Procurement Simulation
Table of Contents Consensus versus Average Forecasting 1 Options 1 Demand Forecast 1 Supplier Selection 2 Change Orders 3 Lessons 3 Appendix A: Simulation Comments 4 Appendix B: Simulation Results 6
Consensus versus Average Forecasting
The consensus forecasts worked well for quick insight into estimated demand for each month. In our first year we used the consensus demand because we did not know the dynamics of the group, and we were relying on their expertise to guide us toward a more accurate forecast. As we progressed through the simulation we came to the realization that the consensus forecasts were often much different than the average estimated demand. After we analyzed the results of the first couple years, we noticed that the average demand was generally more accurate. This led us to the conclusion that the dynamics of the forecasting team were likely distorting the estimates for the consensus number. There were strong personalities within the team that seemed to sway the opinion of the team members to agree with them, thus lowering the accuracy of the estimation.
For the final two years we spent more time looking at the individual opinions of the team and tried to exclude estimates that were exceedingly high or low compared to the rest of the team. This gave us a number that was close to the average estimate of the team and allowed us to make a more accurate forecast. Overall, the consensus forecast was a good tool to use to quickly see whether demand was expected to be high or low. The biggest downside is the lack of accuracy. The average demand was a more accurate tool as long as we took the time to check each individual opinion to see how the number was made up. Our most successful forecasts seemed to come out of a combination of both the consensus and average.
Options
In general we tried to look at the overall benefits the option would provide. First we looked at the effect the option had on the consensus demand forecast. If
Consensus versus Average Forecasting
The consensus forecasts worked well for quick insight into estimated demand for each month. In our first year we used the consensus demand because we did not know the dynamics of the group, and we were relying on their expertise to guide us toward a more accurate forecast. As we progressed through the simulation we came to the realization that the consensus forecasts were often much different than the average estimated demand. After we analyzed the results of the first couple years, we noticed that the average demand was generally more accurate. This led us to the conclusion that the dynamics of the forecasting team were likely distorting the estimates for the consensus number. There were strong personalities within the team that seemed to sway the opinion of the team members to agree with them, thus lowering the accuracy of the estimation.
For the final two years we spent more time looking at the individual opinions of the team and tried to exclude estimates that were exceedingly high or low compared to the rest of the team. This gave us a number that was close to the average estimate of the team and allowed us to make a more accurate forecast. Overall, the consensus forecast was a good tool to use to quickly see whether demand was expected to be high or low. The biggest downside is the lack of accuracy. The average demand was a more accurate tool as long as we took the time to check each individual opinion to see how the number was made up. Our most successful forecasts seemed to come out of a combination of both the consensus and average.
Options
In general we tried to look at the overall benefits the option would provide. First we looked at the effect the option had on the consensus demand forecast. If