Inventory Forecasting
Businesses will often use past performance for different times of the year as an indicator for the forthcoming months. In many businesses there are likely to be patterns; some times of year where demand for their goods increases, and other times when it decreases. When a business has historical data forecasting may be undertaken using the past results as a guide to the potential future demand.
The first stage of forecasting is to create an index using the existing data. The data is provided on a monthly basis, so the forecast can be created on a monthly basis. The data over the four year period provided is placed into a table, which can be used to assess the average for that each month, as shown in table 1.
Table SEQ Table * ARABIC 1; Monthly Average Inventory
Month
Year 1
Year 2
Year 3
Year 4
Average
1
18,000
45,100
59,800
35,500
39,600
2
19,800
46,530
30,740
51,250
37,080
3
15,700
22,100
47,800
34,400
30,000
4
53,600
41,350
73,890
68,000
59,210
5
83,200
46,000
60,200
68,100
64,375
6
72,900
41,800
55,200
61,100
57,750
7
55,200
39,800
32,180
62,300
47,370
8
57,350
64,100
38,600
66,500
56,638
9
15,400
47,600
25,020
31,400
29,855
10
27,700
43,050
51,300
36,500
39,638
11
21,400
39,300
31,790
16,800
27,323
12
17,100
10,300
31,100
18,900
19,350
Avg.
38,113
40,586
44,802
45,896
42,349
With the average calculated, each month's actual use may be calculated as a
Using the assumptions given in the case, all elements of income statement and balance sheet can be projected for next three years 2010, 2011 and 2012. Sales cycle of the products of the company is such that sales of a particular product increases initially for few years and then starts to decline as the new technology
Target is the second biggest retail company after Walmart. Native New Yorker, George Draper Dayton first built a company named Dayton Dry Goods Company in 1902 in the Minneapolis area which is now known as target headquarter. Walmart faced the out of stock issue problem last year and now their biggest competitor, Target, also has faced the same problem this year. Target has a problem keeping the availability of the product in their stores in Canada. It resulted in a huge loss of money and closing down their stores. The CEO of Target said that this is a serious problem and must been solved.
Forecasting should include the use of both quantitative and qualitative approaches to forecast demand for its products.
* Customer demand: Rather than basing on history and forecast sale of its products, the company should pay more attention in analyzing some uncontrollable factors such as inflation, recession, and currency exchange rate which may affect customers’ buying behavior.
For example, factory production declines from weeks 10-17 because of low margins. Factories cannot produce normally because of the cost constraints facing them. The positive value seen at week 18 means that factories have addressed cost issues hence can meet market demand. A positive trend emerges during week 18 as seen from factory with 1unit and 9units for distributor. The value of -24units from retailers in the same week means that factories can produce but remain cautious about meeting previous production
* Our company’s sales forecast has been based on performance from previous years along with market circumstances. We are looking at the future of the business objectively which we then can evaluate past to
We first predict the annual demand for the year 1972 based on trend for 4 months of 1972 based on corresponding months of 1971.
Greaves provided five years and two months of annual sales data. Using Stat Tools, the following analysis were run: Moving Average, Exponential Smoothing Simple, Exponential Smoothing Holt’s, and Exponential Smoothing Winter’s. Following a comparison on the average on all models, the Exponential Smoothing Winter’s was found to be the most suitable model for the case. A graph analysis is captured below.
This will be a basic forecast created from pro-forma financial statements, using basic forecasting procedures.
Greaves provided five years and two months of annual sales data. Using Stat Tools, the following analysis were run: Moving Average, Exponential Smoothing Simple, Exponential Smoothing Holt’s, and Exponential Smoothing Winter’s. Following a comparison on the average on all models, the Exponential Smoothing Winter’s was found to be the most suitable model for the case. A graph
The full report shows all the forecasting data for 2012 – 2016, it clearly estimate the financial trend of our company (attachment). For the data used in this model, some of them are current data, the other are historical or most recently or average number. It only depends on actually situation – for which method is much more realistic.
Forecasting is the methodology utilized in the translation of past experiences in an estimation of the future. The German market presents challenges for forecasting techniques especially for its retail segment. Commercially oriented organizations are used to help during forecasting as general works done by academic scientists are not easy to come across (Bonner, 2009).
We first predict the annual demand for the year 1972 based on trend for 4 months of 1972 based on corresponding months of 1971.
But even this is not possible in case of a new product or innovation. A forecast of sales, demand, cash, requirements and several such business valuables are extremely essential for a business in order to be able to appropriately plan and conduct its operations in an effective and efficient manner. Yet, forecasts cannot be made accurately as there are several factors and changes in the current environment that leads to variations in forecasts and impacts or causes a manager to make changes in the forecasts.
Before, the concept of demand forecast was to serve the key functional groups in achieving their own interest. Facing the new challenges, forecast needed to be more accurate. And therefore it needed a new concept that is to have a consensus forecasting that would accurately reveal market demand and align the needs of key actors in the forecasting process. Leitax implemented two specific changes in forecasting process. The first one is to switch the focus from sell-in to sell-through and second one is to ignore capacity constraints.