A flexible and adaptable planning and control system for an MTO supply chain system

Özbayrak, Mustafa; Papadopoulou, Theopisti C.; Samaras, Efstratios

doi:10.1016/j.rcim.2005.12.011

Cited by 23 publications

(7 citation statements)

References 25 publications

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“…The disruption impact on supply chain performance depends on both proactive resilience measures and recovery contingency plans (Tang and Tomlin 2008). Ozbayrak, Papadopoulou, and Samaras (2006) and Tang and Tomlin (2008) consider flexibility as a capabiltiy that enables timely and responsive reaction to changes in the supply chain environment. Kleindorfer and Saad (2005) consider sourcing flexibility, inventory and capacity excessiveness as major resilience drivers in the supply chain.…”

Section: Flexibility In Manufacturing Service and Supply Chain Systemsmentioning

confidence: 99%

New flexibility drivers for manufacturing, supply chain and service operations

Ivanov

Das

Choi

2018

International Journal of Production Research

114

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Increasing product proliferation, customisation, competition and customer expectations, as well as supply side disruptions, pose significant challenges to firm operations. Such challenges require improved efficiency and resilience in manufacturing, service and supply chain systems. New and innovative flexibility concepts and models offer a prospective route to such operational improvements. Several emerging issues in flexibility, such as risk and uncertainty management, environmental sustainability, optimal strategies under competition, optimal operations with strategic consumer behaviours are being examined in this regard. This overview provides a concise review of these critical research issues, and discusses related papers featured in this special issue. Four major flexibility drivers are classified: disruption risks, resilience and the ripple effect in the supply chain; digitalisation, smart operations and e-supply chains; sustainability and closedloop supply chains; and supplier integration and behavioural flexibility.Keywords: flexibility; flexible manufacturing; supply chain management; supply chain risk management; supply chain coordination; Industry 4.0; manufacturing systems; service supply chain; ripple effect Flexibility in manufacturing, service and supply chain systemsWith increasing product proliferation, reduced unit demand volumes and next-day delivery expectations, flexibility in operations, service and supply chains has become an important focal area for research and industry. This special issue of IJPR aims to present state-of-the-art research in the field of flexibility in manufacturing, supply chain and service operations.A brief background on flexibility is provided in this section. Flexibility refers to the ability of a system to change (adapt) in dynamic environments. Operational system flexibility has been studied extensively in the manufacturing, service and supply chain literature from various strategic, tactical and operative perspectives (Slack 1987;Bordoloi, Cooper, and Matsuo 1999;Koste and Malhotra 1999;D'Souza and Williams 2000;Das 2001;Van Mieghem 2003;Zhang, Vonderembse, and Lim 2003;Wu, Erkoc, and Karabuk 2005;Alp and Tan 2008;Chang 2012;Jain et al. 2013;Choi, Cheng, and Zhao 2016;Cobb 2016;Ivanov, Tsipoulanidis and Schönberger 2017).A substantive part of this research studies capacity flexibility, classifying investigations into two main areas, i.e. decisions on investment in dedicated versus flexible capacity (Li and Tirupati 1994;Bish, Muriel, and Biller 2005), and process flexibility (or capacity adaptability). Process flexibility (Jordan and Graves 1995;Chou et al. 2010) analyses the ability of a company to change manufacturing setups at flexible lines according to demand or supply changes. It relies, in part on the capability to produce multiple products on multiple production facilities or lines (Jordan and Graves 1995;Simchi-Levi and Wei 2015). In particular, the design of a flexible production network under stochastic demand has been studied by Jordan and Graves (199...

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Section: Flexibility In Manufacturing Service and Supply Chain Systemsmentioning

confidence: 99%

New flexibility drivers for manufacturing, supply chain and service operations

Ivanov

Das

Choi

2018

International Journal of Production Research

114

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“…Los sistemas MTO pueden ser más flexibles en el cumplimiento de las diversas órdenes personalizadas, sin embargo, son más sensibles a los cambios que ocurren en forma de flujos inesperados de la demanda. Entonces, dichos sistemas son realmente dependientes de la planificación de la producción y de los mecanismos de control para superar las dificultades impuestas por los mercados globales y la volatilidad (Özbayrak, Papadopoulou, & Samaras, 2006).…”

