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    Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context

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    [EN] Agri-food supply chains are subjected to many sources of uncertainty. If these uncertainties are not managed properly, they can have a negative impact on the agri-food supply chain (AFSC) performance, its customers, and the environment. In this sense, collaboration is proposed as a possible solution to reduce it. For that, a conceptual framework (CF) for managing uncertainty in a collaborative context is proposed. In this context, this paper seeks to answer the following research questions: What are the existing uncertainty sources in the AFSCs? Can collaboration be used to reduce the uncertainty of AFSCs? Which elements can integrate a CF for managing uncertainty in a collaborative AFSC? The CF proposal is applied to the weather source of uncertainty in order to show its applicability.The first author acknowledges the partial support of the Program of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport (FPU15/03595). The other authors acknowledge the partial support of the Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso-Álvarez, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2017). Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context. IFIP Advances in Information and Communication Technology. 506:715-724. https://doi.org/10.1007/978-3-319-65151-4_64S715724506Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. Supply Chain Manag. Int. J. 11, 379–384 (2006)Matopoulos, A., Vlachopoulou, M., Manthou, V., Manos, B.: A conceptual framework for supply chain collaboration: empirical evidence from the agri-food industry. Supply Chain Manag. Int. J. 12, 177–186 (2007)Ahumada, O., Villalobos, J.R.: Application of planning models in the agri-food supply chain: a review. Eur. J. Oper. Res. 196, 1–20 (2009)Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosyst. Eng. 120, 47–64 (2014)van der Vorst, J.G., Da Silva, C.A., Trienekens, J.H.: Agro-industrial supply chain management: Concepts and applications. FAO (2007)Borodin, V., Bourtembourg, J., Hnaien, F., Kabadie, N.: Handling uncertainty in agricultural supply chain management: a state of the art. Eur. J. Oper. Res. 254, 348–359 (2016)van der Vorst, J.G.A.J., Beulens, A.J.M.: Identifying sources of uncertainty to generate supply chain redesign strategies. Int. J. Phys. Distrib. Logist. Manag. 32, 409–430 (2000)Klosa, E.: A concept of models for supply chain speculative risk analysis and management. J. Econ. Manag. 12, 45–59 (2013)Samson, S., Reneke, J.A., Wiecek, M.M.: A review of different perspectices on uncertainty and risk and an alternative modeling paradigm. Reliab. Eng. Syst. Saf. 94, 558–567 (2009)Backus, G.B.C., Eidman, V.R., Dijkhuizen, A.A.: Farm decision making under risk and uncertainty. Neth. J. Agric. Sci. 45, 307–328 (1997)van der Vorst, J.G.: Effective food supply chains; Generating, modelling and evaluating supply chain scenarios. (2000)Amorim, P., Günther, H.O., Almada-Lobo, B.: Multi-objective integrated production and distribution planning of perishable products. Int. J. Prod. Econ. 138, 89–101 (2012)Amorim, P., Meyr, H., Almeder, C., Almada-Lobo, B.: Managing perishability in production-distribution planning: a discussion and review. Flex. Serv. Manuf. 25, 389–413 (2013)Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarria, D., Menesatti, P.: A review on agri-food supply chain traceability by means of RFID technology. Food Bioprocess Technol. 6, 353–366 (2013)Pahl, J., Voss, S.: Integrating deterioration and lifetime constraints in production and supply chain planning: a survey. Eur. J. Oper. Res. 238, 654–674 (2014)Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of Mathematical models for supporting the order promising process under Lack of Homogeneity in product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016)Zwietering, M.H., van’t Riet, K.: Modelling of the quality of food: optimization of a cooling chain. In: Management Studies and the Agri-business: Management of Agri-chains, Wageningen, The Netherlands, pp. 108–117 (1994)Akkerman, R., Farahani, P., Grunow, M.: Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. Spectrum 32, 863–904 (2010)Apaiah, R.K., Hendrix, E.M.T., Meerdink, G., Linnemann, A.R.: Qualitative methodology for efficient food chain design. Trends Food Sci. Technol. 16, 204–214 (2005)Lehmann, R.J., Reiche, R., Schiefer, G.: Future internet and the agri-food sector: State-of-the-art in literature and research. Comput. Electron. Agric. 89, 158–174 (2012)Kusumastuti, R.D., van Donk, D.P., Teunter, R.: Crop-related harvesting and processing planning: a review. Int. J. Prod. Econ. 174, 76–92 (2016)Dreyer, H.C., Strandhagen, J.O., Hvolby, H.H., Romsdal, A., Alfnes, E.: Supply chain strategies for speciality foods: a Norwegian case study. Prod. Plan. Control 27, 878–893 (2016)Baghalian, A., Rezapour, S., Farahani, R.Z.: Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur. J. Oper. Res. 227, 199–215 (2013)Aggarwal, S., Srivastava, M.K.: Towards a grounded view of collaboration in Indian agri-food supply chains: a qualitative investigation. Br. Food J. 115, 1085–1106 (2016)Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M.: A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: a system dynamics approach. Comput. Electron. Agric. 93, 37–45 (2013)Opara, L.U.: Traceability in agriculture and food supply chain: a review of basic concepts, technological implications, and future prospects. J. Food Agric. Environ. 1, 101–106 (2003)Kruize, J.W., Wolfert, S., Goense, D., Scholten, H., Beulens, A., Veenstra, T.: Integrating ICT applications for farm business collaboration processes using Fl Space. In: 2014 Annual SRII Global Conference, pp. 232–240. IEEE (2014)Oriade, C.A., Dillon, C.R.: Developments in biophysical and bioeconomic simulation of agricultural systems: a review. Agric. Econ. 17, 45–58 (1997)Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: value creation in a knowledge society. In: Wang, Kesheng, Kovacs, G.L., Wozny, Michael, Fang, Minglun (eds.) PROLAMAT 2006. IIFIP, vol. 207, pp. 26–40. Springer, Boston, MA (2006). doi: 10.1007/0-387-34403-9_4Prima Dania, W.A., Xing, K., Amer, Y.: Collaboration and sustainable agri-food supply chain: a literature review. MATEC Web Conf. 58 (2016)Simatupang, T.M., Sridharan, R.: The collaborative index: a measure for supply chain collaboration. Int. J. Phys. Distrib. Logist. Manag. 35, 44–62 (2005)Fischer, C., Hartmann, M., Reynolds, N., Leat, P., Revoredo-Giha, C., Henchion, M., Albisu, L.M., Gracia, A.: Factors influencing contractual choice and sustainable relationships in European agri-food supply chains. Eur. Rev. Agric. Econ. 36, 541–569 (2009

