Design and operation of manufacturing networks for mass customisation

Mourtzis, Dimitris; Doukas, M.; Psarommatis, Foivos

doi:10.1016/j.cirp.2013.03.126

Cited by 86 publications

(45 citation statements)

References 12 publications

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“…The new paradigm of mass customization is forcing manufacturers to redesign and change products constantly [33][34][35]. As a consequence, products life cycles have been decreasing making sales at the SKU (Stock Keeping Unit) level in a particular store difficult to forecast, as time series for these products tend to be short.…”

Section: Discussionmentioning

confidence: 99%

Performance of state space and ARIMA models for consumer retail sales forecasting

Ramos

Santos

Rebelo

2015

Robotics and Computer-Integrated Manufacturing

176

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a b s t r a c tForecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

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Section: Discussionmentioning

confidence: 99%

Performance of state space and ARIMA models for consumer retail sales forecasting

Ramos

Santos

Rebelo

2015

Robotics and Computer-Integrated Manufacturing

176

View full text Add to dashboard Cite

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“…Recent complexity studies deal with the emerging aspects of increasing complexity of manufacturing activities and the dynamic nature of supply chains [93]. The importance of managing the complexity in supply chains is evident, as recent studies depict that lower manufacturing network complexity is associated with reduced costs and overall network performance [38,94]. A complete and comprehensive review of complexity in engineering design and manufacturing is presented in [95][96][97].…”

Section: Supply Chain Controlmentioning

confidence: 99%

“…In the first, the coordination decisions are taken by a central body, often the leading supply chain OEM, whereas in the second, each member independently makes its own operational decisions. The decentralised topology has been proven to improve the performance in the context of MC [38,55]. A supply chain that is commissioned to provide a variety of customised products requires a total systems approach to managing the Fig.…”

Section: Supply Chain Coordinationmentioning

confidence: 99%

“…A typical modern manufacturing network is composed of cooperating original equipment manufacturer (OEM) plants, suppliers, distribution centres, and dealers that produce and deliver final products to the market [38]. The topics discussed include supplier selection, supply chain coordination, initial network configuration, manufacturing network complexity, inventory management, capacity planning, warehousing, lot sizing, ICT support tools, and dynamic process planning, monitoring, and control.…”

Section: Manufacturing Network Life Cycle and Mass Customisationmentioning

confidence: 99%

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Challenges and future perspectives for the life cycle of manufacturing networks in the mass customisation era

Mourtzis

2016

Logist. Res.

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Manufacturers and service providers are called to design, plan, and operate globalised manufacturing networks, addressing to challenges of increasing complexity in all aspects of product and production life cycle. These factors, caused primarily by the increasing demand for product variety and shortened life cycles, generate a number of issues related to the life cycle of manufacturing systems and networks. Focusing on the aspects that affect manufacturing network performance, this work reviews the exiting literature around the design, planning, and control of manufacturing networks in the era of mass customisation and personalisation. The considered life cycle aspects include supplier selection, initial manufacturing network design, supply chain coordination, complexity, logistics management, inventory and capacity planning and management, lot sizing, enterprise resource planning, customer relationship management, and supply chain control. Based on this review and in correlation with the view of the manufacturing networks and facilities of the future, directions for the development of methods and tools to satisfy product-service customisation and personalisation are promoted.

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“…This type of network allows manufacturing enterprises to communicate with suppliers and customers and exchange detailed data with them (Wiendahl and Lutz, 2002). Manufacturing Networks consist of original equipment manufacturer (OEM) plants, dealers, and suppliers which may be geographically dispersed (Mourtzis et al 2013). The benefit of using a Manufacturing Network is the ability to integrate both large enterprises and SMEs characteristics together; for example, critical mass in large enterprises and niche markets in SMEs (Butala and Sluga, 2006).…”

Section: Manufacturing Network and Modelsmentioning

confidence: 99%