Section: R E S U M Ounclassified

“…Asimismo, se genera la necesidad de tener inventarios excesivos con el fin de mantener el nivel de servicio (Özbayrak, Papadopoulou, & Samaras, 2006). De igual manera, debido a que la demanda de dichos productos crece de forma rápida e impredecible, las empresas se enfrentan a más riesgo de escasez en las primeras etapas.…”

Section: Análisis Dinámico De La Capacidad De Respuesta De Una Cadenaunclassified

Análisis dinámico de la capacidad de respuesta de una cadena de suministros de productos tecnológicos. Caso Samsung

2016

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R e s u m e nSe presentan los resultados de la simulación de una cadena de suministro de productos tecnológicos, orientado a la flexibilidad de producción desde una perspectiva de capacidad de respuesta en un contexto de producción bajo pedido. Se caracteriza la cadena de suministros identificando los elementos que constituyen el sistema para la formulación del caso de estudio y modelo dinámico, el cual es programado, calibrado y analizado en Vensim DSS. Los resultados reflejan que incrementos en la demanda implican ampliaciones en la capacidad de la planta a una tasa determinada por una política de mejora, condicionando la respuesta de la cadena. En las primeras etapas, la capacidad está por debajo de la demanda; en la fase de declive, gran parte de las instalaciones quedan ociosas; además, elementos como el tiempo de procesamiento, tiempo de entrega y tipo de orden son factores determinantes en la respuesta de la cadena. Más aún, la exactitud de los pronósticos para anticipar aumentos inesperados en la demanda contribuye a mejorar la respuesta de la cadena. Se concluye que el comportamiento de los productos tecnológicos crea grandes dificultades en la gestión de las cadenas de este tipo, dado que su obsolescencia y rápido crecimiento requieren que los eslabones de la cadena generen una respuesta sincronizada e inmediata, con el propósito de obtener un alto nivel de servicio. Sin embargo, esto implica inversiones significativas. PalabRas c l av eProductos con ciclo de vida corto, productos tecnológicos, cadena de suministro, flexibilidad, capacidad de respuesta, simulación dinámica, Vensim DSS.

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“…Implementing VMI in LED-CM plant is complex because the special features of specification of a product composed of the chips of different bins exist in this plant. Although the allocations of inventory to orders is a popular research topics (Fowler et al, 2010, Hop and Kawtummachai 2005, Hoque 2008, Monthatipkul and Kawtummachai, 2007, Ozbayrak et al, 2006, Pibernik and Yadav,, 2009, Razmi and Rafiei 2010, Shafieezadeh and Sadegheih, 2014, the allocation of chips from bins to replenishment orders (Ros) for LED-CM plants is sparse in the literatures. Deciding how many chips of each feasible bin must be allocated to a replenishment order (RO) affects VMI performance in LED-CM plants; a model of allocating chips from bins to ROs for LED-CM plants is thus proposed in this study.…”

Section: Introductionmentioning

confidence: 99%

A replenishment model for VMI warehouse of LED-CM plants

Su¹,

Wu²,

Wei³

2017

Pressacademia

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Purpose-The LED Chip Manufacturing (LED-CM) is an important process in the LED supply chain. Due to customer pressure or to providing better services in order to enhance competitiveness, the Vendor Managed Inventory (VMI) strategy is applied in LED-CM plant in recent years. The chips in the VMI warehouse of the customers must meet the specifications of the customer requirements. Basically, Chips of a specification of the customer requirements compose of the chips of different bins. A bin is defined by the definite electrical functions of LED chips. Deciding how many chips of each feasible bin must be allocated to a replenishment order (RO) affects the performance of VMI in LED-CM plant. Methodology-A model of allocating chips from bins to ROs for LED-CM plants is proposed in this paper. Three objectives, i.e., maximizing ROs of delivery, maximizing throughput of delivery and maximizing the total left chips of the feasible bins of potential ROs, are considered in the proposed model to obtain the best result. Findings-The obtained ROs of delivery or throughput of delivery could serve as a valuable informance for LED-CM plants. Conclusion-Results show that employing the proposed methodology can facilitate LED-CM plants to implement the VMI service effectively.

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A flexible and adaptable planning and control system for an MTO supply chain system

Cited by 23 publications

References 25 publications

New flexibility drivers for manufacturing, supply chain and service operations

New flexibility drivers for manufacturing, supply chain and service operations

Análisis dinámico de la capacidad de respuesta de una cadena de suministros de productos tecnológicos. Caso Samsung

A replenishment model for VMI warehouse of LED-CM plants

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