Multi-Agent System Interaction in Integrated SCM\ud

Coordination between organizations on strategic, tactical and operation levels leads to more effective and efficient supply chains. Supply chain management is increasing day by day in modern enterprises.. The environment is becoming competitive and many enterprises will find it difficult to survive if they do not make their sourcing, production and distribution more efficient. Multi-agent supply chain management has recognized as an effective methodology for supply chain management. Multi-agent systems (MAS) offer new methods compared to conventional, centrally organized architectures in the scope of supply chain management (SCM). Since necessary data are not available within the whole supply chain, an integrated approach for production planning and control taking into account all the partners involved is not feasible. In this study we show how MAS architecture interacts in the integrated SCM architecture with the help of various intelligent agents to highlight the above problem

Challenges of cloud technology in manufacturing environment

The rapid growth Information systems and advanced network technologies have significant impact on enterprises around the world. Enterprises are trying to gain competitive advantage in open global markets by using the latest technologies, along with advanced networks, to create collaboration, reduce costs, and maximize productivity. The combination of latest technologies and advanced manufacturing networks technologies lead to growth of new manufacturing model named Cloud Manufacturing which can shift the manufacturing industry from product-oriented manufacturing to services-oriented manufacturing. This paper explores the literature about the current Manufacturing problems, understands the concept of Cloud Computing Technology, introduces Cloud Manufacturing and its role in the enterprise, and investigates the obstacles and challenges of adopting Cloud Manufacturing in enterprises

FairShares Model

The attached document provides an introduction to the FairShares Model, a new brand and concept for self-governing social enterprises operating under Company and Co operative Law

SMEs; Virtual research and development (R&D) teams and new product development: A literature review

Small and medium-sized enterprises (SMEs) are indeed the engines of global economic growth. Their continued growth is a major subject for the economy and employment of any country. Towards that end, virtual research and development (R&D) could be a viable option to sustain and ease the operations of SMEs. However, literature shows there has not been a great deal of research into the diverse characteristic of virtual R&D teams in SMEs. This article provides a comprehensive literature review on different aspects of virtual R&D teams collected from the reputed publications. The purpose of the literature review is to provide an outline on the structure and dynamics of R&D collaboration in SMEs. Specifying the rationale and relevance of virtual teams, the relationship between virtual R&D team for SMEs and new product development (NPD) has been examined. It concludes with identifying the gaps and feebleness in the existing literature and calls for future research in this area. It is argued to form of virtual R&D team deserves consideration at top level management for venturing into the new product development within SMEs

