Inter-dependent lead-time and ordering cost reduction strategy: a supply chain model with quality control, lead-time dependent backorder and price-sensitive stochastic demand

Roy, Monami Das; Sana, Shib Sankar

doi:10.1007/s12597-020-00499-w

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…However, the risk of acute exposure in cattle owing to grease and Pb-contaminated engine oil has decreased as Pb use is limited in many countries ( 92 ). Acute Pb toxicity in buffaloes and cattle affecting essential trace mineral profiles was caused by contamination of vegetation and pastures near battery manufacturing smelters (battery recycling units) and lead zinc smelters ( 61 , 93 , 94 ). Due to their innate eating habits, cattle are more likely to get poisoned.…”

Section: Heavy Metals: Their Toxicity Mechanism and Effectsmentioning

confidence: 99%

A review of important heavy metals toxicity with special emphasis on nephrotoxicity and its management in cattle

Tahir

Alkheraije

2023

Front. Vet. Sci.

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Toxicity with heavy metals has proven to be a significant hazard with several health problems linked to it. Heavy metals bioaccumulate in living organisms, pollute the food chain, and possibly threaten the health of animals. Many industries, fertilizers, traffic, automobile, paint, groundwater, and animal feed are sources of contamination of heavy metals. Few metals, such as aluminum (Al), may be eliminated by the elimination processes, but other metals like lead (Pb), arsenic (As), and cadmium (Ca) accumulate in the body and food chain, leading to chronic toxicity in animals. Even if these metals have no biological purpose, their toxic effects are still present in some form that is damaging to the animal body and its appropriate functioning. Cadmium (Cd) and Pb have negative impacts on a number of physiological and biochemical processes when exposed to sub-lethal doses. The nephrotoxic effects of Pb, As, and Cd are well known, and high amounts of naturally occurring environmental metals as well as occupational populations with high exposures have an adverse relationship between kidney damage and toxic metal exposure. Metal toxicity is determined by the absorbed dosage, the route of exposure, and the duration of exposure, whether acute or chronic. This can lead to numerous disorders and can also result in excessive damage due to oxidative stress generated by free radical production. Heavy metals concentration can be decreased through various procedures including bioremediation, pyrolysis, phytoremediation, rhizofiltration, biochar, and thermal process. This review discusses few heavy metals, their toxicity mechanisms, and their health impacts on cattle with special emphasis on the kidneys.

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Section: Heavy Metals: Their Toxicity Mechanism and Effectsmentioning

confidence: 99%

A review of important heavy metals toxicity with special emphasis on nephrotoxicity and its management in cattle

Tahir

Alkheraije

2023

Front. Vet. Sci.

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“…Together, these definitions underscore the elapsed time across multiple stages within the order-fulfillment process, such as order processing, production, transportation and delivery. The dimensions of lead-time management include fixed lead time, preprocessing lead time, processing lead time and postprocessing lead time (Angius and Colledani, 2020;Roy and Sana, 2021;R€ uttimann and St€ ockli, 2022).…”

Section: Literature Review and Hypothesis Development 21 Theoretical ...mentioning

confidence: 99%

“…Preprocessing lead time directly impacts delivery performance, customer satisfaction, on-time delivery, production cycle time and inventory levels (Alzoubi et al, 2022). By reducing preprocessing lead time, businesses can improve performance across these dimensions, resulting in cost savings, increased customer loyalty and enhanced overall supply chain performance (Roy and Sana, 2021).…”

Section: Literature Review and Hypothesis Development 21 Theoretical ...mentioning

confidence: 99%

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Lead-time management, information sharing and performance of the motor industry in Zimbabwe

Kanyepe,

Zizhou,

Alphaneta

et al. 2023

EJMS

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PurposeThis study examines the moderating role of information sharing on the effect of lead-time management on the performance of firms in the Zimbabwean motor industry.Design/methodology/approachData were collected using Likert-based structured questionnaires from a sample of 105 employees in Zimbabwe. In addition, Pearson Correlation, Linear Regression and Moderation Regression analysis were employed to test the relationship between study variables.FindingsThe study found that fixed lead time, preprocessing lead time, processing lead time and postprocessing lead time significantly influence the performance of firms in the motor industry. The results also demonstrate that information sharing moderates the effect of lead-time management on firm performance in the motor industry.Practical implicationsFirms in the motor industry should establish long-term relationships with their suppliers and implement effective communication channels for timely and frequent information exchange regarding production schedules, inventory levels, quality standards and potential disruptions.Originality/valueThe current study aims to contribute to the scientific discourse on lead-time management, information sharing and performance in the motor industry. Furthermore, it extends knowledge on the performance of the motor industry in the African region.

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“…In the literature, various studies addressing the Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ) have been published, taking into account backorders [ 3 , 4 , 5 , 6 ]. These approaches include: (i) the coordination and minimization of total costs of the supply chain with backorders [ 7 , 8 ] among other factors, such as with stochastic supply distribution [ 9 , 10 ]; (ii) the inventory problem addressed with backorders [ 11 , 12 , 13 , 14 ] and safety stocks [ 15 ], multi-objective optimization formulations with fixed backorder, and time-weighted backorder [ 16 ], stochastic demand and price discount [ 17 , 18 ], the integration of human errors [ 19 ], customers’ preferences [ 20 ], or customers’ behavior [ 21 ], and from the energy-efficient perspective with the aim to minimize carbon emissions [ 22 ]; (iii) fuzzy logic to model the demand or the order quantity for finding the optimal stock quantity [ 23 , 24 , 25 ]; (iv) heuristic approaches for optimizing the inventory systems [ 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management

Ntakolia

Kokkotis

Karlsson³

et al. 2021

Sensors

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Global competition among businesses imposes a more effective and low-cost supply chain allowing firms to provide products at a desired quality, quantity, and time, with lower production costs. The latter include holding cost, ordering cost, and backorder cost. Backorder occurs when a product is temporarily unavailable or out of stock and the customer places an order for future production and shipment. Therefore, stock unavailability and prolonged delays in product delivery will lead to additional production costs and unsatisfied customers, respectively. Thus, it is of high importance to develop models that will effectively predict the backorder rate in an inventory system with the aim of improving the effectiveness of the supply chain and, consequentially, the performance of the company. However, traditional approaches in the literature are based on stochastic approximation, without incorporating information from historical data. To this end, machine learning models should be employed for extracting knowledge of large historical data to develop predictive models. Therefore, to cover this need, in this study, the backorder prediction problem was addressed. Specifically, various machine learning models were compared for solving the binary classification problem of backorder prediction, followed by model calibration and a post-hoc explainability based on the SHAP model to identify and interpret the most important features that contribute to material backorder. The results showed that the RF, XGB, LGBM, and BB models reached an AUC score of 0.95, while the best-performing model was the LGBM model after calibration with the Isotonic Regression method. The explainability analysis showed that the inventory stock of a product, the volume of products that can be delivered, the imminent demand (sales), and the accurate prediction of the future demand can significantly contribute to the correct prediction of backorders.

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Inter-dependent lead-time and ordering cost reduction strategy: a supply chain model with quality control, lead-time dependent backorder and price-sensitive stochastic demand

Cited by 10 publications

References 32 publications

A review of important heavy metals toxicity with special emphasis on nephrotoxicity and its management in cattle

A review of important heavy metals toxicity with special emphasis on nephrotoxicity and its management in cattle

Lead-time management, information sharing and performance of the motor industry in Zimbabwe

An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management

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