Acquiring supply chain agility through information technology capability: the role of demand forecasting in retail industry

Bai, Bingfeng

doi:10.1108/k-09-2021-0853

Cited by 6 publications

(4 citation statements)

References 103 publications

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“…FMCG companies often operate in a complex environment characterized by high demand volatility, short product lifecycles, numerous SKUs (stock-keeping units) and intricate distribution networks [110]. These complexities are amplified by factors such as changing consumer preferences, globalization, regulatory requirements and the need to maintain a balance between supply and demand [111]. Managing this complexity is crucial for FMCG companies to ensure efficiency in the supply chain, minimize costs, optimize inventory and meet customer demands promptly.…”

Section: Managerial and Policy Implicationsmentioning

confidence: 99%

Exploring the Challenges of Industry 4.0 Adoption in the FMCG Sector: Implications for Resilient Supply Chain in Emerging Economy

Shakur,

Lubaba,

Debnath

et al. 2024

Logistics

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Background: Fast-moving consumer goods (FMCG) supply chains are experiencing various challenges due to the interactions between consumers and decision-makers during physical distribution, manufacturing, wholesale and retail. One possible strategy to address these challenges for smoothing the supply chain (SC) and logistics operations is to adopt Industry 4.0 (I4.0) based technologies in the FMCG business processes. In this regard, digitalization and automation of the FMCG supply chain can be strengthened by the alluring properties of I4.0 technologies. Methods: This study identified nine significant challenges through a literature review and expert validation. Later, the challenges were evaluated using a novel multicriteria decision-making (MCDM) framework, the Bayesian best worst method (BWM). Results: The findings indicated that “requirement for substantial investment and resources”, “incompatible technological infrastructure” and “poorly structured value chain” are the most significant challenges to implementing I4.0 in the FMCG industry. Conclusions: The study is expected to significantly contribute to improving the FMCG supply chain’s resilience, sustainability, visibility, traceability and responsiveness. Additionally, the research can provide industrial practitioners valuable insights into implementing I4.0 in FMCG and similar sectors and thus promote SC sustainability and resilience in those industries.

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Section: Managerial and Policy Implicationsmentioning

confidence: 99%

Exploring the Challenges of Industry 4.0 Adoption in the FMCG Sector: Implications for Resilient Supply Chain in Emerging Economy

Shakur,

Lubaba,

Debnath

et al. 2024

Logistics

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“…Models which do not use ML algorithms cannot get to know their customers more sincerely. Generally, these types of models cannot gain a deeper understanding of the system which is under consideration (Bai, 2022). While ML algorithms can learn the connections and then set product development and marketing initiatives related to customer behavior by collecting customer data, identifying behaviors and relating them to each other over time.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques

Shakibaei

Farhadi-Ramin

Alipour-Vaezi

et al. 2023

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PurposeEvery day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.Design/methodology/approachA new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.FindingsFor this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.Originality/valueObtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

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“…In this context, analyzing market demand history becomes crucial in understanding and explaining these dynamics (Robinson and Hsieh, 2016). Moreover, demand forecasting accuracy has been identified as a crucial factor in improving SC responsiveness and reducing costs, leading to an increasing number of studies emphasizing its importance (Friscia et al , 2004; Bai, 2022). The research conducted by Friscia et al (2004) demonstrated that the top-performing demand forecasters experienced significant advantages compared to their counterparts.…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of inbound logistics capabilities on supply chain resilience: insight from Vietnamese textile industry

Thị

Khanh

Minh

et al. 2023

MBE

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Purpose This study aims to examine the impact of inbound logistics on dynamic supply chain capabilities and, subsequently, on supply chain resilience in the Vietnamese textile industry. Design/methodology/approach A conceptual framework based on a resource-based view was empirically tested using partial least squares structural equation modeling and data collected from 215 Vietnamese textile enterprises from December 2021 to March 2022. Findings The research shows that inbound logistics capability positively affects dynamic supply chain capabilities. In particular, the study has ratified reengineering as the chief factor that textile firms should consider when building a resilient supply chain. Originality/value This study considers the Vietnamese textile industry to assess the indirect effect of inbound logistics on supply chain resilience through dynamic supply chain capabilities in a theoretical sense while assisting managers in comprehending the functions of supply chain collaboration, agility and reengineering as the foundation for supply chain resilience in a managerial sense.

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Acquiring supply chain agility through information technology capability: the role of demand forecasting in retail industry

Cited by 6 publications

References 103 publications

Exploring the Challenges of Industry 4.0 Adoption in the FMCG Sector: Implications for Resilient Supply Chain in Emerging Economy

Exploring the Challenges of Industry 4.0 Adoption in the FMCG Sector: Implications for Resilient Supply Chain in Emerging Economy

Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques

Impacts of inbound logistics capabilities on supply chain resilience: insight from Vietnamese textile industry

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