Review and analysis of artificial intelligence methods for demand forecasting in supply chain management

Mediavilla, Mario Angos; Dietrich, Fabian; Palm, Daniel

doi:10.1016/j.procir.2022.05.119

Cited by 36 publications

(24 citation statements)

References 18 publications

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“…directly from the material in a process of generalization. Thereby, the qualitative content analysis approach outlined by Mayring (2015) and Mohaghegh and Furlan (2020) was taken as a basis. This systematic nature of the approach ensures traceability and verification of the procedure, enhancing its inter-subjectivity.…”

Section: Data Extraction Analysis and Synthesismentioning

confidence: 99%

“…For example, advancements in machine learning, artificial intelligence, cloud computing, blockchain and big data analytics offer significant potential to improve the accuracy of demand forecasting (e.g. Mediavilla et al, 2022;Feizabadi, 2022;Carbonneau et al, 2008). This could help in reducing the uncertainties that often lead to the bullwhip effect.…”

Section: Managerial Implications and Future Researchmentioning

confidence: 99%

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Causes of the bullwhip effect: a systematic review and categorization of its causes

Brauch,

Mohaghegh,

Größler

2024

MRR

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Purpose One pertinent dynamic phenomenon in supply chains is the amplification of order variance, i.e. the bullwhip effect. Its continued significance is underscored in contemporary empirical research. While numerous publications have pinpointed various causes of the bullwhip effect, there remains a gap in their systematic consolidation. The purpose of this paper is to compile a comprehensive list of the causes of the bullwhip effect from existing literature and categorize them appropriately. Design/methodology/approach This study conducts a systematic literature review to offer a comprehensive overview of bullwhip effect causes addressed in the existing literature. The identified causes are categorized using a qualitative content analysis approach. Findings The study shows the diversity of the causes of the bullwhip effect and their interdependencies. In addition, this study demonstrates that, at the highest level of aggregation, causes of the bullwhip effect can be classified into four main categories: causes inherent in the system structure, causes related to uncertainty, causes related to misaligned incentives and causes related to inadequate cognition of the situation. Originality/value The work provides an extensive overview and categorization of bullwhip effect causes, offering valuable insights for both researchers and practitioners seeking a deeper understanding of this phenomenon. In addition, it underscores managerial implications and highlights future research opportunities.

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Section: Data Extraction Analysis and Synthesismentioning

confidence: 99%

Section: Managerial Implications and Future Researchmentioning

confidence: 99%

Causes of the bullwhip effect: a systematic review and categorization of its causes

Brauch,

Mohaghegh,

Größler

2024

MRR

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“…Accurate forecast of demand is highly pronounced in the apparel industry by the emerging consumer style, seasonal trends and global market conditions which is a signi cant obstacle for accurate demand forecasting. Although traditional approaches based only on the past sales data are rare, they are still challenged by fashion uncertainty, therefore, ine ciencies in the supply chain management remain unavoidable (Mediavilla, Dietrich and Palm 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The apparel supply chain faces inherent challenges related to the procurement of raw materials, manufacturing processes, distribution, and retail. The traditional supply chain model often lacks the agility required to adapt to sudden shifts in demand, leading to operational ine ciencies (Mediavilla, Dietrich and Palm 2022).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Demand Forecasting in the Supply Chain Management of Apparel Industry

Ranawaka,

Jawwadh

2024

Preprint

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Accurate demand forecasting is a key component of a well-built supply chain management process in the ever-changing apparel industry, where precise predictions are vital for optimizing the production, inventory, and transportation levels. Traditional methods on numerous occasions fail to comprehensively understand the nature of this field, thus resulting in inefficiencies within the Sri Lankan apparel supply chain. The research answers this problem through the identification and development of the context-specific methods that are effective for enhanced demand forecasting in the apparel supply chain. The project explores the use of deep learning, particularly Long Short-Term Memory (LSTM) networks and their combinations with other models (CNN, ARIMA, BPNN) to develop a demand forecasting application. Experiments with six models identified a CNN-LSTM architecture as the optimal solution, achieving the lowest MAE of 2.9710, MAPE of 24.6802, MSE of 85.0358, and RMSE of 9.2215. Hyperparameter tuning and cross-validation were employed to optimize and validate the chosen model.

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“…Kantasa-ard et al [15] highlighted that, due to the more accurate forecast of future demand achieved with AI applications, organizations are making better plans for supply chain activities and operations with impacts on shipping times, inventories, and overall expenses. Many applications based on AI have been developed in recent years, such as those combining traditional forecasting methods with machine learning [16,17]. However, choosing the appropriate AI application for demand planning can be challenging, as each method has certain strengths and weaknesses, mainly due to demand volatility and complexity [18].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Capabilities for Demand Planning Process

de Mattos,

Correia,

Kissimoto

2024

Logistics

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Background: Technological advancements, particularly in Artificial Intelligence (AI), are revolutionizing operations management, especially in the domain of supply chain management. This paper delves into the application of AI in demand planning processes within the supply chain context. Drawing upon a comprehensive review of the existing literature, the main objective of this study is to analyze how AI is being applied and adopted in the demand planning process, identifying the resources needed to build the capacity of AI in the demand process, as well as the mechanisms and practices contributing to AI capability’s advancement and formation. Methodology: The approach was qualitative, and case studies of three different companies were conducted. Results: This study identified crucial resources necessary for fostering AI capabilities in demand planning. Our study extends the literature on AI capability in several ways. First, we identify the resources that are important in the formation of the capacity to implement AI in the context of demand planning. Conclusions: This study’s practical contributions underscore the multifaceted nature of AI implementation for demand planning, emphasizing the importance of resource allocation, human capital development, collaborative relationships, organizational alignment, and relational capital and AI.

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Review and analysis of artificial intelligence methods for demand forecasting in supply chain management

Cited by 36 publications

References 18 publications

Causes of the bullwhip effect: a systematic review and categorization of its causes

Causes of the bullwhip effect: a systematic review and categorization of its causes

Optimization of Demand Forecasting in the Supply Chain Management of Apparel Industry

Artificial Intelligence Capabilities for Demand Planning Process

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