THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes

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

Using Collaborative Immersive Environments and Building Information Modeling Technology for Holistic Planning of Production Lines

Large and complex building projects need many different experts from different engineering disciplines for different matters. But these experts each use their own IT tools that produce a lot of heterogeneous data. This leads to a strong fragmentation of competencies, what causes problems for interdisciplinary collaboration, because the data might be inconsistent, redundant or there are no interfaces to combine the data. These problems in collaboration increase the risk of planning mistakes that might significantly impair the overall project success. So only one database should be used for all engineering tasks to improve the transdisciplinary collaboration. The Building Information Modelling (BIM) working methodology enables the digital collaboration of virtual production planning and architecture tasks for developing a building. By means of lean optimization in combination with early integration of future-oriented production facilities, process-relevant production data can be included in the planning phase before construction begins. This article presents a real time immersive 3D virtualization system using the digital twin of complex buildings with a modern production line as the single source of truth and creates a consistent integrated data model, that enables transdisciplinary collaboration of all involved engineering disciplines. In this way, a continuous comparison can be made between the real construction project and its digital twin in an interactive, intuitive and collaborative manner. The same model is also used by production planners to optimize the material flow and in general the value chain of a production line through a holistic planning, which brings many benefits for all stakeholders

KNOWLEDGE MANAGEMENT THROUGH BIM IN CONSTRUCTION SUPPLY CHAINS

ABSTRACT Collaborative working is the key driver for delivering projects in construction industry. In construction supply chains where there is huge knowledge and information flow between the contractors, subcontractors, suppliers and distributors, it is essential to create a collaborative environment during the projects from the bidding phase to the delivery to client. There are some key virtual collaborative tools which have been started to be utilized in major civil engineering projects. The recent concept Building Information modeling (BIM) has been utilized in some major and prestigious construction projects where architects, structural engineers, suppliers, contractors and sub-contractors can work within a three dimensional platform to achieve certain tasks as design, planning, resource allocation, logistics planning, clash detection, coordination and production of design drawings. This paper first explains the recent trends in construction supply chain management, knowledge management and Building Information Modeling. Then, it discusses the integration of Building Information Modeling into construction supply chains for improving information and knowledge management practices throughout the lifecycle of the project which is called as Building Knowledge Model (BKM)

Models for supply chain negotiation in collaborative relationships

Nowadays, firms are increasingly building collaborative relationships with their partners in order to improve the global performance of the supply chain in which they are involved. Such collaborative relationships require information exchange or share and negotiation. In this paper, we first formalize some practices of collaboration from case studies of the aeronautical area then suggest some models for negotiation, allowing a supply chain member to publish hidden constraints and share risks/costs in order to achieve a win-win situation

Designing Enterprise Resources Planning Application for Integrating Main Activities in a Simulator Model of SCM Network Distribution

Collaborative supply chain is a specific topic in supply chain management and studied by industrial engineering students in supply chain management course. Unfortunately, conventional learning media cannot explain the phenomenon of collaborative supply chain to the students. This study aimed to design a dynamic learning media so that inter-company collaboration and information sharing on the activities of Supply Chain entities can be explained effectively to the students. The problem was solved using 3 (three) steps. First, the distribution network was described using mock up. It consists of miniature trucks, miniature network and miniature of the manufacturer-distributor-retailer embedded with tag and reader of RFID. Second, the Enterprise Resources Planning application was developed for supporting business activities. Third, we developed the integrator consists of monitor’s user interface and practice modules. The result of the research - an SCM-Simulator – will be able to improve learning skills of industrial engineering graduates, especially abilities to identify, formulate, and solve the activities of tactical plan & operational routines of Supply Chain entities. However, distribution module designed is for limited scale laboratory study of simple objects. Keywords: Distribution Network, Enterprise Resource Planning, Industrial Engineering Education, SCM Simulator,and Learning Media

Bio-inspired Factories of the Future

The biological transformation of added value is seen as one of the key aspects in applied research. Bio- inspired methods and technologies will affect factories of the future and enable them to cope with changing boundary conditions and the rising necessity of sustainability. This results in a higher demand for flexibility and transformation ability of the comprised production systems. To elaborate topics like these, Fraunhofer initiated strategic collaborative research projects. The current project aims at developing aspects of the biological transformation, whereof organic bio-inspired factories is one. Different research focal points were identified as enabling technologies on different levels of the well-established automation pyramid. The paper highlights the aspects “facility layout planning”, “behavioral modeling of production systems” and “skill-based controller programming” as enabling technologies. Solution approaches for the addressed aspects are discussed and future steps towards a flexible and sustainable production are shown