1,721,088 research outputs found
Application of project management techniques to a software for telecommunication complex project
No full textManaging complex project that involves software design, implementation and testing is a very difficult task that becomes even worse when developing the first project of a given kind. The telecommunications industry affords this problem with the third generation mobile networks (UMTS). Although project planning is periodically revised (once a month), differences in tasks'durations and experienced delays cannot be used to improve prediction of project's duration. This paper presente the application of project management techniques (Critical Path, Critical Chain and Statistical Simulation) to the UMTS project, highlighting problems and difficulties. In order to predict a reliable ending date for the project an original approach will be presente
Simulating A Non Multi Echelon Supply Chain Model Using Berkeley Madonna Software: A System Dynamics Approach
No full textArtificial intelligence for supporting forecasting in maritime sector
No full textThe importance of the time series of data has always been of great relevance. A main use of them is the prediction of the future values of the quantities of interest. On this purpose, a lot of models have been created so far, as AR, MA, ARMA, ARMAX, ARIMA and so on. In the last years, the interest on Artificial Intelligence and Neural Network has grown a lot and a lot of studies were conducted to enable their use in different fields. This paper has the aim to show the possibility to use a system based on Artificial Intelligence to analyze the time series of index and future on the chartering of ships in order to predict the future values of them. The Neural Network is trained with the data of the last 3 years and the results obtained have be compared with those coming from ARIMA model and Carbon Copy model. The first aim of this paper is thus showing if the Neural Network performs better than the other 2 models and what day (first, third or fifth) is the best for the prevision made. The second purpose of this paper is establishing if the knowledge of the trend of the quantity value influences the results. The Neural Network has been trained both with a bullish trend and a bearish trend, then the results have been compared to prove if setting the right trend improve the quality of the prediction
System Dynamics Model For The Simulation Of A Non Multi Echelon Supply Chain: Analysis and Optimization Utilizing The Berkeley Madonna Software
Get PDF— In today’s global market, managing the entire supply chain becomes a key factor for a successful business. World-class organizations realize that non-integrated manufacturing and distribution processes together with poor relationships with suppliers and customers are a huge limit for their success. One of the most important aspect affecting the performance of a supply chain is the management of inventories. Inventory management in the supply chain system is quite a complex issue because demand at the upstream stage is dependent on orders from the downstream stage, and the final downstream stage receives orders from the market in uncertain conditions. Uncertainty is one of the major obstacle which limits the creation of an effective supply chain inventory model, able to optimize times and costs. Being the management of a complex inventory model too difficult to analyze with traditional analytical mathematical methods, computer simulation is widely used to study this kind of problems. This paper has the goal of modeling a single echelon supply chain and optimizing its inventories levels so to reduce the bullwhip effect and consequently minimize the supply chain costs. The supply chain here proposed consists of five stages – customer, retailer, wholesaler, distributor and factory – and its modeling is carried out through a system dynamics approach, utilizing the Berkeley Madonna software
Last mile logistics in maritime terminals, tools and techniques for improving performances
No full textImpact of a disruption in a supply chain: An assessment model
No full textThis paper aims to propose a model for the evaluation of the economic impact caused by a disruption of a supply chain. The model evaluates the impact on companies (nodes) upstream and downstream after identifying disrupted nodes. The impact on individual nodes is calculated in terms of Business Interruption (representing the number of days that the node does not generate value) and the turnover produced by the node. The paper describes the main operating logics of the model and the method used for their verification and validation. The work was developed using the anylogic simulation tool
Modelling of Impact Caused by Flood after Water Flow Optimization at Volga-Kama River Basin
No full textIn this paper two different water flood scenarios at Volga-Kama river in Russia are presented. First scenario is based on the existing water management approach that has been implemented in the period from 23 August 2013 until 22 August 2014. The second scenario is based on the original management approach aimed at mitigation of flood impact to the populated areas. The flood propagation has been modelled in HEC-RAS software, while post-processing and impact analysis were performed in QGIS 3.0. The results show that proposed original management approach allows to decrease the impact caused by inundation at Volga-Kama river basin by two times compared to the one implemented by the operator management approach. This result is achieved due to mitigation of the flood in highly populated areas and allowing additional water discharge among water management facilities in the areas with low population
Supply Chain Resilience in SMEs: Integration of Generative AI in Decision-Making Framework
No full textSupply chains for Small and medium-sized enterprises (SMEs) are more vulnerable to increasing occurrence of disruption events. These disruptions have a significant impact on their operations' continuity and performance which results in a loss of customer satisfaction and their market share. Building a resilient SC is a significant challenge for such enterprises due to their limited resources. In this paper, we present the integration of generative AI in a decision-making framework to enhance supply chain resilience (SCR) for SMEs. The ability of Artificial intelligence to generate new scenarios and analyze large data has been integrated at different stages of the decision-making framework. Starting from the initial stage of the framework, AI-generated novel scenarios aid in risk identification. Later, the decision-making framework can get insights from AI in order to trigger optimal mitigation strategies. The AI-integrated framework aims to speed up the decision-making process by identifying, analyzing, and evaluating optimal mitigation strategies to overcome disruption risks and to achieve the objective of SC resilience. Our study provides a practical tool for SMEs to achieve resiliency for their SCs
Supply Chain Management Dynamic Negotiation Using Web Integrated Logistics Designer (WILD II)
No full textLean manufacturing in Italian companies: a survey on correlations between practices adoption and plant performance
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