[solved]Business and Management: Forecasting

Business and Management: Forecasting

Name

Affiliation

A forecast is a tool used to make decisions about the future of a business in terms of market demand by analyzing data from past sales. Forecasting has been instrumental for organizations and businesses in facilitating accurate planning and ensuring the future success of businesses that have used it.

1. Prepare weekly forecasts for the next four weeks for both products.

In the plotting of data for the first product, a linear pattern is revealed with an exception where the demand is unexpectedly high. I treated the unexpected spike as an outlier which prompted me to substitute its value with the average from the previous and subsequent weeks. While plotting data from the second product, there is an evident sequence of sudden increments in demand that happen once every four weeks. The complicated nature of the second product’s data would make forecasting a challenge in respect to the second product.

Putting into consideration the disregard of the manager of the second product by the manager, I opted for the judgmental method to make an estimated forecast of the sales for the subsequent weeks. In other words, I used the average of the sales data for all the weeks and added four. The resulting figure was the predicted sale for all subsequent weeks.

Data provided from the 14 weeks was plotted in a scatter graph and the linear regression line identified as sales = 3.46 multiplied by week + 48.28.

The calculation of the 4 weeks forecast is shown in the table. Just like the data provided and the determination of the regression line, the forecast shows the demand for the first product increasing steadily in every week. In the second product, the nature of data provided shows a sudden increase in demand after every four weeks with a steady increase of one unit in the other weeks.

2. Describe the forecasting method you chose and explain why that forecasting method is best suited to the scenario.

I used the time series analysis (Choi et al, 2017) as a forecasting method because the statistics provided in this case are consistent with the requirements for this forecasting method. Time series analysis is used when many years, months or weeks’ worth of data for a product is available and when trends are clear and relatively stable. Information provided in this situation consisted of data collected for two products over a period of 14 weeks which also showed relative consistency. This method helps the forecaster, in this case the manager, get an idea of the current rate of sales and the speed at which they increase or decrease so that they can make informed projections. For this reason, time series forecasting method was most appropriate.

3. Explain why you did, or did not, choose the same forecasting method for each product.

My choice for forecasting methods was not the same for the two products. I opted for different approaches because the nature of the data presented for both products was different and as such, one forecasting method could not be effective in making a reliable forecast. Data for the first product displayed a consistent increase in demand with a significant difference in week seven which was explained as an unusual order because of increase in demand. The linear nature of the data in general prompted for a time series forecasting method to produce effective and reliable results. Data for the second product presented a scenario where there were periodic spikes in demand that occurred three times at an interval of 4 weeks during the entire period of 14 weeks. The nature of the data in the second product and the fact that they were different, directed me towards a different forecasting method that considered the manager’s intuition about the second product.

4. What are the benefits of using a formalized approach to forecasting these products?

Businesses and organizations employ forecasting strategies in production management to improve their products’ capacity to sell and to have a better grasp of what the market demands are, depending on economic factors and other market variations. The operations departments are responsible for planning for the possible outcomes that may arise. The potential benefits of using this formalized approach to forecasting is that it makes the utilization of the computer easier in regard to data analysis, which can be a daunting and tedious task when done manually (Jacobs et al, 2014). A formalized approach also eases the quantification of information. An informal approach to forecasting on the other hand is bound to be ineffective due to individual bias in the application of personal intuition. Personal instinct is also not measurable and therefore does not qualify to be a useful variable in making any kind of projections.

A formalized approach to forecasting also has the advantage of being able to maintain objectivity even when the problem is more significant or when the amount of data being handled gets larger. Conversely, it would be impossible for a less formalized approach to maintain impartiality because an individual’s perception is not able to analyze a large volume of information.

References

Choi, T. M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.

Jacobs, F. R., Chase, R. B., & Lummus, R. R. (2014). Operations and supply chain management (pp. 533-535). New York, NY: McGraw-Hill/Irwin.