Downstream Demand Inference in decentralized supply chains

Tliche, Youssef; Taghipour, Atour; Canel-Depitre, Béatrice

doi:10.1016/j.ejor.2018.09.034

Cited by 32 publications

(12 citation statements)

References 33 publications

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“…Industrial firms use inventory management technology to share manufacturing knowledge but may ignore lead-time uncertainty, especially if demand is stable over long periods. Therefore, it is critical to estimate lead-time fluctuations in probabilistic demand situations (Ghafour, 2018; Tliche et al , 2019). In this vein, Dolgui et al (2013) reported that safety lead times may have a greater impact on firm performance than safety stock, especially for multi-level inventory systems, thereby contributing to firm responsiveness to market demand.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…As a collaborative approach, external integration (e.g. upstream and downstream) provides critical knowledge to partners along with the SC that promotes their dynamic capabilities to respond more proactively to potential customer needs (Luu, 2017; Tliche et al , 2019). Each partner along the SC must promote the end customer value from their respective viewpoints and integration between supply- and demand-side activities (Arslanagic–Kalajdzic et al , 2019; Jeong and Hong, 2007; Medrano and Olarte–Pascual, 2016) can provide optimal solutions for demand based on shared information (Engelseth and Wang, 2018; Sacristán–Díaz et al , 2018; Tliche et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

“…upstream and downstream) provides critical knowledge to partners along with the SC that promotes their dynamic capabilities to respond more proactively to potential customer needs (Luu, 2017; Tliche et al , 2019). Each partner along the SC must promote the end customer value from their respective viewpoints and integration between supply- and demand-side activities (Arslanagic–Kalajdzic et al , 2019; Jeong and Hong, 2007; Medrano and Olarte–Pascual, 2016) can provide optimal solutions for demand based on shared information (Engelseth and Wang, 2018; Sacristán–Díaz et al , 2018; Tliche et al , 2019). Downstream SC partners may be more sensitive to demand disruptions (Ivanov, 2018) and can contribute to firm responsiveness to customer needs to create near-optimal solutions to demand problems and create new markets (Stott et al , 2016; Tliche et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

“…However, weak downstream integration increases the probability of acquiring distorted or erroneous information, which, in turn, leads to poor market response (Jeong and Hong, 2007; Luu, 2017; Tliche et al , 2019; Zhang and Zhu, 2019). Therefore, the level of market responsiveness (MR) is contingent on the availability of firm capabilities such as market sensing and investments in generating market knowledge (Mcdonald, 2016; Noor and Aljanabi, 2016; Tliche et al , 2019). Prior studies have emphasized the synergic interaction between SCs and MR in creating customer value beyond what is conventionally associated with marketing.…”

Section: Introductionmentioning

confidence: 99%

“…To address this uncertainty, forecasting has been proposed as an essential method to estimate market trends to proactively respond to market demand (Flynn et al , 2010; Tliche et al , 2019). However, at least four fundamental questions require further investigation.…”

Section: Introductionmentioning

confidence: 99%

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Supply chain management and market responsiveness: a simulation study

Aljanabi

Ghafour

2020

JBIM

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Purpose This study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement). Design/methodology/approach Simulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist. Findings The demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research. Originality/value This study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supply chain management and market responsiveness: a simulation study

Aljanabi

Ghafour

2020

JBIM

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A Benders decomposition approach for a new sustainable pharmaceutical supply chain network: a case study in France

Goodarzian,

Abraham,

Muñuzuri

et al. 2024

Ann Oper Res

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The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective

Ghafour

Aljanabi

2022

Oper Manag Res

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Strengthen the resilience of supply chains was observed to be critical issue by firms to confront disruptions triggered by unprecedented demand and severe disasters. However, the extraordinarily challenging disruptions of COVID-19 pandemic, unlike any disasters seen in recent times. This study aims to provide a practical solution to supply chain (SC) disruptions by estimating the best forecasting models for demand fluctuations in the context of food and beverages. A method is proposed to predict SC disruptions and enhance SC resilience. Double exponential smoothing (DES) and the ARIMA model are adopted as forecasting approaches to estimate demand and optimum inventory quantities during three different periods of disruption associated with the COVID-19 outbreak. A downstream SC involving 2 distributors and 56 retailers is considered to elaborate inventory measurements (optimal inventory levels and total costs). The results demonstrate that distributors can reduce costs by dispensing with some retailers, particularly those who order low quantities and thus incur unjustified expenses. Furthermore, high accuracy is obtained, with minimal differences between the real data and the model’s forecast. Existing research has largely ignored supply disruptions in the distributor-retailer relationship. This study provides distributors and SC managers important knowledge on SC disruptions and identifies appropriate forecasting methods to increase SC resilience. It also provides distributors and other SC managers unprecedented insights on tackling crises of stability like COVID-19.

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Downstream Demand Inference in decentralized supply chains

Cited by 32 publications

References 33 publications

Supply chain management and market responsiveness: a simulation study

Supply chain management and market responsiveness: a simulation study

A Benders decomposition approach for a new sustainable pharmaceutical supply chain network: a case study in France

The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective

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