Data on the Company
Amazon, Inc. has grown towards becoming one of the notable companies not only in the United States but in the world reaching a valuation of $1 trillion. It is important to consider data from the company, which will help in projecting the company’s overall capacity to meet expected goals. In the last financial year, one of the key factors to note is that it the demand for products offered within the company has increased by a 13.7% margin, which is according to the company’s financial data. The evaluation of the demand forecast for the company indicates that one of the key drivers for demand at Amazon, Inc. is the shifting expectations among individual customers, who are the key stakeholders that the company is considering when making specific decisions. Additionally, it can also be noted that the profit margins associated with Amazon, Inc. have also gone up by a margin of 19.5% (Zhu & Liu, 2018).
2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | |
Annual Revenue ('million) | 177,866 | 135,987 | 107,006 | 88,988 | 74,452 | 61,093 | 48,077 | 34,204 | 24,509 | 19,166 |
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Table 1: Amazon Annual Revenue for 10 Years (in dollars)
Forecasting Method
To help in effective forecasting for the company, the method selected is the moving average method, weighted average, exponential smoothing, which seeks to embark on forecasting based on an in-depth analysis of changes in driving factors for demand. One of the key advantages of using this method is that it will help in the evaluation of key factors driving change within the consumer market to help in defining whether the demand is expected to go higher. In the case of Amazon, Inc., this serves as the best forecasting method, as the approach taken will not only seek to analyze the company’s current position but will also seek to reflect on key factors driving demand, which would help in highlighting expected outcomes in future.
Table 1: Moving average method
Moving average=mean of demand in n periods /n
Year | Annual Revenue | 3-month moving Averages |
2008 | 19,166 | |
2009 | 24,509 | |
2010 | 34,204 | |
2011 | 48,077 | (19166+24509+34204)=25,960 |
2012 | 61,093 | (24509+34204+48077)=35,597 |
2013 | 74,452 | (34204+48077+61093)=47,791 |
2014 | 88,988 | (48077+61093+74452)=61,207 |
2015 | 107,006 | (61093+74452+88988)=74,844 |
2016 | 135,987 | (74452+88988+107006)=90,149 |
2017 | 177,866 | (88988+107006+135987)=110,660 |
Table 2: Weighted Moving Average
Year | Annual Revenue | Weighted averages |
2008 | 19,166 | |
2009 | 24,509 | |
2010 | 34,204 | |
2011 | 48,077 | (3*19166)+(2*24509)+(34204)/6=23,453 |
2012 | 61,093 | (3*24509)+(2*34204)+(48077)/6=31002 |
2013 | 74,452 | (3*34204)+(2*48077+(61093)/6=43310 |
2014 | 88,988 | (3*48077)+(2*61093+(74452)/6=56,812 |
2015 | 107,006 | (3*61093)+(2*74452+(88988)/6=70,195 |
2016 | 135,987 | (3*74452)+(2*88988+(107006)/6=84,723 |
2017 | 177,866 | (3*88988)+(2*107006+(135987)/6=192,321 |
Table 3: Exponential Smoothing
Smoothing constant is 0.1
Year | Annual Revenue | 3-month moving Averages | Exponential smoothing |
2008 | 19,166 | ||
2009 | 24,509 | ||
2010 | 34,204 | ||
2011 | 48,077 | 25,960 | 25,960+0.1(48077-25960)=28,171.70 |
2012 | 61,093 | 35,597 | 35,597+0.1(61093-35597)=38,146.60 |
2013 | 74,452 | 47,791 | 47,791+0.1(74452-47791)=50,457.10 |
2014 | 88,988 | 61,207 | 61207+0.1(88988-61207)=63,985.10 |
2015 | 107,006 | 74,844 | 74844+0.1(107006-74844)=78,060.20 |
2016 | 135,987 | 90,149 | 90149+0.1(135987-90149)=94,732.80 |
2017 | 177,866 | 110,660 | 110660+0.1(177866-110660)=117,380.60 |
Selected Method
The appropriate method for forecasting is the exponential smoothing approach. It forecasts the trends well and does not require extensive historical data. On the contrary, the weighted and moving average methods are less sensitive to changes. The two approaches require detailed historical data and do not forecast the trends well. Therefore, the exponential smoothing is better than the weighted and moving average approach.
