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 the demand forecast went up, we decided to add the option to the phone because we believed we would be able to sell more units. The second deciding factor was whether or not the option increased profit for the model. We decided to add any option that would increase profit for the model. If the option increased both profit and consensus demand, we checked the graphs to look at the standard deviation. We avoided options that had a high standard deviation because we believed that there was more risk that the estimate would be inaccurate. A lower standard deviation indicated a higher level of confidence with the effect the option would have on demand. In retrospect we relied fairly heavily on the consensus estimates to choose the options. We realize that the consensus demand estimates were often inaccurate, and could have led us to choose options that were not beneficial to the models we were trying to sell. Just as we switched to using average demand estimates for our forecasting, we should have switched to using individual estimates when we were choosing options. It would have provided a greater level of accuracy and could have helped us make better decisions.
Demand Forecast
Initially we were reluctant to order too much stock because we were focused on the high cost of inventory becoming obsolete. This led to us experience a number of stock outs in our first year. We realized that this was costing us a substantial amount in lost profits. Due to this we changed our strategy for the following years to focus on ensuring we had products available to sell. Example of this would be in the first year, where we found the inventory cost of $4 per month and markdown loss was $17 per unit, but the profits were $70 for Model A and $90 for Model B. Using this as an example and a lesson learned from first year, we concluded that it would beneficial to carry excess inventories to compensate for fluctuations in demand, and that it is better to have more inventory and have the holding costs, than give up potential revenue for not having enough revenue . Carrying more inventories allowed us to sell more products and did not increase our holding costs to the point where profit was negatively impacted. Even with our change in strategy we still struggled with stock outs due to unanticipated fluctuations in demand. We were continuously conscious of the high costs of obsolete inventory and tried to avoid them by carrying just enough inventory to have a minimal amount left over, but we usually ended up with significant stock outs or a large amount of left over product.
In Retrospect if we were to run the simulation again we would focus on establishing that we had enough product to meet capacity. The lost profit due to selling out was incrementally high compared to the holding costs and obsolete costs, and if our inventories got to higher may have issued a change order, to try and finish will relatively low stock at the year instead of being reluctant to order much stock like we did in the first year. Also, we would have invested more in the marketing analysis; we found throughout the simulation that obtaining, as much information about demand is a very strong factor that will influence the accuracy of demand estimates as well as performance.
Supplier Selection
In the first year we avoided the suppliers with the higher set up costs and opted for suppliers with longer lead times and less capacity. We believed that these suppliers would provide us with some cost savings while still being able to meet our conservative demand estimate needs. We decided to allocate only on the basis of capacity, giving the supplier with the highest capacity to the model that was expected to sell more units. The supplier with the smaller capacity abilities was given to the model with less expected demand. During the simulation of the first year we realized that we were having serious flexibility issues with both demand and lead time. The supplier that had larger capacity had a lead time of four months and were producing well over what we had anticipated. By the time we realized we were over ordering stock, it was too late to make changes that would be profitable. Our second supplier with smaller capacity had great flexibility on time, but when we realized we needed much more stock to make greater profits, we found that the supplier was unable to meet these needs.
In the second year we knew we had to address these issues with flexibility in order to become more profitable. We decided to use the supplier with the longer lead time and greater capacity to provide both models with some level of stock that we knew would be in demand. That way we would not be forced to make any changes to the order from this supplier. We chose our second supplier based on the shorter lead time so that we could make quick changes when we experienced fluctuations in demand. This supplier was used to provide any additional stock that the first supplier did not cover. The flexibility in lead time allowed us to cut orders or add orders based on their capacity. As we were using suppliers with cheaper set up costs, we were still occasionally running into capacity constraints. In the third and fourth year we decided to switch our local supplier to one that could meet greater capacity demands. This allowed us greater flexibility in order changes which proved to be valuable. In retrospect we should have considered more than capacity in the first year. We discounted the necessity of flexibility in a market where demand is very unpredictable. In the future we would put higher priority on flexibility in order to capitalize on fluctuations in demand.

