The reduction of logistics costs
1enabled organizations to expand their markets worldwide, however, increased
2 the difficulty in grabbing market share from their competitors. Clients now have at their disposal a large number of choices,
3 thus price is not anymore the only purchase decision driver.
Organizations strives toward excellence, spending their resources to meet and even surpass customer expectations, providing the right item in the right quantity at the right time at the right place for the right price in the right condition to the right customer.
4
In order to achieve such level of excellence,
5 their supply chains require quickly response to market fluctuations, frequently of random nature.
6Growing larger each day, with many facilities scattered around the globe, the increasing complexity of supply chains make
7 the task of integrating its
8 components under an unified policy management
9 being
10 not a trivial solution. Conventional analytical models applied to large systems featuring stochastic behavior are too complex to be solved. With computer power
11 costs decreasing each day, computer simulation is a powerful methodology to perform
12 what-if scenario analysis and optimization of the whole supply chain.
Among many simulation methodologies available, discrete event simulation enables the supply chain´s dynamic behavior being unfolded
13 whileundergoing demand and lead time uncertainties. Despite
14 being a powerful tool for quantitative analysis, modeling systems with such technique is
15 very hard
16 and commercial discreteeventsimulation
17 platforms are cost prohibitive.
Moreover, they demand prior programming language knowledge and training.
18 This work proposes a small supply chain modeling
19 and implementation by means of discrete event simulation. The simulator will measure the influences of demand and lead time uncertainties, jointly
20 with different inventory policies, over important performance indicators such as inventory costs and service level.