Table 4: MAD
Year | Annual Revenue | Forecast with 0.1 | Absolute Deviation |
2008 | 19,166 | 19,161 | 5 |
2009 | 24,509 | 19,161+0.1(19166-19161)=19,161.5 | 5347.50 |
2010 | 34,204 | 19161.50+0.1(24509-191161.50)=19696.3 | 14507.70 |
2011 | 48,077 | 19696.30+0.1(34204-19696.30)=21,196.07 | 26880.30 |
2012 | 61,093 | 21,196.07+0.1(48,077-21,196.07)=23,884.17 | 37,208.83 |
2013 | 74,452 | 23,884.17+0.1(61,093-23,884.17)=27605.05 | 46846.95 |
2014 | 88,988 | 27,605.05+0.1(74,452-27605.05)=32289.75 | 56698.25 |
2015 | 107,006 | 32289.75+0.1(88988-32289.75)=37959.58 | 69046.42 |
2016 | 135,987 | 37959.58+0.1(107,006-37959.75)=44868.22 | 91118.78 |
2017 | 177,866 | 44868.22+0.1(135987-44868.22)=53980.10 | 123885.90 |
Sum of absolute deviation=471,545.63
MAD=Sum of deviation/n
=471,545.63/10
=47,154.56
Table 5: MSE
Year | Annual Revenue | Absolute Deviation | (ERROR) 2 |
2008 | 19,166 | 5 | 5 2 =25 |
2009 | 24,509 | 5347.50 | 5347.50 2 =28,595,756.25 |
2010 | 34,204 | 14507.70 | 14507.70 2 =210,473,359.30 |
2011 | 48,077 | 26880.30 | 26880.30 2 =722,550,528.1 |
2012 | 61,093 | 37,208.83 | 37208.83 2 =1384497030 |
2013 | 74,452 | 46846.95 | 46846.95 2 =2194634850 |
2014 | 88,988 | 56698.25 | 56698.25 2 =3214691553 |
2015 | 107,006 | 69046.42 | 69046.42 2 =4767822402 |
2016 | 135,987 | 91118.78 | 91118.78 2 =8302632069 |
2017 | 177,866 | 123885.90 | 123885.90 2 =1.5*10 10 |
MSE=Sum of Error 2 /n
=9305456128/10
=950545612.80
Table 6: MAPE
Year | Annual Revenue | Absolute Deviation | Absolute Percent Error |
2008 | 19,166 | 5 | 100(5/19166)=0.026% |
2009 | 24,509 | 5347.50 | 100(5347.50/24509)=21.82% |
2010 | 34,204 | 14507.70 | 100(14507.70/34204)=42.42% |
2011 | 48,077 | 26880.30 | 100(26880.30/48077)=55.91% |
2012 | 61,093 | 37,208.83 | 100(37208.83/61093)=60.90% |
2013 | 74,452 | 46846.95 | 100(46846.95/74452)=62.92% |
2014 | 88,988 | 56698.25 | 100(56698.25/88988)=63.71% |
2015 | 107,006 | 69046.42 | 100(69046.42/107006)=64.52% |
2016 | 135,987 | 91118.78 | 100(91118.78/135987)=67% |
2017 | 177,866 | 123885.90 | 100(123885.90/177866)=69.65% |
MAPE=Absolute percent error/n
=508.876%/10
=50.89%
Forecasting Future Revenue
Using the forecasting method, as described above, forecasting future demand for Amazon, Inc. will focus on a process of examining how the demand for Amazon products has progressed within the last financial year. From the analysis, it is clear that the demand for its products has increased by a rate of 13.7%, which is important towards defining future demand (Zhu & Liu, 2018). Using this statistic, it becomes clear that Amazon is expected to experience a significant rise in its orders not only within the American continent but also in other parts of the world. Amazon ought to anticipate a significant rise in the demand for its products moving into the future taking into account that more customers are gaining confidence in the quality of products offered by the company.
