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The results indicate that the ideal DF can be set equal to L. Next, the three methods will be compared using a theoretical environment of demand and demand variance. In the theoretical environment, a demand data stream is generated using a normal distribution with an average demand of units per day and a standard deviation of The improved AFR would confirm the reasoning behind the way the method is implemented.

Given that customer service is more important, the calculation was adapted to allow for a better AFR. The unintended consequence of this was that stock holding has been increased significantly. From the above results, it is clear that in the case of the local supply chains, the STS method has merits as a replacement method that will increase the AFR, but will require less stock than the MIP Actual method.

The final comparison between the three order placement algorithms will focus on two selected data sets. Data set one represents the demand for fast moving parts, and data set two represents the demand for erratic moving parts. In 8 of 12 parts investigated, the STS method required lower stock levels. In 11 of 15 cases, the stock holding required was lower than the MIP Theory method. Of these, four parts achieved the same or better AFR with lower stock holding. Running the simulation for slow moving local parts shows that in all cases, the MIP Actual method improves the AFR, but with stock holding increased by four times.

It also requires stock holding to be increased, but in only two cases is the stock required double that of the MIP Theory method. Running the simulation for erratic moving local parts shows that the MIP Actual method improved the AFR in 15 of 16 cases, in all cases increasing the stock holding. In only three cases, the stock increase was less than double.

In five cases, the stock holding was reduced. The results show that the MIP Actual method would provide equivalent performance to the other methods, but with the lowest stock holding Table 7. In this article, four key issues were addressed. The theory behind the MIP method and how it is implemented was analysed. The difference between the theoretical and actual implementation was identified. An alternative STS method, focusing on stock targeting, was derived theoretically.

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An SDSM was developed to analyse the performance of the ordering algorithms on the supply chain. The SDSM is limited in that it does not simulate a unique demand for every part, but rather a demand distribution based on a fixed set of variables. The analysis of the actual data sets is limited in that actual stock holding data are not available, and therefore, the results cannot be compared to the actual situation.

The STS method was analysed to confirm that it would not result in the bullwhip effect by evaluating the impact of damping on the supply chain performance.

Following the analysis, the DF was set equal to L. The three order algorithms were compared in a theoretical environment. In all cases, this was in excess of 10 times more than the STS method. Two actual data sets were used to evaluate the effectiveness of the three algorithms in practice. The STS method, in general, provides an increase in the AFR, except for the erratic moving parts, with a lower increase in stock holding. The results obtained at this point suggest that the STS method be further investigated as an alternative solution to the MIP Actual method to ensure that the warehouse space constraint is managed effectively.

The effectiveness of the three methods to ensure the target AFR is achieved with the least amount of stock against various theoretical demand patterns log-normal or gamma distributions should be investigated to determine if the methods are applicable to specific demand patterns. The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.


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The study forms part of A. He proposed and developed the STS inventory management method. He constructed the SDSM for analysing the three inventory management methods. He also performed all the theoretical and actual data simulations.

Supply Chain Simulation A System Dynamics Approach for Improving Performance

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Supply chain simulation : a system dynamics approach for improving performance

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Original Research. System dynamics comparison of three inventory management models in an automotive parts supply chain.