This document discusses inventory management and seasonal demand. It provides data on winter inventory levels over four years for 12 months. It includes typical demand patterns and discusses just-in-time inventory systems. Tables show raw inventory data and calculations of the mean, median, mode, and standard deviation for each year's data. The conclusion discusses the importance of forecasting accuracy for inventory planning.

1. KAREN FAITH ADAMS, KAREN JONES
QRB/501 – QUANTITATIVE REASONING FOR BUSINESS
MARCH 26, 2012
ARI RAHMAN

2. Introduction
 Inventory management systems are a vital part of
businesses
 Observing inventory levels is necessary for creating
revenue and profits.
 Mangers need to develop , analyze frequency
distributions, locate the mean, median and mode and
show a normal distribution of raw data collect

3. Just-In-Time Inventory System
 Production and the purchase of materials do not begin until a customer
places an order for a product.
 Eliminates waste and makes the company efficient
 Companies are able to do away with their warehouses
 Funds that were tied up in inventories can be used elsewhere
 Defect rates are reduced, resulting in less waste and greater customer
satisfaction

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

6. Measure of Dispersion

7. Winter Historical Inventory Data
Frequency/Normal Distribution
Month Year 1 Year 2 Year 3 Year 4
1 15,400 10,300 25,020 16,800
2 15,700 22,100 30,740 18,900
3 17,100 39,300 31,100 31,400
4 18,000 39,800 31,790 34,400
5 19,800 41,350 32,180 35,500
6 21,400 41,800 38,600 36,500
7 27,700 43,050 47,800 51,250
8 53,600 45,100 51,300 61,100
9 55,200 46,000 55,200 62,300
10 57,350 46,530 59,800 66,500
11 72,900 47,600 60,200 68,000
12 83,200 64,100 73,890 68,100
MEAN 38,113 40,586 44,802 45,896
MEDIAN 24,550 42,425 43,200 43,875
MODE Non-Modal Non-Modal Non-Modal Non-Modal
STDEV 24739.13099 13336.54409 15407.74471 19185.84301
RANGE 67,800 53,800 48,870 51,300

10. Conclusion
 Forecast accuracy is a critical component of Gilbarco’s success in today’s
markets, particularly when competing on a global scale.
 Inventory is Gilbarco’s largest asset
 Forecasting techniques are quantitative. Quantitative techniques of
forecasting are best used when changes are infrequent.

11. • Accounting For Management. (2011). Retrieved from
http://www.accountingformanagement.com/just_in_time.htm
• University of Phoenix. (2012). Winter Historical Inventory
Data, University of Phoenix Material.. Retrieved from University of
Phoenix