Paper Example on Operations Forecasting

Introduction

Macy's is a department store in America founded in 1958. As at 2017, the company has 669 stores with the "Macy's" nameplate in operation across the United States, Guam and Puerto Rico. Their Herald Square is the biggest department stores around the globe and covers almost an entire New York City block with a space of about 1.1 million square feet.

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Simple Moving Average

The simple Moving average is calculated by calculating the average of annual sales of a company over a specific timeframe or a number of years. The majority of moving average is based on the closing sales, at the end of the stipulated timeline. For example, a six-year moving average will include the sum of closing sales for the six years divided by 6. This means that the moving average is simply the average that moves. This is because as shown in the excel worksheet, the old data is repetitively dropped or abandoned as new data is used. For this reason, therefore, the calculations of the moving average occurs along a similar timescale.

In relation to the excel worksheet calculations, the initial year used in the moving average sales covers the last five years of Macy's sales revenues. On the other side, the second year of moving average sales calculations indicates a drop in the first Macy's sales $25,778 and is added to the new sales achieved ("Macy's, Inc.", 2018). Subsequently, the third year of the moving average continuous through by dropping the second sales data and adding up the latest sales data acquired. As noted in the calculation, the sales values consciously increase as the year progresses.

In comparison to the exponential moving average method, this method is simpler and easier to conceptualise. Nevertheless, the moving average value method often results to a value below the previous sales which caused the moving average to trail or lag.

Exponential Moving Average Calculation

Exponential moving averages as indicated in the excel worksheet calculation is known of minimizing the trail through applying a lot of weight to recent sales data. Subsequently, the weighting applied in the computation used on the latest prices relies on the number of years used to calculate the moving average (Krige, 2010). As shown, there exist three stages used to calculate the exponential moving average. In order to find the exponential moving average, the initial step is to calculate the simple moving average. The reason behind this is because the exponential moving average has to start somewhere so that the simple moving average can be applied to the preceding in the first calculation. The calculation that follows is the computation of weighting multiplier, and the third and final step involves the calculation of the exponential moving average.

In the excel worksheet provided, the 5-year timeline of exponential moving average applied a weighting of 33% to the latest sales value registered. This means that the exponential moving average can also be labelled 33% exponential moving average. Nevertheless, it is better to appreciate that the weighting for shorter timeframe is given more consideration more than the weighting used for a more extended period of time and in this case, therefore, Macy's 5 year duration is appropriate.

Naive Method

This method can only be applied in case of a time series data. In this case, all forecast are subjected to the value registered by the last observation. This, therefore, means that the most recent data presented in the operation database is what is used as the forecasted value for the subsequent period. In this case, therefore, the forecasted data is the last period of Macy's sales operations or the year 2017.

From the three methods of operation forecast used at Macy's, the most preferred method is the exponential moving average. This is related to the fact that despite being complicated in comparison to others, it presents a well-calculated forecasted value and considers numerous factors and therefore producing a value that is most accurate and realistic (Krige, 2010). At the same time, the exponential moving average approach is best because it reduced the trail seen in the simple moving average method and supports the application of shorter predictive durations which ultimately increases accuracy.

Conclusion

In a nutshell, the operation forecast of Macy or other company operating in an entirely different sector can produce mixed results, either positive or negative financial metrics. The advantage is that sales or operation forecast can lead to improved customer service when demand and supply are projected correctly. In this case, goods are produced in line with the consumer demands, and this means that there is high satisfaction level. On the other side, in case there is a wrong forecast, customer demands will not be met and hence the high level of dissatisfaction and thus losses. This is dangerous to the financial projections and profit revenues.

References

Krige, D. G. (2010). Two-dimensional weighted moving average trend surfaces for ore evaluation (pp. 13-79). Johannesburg: South African Institute of Mining and Metallurgy.

Macy's, Inc. (2018). Retrieved from http://investors.macysinc.com/phoenix.zhtml?c=84477&p=irol-sales