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Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three 'pillars' of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a 'boomerang' effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems. [ABSTRACT FROM AUTHOR]

The hybrid assembly-to-order (ATO) supply chain, combining make-to-stock and make-to-order (MTS-MTO) production, separated by a customer order decoupling point (CODP), is well recognised in many sectors. Based on the well-established Inventory and Order Based Production Control Systems (the IOBPCS family), we develop a hybrid ATO system dynamics model and analytically study the impact of nonlinearities on its dynamic performance. Nonlinearities play an important, sometimes even a dominant, role in influencing the dynamic performance of supply chain systems. However, most IOBPCS based analytical studies assume supply chain systems are completely linear and thereby greatly limit the applicability of published results, making it difficult to fully explain and describe oscillations caused by internal factors. We address this gap by analytically exploring the non-negative order and capacity constraint nonlinearities present in an ATO system. By adopting nonlinear control engineering and simulation approaches, we reveal that, depending on the mean and amplitude of the demand, the non-negative order and capacity constraints in the ATO system may occur and their significant impact on system dynamics performance should be carefully considered. Failing to monitor non-negative order constraints may underestimate the mean level of inventory and overestimate the inventory recovery speed. Sub-assemblers may suffer increased inventory cost (i.e. the consequence of varying inventory levels and recovery speed) if capacity and non-negative order constraints are not considered at their production site. Future research should consider the optimal trade-off design between CODP inventory and capacity and the exploration of delivery lead-time dynamics. [ABSTRACT FROM AUTHOR]

The relationship between volume, variety, and process choice is a fundamental tenet of manufacturing research and practice, and through the product-process matrix managers balance trade-offs between the traditionally dichotomous objectives of flexibility and cost in process selection. In this paper, we examine the adherence of Additive Manufacturing systems to traditional trade-offs, and identify circumstances where they deviate from these established norms. Using engineering philosophy we develop an extension of the product-process matrix to accommodate both variety and customisation measures, which is used to evaluate case study research conducted with five major Additive Manufacturing companies. Fifteen case studies inform the research, drawn from a broad range of industry sectors. A qualitative approach was taken, using semi-structured interviews and process observation. The study demonstrates that Additive Manufacturing systems can support both alignment and disjunction to established theory. For many cases, a general conformance to the traditional product-process matrix 'diagonal' is evidenced. However, several cases show significant deviation, demonstrating the achievement of both variety and volume for both batch and line production. Through a detailed exploration of the focal cases, we highlight the characteristics of both products and Additive Manufacturing systems that can help overcome traditional trade-off constraints. Extends the strategically important concept of the product-process matrix to accommodate both variety and customisation measures. Uses empirical data gained from five commercial Additive Manufacturing companies to examine the implications for the Product Process Matrix. Examines characteristics of Additive Manufacturing and its products that support alignment and divergence from established theory. [ABSTRACT FROM AUTHOR]

Infrastructure projects exemplify engineer-to-order supply chains, where there is a high degree of complexity and uncertainty associated with developing a 'unique' product. While there is much advocacy of translating operational excellence techniques from high volume manufacturing sectors, we argue that such an approach is based on a mis-presumption of order and structure at all systems levels. We suggest an alternative 'travel of an idea' from the knowledge management discipline, a phenomenological framework describing contexts in terms of ordered and un-ordered, which directs us towards the need for diverse management approaches if we are to minimise the risk of project underperformance and failure. We contemplate the value of the framework and reflect on the contribution it can make to the construction industry specifically and to engineer-to-order production systems more generally; we provide a basis to bring a healthy challenge when 'travelling ideas' and expose how unthinking choices can be expected to fail. [ABSTRACT FROM AUTHOR]