Change Orders
When the decision came up to make a change order, we first looked at the capacity capabilities of our suppliers to determine if they could meet the needs of a new order. If they could not meet capacity to a level that would offset the 2 million dollar cost to change the order, we did not change the order. If the supplier had the capacity capabilities or if we were trying to reduce our order, we then looked at the lead time of the supplier. Since our base inventory supplier had a lead time of four months, we decided that it would take too long to see the effects of a change order to offset the cost of sending a change order request. If we felt it was necessary to change our order with them, we knew it had to be as early in the year as possible to see the most benefit. However, we were still reluctant to make changes with them. Our local supplier had no lead-time so they were very effective at meeting our new requests. Also, if our local supplier was at full capacity, we never chose to implement a change order to our global supplier due to the long lead-time. We gave priority to addressing stock outs opposed to having too much inventory. As we stated before, we found that stocking out was more costly than holding too much inventory. We also found that it was much easier to place a request for a reorder when we had invested in market analysis. With a market analysis we were more certain of the demand, which made it possible to calculate the benefit of requesting a change order, and there was less risk of requesting a change order when it was not necessary.
Lessons
There were distinguishable lessons to take away from this simulation. Firstly, we learned that it is important to take the time to understand team dynamics when estimating demand. Different personalities can negatively impact the accuracy of a consensus forecast. It is necessary to look at all the information provided to gain a big picture understanding of differing opinions. This simulation also provided critical insight into how difficult forecasting can be in particular markets. We learned that it is not always a good idea to rely solely on a demand forecast to select suppliers. It is a good idea to select suppliers that will be able to accommodate changes in demand as information becomes available. Stemming from this, we learned that supplier flexibility within an unpredictable market is one of the most important factors. The right mix of capacity and lead times are critical to be able to match supply with demand.
The responsiveness of suppliers was a key aspect to meeting new demand levels, as long as the supplier also had the correct capacity capabilities. This was critical to highlighting the need to select multiple suppliers in order to gain benefits from both capacity and responsiveness. It also demonstrated that the whole process is inter-connected, starting with demand forecasts. If the forecasts are not accurate, then the options might not reflect the consumer preferences accurately. The suppliers and production numbers that have been chosen will be inaccurate and this may result in high inventory cost, obsolescence costs, and change order costs.

Appendix A: Simulation Comments
Year One
Design Room

The consensus demand will increase by 2K with the options we selected which seemed to be the highest of all the possible combinations of all the options. There is no impact on the profitability of either model without decreasing the consensus demand.

Forecasting Room

With no previous experience in demand forecasting we trusted our team to come up with a reliable consensus number, as they are trained professionals within the industry. As this is the first year of production we expect to learn from the results and adjust our estimates for the coming years. We have documented the estimates of each team member for analysis in the upcoming years.

Production Room

* Couldn’t access comments from simulation for this section*

Year Two
Design Room

We chose this combination of options because it increased the consensus demand forecast by 1K and also increased profits per model by $3. We feel that this is the best design.

Forecasting Room

We removed the outliers in the data to try and come up with a more stable average demand. Based on our results with the removal of the outliers the demand for Model A should be 50.4, and Model B should be 28.75. From this we rounded our estimates to the nearest number with a factor of 5.

Production Room

* Couldn’t access comments from simulation for this section*

Year Three
Design Room

We removed the outliers in the data to try and come up with a more stable average demand. Based on our results with the removal of the outliers the demand for Model A should be 50.4, and Model B should be 28.75. From this we rounded our estimates to the nearest number with a factor of 5.

Forecasting Room

The board seemed to be optimistic about demand and confident about their forecasts. We were a little hesitant to share their optimism due to previous years so we chose conservative numbers. We decided to chose numbers that more closely reflected the average demand estimates opposed to the consensus.

Production Room

* Couldn’t access comments from simulation for this section*

Year Four
Design Room

The speaker’s option gives an increase in demand and profit. It does not seem to have much deviation. It was the only option to increase both consensus demand and average demand.

Forecasting Room

We think this will be a good year for our models. We considered Joe to be lowballing the demand estimate because we decided to exclude the feature he wanted in the Model. Because of this we excluded his estimate from the data we used to make our decision

Production Room

* Couldn’t access comments from simulation for this section*

Appendix B: Simulation Results