Performance evaluation of SCM in JIT environment

Kojima, Mitsutoshi; Nakashima, Keisuke; Ohno, Katsuhisa

doi:10.1016/j.ijpe.2007.11.017

Cited by 40 publications

(9 citation statements)

References 8 publications

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“…La tabla 1 muestra esta clasificación de los indicadores por cuartiles. [2] , [3] , [8] , [15] , [17] , [23] , [31] , [44] , [48] , [54] , [56] , [60] , [65] , [66] , [79] , [81] , [98] , [105] , [111] , [112] , [114] , [115] , [117] 23 7,7 7,7 1 inventory level [4] , [13] , [21] , [23] , [26] , [31] , [35] , [48] , [50] , [58] , [59] , [60] , [61] , [65] , [66] , [105] , [109] , [117] 18 6,0 13,8 1 holding cost [1] , [12] , [19] , [35] , [46] , [57] , [62] , [74] ,…”

Section: Análisis De Los Resultadosunclassified

Planteamiento De Un Modelo De Evaluación De Lean Supply Chain Management

Garcia-Buendia

Fuentes

Marín

2019

ReE

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En este trabajo se plantea un modelo de evaluación de la gestión lean en la cadena de suministro, a partir de los indicadores de resultados más utilizados en la literatura. Se parte de una Revisión Sistemática de la Literatura realizada con anterioridad por los autores, para identificar los indicadores que más consenso han recibido, y se realiza una clasificación en función de la naturaleza de los mismos, según si es operativa, financiera o de mercado. Este modelo tiene como finalidad servir de fundamento para la elaboración de un Cuadro de Mando. De este modo, se propone un modelo teórico para evaluar los resultados derivados de la gestión lean de la cadena de suministro, agrupando los indicadores en enfoques o perspectivas de interés para que los directivos puedan orientar su toma de decisiones, de forma similar a un cuadro de mando integral.

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Section: Análisis De Los Resultadosunclassified

Planteamiento De Un Modelo De Evaluación De Lean Supply Chain Management

Garcia-Buendia

Fuentes

Marín

2019

ReE

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“…Kojima et al [4] adapt the kanban contol as one of the most powerful tools in lean manufacturing to a supply chain under stochastic demand and optimize the number of kanbans. Nakashima and Gupta [7] and Nakashima et al [8] model and analyze disassembly system in the automotive industry using kanban contol systems.…”

Section: Previous Workmentioning

confidence: 99%

Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain

Nakashima

Sornmanapong²,

Ehm³

et al. 2014

IJBER

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As an innovation in the semiconductor industry grows speedy, supply chain processes have not followed up. The variability in semiconductor supply chain have increased and been more complicated. These results in accurately forecast demand and set inventory target. Demand and supply are more and more stochastic and non-stationary. Inventory is one of the methods that companies are able to buffer themselves from complex and variable environment, while still being able to satisfy customer needs. We explore the variability of semiconductor industry in automotive industry. On the supply side, we evaluate variability in complexities of manufacturing process and also products are composed with multiple parts efforts to stochastic production lead-time. However in this paper, we disregard the variability arising from supply side so we assumed lead-time is fixed at 16 weeks. For demand side, the phenomenon is known as the bullwhip effect, the demand variability increases as one move up a supply chain, severely effects to semiconductor supply chain. This results the stochastic demand process is not well understood. Thus we evaluate the stochastic in demand as two aspects: 1) the dispersion of historical demand data from its mean which denoted as standard deviation of demand, 2) the difference between the actual demand and forecast data which denoted as standard deviation of forecast error. We use them as a proxy for demand variability. Then we apply the data to the base stock model. Then, we determine what each variability parameter contributes to inventory. The inventory model represents the semiconductor manufactory's inventory with actual statistical data which provided from semiconductor company to calculate inventory target required to meet the desired customer service level.

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“…The SOM strategy is different from other traditional manufacturing paradigms, e.g., Lean Manufacturing (Shah and Ward, 2003), Flexible Manufacturing (Konak et al, 2008), Computer-Integrated Manufacturing (Altuntas et al, 2007), Just-In-Time Manufacturing (Kojima et al, 2008), Agile Manufacturing (Zhang, 2011), Green Manufacturing (Ahiska and King, 2010), Mass Customization Manufacturing (Anzanello and Fogliatto, 2011), Virtual Enterprise (Monroy and Arto, 2010), Marketing-Manufacturing Integration (MMI) (Paiva, 2010), Re-Manufacturing (Kenné et al, 2012). General speaking, all of these manufacturing paradigms or concepts have some characteristics as follows: (1) value is mainly created by manufacturing the tangible goods.…”

Section: Literature Reviewmentioning

confidence: 99%

An analytical study on service-oriented manufacturing strategies

Zhen

2012

International Journal of Production Economics

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Performance evaluation of SCM in JIT environment

Cited by 40 publications

References 8 publications

Planteamiento De Un Modelo De Evaluación De Lean Supply Chain Management

Planteamiento De Un Modelo De Evaluación De Lean Supply Chain Management

Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain

An analytical study on service-oriented manufacturing strategies

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