Demand‐Driven Inventory Optimization and Replenishment

Davis, Robert A.

doi:10.1002/9781118747841

Cited by 12 publications

(10 citation statements)

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“…La optimización del inventario requiere un análisis con cálculos donde intervienen muchas variables como: demanda, clientes, nivel de servicio, tiempos de reabastecimiento, costos, localidades, regiones, y capacidad de producción. Pero también es crucial el conocimiento y la comunicación a lo largo de toda la cadena de suministro para reducir el efecto látigo, caso contrario la optimización será imposible de mantenerla (Davis, 2016).…”

Section: Marco Teóricounclassified

Desbalance entre costo del inventario y nivel de servicio de producto terminado

Cepeda¹,

Acuña

2020

IRJ

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El presente trabajo tiene como principal objetivo desarrollar una herramienta de control y gestión para el inventario de producto terminado para una empresa industrial con la ayuda de herramientas tecnológicas como Excel y el software R Studio. Por tal razón, se analizarán las variables que intervienen o afectan directa e indirectamente al inventario como: datos históricos de venta, datos atípicos de ventas, pronóstico de demanda futura, modelo de proyección, tiempos de reabastecimiento, nivel de servicio, stock de seguridad, inventario promedio e inventario óptimo. Además, el modelo de optimización del inventario está basado en el análisis estadístico de datos como: coeficientes de variación, desviación estándar, valores mínimos y máximos y cuartiles. Debido a la confiabilidad de los datos e información estratégica de la empresa, el presente estudio tiene sus limitaciones y por lo tanto se generan supuestos que serán detallados para que la herramienta a futuro pueda ser utilizada, evaluada y mejorada según amerite.

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Section: Marco Teóricounclassified

Desbalance entre costo del inventario y nivel de servicio de producto terminado

Cepeda¹,

Acuña

2020

IRJ

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“…Furthermore it is possible to generate cumulative probability functions for several time points over the product’s life-cycle to inform a replenishment schedule (Davis, 2013). This is shown in Figure 7.…”

Section: Simulating a Probabilistic Forecastmentioning

confidence: 99%

Articulating the fashion product life-cycle

Spragg¹

2017

JFMM

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Purpose The purpose of this paper is to describe a framework for predicting the seasonal demand for fashion based on the Bass Diffusion model of demand and the Newsvendor inventory management model. Sales and inventory management simulations based on these models have been implemented in the Julia programming language to demonstrate how theoretical models can be made operational using open-source software for the benefit of small- to medium-scale enterprises with limited resources. Design/methodology/approach The paper employs simulation models implemented in the Julia programming languages to explore the use of theoretical models of demand on the seasonal management of fashion inventory. Findings The research demonstrates that further exploration of the framework described is worth undertaking. Research limitations/implications The paper employs theoretical models and is therefore conceptual in its outlook. Nonetheless it provides insights that could be explored further if real-world sales data would become available. Practical implications The framework provides simple models for predicting the demand for fashion that allows “what-if” scenarios to be constructed based on the manipulation of a few model parameters. Social implications The framework provides low-cost analytical tools that support small fashion enterprises with regards to market understanding and the operational management requirements necessary to support those markets. Originality/value The analytical use of the Bass model of demand with the Newsvendor model of inventory management within a fashion context.

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“…Most of these solutions are inspired on Enterprise Service Bus (ESB), web portals coupled with database server (DBMS), or ERP's model. In these cases, SC partners are connected to the gates and have a view of the logistics unit for which they have access right [16]. For models based on web services, each actor in the supply chain requests web operations from its collaborator to get the desired information.…”

Section: Overviewmentioning

confidence: 99%

Publish and Subscribe Pattern for Designing Demand Driven Supply Networks

Gnimpieba

Nait-Sidi-Moh

Durand³

et al. 2016

Product Lifecycle Management in the Era of Internet of Things

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Logistic flows, business process management and collaboration remain a major problem in the supply chain. In this article we are going through this issue to propose an integrative and collaborative approach. More precisely, we develop a cloud-based and service-oriented bus for business interoperability for logistic flows. Though the bus, we define protocols and standards allowing the integration of data formats which are for the most proprietary and heterogeneous. The bus also allows data sharing between processes and actors involved in the flow. Key features of this bus are event handling and processing from the physical flow and real-time notification to stakeholders.

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Demand‐Driven Inventory Optimization and Replenishment

Cited by 12 publications

References 0 publications

Desbalance entre costo del inventario y nivel de servicio de producto terminado

Desbalance entre costo del inventario y nivel de servicio de producto terminado

Articulating the fashion product life-cycle

Publish and Subscribe Pattern for Designing Demand Driven Supply Networks

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