Essays on Impact of Working Environment on Job Satisfaction Assignment

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The paper "Impact of Working Environment on Job Satisfaction" is a wonderful example of an assignment on management. (a)The moving average will rely on historical data to predict the next period's forecastMAtn = (At+ At-1+ At-2 +At-3+ … .+ At-n+1 )/nThe moving average for the next 3 weeksperiodActual Demand3 period Moving Average tAtMA1142214531451444147145.7514614661451467152147.78153147.79157150101611541116615712167161.313170164.714116167.7151201511613513517124123.618125125The moving average for the next 3 weeks is 135, 124 and 125(b)Simple exponential smoothing model to forecast demand for the next 3 weeksperiodActual DemandForecast error Simple exponential smoothing (0.2)Simple exponential smoothing (0.5)114200021453145.6146.531453145.6145.641475148149.251464146.814861453145.6146.5715210154157815311155.2158.5915715160164.51016119164.8170.51116624170.81781216725172179.51317028175.618414116-26110.810315120-22115.610916135-7133.6131.517124-1812011518125-17121.6116.5(c)The moving average is slightly better when compared to the exponential smoothing.

This is because the moving average had a small deviation from the initial figure. The demand for exponential smoothing was calculated using the same range for the moving averages to enable reasonable comparison (Wikner, 2006). However, the disadvantage of using the moving average in forecasting demand is that the averages normally stay within the past ranges and hence this requires broad record-keeping (Loh, 2005). On the other hand, exponential smoothing smoothens the data but it has significant variation when compared to the moving average. (d)Other factors that should be taken into consideration when forecasting demand include the level of accuracy desirable, the time duration to be forecast, the cost-benefit of the forecast to the organization as well as the available time to perform the analysis.

In addition, there are techniques that provide potentially higher accuracy but do not use past data or any other data that might be costly to obtain (Wikner, 2006). A technique that can be used to accurately forecast demand is the linear approximation formula. This method is used to calculate a trend from the number of sales period history as well as to forecast the trend.

The trend is recalculated accordingly in order to detect any change.


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