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.
Abdul R & Raheela M. (2015). Impact of Working Environment on Job Satisfaction.Procedia Economics and Finance. 23(1), pp:717-725.
Frieden T. (2010). Restaurant Design. Am J. 100(4), pp:590–5
Hahn, P.M. & Krarup. (2001). A hospital facility layout problem finally solved. J. Journal of Intelligent Manufacturing. (5)12, pp: 487-496.
Hahn, P. M., Hightower, W., Johnson, T., Guignard-Spielberg, M. & Roucairol, C. (2001). Tree elaboration strategies in branch and bound algorithms for solving the quadratic assignment problem. Yugoslav Journal of Operations Research.11, 41-60.
Howard L, Tim B, Palie S. (2013). The servitization of manufacturing: A systematic literature review of interdependent trends. International Journal of Operations & Production Management. 33(11/12) pp:1408-1434,
Hales B & Pronovost P. (2006). The checklist – a tool for error management and performance improvement. J Crit Care. 21(3):231–5
Henderson, D. A. (2008).Demand. Retrieved from Library of Economics and Liberty: http://www.econlib.org/library/Enc/Demand.html
Jacobs, F. R., & Chase, R. B. (2015).Operations and Supply Chain Management(14th ed.). New York, NY: McGraw-Hill.
Loh, E.. (2005). Profiting from moving averages and time-series forecasts: Asian-pacific evidence. Asia Pacific Journal of Economics & Business. 9(1), pp: 62-82.
Pitts S & Rob K. (2014). The Role of Business Ethics: Incorporating Values and Ethics into Business Decisions. Journal of Legal, Ethical and Regulatory Issues. 1(1).
Smith, Laura A., Maull, Roger and Ng, Irene C. L. (2012) Servitization and operations management: a service-dominant logic approach. Working Paper. Coventry: Warwick Manufacturing Group. WMG Service Systems Research Group Working Paper Series (Number 10/12).
Topalović S. (2015). The Implementation of Total Quality Management in Order to Improve Production Performance and Enhancing the Level of Customer Satisfaction. Procedia Technology. 19(1), pp: 1016-1022.
Usman E. (2012). Factors important for the selection of fast food restaurants: an empirical study across three cities of Pakistan. British Food Journal. 114(9), pp.1251-1264.
Wikner, J. (2006). Analysis of smoothing techniques: application to production-inventory systems. Kybernetes. 35(9), pp: 1323-1347.