Free Essay Descriibing the Advantages of Using Formalized Forecasting Approach

Forecasting entails the use of past data and information to predict future events and outcomes. This approach is associated with some benefits that M&L Manufacturing Company can capitalize on and take advantage of. Formalized forecasting approach provides great efficiency in utilizing computer power and in the quantification of information. Notably, less formalized forecasting approaches that rely on intuition may undergo personal bias that can rigorously affect the forecasts. As a result of the setback associated with the intuition, formalized forecasting approach becomes effective and more reliable as long as the data under study is voluminous.

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It assists the company's management to boost inventory management, and production planning (Stevenson, 2018). This is essential to the company since it would ensure that the management has reliable estimates of the expected demand for the two products. This will in the coming periods assist the company in planning for optimum production capacity. Moreover, the manufacturing and distribution of the two products in the market would be much easier in such a way that it accommodates the prevailing demand. Cases of under-stocking and over-stocking will also be eliminated hence making the company be opportunistic in any sales opportunity and also keeping storage cost that takes place when the production process is uneven at its minimum, (Pandey et al., 2014).

Formalized forecasting procedure is advantageous since it assists the management in the mitigation of risk and uncertainty that is brought about by difficulty in the prediction of the demand pattern for the products are likely to behave. It assists management to extrapolate and predict the pattern taken by demand way into the future, (Foltz, 2012). With this technique, therefore, the management of the company can have a clear picture of how demand for the two products will behave in the near future and develop an effective strategy to ensure sales do not go down over time.

In addition to this advantage, this approach helps the company to be certain of what types of products are highly demanded in the market and supply them in a manner that suits the final consumer. This will eventually lead to increased customer satisfaction and loyalty making demand and sales more efficient, and in the long run, it will boost the company profits, (Coke &Helo, 2016).

This approach, particularly when dealing with historical prices, is advantageous since it assists the company in creating an impression of past sales pattern. Managers experience scenarios when sales deteriorate and investigate reasons for such deterioration, (Randhear& Al-Aali, 2015). This is essential in policy making and strategy formulation in an attempt to be guaranteed that such scenarios do not take place again. Moreover, the approach also enhances management efforts to access information of demand related opinions for different products and makes pricing modifications for improved cash flows for the company.

Forecasting also helps M&L Manufacturing Company in the identification of unique business lines. Forecasting, particularly when viewed in customer's perception and in relation to existing products and services and new products and services they would like to have in the market, can be intuitive in the creation on new lines of business. The new business lines eventually leads to improved sales, cash flows and boosted company revenue.

Product 1

A linear trend is evident when a scatter plot for the first product is plotted. It is worth noting that demand for the product in week 14 is exempted. The data value for week 7 is extremely elevated and does not cross the line of best fit. The point is referred to as an outlier. Outliers can be dealt with using different ways. Replacing it with an average of nearest two data points close to it is the most simple and common way of handling such a problem. An outlier is evident in Week 7 and the value to be used is arrived at as (67++76)/2=71.5. Since the data points cut the line of best fit, the common linear regression technique is used in forecasting demand for the first product, (Bai et al., 2014). This technique is selected since it eliminates the total of squared residuals in a regression model, making efficient and reliable estimates achievable to be used in forecasting, (Srinivasan, 2012).

Figure 1: Scatter Graph of raw data for product 1.

Observation: Week 7 has an outlier as is evident in the graph.

Figure 2: Scatter Graph of data modified for the outlier for product 1

Observation: After correcting for the outlier in week 7, all the data points fit in a line of best fit.

Table 1: Regression Analysis for Demand for Product 1

Sum 105 1,020.3 8,449.5 1,015

y=1,020.3, t=105, t2=1015, ty=8,449.5, y/n=72.89 t/n=7.5, n=14 and t is time in weeks while y is demand for product 1. A regression model of the following form can be formed:

Y=a+bt where y and t are as defined above, and a is the intercept while b is the slope of the line of best fit. A relationship exists between time and demand for product 1.

a = (y/n)-b (t/n) =72.89-3.499120879(7.5) =46.64659341

b= (ty-nt/ny/n)/ (t2-n (t/n) 2) =3.499120879

Therefore the model for product 1 is: y=46.6466+3.4991t... (1)

Estimated values for the next four weeks will be obtained when t takes the values of 15, 16, 17 and 18. These values are plugged into equation 1 where there is t.

Period Forecast (y=46.6466+3.4991t)

15 - 99.1331

16 - 102.6322

17 - 106.1313

18 - 109.6304

Product 2

The data for product 2 shows a complicated scatter plot pattern. The pattern can't produce a line of best fit. There seems to be a spike in an interval of every 3 weeks. The values between the observed spikes are comparatively close to each other.

Moreover, closer observation reveals that the data appears to be moving upwards a unit every week for the periods between the spikes. This renders a simple regression technique inappropriate for this kind of data, and thus it will not be required. Therefore an intuitive technique would be appropriate for this kind of data, (Besheras et al., 2013). This method involves the use of an average of the 4 non-spike periods and an additional 1.0 to predict the next 4 non-spike periods. Week 15 forecast is arrived at by taking the average of the first non-spike period plus 1.0. Week 16 is arrived at by taking the average of 3 spike periods plus 1.0, week 17 is arrived at by taking the average of the second non-spike period plus 1.0 and week 18 is arrived at by taking the average of the third non-spike period plus 1.0.

Product 2 forecast

References

Bai, C., Hong, M., Wang, D., Zhang, R., & Qian, L. (2014). Evolving an Information Diffusion Model Using a Genetic Algorithm for Monthly River Discharge Time Series Interpolation and Forecasting. Journal of Hydrometeorology,15(6), 2236-2249. Retrieved from http://www.jstor.org/stable/24914897

Beshears, J., Choi, J., Fuster, A., Laibson, D., &Madrian, B. (2013). What Goes Up Must Come Down? Experimental Evidence on Intuitive Forecasting. The American Economic Review,103(3), 570-574. Retrieved from http://www.jstor.org/stable/23469795

Coker, J., Helo, P. (2016). Demand-supply balancing in manufacturing operations, Benchmarking, 23(3), 564-583.

Foltz, B. (2012). Operations management 101: Measuring forecast error [Video file]. Retrieved from https://www.youtube.com/watch?v=m0Wbj0-yJ5c.

Pandey, P., Kumar, S., Shrivastava, S. (2014). A unified strategy for forecasting of a new product, Decision, 41(4), 411-424.

Randheer, K., Al-Aali, A. (2015). What, who, how and where: Retailing industry in Saudi Arabia, Journal of Competitiveness Studies, 23(3), 54-69.

Srinivasan, G. (2012). Mod-02lec-02 Forecasting-Time series models-simple exponential smoothing[Video file]. Retrieved from https://www.youtube.com/watch?v=k9dhcflyOFc

Stevenson, W. (2018). Operations management (13 ed.) New York, NY: McGraw-Hill Irwin. ISBN-13:9781259667473.