Customer specific requirements is one of the key drivers in a supply chain strategy based on mass customization. At the same time there are intense pressure on price and short delivery lead times providing a complex environment for managing the supply chain. The complexity can be reduced by introducing the concept of a customer order decoupling point (CODP). The CODP separates the supply chain into two key processes that can be categorized as make-to-order (MTO) and make-to-stock (MTS) and constitutes the two key building blocks of a system based on the mass customization concept. The dynamic properties of the MTS environment have been extensively researched using the automatic pipeline-, inventory-, and order-based production control system (APIOBPCS) archetype. In contrast to MTS systems, the customer facing MTO process, which is usually exposed to a more volatile demand stream suitable for an agile strategy, is less well investigated. Agility does, however, frequently not mean that infinite capacity is available but that there is some surplus capacity available and that the capacity can be adapted to the customer requirements in certain time frames. When the adaptation is not instant but requires some time to take effect the delay creates a dynamic backlog represented by the order book which affects the delivery lead time. Since controlling lead times is a key factor in providing reliable delivery promises an order book control logic is introduced. It is also shown that the new MTO model of the customer facing part of a mass customization system is an extension to the well established APIOBPCS framework. Index Terms--Dynamic analysis, make-to-order, mass customized supply chains, performance measurement, simulation.

In this paper we study a hybrid system with both manufacturing and remanufacturing. The inventory control strategy we use in the manufacturing loop is an automatic pipeline, inventory and order based production control system (APIOBPCS). In the remanufacturing loop we employ a Kanban policy to represent a typical pull system. The methodology adopted uses control theory and simulation. The aim of the research is to analyse the dynamic (as distinct from the static) performance of the specified hybrid system. Dynamics have implications on total costs in terms of inventory holding, capacity utilisation and customer service failures. We analyse the parameter settings to find preferred 'nominal', 'fast' and 'slow' values in terms of system dynamics performance criteria such as rise time, settling time and overshoot. Based on these parameter settings, we investigate the robustness of the system to changes in return yield and the manufacturing/remanufacturing lead time. Our results clearly show that the system is robust with respect to the system dynamics performance and the remanufacturing process can help to improve system dynamics performance. Thus, the perceived benefits of remanufacturing of products, both environmentally and economically, as quoted in the literature are found not to be detrimental to system dynamics performance when a Kanban policy is used to control the remanufacturing process. Keywords: Hybrid system; Combined manufacturing and remanufacturing; Dynamic performance, 1. Introduction With the growing concern for environmental protection from the customers' perspective and stricter environment legislations issued by the governments, more and more world leading companies have begun to [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.09.044 Byline: Nina F. Thornhill (a), Mohamed M. Naim (b) Keywords: Uncertainty; Variability; Time series; Frequency spectrum; Multivariate analysis; Spectral principal component analysis Abstract: Variability in the information flows within a supply network requires production companies to either track the variations, hence leading to increased production on-costs, or to buffer themselves via the use of inventory which leads to stock holding costs. Customer demands generate variability, often in the form of seasonal patterns, but must be satisfied. In contrast, 'rogue seasonality', i.e. unintended variability, may be generated by a company's own internal processes such as inventory and production control systems. Importantly, rogue seasonality may propagate through a supply network. Thus there is a motivation for automated detection of network-wide rogue seasonality and for the diagnosis of its root cause. In this article, a data-driven technique known as spectral principal component analysis is used to detect and characterise cyclical disturbances in a supply network that indicate seasonality. All the information and material flows participating in each disturbance are detected, and the distribution of each disturbance enables a hypothesis to be reached about its root cause. The technique is applied to a supply network consisting of four autonomous business units in the steel industry. Two main cyclical disturbances were detected and diagnosed. One was found to be rogue seasonality and the other was externally induced by the pattern of customer orders. Author Affiliation: (a) Department of Electronic and Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, UK (b) Logistics Systems Dynamics Group, Cardiff Business School, Aberconway Building, Colum Drive, Cardiff, Wales CF10 3EU, UK Article History: Received 25 September 2003; Accepted 8 September 2004