Production and Operation Management
PRODUCTION & OPERATIONS MANAGEMENT
Forecasting helps managers and businesses develop meaningful plans and reduce uncertainty of events in the future. Managers want to match supply with demand; therefore, it is essential for them to forecast how much space they need for supply to each demand. 1.1 QUANTITATIVE TECHNIQUES * LINEAR TREND
Show steady, straight-line increases or decreases where the trend-line can go up or down and the angle may be steep or shallow. The concept describes the purposes and uses of linear trend forecasting and the main ingredients necessary for implementation of this forecasting procedure.
Linear trend forecasting is used to impose a line of best fit to time series historical data (Harvey, 1989; McGuigan et al., 2011). It is a simplistic forecasting technique that can be used to predict demand (McGuigan et al., 2011), and is an example of a time series forecasting model.
* CYCLICAL COMPONENT
In weekly or monthly data, the cyclical component describes any regular fluctuations. It is a non-seasonal component which varies in a recognizable cycle.
* SEASONAL COMPONENT
In weekly or monthly data, the seasonal component, often referred to as seasonality, is the component of variation in a time series which is dependent on the time of year. It describes any regular fluctuations with a period of less than one year. For example, the costs of various types of fruits and vegetables, unemployment figures and average daily rainfall, all show marked seasonal variation.
* TIME SERIES ANALYSIS
A time series is a sequence of observations which are ordered in time (or space). If observations are made on some phenomenon throughout time, it is most sensible to display the data in the order in which they arose, particularly since successive observations will probably be dependent. Time series are best displayed in a scatter plot. The series value X is plotted on the vertical axis and time t on the
Forecasting helps managers and businesses develop meaningful plans and reduce uncertainty of events in the future. Managers want to match supply with demand; therefore, it is essential for them to forecast how much space they need for supply to each demand. 1.1 QUANTITATIVE TECHNIQUES * LINEAR TREND
Show steady, straight-line increases or decreases where the trend-line can go up or down and the angle may be steep or shallow. The concept describes the purposes and uses of linear trend forecasting and the main ingredients necessary for implementation of this forecasting procedure.
Linear trend forecasting is used to impose a line of best fit to time series historical data (Harvey, 1989; McGuigan et al., 2011). It is a simplistic forecasting technique that can be used to predict demand (McGuigan et al., 2011), and is an example of a time series forecasting model.
* CYCLICAL COMPONENT
In weekly or monthly data, the cyclical component describes any regular fluctuations. It is a non-seasonal component which varies in a recognizable cycle.
* SEASONAL COMPONENT
In weekly or monthly data, the seasonal component, often referred to as seasonality, is the component of variation in a time series which is dependent on the time of year. It describes any regular fluctuations with a period of less than one year. For example, the costs of various types of fruits and vegetables, unemployment figures and average daily rainfall, all show marked seasonal variation.
* TIME SERIES ANALYSIS
A time series is a sequence of observations which are ordered in time (or space). If observations are made on some phenomenon throughout time, it is most sensible to display the data in the order in which they arose, particularly since successive observations will probably be dependent. Time series are best displayed in a scatter plot. The series value X is plotted on the vertical axis and time t on the