    formulas of revised mrp

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    Information sharing among supply chain echelons is now an acquired result. As a consequence, most of the traditional techniques and procedures in production management must be revised and updated, exploiting the opportunities provided by new technologies. This paper presents an improved version of Material Requirement Planning procedure, which assumes information sharing capabilities and permits the creation of new business opportunities. In Orlickyʹs MRP, orders are computed considering the parent items gross requirements. On the contrary, here the order release procedure related to a certain item is computed both by exploiting all the information sharing advantages and by introducing a drastic innovation to the main process functioning. As a result, the proposed algorithm copes better with demand uncertainty, lowers the system nervousness and also removes the need for continuous forecast adjustments, thereby improving the ease in managing the material flow, allowing the development of new forms of collaboration among different supply chain partners and the creation of new business networks. The algorithm is presented in formulas to describe in detail each procedure step and calculations

    Supply chain uncertainty:a review and theoretical foundation for future research

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    Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative)

    A conceptual framework for crop-based agri-food supply chain characterization under uncertainty

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    [EN] Crop-based Agri-food Supply Chains (AFSCs) are complex systems that face multiple sources of uncertainty that can cause a significant imbalance between supply and demand in terms of product varieties, quantities, qualities, customer requirements, times and prices, all of which greatly complicate their management. Poor management of these sources of uncertainty in these AFSCs can have negative impact on quality, safety, and sustainability by reducing the logistic efficiency and increasing the waste. Therefore, it becomes crucial to develop models in order to deal with the key sources of uncertainty. For this purpose, it is necessary to precisely understand and define the problem under study. Even, the characterisation process of this domains is also a difficult and time-consuming task, especially when the right directions and standards are not in place. In this chapter, a Conceptual Framework is proposed that systematically collects those aspects that are relevant for an adequate crop-based AFSC management under uncertainty.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015Alemany Díaz, MDM.; Esteso, A.; Ortiz Bas, Á.; Hernández Hormazabal, JE.; Fernández, A.; Garrido, A.; Martin, J.... (2021). A conceptual framework for crop-based agri-food supply chain characterization under uncertainty. Studies in Systems, Decision and Control. 280:19-33. https://doi.org/10.1007/978-3-030-51047-3_2S1933280Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. 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Manage. 33, 114–158 (2013)Lemma, Y., Kitaw, D., Gatew, G.: Loss in perishable food supply chain: an optimization approach literature review. Int. J. Sci. Eng. Res. 5, 302–311 (2014)Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosyst. Eng. 120, 47–64 (2014)Van der Vorst, J.G., Da Silva, C.A., Trienekens, J.H.: Agro-industrial Supply Chain Management: Concepts and Applications. FAO (2007)Hernandez, J., Mortimer, M., Patelli, E., Liu, S., Drummond, C., Kehr, E., Calabrese, N., Iannacone, R., Kacprzyk, J., Alemany, M.M.E., Gardner, D.: RUC-APS: enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems. In: 11th International Conference on Industrial Engineering and Industrial Management, Valencia, Spain (2017)Miles, M.B., Huberman, A.M.: Qualitative Data Analysis: An Expanded Sourcebook. Sage Publications, Thousand Oaks (1994)Alemany, M.M.E., Alarcón, F., Lario, F.C., Boj, J.J.: An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Comput. Ind. 62, 519–540 (2011)Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M.: A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: a system dynamics approach. Comput. Electron. Agric. 