Team “B” is presenting an inventory proposal to address the problem University of Phoenix is encountering regarding the Summer Historical Inventory Data and the benefits expected to achieve by implementing a solution. Time series data is converted to indices and analyzed using the slope-intersect formula to calculate the busy and slow months. To close, a histogram, and a forecast of future inventory costs will be presented.
Proposal
The University of Phoenix is one of the largest campus and internet-based schools in the country. The school’s online and campus classes require the university to have on hand enough supplies to satisfy an extremely large number of students, instructors, and faculty members. With new classes beginning every five, six, and nine weeks online in conjunction with semester and summer sessions at campus sites; classes involves several instructors and students that deem it necessary to have on hand a large amount of supplies in stock. This problem forced the university to develop a much needed system to ensure the needed supplies are on hand for it faculty and students as needed.
Team “B” used the information given in the University of Phoenix Summer Historical Inventory to create indices for each month during the four year period. Below is the table used for the calculations (see figures 1-3):

Figure 1.
The table used for the calculations: | | | Actual Demands (in units) | | | | | | | | | Month | Year 1 | Year 2 | Year 3 | Year 4 | Forecast | 1 | 18,000 | 45,100 | 59,800 | 35,500 | | 2 | 19,800 | 46,530 | 30,740 | 51,250 | | 3 | 15,700 | 22,100 | 47,800 | 34,400 | | 4 | 53,600 | 41,350 | 73,890 | 68,000 | | 5 | 83,200 | 46,000 | 60,200 | 68,100 | | 6 | 72,900 | 41,800 | 55,200 | 61,100 | | 7 | 55,200 | 39,800 | 32,180 | 62,300 | | 8 | 57,350 | 64,100 | 38,600 | 66,500 | | 9 | 15,400 | 47,600 | 25,020 | 31,400 | | 10 | 27,700 | 43,050 | 51,300 | 36,500 | | 11 | 21,400 | 39,300 | 31,790 | 16,800 | | 12 | 17,100 | 10,300 | 31,100 | 18,900 | |

Figure 2. Histogram

Figure 3. Line graph

The list indicates a large amount of stock is required for each month of each year. The means and index of each month for each four year period will be indicated by the Mean in Red and Index in Blue (See figures 4-6).
Figure 4.
January February March April May June
$39,600 $37,080 $30,000 $59,210 $64,375 $57,750
0.935 0.876 0.708 1.4 1.52 1.37
July August September October November December
$47,370 $56,637.5 $29,855 $39,637.5 $27,322.5 $19,350
1.12 1.34 0.705 0.936 0.645 0.457
Figure 5. Seasonal Index | Month 1 | 0.93509 | Month 2 | 0.87558 | Month 3 | 0.7084 | Month 4 | 1.39815 | Month 5 | 1.52011 | Month 6 | 1.36367 | Month 7 | 1.11856 | Month 8 | 1.3374 | Month 9 | 0.70498 | Month 10 | 0.93597 | Month 11 | 0.64518 | Month 12 | 0.45692 |

Figure 6. Histogram

The calculation of the mean and index contributes in determining what the annual increase of the inventory. To do this we will us the slope intercept formula (y = mx + c). The equation will take the yearly total for the four year so the equation will be y= 12x+4 for this equation we can see that the increase is 25%.
The busiest month of the year based on the table is the month of May with a mean amount of $64,375 and an index of 1.52. Team “B” believes the elevated inventory needs are a result of new student enrollment to the university from students seeking to further his or her education beyond a high school degree and graduations. The slowest month of the year would be the month of December with an income of $19,350 and an index of 0.457. Team “B” believes at this time of the year, new student enrollment is low and students are not buying supplies because of preparation to go home for the holiday.
We calculated the monthly averages over this four year period and used these figures to determine the annual percentage of growth. By averaging this annual growth, we forecast a 6.5% increase in year five to calculate a monthly average of 48,846.93. By multiplying the monthly index by the forecast monthly average (48,846.93) the forecast the monthly demand for year five was determined (see figures 7-10).
Figure 7. Month | Year 1 | Year 2 | Year 3 | Year 4 | Forecast Year 5 | 1 | 18,000 | 45,100 | 59,800 | 35,500 | 45,676.18 | 2 | 19,800 | 46,530 | 30,740 | 51,250 | 42,769.49 | 3 | 15,700 | 22,100 | 47,800 | 34,400 | 34,603.17 | 4 | 53,600 | 41,350 | 73,890 | 68,000 | 68,295.09 | 5 | 83,200 | 46,000 | 60,200 | 68,100 | 74,252.61 | 6 | 72,900 | 41,800 | 55,200 | 61,100 | 66,611.09 | 7 | 55,200 | 39,800 | 32,180 | 62,300 | 54,638.42 | 8 | 57,350 | 64,100 | 38,600 | 66,500 | 65,327.88 | 9 | 15,400 | 47,600 | 25,020 | 31,400 | 34,435.91 | 10 | 27,700 | 43,050 | 51,300 | 36,500 | 45,719.40 | 11 | 21,400 | 39,300 | 31,790 | 16,800 | 31,514.82 | 12 | 17,100 | 10,300 | 31,100 | 18,900 | 22,319.04 | Avg. | 38,112.50 | 40,585.83 | 44,801.67 | 45,895.83 | 48,846.93 | | Percent increase | 6.50% | 10.40% | 2.40% | 6.50% |

Figure 8. Histogram

Figure 9.

Figure 10.

Conclusion In conclusion, the inventory reported an increase over the four years reported. Based on our forecast calculations, the university can expect a 6.50% forecasted increase for the fifth year. For the university to accommodate the increase successfully, additional resources may need to be employed.

References
Time-Critical Decision Making for business (2009), http://home.ubalt.edu/ntsbarsh/stat-data/forecast.htm Typical Seasonal Demand for Summer Highs (2012), University of Phoenix Student Lab. https://portal.phoenix.edu/home.html