Optimising safety stocks and reorder points when the demand and the lead-time are probabilistic in cement manufacturing

Ghafour, Karzan Mahdi

doi:10.1504/ijpm.2018.091672

Cited by 5 publications

(6 citation statements)

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“…When the duration of supply disruption is unknown and potential solutions are unclear (Ghafour 2018 ; Aljanabi 2021 ; Boehme et al 2021 ), firms seeking resilient SCs should focus on situational responses to dynamic market changes rather than proactive procedures (Ponomarov and Holcomb 2009 ; Ivanov and Das 2020 ). 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 ).…”

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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“…Thus, an SC’s level of responsiveness can increase or decrease as the production system moves downstream within the SC depending on the understanding of customer needs (Martinelli and Tunisini, 2019; Reichhart and Holweg, 2007; Truong et al , 2017). Customer-oriented firms are more willing to maximize their customer value through decreasing lead time, which, in turn, minimizes cost and inventory (Ghafour, 2018; Jüttner et al , 2010; Sacristán–Díaz et al , 2018). Effective and efficient sharing of required knowledge along the SC is crucial to decrease lead time, improve the logistical value and promote the firm’s dynamic capabilities and manufacturers’ responsiveness (Jüttner et al , 2010; Lee and Song, 2015; Luu, 2017; Sacristán–Díaz et al , 2018; Zhang et al , 2015).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…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%

“…The service level ( SL) plays an important role in optimizing both the safety stock S and reorder level R. To identify the probability SL for the demand during lead time D L that is lower than the reorder level R (Ghafour, 2018), the following equation is used: …”

Section: Optimizing the Reorder Level And Safety Stockmentioning

confidence: 99%

“…Because of the stability of manufactured materials distributed to different destinations, some previous studies have proposed constant or fixed lead times or simply ignored this parameter (Demeter and Golini, 2014). However, such an assumption does not appear to apply to all cases and it is critical to estimate lead-time fluctuations under probabilistic demand situations (Dolgui et al , 2013; Ghafour, 2018; Tliche et al , 2019). Efficient inventory policies can establish strong MR by coordinating the flows of information to related SC members (Jüttner et al , 2010; Lee and Song, 2015; Newell et al , 2019; Sacristán–Díaz et al , 2018; Zhang and Zhu, 2019), but the flexibility of the delivery system to meet multi-period demand under lead-time uncertainty is a more complicated consideration than periodic demand.…”

Section: Managerial Implicationsmentioning

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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Machine Learning Applications for Demand Driven in Supply Chain: Literature Review

Garinian¹,

Salais-Fierro²,

Marmolejo-Saucedo³

et al. 2023

Engineering Cyber-Physical Systems and Critical Infrastructures

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Optimising safety stocks and reorder points when the demand and the lead-time are probabilistic in cement manufacturing

Cited by 5 publications

References 0 publications

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

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

Supply chain management and market responsiveness: a simulation study

Machine Learning Applications for Demand Driven in Supply Chain: Literature Review

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