Additive Manufacturing (AM) technology has the potential to significantly improve supply chain dynamics. The purpose of this paper is to investigate the impact of AM on spare parts supply chain. Three supply chain scenarios are investigated in this paper, namely conventional supply chain, centralised AM-based supply chain and distributed AM-based supply chain. Based on system dynamics simulations, this paper specifically compares three supply chain scenarios, in terms of total variable cost and carbon emission. The results show the spare part supply chain utilising AM is indeed superior to the traditional one in sustainable performance. It is also expected that AM can facilitate the spare parts supply chain to achieve more economic benefits along with its development. To our knowledge, this paper is one of the early studies that explores the impact of AM on supply chain performance and quantitatively examines the superiority of utilising AM in spare parts supply chain. Some suggestions are also provided to help managers adopting AM in their spare parts supply chains.

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Additive manufacturing (AM), alongside technological developments, has been used in the production of spare parts with positive results for spare parts supply chains. In this study, we investigate spare parts supply chains serving heterogeneous demands from multiple service locations under the mode of make-to-order. We aim to compare different configurations (i.e. centralised and distributed) of spare parts supply chains in terms of their performance (e.g. sojourn time and cost) and to further propose suggestions to better configure AM-based spare parts supply chains by effectively allocating AM machines at service locations (SLs) or regional distribution centres (RDCs). In order to realise these research objectives, the simulation approach is used as the main research method. Different from the existing perception, our results illustrate that the distributed deployment of AM machines does not always guarantee a quick response, and that centralised configuration is desirable when the demand rate is relatively high due to the pooling effect. The distributed configuration, however, can still be suitable, considering the development of AM technology. Our results also indicate the possibility of a mixed configuration of AM-based supply chains with the potential for outperforming the purely centralised/distributed configuration. The criteria to design such a mixed configuration are also offered.

Purpose: The purpose of this paper is to find the driving factors for effective and efficient management of international manufacturing network (IMN) which has become increasingly important due to the intensive competition existing in the manufacturing industry. This paper presents a magnified view of different constructs of IMN and identifies the qualitative factors which are broadly classified under different constructs like political, economic, social, technological, legal and environmental. Design/methodology/approach: Principal component analysis is applied to club identified factors into political, economic, social, technological and legal categories. PESTLE–SWOT approach is used to shortlist the identified factors using the elimination algorithm. Using analytical hierarchy process, weightages and rank of the identified factors are evaluated. Interpretive structural modeling is applied to understand inter-relationship among factors, and to analyze the driving power and dependence of the factors. Findings: The results obtained from the above approaches are compared to identify most significant factors and the list of IMN enablers is presented using Venn Diagram. Government stability, Economic freedom, economic stability, environmental sustainability and raw material availability are coming out to be the driving factors across all techniques. Finally, the paper provides weights of the driving indicators common across all multi-criteria decision-making techniques considered. Research limitations/implications: The proposed work provides significant information about the qualitative factors needed to be considered while designing and developing IMN. As identified by literature review and through expert opinions, not all 29 factors responsible for IMN development are crucial. Many factors are either interdependent or driven by others. The paper provides a useful analysis about the driving factors that need to be taken into account. Originality/value: The study presents a comprehensive analysis of the IMN enablers. Furthermore, it provides managerial and theoretical implications to deal with the complexities encountered while establishing IMN.

The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.

Considering the increased significance of both internationalisation and externalisation of manufacturing, this paper aims to provide an integrated understanding of internationalisation and externalisation and seek to identify further development trends. First, this paper investigates and summarises the existing literature in order to examine what has been done on internationalisation and externalisation. An adapted matrix is further presented in order to illustrate a roadmap for the manufacturing system evolution. Claiming that conceptual insights tell only one of the two worlds (i.e. research and practice) of operations management, this paper successively explores what is actually happening in practice on the basis of three case studies selected from the global operations networks (GONE) project, which is a joint-effort between several Danish universities and aims to deepen the understandings on GONE. The empirical findings suggest that the discussions on internationalisation and externalisation need to be extended from manufacturing activity to other value chain activities. Two development trajectories are further identified and combined. Their similarities and differences are correspondingly compared. Based on these findings, some managerial recommendations are given at the end.