is critical for companies and supply chains. Thanks to AI, businesses can accurately predict customer behavior. The study aims to provide a comprehensive review of how AI has been applied to forecast demand over the last decade. This research collects articles published between 2013 and 2023. According to the findings, AI is increasingly being used for demand forecasting in recent years. Energy and water demand forecasts receive the most attention. Long Short-Term Memory has gained prominence because of its advantages. Besides, this study will highlight the challenges of the adoption of AI. One of these challenges is selecting different reliable and suitable forecasting inputs for each AI method. This review will help supply chain managers and analysts select and implement suitable forecasting methods. Furthermore, this study will suggest some future research directions.

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Section: Ai Methodsmentioning

confidence: 93%

Applications of Artificial Intelligence for Demand Forecasting

Nguyen

2023

OSCM: An Int. Journal

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“…The additional case studies confirmed the advantages of the proposed method, which reported a reduction of 25.6% in terms of mean absolute percentage error compared to the conventional forecasting method. Other studies investigating the integration of macroeconomic indicators when forecasting demand data can also be found in [20,21,28]. In [20], gross domestic product, unemployment rate, crude oil price, purchasing managers' indices, and copper price were used as input to compare the forecasting accuracy of several traditional and ML models.…”

Section: And DL Predictive Approaches For Scrm-exploiting Macroeconom...mentioning

confidence: 99%

“…In [20], gross domestic product, unemployment rate, crude oil price, purchasing managers' indices, and copper price were used as input to compare the forecasting accuracy of several traditional and ML models. Conversely, in [21,28], DL models started to be adopted for the task, with the results highlighting the greater capability of these models to provide more accurate forecasts.…”

Section: And DL Predictive Approaches For Scrm-exploiting Macroeconom...mentioning

confidence: 99%

“…However, according to [19], relying only on internal company data or past historical delivery data does not allow for anticipating market changes. Lastly, even if autocorrelation in data has often been considered in demand forecasting [19][20][21], autocorrelation in delivery delay data has never been adopted to build a forecasting model to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach to Predict Supply Chain Delivery Delay Risk Based on Macroeconomic Indicators: A Case Study in the Automotive Sector

Gabellini,

Civolani,

Calabrese

et al. 2024

Applied Sciences

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The development of predictive approaches to estimate supplier delivery risks has become vital for companies that rely heavily on outsourcing practices and lean management strategies in the era of the shortage economy. However, the literature that presents studies proposing the development of such approaches is still in its infancy, and several gaps have been found. In particular, most of the current studies present approaches that can only estimate whether suppliers will be late or not. Moreover, even if autocorrelation in data has been widely considered in demand forecasting, it has been neglected in supplier delivery risk predictions. Finally, current approaches struggle to consider macroeconomic data as input and rely mostly on machine learning models, while deep learning ones have rarely been investigated. The main contribution of this study is thus to propose a new approach that for the first time simultaneously adopts a deep learning model able to capture autocorrelation in data and integrates several macroeconomic indicators as input. Furthermore, as a second contribution, the performance of the proposed approach has been investigated in a real automotive case study and compared with those studies resulting from approaches that adopt traditional statistical models and models that do not consider macroeconomic indicators as additional inputs. The results highlight the capabilities of the proposed approach to provide good forecasts and outperform benchmarks for most of the considered predictions. Furthermore, the results provide evidence of the importance of considering macroeconomic indicators as additional input.

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“…Diversos domínios de aplicação têm se beneficiado com o uso de técnicas de inteligência artificial (IA) (DOLLIN, 2021) e o setor têxtil também tem sido beneficiado por tais técnicas sob algumas perspectivas, como em (KAPUT, 2022) e (YASIR, 2022). Muitos estudos no setor procuraram aplicar técnicas de inteligência artificial para apoiar o conceito de vestuário inteligente, algumas vezes em cenários associados com Internet das coisas (IoT) e considerando que as peças de roupa possam estar conectadas à Internet.…”

Section: Introductionunclassified

Modelos preditivos para o setor têxtil: estudo de Caso de uma empresa de confecções do polo Agreste de Pernambuco.

Pimentel

Silva²,

Andrade³

et al. 2022

RMP

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O setor têxtil tem apresentado crescimento constante nos últimos anos e o Brasil ocupa a quarta posição mundial no nicho de vestuário. A crescente demanda de têxteis reforça a importância de tecnologias e sistemas inteligentes que contribuam para a continuidade de expansão do setor. Neste sentido, este artigo analisa diferentes abordagens para predição de variáveis importantes para uma empresa têxtil, de modo a permitir o uso dos modelos preditivos obtidos como auxiliares em futuras ferramentas de otimização e planejamento. Foi desenvolvido um sistema com interface gráfica para facilitar a visualização e manipulação dos dados e a proposta foi avaliada sobre os dados de uma empresa parceira do Polo Agreste considerando a técnica de predição com melhor desempenho nos estudos anteriores. Os resultados obtidos reforçam que a abordagem é promissora, apresentando um erro quadrático médio de 1,20×10-2 para a predição do valor total produzido e 4,37×10-2 para o custo de produção.

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Machine learning–assisted efficient demand forecasting using endogenous and exogenous indicators for the textile industry

Cited by 13 publications

References 44 publications

Applications of Artificial Intelligence for Demand Forecasting

Applications of Artificial Intelligence for Demand Forecasting

A Deep Learning Approach to Predict Supply Chain Delivery Delay Risk Based on Macroeconomic Indicators: A Case Study in the Automotive Sector

Modelos preditivos para o setor têxtil: estudo de Caso de uma empresa de confecções do polo Agreste de Pernambuco.

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