On the Property Rights System of the State Enterprises in China

Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error

A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products

This is an Accepted Manuscript of an article published by Taylor & Francis in Production Planning & Control: The Management of Operations on 2014, available online: http://www.tandfonline.com/10.1080/09537287.2013.798085Uniform product deliveries are required in the ceramic, horticulture and leather sectors because customers require product homogeneity to use, present or consume them together. Some industries cannot prevent the lack of homogeneity in products in their manufacturing processes; hence, they cannot avoid non-uniform finished products arriving at their warehouses and, consequently, fragmentation of their stocks. Therefore, final uniform product amounts do not match planned production ones, which frequently makes serving previous committed orders with homogeneous quantities impossible. This paper proposes a model-driven decision support system (DSS) to help the person in charge of delivery management to reallocate the available real inventory to orders to satisfy homogenous customer requirements in a collaborative supply chain (SC). The DSS has been validated in a ceramic tile collaborative SC.This research has been carried out within the framework of the project funded by the Spanish Ministry of Economy and Competitiveness (Ref. DPI2011-23597) and the Polytechnic University of Valencia (Ref. PAID-06-11/1840) entitled 'Methods and models for operations planning and order management in supply chains characterized by uncertainty in production due to the lack of product uniformity' (PLANGES-FHP). Also, we thank the comments and suggestions made by the Editors and the Reviewers. In our opinion, these changes have improved the quality of the paper.Boza García, A.; Alemany Díaz, MDM.; Alarcón Valero, F.; Cuenca, L. (2014). A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products. Production Planning and Control. 25(8):650-661. https://doi.org/10.1080/09537287.2013.798085S650661258Abid, C., D’amours, S., & Montreuil, B. (2004). Collaborative order management in distributed manufacturing. International Journal of Production Research, 42(2), 283-302. doi:10.1080/00207540310001602919Akkermans, H., Bogerd, P., & van Doremalen, J. (2004). Travail, transparency and trust: A case study of computer-supported collaborative supply chain planning in high-tech electronics. European Journal of Operational Research, 153(2), 445-456. doi:10.1016/s0377-2217(03)00164-4Alarcón, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011Alarcón, F., Alemany, M. M. E., & Ortiz, A. (2009). Conceptual framework for the characterization of the order promising process in a collaborative selling network context. International Journal of Production Economics, 120(1), 100-114. doi:10.1016/j.ijpe.2008.07.031Alemany, M. M. E., Alarcón, F., Lario, F.-C., & Boj, J. J. (2011). An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Computers in Industry, 62(5), 519-540. doi:10.1016/j.compind.2011.02.002Alemany, M. M. E., Alarcón, F., Ortiz, A., & Lario, F.-C. (2008). Order promising process for extended collaborative selling chain. Production Planning & Control, 19(2), 105-131. doi:10.1080/09537280801896011Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022Arshinder, Kanda, A., & Deshmukh, S. G. (2008). Supply chain coordination: Perspectives, empirical studies and research directions. International Journal of Production Economics, 115(2), 316-335. doi:10.1016/j.ijpe.2008.05.011Azevedo, A. ., & Sousa, J. . (2000). A component-based approach to support order planning in a distributed manufacturing enterprise. Journal of Materials Processing Technology, 107(1-3), 431-438. doi:10.1016/s0924-0136(00)00680-4Balakrishnan, A., & Geunes, J. (2000). Requirements Planning with Substitutions: Exploiting Bill-of-Materials Flexibility in Production Planning. Manufacturing & Service Operations Management, 2(2), 166-185. doi:10.1287/msom.2.2.166.12349Bhakoo, V., Singh, P., & Sohal, A. (2012). Collaborative management of inventory in Australian hospital supply chains: practices and issues. Supply Chain Management: An International Journal, 17(2), 217-230. doi:10.1108/13598541211212933Bititci, U., Turner, T., Mackay, D., Kearney, D., Parung, J., & Walters, D. (2007). Managing synergy in collaborative enterprises. Production Planning & Control, 18(6), 454-465. doi:10.1080/09537280701494990Boza, A., Ortiz, A., & Cuenca, L. (2010). A Framework for Developing a Web-Based Optimization Decision Support System for Intra/Inter-organizational Decision-Making Processes. IFIP Advances in Information and Communication Technology, 121-128. doi:10.1007/978-3-642-14341-0_14Framinan, J. M., & Leisten, R. (2009). Available-to-promise (ATP) systems: a classification and framework for analysis. International Journal of Production Research, 48(11), 3079-3103. doi:10.1080/00207540902810544Gomes da Silva, C., Figueira, J., Lisboa, J., & Barman, S. (2006). An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming. Omega, 34(2), 167-177. doi:10.1016/j.omega.2004.08.007Hernández, J. E., Poler, R., Mula, J., & Lario, F. C. (2010). The Reverse Logistic Process of an Automobile Supply Chain Network Supported by a Collaborative Decision-Making Model. Group Decision and Negotiation, 20(1), 79-114. doi:10.1007/s10726-010-9205-7Holweg, M., & Pil, F. K. (2007). Theoretical perspectives on the coordination of supply chains. Journal of Operations Management, 26(3), 389-406. doi:10.1016/j.jom.2007.08.003Jagdev, H. S., & Thoben, K.-D. (2001). Anatomy of enterprise collaborations. Production Planning & Control, 12(5), 437-451. doi:10.1080/09537280110042675Kubat, C., Öztemel, E., & Taşkιn, H. (2007). Decision support systems in production planning and control. Production Planning & Control, 18(1), 1-2. doi:10.1080/09537280600940572Lambert, D. M., & Cooper, M. C. (2000). Issues in Supply Chain Management. Industrial Marketing Management, 29(1), 65-83. doi:10.1016/s0019-8501(99)00113-3Lejeune, M. A., & Yakova, N. (2004). On characterizing the 4 C’s in supply chain management. Journal of Operations Management, 23(1), 81-100. doi:10.1016/j.jom.2004.09.004Okongwu, U., Lauras, M., Dupont, L., & Humez, V. (2011). A decision support system for optimising the order fulfilment process. Production Planning & Control, 23(8), 581-598. doi:10.1080/09537287.2011.566230Pibernik, R. (2006). Managing stock‐outs effectively with order fulfilment systems. Journal of Manufacturing Technology Management, 17(6), 721-736. doi:10.1108/17410380610678765Poler, R., Hernandez, J. E., Mula, J., & Lario, F. C. (2008). Collaborative forecasting in networked manufacturing enterprises. Journal of Manufacturing Technology Management, 19(4), 514-528. doi:10.1108/17410380810869941Romano, P. (2003). Co-ordination and integration mechanisms to manage logistics processes across supply networks. Journal of Purchasing and Supply Management, 9(3), 119-134. doi:10.1016/s1478-4092(03)00008-6Zschorn, L. (2006). An extended model of ATP to increase flexibility of delivery. International Journal of Computer Integrated Manufacturing, 19(5), 434-442. doi:10.1080/0951192050039903

Virtual R&D teams in small and medium enterprises: a literature review

Small and medium enterprises (SMEs) are the driving engine behind economic growth. While SMEs play a critical role in generating employment and supporting trade, they face numerous challenges, the prominent among them are the need to respond to fasting time-to-market, low-cost and rapid solutions to complex organizational problems. Towards that end, research and development (R & D) aspect deserves particular attention to promote and facilitate the operations of SMEs. Virtual R & D team could be a viable option. However, literature shows that virtual R & D teaming in SMEs is still at its infancy. This article provides a comprehensive literature review on different aspects of virtual R & D teams collected from the reputed publications. The purpose of the state-of-the-art literature review is to provide an overview on the structure and dynamics of R & D collaboration in SMEs. Specifying the foundation and importance of virtual teams, the relationship between virtual R & D team and SMEs has been examined. It concludes with the identification of the gaps in the existing literature's and calls for future research. It is argued that setting-up an infrastructure for virtual R & D team in SMEs still requires a large amount of engineering efforts and deserves consideration at top level management