177,173 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
A System Dynamics Model For The Simulation Of A Non Multi Echelon Supply Chain: Analysis and Optimization Utilizing The Berkeley Madonna Software
No full textIn 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
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
No full text— 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 textAn innovative stochastic approach to forecast the demand of new products
No full textStarting in the second half of last century, researchers and managers have gradually paid more attention to that chapter of decision-making sciences that goes by the name of "Demand Analysis." In the literature today, there are a wide range of templates to allow you to predict future demand for goods and services, with the obvious ultimate goal of providing managers with quantitative elements to improve the different planning processes related to this type of decision (purchasing raw materials, semi-finished materials, optimum input and output warehouse inventory, production planning). The matrix common to all the most widely-used models is the use of the consolidated historical data to "train" the prediction algorithm. In this paper, the authors describe a method to study those cases where the historical data is not available (new markets, new products, new technologies, etc.) and the stochastic element consequently takes on extreme importance in defining the prediction process
Improve supply chain management using neural networks and regresive KPI relationship metamodels
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