Suppose you are the marketing manager of bayer and co ahmedabad which are the techniques you will apply in forecasting demand of a product yet to be manufactured?

Answer 1

Forecasting is the process of making projections of demand for products by examining past and present performance levels, combined with an assessment of available products and markets. This may be carried out within the government service or by individual companies in a purely commercial context. The following approaches can be used:

· Target setting;

· Growth trends;

· Growth rates adjusted for new technology adoption;

· Sampling.

Target setting This method is commonly used in developing countries where government is directly involved in planning and seed supply. In a centrally managed economy, targets are likely to be set at a national level and production plans fixed for each region.

India is an example of a more open economy where both the public and private sectors coexist in a well-developed seed industry, but where the government retains a coordinating function and has the ultimate responsibility for the security of seed supply. The Ministry of Agriculture sets the targets and organizes meetings to establish the supply situation and production plans of the various organizations involved.

Companies may opt to set a target for an ideal sales level while, at the same time, recognizing that this is unlikely to be achieved and budgeting for a more achievable situation.

Growth trends. This approach is based on the assumption that the rate of growth of seed demand as seen in past years will continue. This may give unrealistically high forecasts and will depend on the stage of market development for improved seeds. Small increases in volume in the early stages of improved seed use will represent a large increase in percentage terms, which may not be possible to sustain.

Growth rates adjusted for new technology adoption. Using this approach a given region is considered on the basis of degrees of new technology uptake and the likely speed of change. Each part of the region can then be categorized as 'low' to 'medium' or 'high' growth, better reflecting the overall situation.

Sampling. The accuracy of the above approaches can be improved if sample groups of farmers are questioned to gauge their anticipated demand for seed. This exercise is more reliable where there is a reasonable awareness of the benefits of using improved seeds.

v Definition of Demand: The amount of a particular economic good or service that a consumer or group of consumers will want to purchase at a given price. The demand curve is usually downward sloping, since consumers will want to buy more as price decreases. Demand for a good or service is determined by many different factors other than price, such as the price of substitute goods and complementary goods. In extreme cases, demand may be completely unrelated to price, or nearly infinite at a given price. Along with supply, demand is one of the two key determinants of the market price.

v Forecasting product demand has always been a crucial challenge for managers as they play an important role in making many business critical decisions such as production and inventory planning. These decisions are instrumental in meeting customer demand and ensuring the survival of the organization. This paper introduces a novel Fuzzy Cerebellar-Model-Articulation-Controller (FCMAC) with a Truth Value Restriction (TVR) inference scheme for time-series forecasting and investigates its performance in comparison to established techniques such as the Single Exponential Smoothing, Holt's Linear Trend, Holt-Winter's Additive methods, the Box-Jenkin's ARIMA model, radial basis function networks, and multi-layer perceptrons. Our experiments are conducted on the product demand data from the M3 Competition and the US Census Bureau. The results reveal that the FCMAC model yields lower errors for these data sets. The conditions under which the FCMAC model emerged significantly superior are discussed.

If I were a Marketing Manager of Bayer & Company, Ahmedabad. I would use the following Demand forecasting method for a product yet to be manufactured.

· Growth trends.This approach is based on the assumption that the rate of growth of seed demand as seen in past years will continue. This may give unrealistically high forecasts and will depend on the stage of market development for improved seeds. Small increases in volume in the early stages of improved seed use will represent a large increase in percentage terms, which may not be possible to sustain.

1. A method for forecasting demand for a product based on sales results of the product, comprising:

setting plural models as a neural network;

identifying sales results of a first period;

inputting the identified sales results of the first period to each of the models to make the neural network of each model learn from inputs and produce data as close as possible to sales results of a second period following the first period:

storing a forecast demand value of a predetermined time outputted by each of the neural networks;

selecting a model from the learned neural networks which has a forecast demand value closest to the sales results of the predetermined time; and

inputting latest sales results identified by the learned neural network corresponding to the selected model to forecast demand.

2. A demand forecasting method of claim 1, further comprising an outputting device outputting a calculated error between the sales results and demand forecasting result.

3. A method of claim 1, wherein said model is a model incorporating position data indicating period position on a calendar as a processing element, and the position data is fed in the neural network together with the sales results.

4. A method of claim 1, wherein said model is a model incorporating the position data indicating the position of a calendar period as a processing element, and the position data is fed in the neural network together with the sales results.

5. A demand forecasting system using the method of claim 4.

6. A demand forecasting system of claim 5, further comprising an output device outputting a calculated error between the sales results and demand forecasting result.

7. A method of claim 1, wherein the sales results used in learning of the neural network of 13 months dating back from a learning point is acquired.

8. A demand forecasting system using the method of claim 7.

9. A demand forecasting system of claim 8, further comprising an output device outputting the error between the sales results and demand forecasting result.

10. A method of claim 1, wherein demand forecasting in a first period unit forecast by the neural network is reflected in the demand forecasting in a second period unit composed of a set of first period units.

11. A demand forecasting system using the method of claim 10.

12. A demand forecasting system of claim 11, further comprising an output device for outputting the error between the sales results and demand forecasting result.

13. A computer readable storage media storing a process of forecasting the demand for a product on the basis of the sales results of the product comprising:

setting plural models as a neural network;

identifying sales results of a first period;

inputting the identified sales results of the first period to each of the models to make the neural network of each model learn from inputs and produce data as close as possible to the sales results of a second period following the first period;

storing a forecast demand value of a predetermined time outputted by each of the neural networks;

selecting a model from the learned neural networks which has a demand value closest to the sales results of the predetermined time; and

inputting a latest sales results identified by the learned neural network corresponding to the selected model to forecast a demand.

14. A recording medium of claim 13, wherein said model is a model incorporating a position data indicating position of a calendar period as a processing element, and further including program code means for causing said computer to feed the position data in the neural network together with the sales results.

15. A recording medium of claim 13, wherein the identifying sales results includes acquiring results of 13 months dating back from the learning point.

16. A recording medium of claim 13, further including causing said computer to reflect the demand forecasting in a first period unit forecast by the neural network in the demand forecasting in a second period unit composed of a set of the first period units.

17. A recording medium of claim 14, wherein identifying sales results includes acquiring results of 13 months dating back from the learning point.

18. A recording medium of claim 14, further comprising reflecting the demand forecasting in a first period unit forecast by the neural network in the demand forecasting in a second period unit composed of a set of the first period units.

19. A recording medium of claim 5, further comprising reflecting the demand forecasting in a first period unit forecast by the neural network in the demand forecasting in a second period unit composed of a set of the first period units.

20. A demand forecasting method comprising:

creating a plurality of neural network models to forecast demand based on different time periods;

identifying sales results of a first period and entering the results into each of the models to allow each model to learn and forecast demand for a second period;

comparing the forecast demand from each of the models for the second period with actual sales results to compute an error of each model; and

selecting the model with the smallest error.

21. A computer readable storage medium storing software to implement a demand forecasting method performing;

creating a plurality of neural network models to forecast demand based on different time periods;

identifying sales results of a first period and entering the results into each of the models to allow each Model to learn and forecast demand for a second period;

comparing the forecast demand from each of the models for the second period with actual sales results to compute an error of each model; and

selecting the model with the smallest error.

22. A demand forecasting system comprising:

Neural network models forecasting demand based on different time periods;

an inputting device inputting sales results of a first period into each of the models;

a comparing device comparing a forecast demand from each of the models for a second period with actual sales results to compute an error of each model; and

a selecting device selecting the model with the smallest error.