Developing a M/G/C-FCFS queueing model with continuous review (R, Q) inventory system policy in a cement industry

Ghafour, Karzan Mahdi; Ramli, Razamin; Zaibidi, Nerda Zura

doi:10.3233/jifs-152509

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…The recent SC literature suggests that maintaining collaborative partner relationships to estimate and manage SC risks is indispensable not only to strengthen SC activities (e.g., inventory control, demand and supply management) but also to develop the resilience required to handle disruptions during and after COVID-19 in a competitive manner (Cao et al 2021 ; Lotfi and Larmour 2021 ; Sharma et al 2021 ; Zhang et al 2021 ). SC partners can pursue collaboration by sharing information, which plays a fundamental role in overall SC performance, reducing communication barriers, and providing near-optimal solutions for SC disruptions (Ponomarov and Holcomb 2009 ; Ghafour et al 2017 ; Aljanabi 2018 ; Tliche et al 2019 ; Cao et al 2021 ; Swierczek 2020 ) noted that sharing information on real demand enables the identification of disruptions and increases the flexibility of SCs to confront demand variability. Such knowledge management via sharing critical information can also be a supportive analysis tool in dealing with scenario planning and rectifying the impacts of COVID-19 disruptions (Chowdhury et al 2021 ; Sharma et al 2021 ), particularly for downstream operations, which have a greater impact on SC performance than upstream operations (Olivares-Aguila and ElMaraghy 2021 ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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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Section: Theoretical Backgroundmentioning

confidence: 99%

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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“…While creating superior customer value represents a key issue, it is equally crucial to maintain suitable product availability to increase MR, especially for low-value products such as cement and other construction materials (Arslanagic–Kalajdzic et al , 2019; Pujawan et al , 2015). Lafarge has recently increased their production capacity to 2.4 million tons per year but still faces difficulty in increasing its MR due to problems in demand and lead time (Lafarge, 2017; Lafarge, 2018), which are subject to probabilistic theory and highly uncertain (Ghafour et al , 2016a, 2017; Humair et al , 2013; Lin, 2016; Nguyen and Wright, 2015). Studies focusing on the SC of firms have recognized that firms need accurate mechanisms to manage their SCs and determine the level of uncertain customer demand.…”

Section: Introductionmentioning

confidence: 99%

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.