Another key factor to consider, when forecasting future demand, is the satisfaction levels that customers have when using Amazon, Inc. for purposes of shopping for different products. An analysis of the company’s financial report indicates that it has been on the forefront in establishing a clear-cut approach through which to maximize on the relationships that it builds with its clients. The relationships are important for Amazon, Inc. to consider, as they play a critical role towards defining the satisfaction levels among individual clients. The outcome is that clients tend to focus on coming back to make other orders, which acts as a clear indication that indeed future demand is expected to go higher than the company may anticipate moving into the future.
Figure 1: Online Sales Growth Targets
On the other hand, Amazon, Inc. has been on the forefront towards introducing new products to its clients as a way of enhancing overall capacity for it to match set out projections. The idea of having to introduce new products means that the company finds itself in a rather unique position, as it becomes much easier for it to build on its capacity towards increasing the number of orders made by its clients. From that view, it can be argued that indeed Amazon, Inc.’s future is expected to have a high rate of demand for specific products and services, as clients develop that element of confidence in the company. The introduction of new products or services in the next financial year would mean that indeed the demand may go higher than projected considering that it will be much easier for customers to make necessary orders from the company.
Decision based on Future Revenue Forecast
Based on the forecasted future demand, as has been presented in the previous section, it is important evaluate the decision that the company may need to undertake as a way of ensuring that it is able to build its capacity to match possible outcomes. The decision will be expected to reflect more on trying to create a rather effective avenue through which to ensure that Amazon, Inc. would be able to deliver on its set out goals and objectives. The forecasted future demand indicates that Amazon, Inc.’s capacity to deliver on set out projections moving into the future are likely to achieve possible outcomes. Additionally, the forecasted future demand also suggests that the company is likely to find itself in a position where it would be expected to expand rapidly in a bid to meeting the demand expectations from the clients within different consumer markets as a way of advancing overall projections of meeting its goals.
From that position, the decision that the company ought to consider is embarking on a process of having to focus on partnerships as one of the ways through which to ensure that it would be able to meet its projected outcomes. The idea of having to embark on partnerships would mean that Amazon, Inc. would be in a better position through which to enhance its capacity toward meeting the changing demands among consumers within different markets. The partnerships will play a critical role towards ensuring that the company would be able to match the forecasted future demands from its clients taking into account that this would help in boosting customer confidence. On the other hand, the partnerships will play a critical role towards ensuring that the company is able to deliver on its set out projects within the coming years taking into account that it is anticipated demand.
One of the strategic expectations for Amazon, Inc. is that the company would be able to align its strategic goals with the shifting demands within the consumer market. That would mean that the company may be expected to refocus much of its efforts towards creating a new approach through which to define its capacity to deliver. From that view, it is clear that Amazon, Inc. may be expected to focus on a structured approach in which it engages in partnerships as one of the ways through which to define its market position. The partnerships are seen as key towards ensuring that the company is able to redefine its understanding of the market while build on its capacity to establish possible success levels.
As part of its market approaches, Amazon, Inc. may embark on a process of having to examine some of its market to determine the possibility of having to engage in partnerships with some of the smaller companies. The smaller companies will be of great value for Amazon, Inc. in ensuring that they are able to deliver on the expectations from their customers and clients while at the same time ensuring that the company is able to learn the shifting demands within the market. That means that Amazon, Inc. finds itself in a rather positive position allowing it to maximize on market demands by embarking on partnerships as one of its strategic goals to help meet the forecasted demand rise.
Forecast Error
The forecast error associated with the projections given for Amazon, Inc. may focus on several key outcomes, which are important to consider in building possible avenue for success in meeting set out objectives. The possible error is that the demands may not be as high as may be projected taking into account that the company may experience a wide array of challenges that may impact its ability to deliver. On the other hand, the challenges may arise from a situation where the company finds itself at a disadvantage in ensuring that the products delivered to the respective customers match the set out consumer demands. That creates a high possibility of an error in forecasting, which would create a major issue for the company in its bid to delivering on set out projections.
References
Zhu, F., & Liu, Q. (2018). Competing with complementors: An empirical look at Amazon. com. Strategic Management Journal , 39 (10), 2618-2642.