93, 37–45 (2013)Kusumastuti, R.D., van Donk, D.P., Teunter, R.: Crop-related harvesting and processing planning: a review. Int. J. Prod. Econ. 174, 76–92 (2016)Zhang, W., Wilhelm, W.E.: OR/MS decision support models for the specialty crops industry: a literature review. Ann. Oper. Res. 190, 131–148 (2011)Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of mathematical models for supporting the order promising process under lack of homogeneity in product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016)Blanco, A.M., Masini, G., Petracci, N., Bandoni, J.A.: Operations management of a packaging plant in the fruit industry. J. Food Eng. 70, 299–307 (2005)Grillo, H., Alemany, M.M.E., Ortiz, A., Fuertes-Miquel, V.S.: Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Appl. Math. Model. 49, 255–278 (2017)Verdouw, C.N., Beulens, A.J.M., Trienekens, J.H., Wolferta, J.: Process modelling in demand-driven supply chains: a reference model for the fruit industry. Comput. Electron. Agric. 73, 174–187 (2010)Amorim, P., Günther, H., Almada-Lobo, B.: Multi-objective integrated production and distribution planning of perishable products. Int. J. Prod. 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    Customer Enquiry Management in a Global Competitive Context: A Comparative Multi-Case Study Analysis

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    Business-to-Business (B2B) relationships, such as between a manufacturer and a customer, are increasingly important during the Customer Enquiry Management (CEM) process, particularly so for non-Make-To-Stock (non-MTS) companies operating in industrial markets. Few empirical studies have explored the CEM practices adopted by firms in practice. A study of the Italian capital goods sector by Zorzini et al. (2007) is a recent exception. Moreover, most studies have approached CEM from a cross-department integrated perspective but in the digital economy, and with globalization, outsourcing and extended supply chains, CEM needs to be approached from a broader supply chain-oriented perspective, incorporating B2B exchanges. This paper builds on the study by Zorzini et al. (2007) by conducting multi-case study research with seven UK-based companies in the capital goods sector, including three sales and support companies with offshore manufacturing. By adopting a cross-national research perspective, it assesses whether the proposed theory applies to other capital goods firms outside Italy. By also adopting a supply chain perspective of CEM it investigates current industry practice in B2B markets and explores whether cross-functional coordination and formalization issues can be extended into a global context. Evidence from the UK generally supports prior theory, confirming links between high levels of coordination, formalization of the CEM process and improved performance. Some refinements are proposed, for example, in order to make the theory suitable for a global context. The characteristics of a supply chain are important factors that affect CEM. This research has managerial implications for improving the CEM process in non-Make-To-Stock (non-MTS) capital goods companies from both an intra and an inter-organisational (B2B) perspective. Coordination with partners along the supply chain is needed at the enquiry stage and constraints linked to global customers should be considered when structuring the

    Managing the trade-off implications of global supply

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    The cost versus response trade-off is a growing logistics issue due to many markets being increasingly characterized by demand uncertainty and shorter product life cycles. This is exacerbated further with supply increasingly moving to low cost global sources. However, the poor response implications of global supply are often not addressed or even acknowledged when undertaking such decisions. Consequently, various practical approaches to minimising, postponing or otherwise managing the impact of the demand uncertainty are often only adopted retrospectively. Even though such generic solutions are documented through case examples we lack effective tools and concepts to support the proactive identification and resolution of such trade-offs. This paper reports on case-based theory building research, involving three cases from the UK and USA used in developing a conceptual model with associated tools, in support of such a process

    Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions

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    "This is an Accepted Manuscript of an article published in International Journal of Production Research on December 2014, available online: http://www.tandfonline.com/10.1080/00207543.2014.920115."In this paper, we formulate the material requirements planning) problem of a first-tier supplier in an automobile supply chain through a fuzzy multi-objective decision model, which considers three conflictive objectives to optimise: minimisation of normal, overtime and subcontracted production costs of finished goods plus the inventory costs of finished goods, raw materials and components; minimisation of idle time; minimisation of backorder quantities. Lack of knowledge or epistemic uncertainty is considered in the demand, available and required capacity data. Integrity conditions for the main decision variables of the problem are also considered. For the solution methodology, we use a fuzzy goal programming approach where the importance of the relations among the goals is considered fuzzy instead of using a crisp definition of goal weights. For illustration purposes, an example based on modifications of real-world industrial problems is used.This work has been funded by the Universitat Politecnica de Valencia Project: 'Material Requirements Planning Fourth Generation (MRPIV)' (Ref. PAID-05-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research. 52(23):6971-6988. doi:10.1080/00207543.2014.920115S697169885223Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427-1433. doi:10.1016/j.ejor.2005.11.049Alfieri, A., & Matta, A. (2010). Mathematical programming representation of pull controlled single-product serial manufacturing systems. Journal of Intelligent Manufacturing, 23(1), 23-35. doi:10.1007/s10845-009-0371-xAloulou, M. A., Dolgui, A., & Kovalyov, M. Y. (2013). A bibliography of non-deterministic lot-sizing models. International Journal of Production Research, 52(8), 2293-2310. doi:10.1080/00207543.2013.855336Barba-Gutiérrez, Y., & Adenso-Díaz, B. (2009). Reverse MRP under uncertain and imprecise demand. The International Journal of Advanced Manufacturing Technology, 40(3-4), 413-424. doi:10.1007/s00170-007-1351-yBookbinder, J. H., McAuley, P. T., & Schulte, J. (1989). Inventory and Transportation Planning in the Distribution of Fine Papers. Journal of the Operational Research Society, 40(2), 155-166. doi:10.1057/jors.1989.20Chiang, W. K., & Feng, Y. (2007). The value of information sharing in the presence of supply uncertainty and demand volatility. International Journal of Production Research, 45(6), 1429-1447. doi:10.1080/00207540600634949Díaz-Madroñero, M., Mula, J., & Jiménez, M. (2013). A Modified Approach Based on Ranking Fuzzy Numbers for Fuzzy Integer Programming with Equality Constraints. Annals of Industrial Engineering 2012, 225-233. doi:10.1007/978-1-4471-5349-8_27DOLGUI, A., BEN AMMAR, O., HNAIEN, F., & LOULY, M. A. O. (2013). A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty. Studies in Informatics and Control, 22(3). doi:10.24846/v22i3y201302Dubois, D. (2011). The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems, 184(1), 3-28. doi:10.1016/j.fss.2011.06.003Grabot, B., Geneste, L., Reynoso-Castillo, G., & V�rot, S. (2005). Integration of uncertain and imprecise orders in the MRP method. Journal of Intelligent Manufacturing, 16(2), 215-234. doi:10.1007/s10845-004-5890-xGuillaume, R., Thierry, C., & Grabot, B. (2010). Modelling of ill-known requirements and integration in production planning. Production Planning & Control, 22(4), 336-352. doi:10.1080/09537281003800900Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy Sets and Systems, 47(1), 81-86. doi:10.1016/0165-0114(92)90062-9Hnaien, F., Dolgui, A., & Ould Louly, M.-A. (2008). Planned lead time optimization in material requirement planning environment for multilevel production systems. Journal of Systems Science and Systems Engineering, 17(2), 132-155. doi:10.1007/s11518-008-5072-zHung, Y.-F., & Chang, C.-B. (1999). Determining safety stocks for production planning in uncertain manufacturing. International Journal of Production Economics, 58(2), 199-208. doi:10.1016/s0925-5273(98)00124-8Inderfurth, K. (2009). How to protect against demand and yield risks in MRP systems. International Journal of Production Economics, 121(2), 474-481. doi:10.1016/j.ijpe.2007.02.005JIMÉNEZ, M. (1996). RANKING FUZZY NUMBERS THROUGH THE COMPARISON OF ITS EXPECTED INTERVALS. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 04(04), 379-388. doi:10.1142/s0218488596000226Jiménez, M., Arenas, M., Bilbao, A., & Rodrı´guez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609. doi:10.1016/j.ejor.2005.10.002Jones, D. (2011). A practical weight sensitivity algorithm for goal and multiple objective programming. European Journal of Operational Research, 213(1), 238-245. doi:10.1016/j.ejor.2011.03.012Lage Junior, M., & Godinho Filho, M. (2010). Variations of the kanban system: Literature review and classification. International Journal of Production Economics, 125(1), 13-21. doi:10.1016/j.ijpe.2010.01.009Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. 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    The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

