BUS606: Operations and Supply Chain Management

The study takes a four-stage chicken supply chain in Taiwan for numerical analysis, with the available two-year 24 pairs data come from January 2004 through December 2005. The architecture of the analysed supply chain is shown as Figure 3, consisted of four states names "feed supplier," "chicken farm," "slaughterhouse," and "meat retailer".

Figure 2: Supply Chain of the Case

The original condition of information sharing of this supply chain is not transparent, and no SCM software is implemented (i.e., R=0%). Therefore, the original supply chain belongs to the decentralised model, where information of "average demand" and "variance of demand" from the meat retailer are not shared for supply chain members of feed supplier, chicken farm, and slaughterhouse.

To calculate the bullwhip effect of this case, Equation (5) can be applied for the supply chain with data sets p=24, supply chain stages k=4, and average lead time L=4 hours (i.e., half of the working day). Moreover, the numerical simulation of the case supply chain under different rates by implementing SCM software is shown in Table 2, and visualised results of the simulation are shown in Figure 3. Note that the original condition is filled gray in Table 2 with R=0%. It is found that an average of the bullwhip effect improvements up to 13.4% in this simulation case. In addition, the results also show that a higher implementation rate of supply chain management software in the supply chain, the more improvements of the bullwhip effect will make.

Table 2: Variance ratios under different rate of implementing SCM software

Furthermore, to discuss the effects of implementing SCM software in different performed supply chain, the lead time L is taken for sensitivity analysis. The numerical simulation of the case supply chain is shown in Table 3, where lead time L varied from 1 through 6 hours. Note that the original condition is filled gray in Table 2 with L=4. It is found that the average of improvements for the bullwhip effect ranges from 0.4% to 28.3% in this sensitivity simulation. Meanwhile, the visualised results of the simulation are shown in Figure 4, where the solid line stands for the original condition of the supply chain without implementing any SCM software, and broken line stands for the supply chain after fully implementing SCM software. It can be concluded that under the same software implementation rate in better performed and worse performed supply chains, the worse one can make more notable improvements for the bullwhip effect.

Table 3: Variance ratios under different lead time

Figure 4: Variance Ratios under Different Lead Timen