UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution

Fu, Wenhan; Chien, Chen–Fu

doi:10.1016/j.cie.2019.07.002

Cited by 94 publications

(34 citation statements)

References 57 publications

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“…Typically, SC demand forecasting studies carried out to date have been proposed to: 1) forecast intermittent demand patterns [11,12,13,14,15]; 2) predict new product demands over its life-cycle using product differentiation information [16]; or even 3) forecast spare parts demand for regular products [17]. Table 1 provides an overview of the discussed literature on SC demand forecasting.…”

Section: Related Workmentioning

confidence: 99%

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

Gonçalves

Cortez

Carvalho

et al. 2021

Decision Support Systems

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Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventoryrelated costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.

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Section: Related Workmentioning

confidence: 99%

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

Gonçalves

Cortez

Carvalho

et al. 2021

Decision Support Systems

View full text Add to dashboard Cite

show abstract

“…Our intervention has led to the development of a predictive modeling framework for a demand forecasting problem that is not Alpha-specific, but general to many firms in manufacturing supply chains (Fu & Chien, 2019). Methodological innovations, like many scientific developments, are driven by empirical irregularities, that is, unsuccessful applications of oft-used theories and methods.…”

Section: Generalizabilitymentioning

confidence: 99%

“…The demand forecasting problem Alpha faces is common to many firms in high-tech manufacturing sectors (Fu & Chien, 2019). Alpha, like these other firms, conducts a periodic review inventory management system.…”

Section: Introductionmentioning

confidence: 99%

Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics

Chuang

Chou

Oliva

2021

J of Ops Management

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Despite its importance to OM, demand forecasting has been perceived as a "problem-solving" exercise; most empirical work in the field has focused on explanatory models but neglected prediction problems that are part of empirical science. The present study, involving one of the leading electronics distributors in the world, aims to improve prediction accuracy under high demand volatility for procurement managers to make better inventory decisions. In response to requests for an integrated forecasting methodology, we undertook an iterative process based on three guiding principles-data pooling, theoryinformed feature engineering, and ensemble-based machine learning. The resulting framework managed to improve forecast accuracy significantly and is applicable to a broad range of situations. We present reflections and insights derived abductively through engagement with managers in this problem situation. This "problem-driven" process corresponds to intervention as a research strategy that can foster theoretical and methodological innovations in OM. Our contribution goes beyond the development of the prediction framework as it elucidates ways OM researchers could leverage theoretical foundations to inform feature derivation and model construction. We posit that this work points to a way forward to the combination of OM principles with the emerging innovations in data science and artificial intelligence.

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“…They enable an assessment and classification of whether a transaction can be classified as normal or fraudulent to reduce product quality risks. Fu and Chien [65] present a data-driven analysis framework that integrates ML technologies and temporal aggregation mechanisms to predict the requirements of intermittent electronic components and, thereby, to reduce supply risks. The empirical study was conducted in a distribution company for electronic components.…”

Section: Number Of Published Papersmentioning

confidence: 99%

“…These latter publications are therefore not listed in Table 1. [18] Conceptual work production supplier risks direct Benjaoran and Dawood [61] Case Study production production risks indirect Blackburn et al [62] Conceptual work & use case production production risks indirect Bouzembrak and Marvin [63] Conceptual work transport food quality/transport risks indirect Brintrup et al [57] Case study transport supplier risks direct Cavalcante et al [58] Conceptual work transport supplier risks direct Constante-Nicolalde et al [64] Conceptual work supply chain quality risks indirect Fu and Chien [65] Conceptual work production supply risks indirect Hassan [19] Conceptual work supply chain supply risks direct Lau et al [66] Conceptual work procurement information risks indirect Layouni et al [20] Survey production transport risks indirect Pereira et al [67] Conceptual work supply chain sales risks indirect Rodriguez-Aguilar et al [22] Conceptual work supply chain supply risks direct Wichmann et al [59] Conceptual work supply chain supplier risks direct Yong et al [23] Conceptual work supply chain supply risks direct…”

Section: Number Of Published Papersmentioning

confidence: 99%

A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management

Schroeder

Lodemann

2021

Logistics

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The main objective of the paper is to analyze and synthesize existing scientific literature related to supply chain areas where machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. Furthermore, we analyzed which risks were addressed in the use cases as well as how ML might shape SCRM. For this purpose, we conducted a systematic literature review. The results showed that the applied examples relate primarily to the early identification of production, transport, and supply risks in order to counteract potential supply chain problems quickly. Through the analyzed case studies, we were able to identify the added value that ML integration can bring to the SCRM (e.g., the integration of new data sources such as social media or weather data). From the systematic literature analysis results, we developed four propositions, which can be used as motivation for further research.

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UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution

Cited by 94 publications

References 57 publications

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics

A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management

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