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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

    An integrated shipment planning and storage capacity decision under uncertainty: a simulation study

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    Purpose – In transportation and distribution systems, the shipment decisions, fleet capacity, and storage capacity are interrelated in a complex way, especially when the authors take into account uncertainty of the demand rate and shipment lead time. While shipment planning is tactical or operational in nature, increasing storage capacity often requires top management’s authority. The purpose of this paper is to present a new method to integrate both operational and strategic decision parameters, namely shipment planning and storage capacity decision under uncertainty. The ultimate goal is to provide a near optimal solution that leads to a striking balance between the total logistics costs and product availability, critical in maritime logistics of bulk shipment of commodity items. Design/methodology/approach – The authors use simulation as research method. The authors develop a simulation model to investigate the effects of various factors on costs and service levels of a distribution system. The model mimics the transportation and distribution problems of bulk cement in a major cement company in Indonesia consisting of a silo at the port of origin, two silos at two ports of destination, and a number of ships that transport the bulk cement. The authors develop a number of “what-if” scenarios by varying the storage capacity at the port of origin as well as at the ports of destinations, number of ships operated, operating hours of ports, and dispatching rules for the ships. Each scenario is evaluated in terms of costs and service level. A full factorial experiment has been conducted and analysis of variance has been used to analyze the results. Findings – The results suggest that the number of ships deployed, silo capacity, working hours of ports, and the dispatching rules of ships significantly affect both total costs and service level. Interestingly, operating fewer ships enables the company to achieve almost the same service level and gaining substantial cost savings if constraints in other part of the system are alleviated, i.e., storage capacities and working hours of ports are extended. Practical implications – Cost is a competitive factor for bulk items like cement, and thus the proposed scenarios could be implemented by the company to substantially reduce the transportation and distribution costs. Alleviating storage capacity constraint is obviously an idea that needs to be considered when optimizing shipment planning alone could not give significant improvements. Originality/value – Existing research has so far focussed on the optimization of shipment planning/scheduling, and considers shipment planning/scheduling as the objective function while treating the storage capacity as constraints. The simulation model enables “what-if” analyses to be performed and has overcome the difficulties and impracticalities of analytical methods especially when the system incorporates stochastic variables exhibited in the case example. The use of efficient frontier analysis for analyzing the simulation results is a novel idea which has been proven to be effective in screening non-dominated solutions. This has provided the authors with near optimal solutions to trade-off logistics costs and service levels (availability), with minimal experimentation times

    The use of information systems for logistics and supply chain management in South East Europe: Current status and future direction

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    This research aims to investigate the current status and future direction of the use of information systems for logistics and supply chain management (LSCM) in South East Europe. The objectives are threefold: (1) to identify major challenges and developments on the use of information systems for LSCM by enterprises, (2) to examine the actual level of satisfaction of current policy on LSCM, and (3) to reveal the actual need of enterprises in South East Europe on effective use of information systems for LSCM. Mixed methodology of literature review and questionnaire survey is adopted in this research. Data collected from 79 enterprises are analysed using descriptive analysis in SPSS. The findings suggest that enterprises in Albania, Bulgaria, Greece, Former Yugoslav Republic of Macedonia (FYROM), Romania, and Serbia and Montenegro, face similar challenges but all are in different stages of developments of LSCM. Their use of information systems explains their heavy focus on supply chain partnership and weakness in demand chain partnership. Major findings suggest that companies and governments alike in that region do not seem to be ready for playing a significant and demanding role in global supply chains. Current deficiencies, including limited abilities in building valuable forward relations, weak strategic planning and organisation, and infrastructural problems, are major obstacles for fast development in LSCM. At the same time though, traces of changing mentalities do exist, setting the ground for improved performance and ultimately for a better position in global business
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