A time series is a set of values that have been recorded over a period of time which can then be represented using a Time Plot. Time Series Forecasting does not explain data to a company but is useful for describing what is happening to the data or creating predictions for what will happen in the future e. g. Sales forecasts. It is also beneficial to the Human Resources Department as it can be used as an aid for them to plan the future resource requirements of the organisation.

We Will Write a Custom Essay Specifically
For You For Only \$13.90/page!

order now

Time Series forecasting is relatively simple to carry out. It has three main components: a random element (freak fluctuations), a trend (raw data) and seasonal components (fluctuations in business activity during the year). The HR Department of Dewhurst Plc uses Time Series Forecasting to gain a better understanding of the general attitude of its employees towards their position of employment. It helps to illustrate trends and changes in the overall attendance of the employees of the company.

Dewhurst monitor (with the help of time series forecasting) the rate at which employees are leaving and joining the company by taking quarterly recordings of the labour turnover, per year. Below are Dewhurst’s figures for the period of 1998-2001. The British operation employs a total number of 520 staff. From initially assessing the data, it is apparent that there are some fluctuations throughout the years however, it remains fairly stable. Moving averages focuses on the average values of the data provided in order to calculate the forecast for a certain time period in the future.

This method calculates the ‘mean’ of the data provided. However, it should not be used if the data contains values that are slightly abstract in comparison to the other values in the set, as it can distort the average figures, making the forecast unreliable. Once the mean has been calculated, using a mean square error, you are able to identify which period would be best to use for the forecast to be most accurate. This method should only be used on data that has no obvious trend as with upward and downward trends, it can under estimate or over estimate the forecasts. Trend Analysis

When it is possible to represent the data pictorially, specifically in the form of a line graph or in a curve, then Trend Analysis is one of the most useful methods to use. As the graph shows, you are able to predict from past results what the future figures for Dewhurst’s staff turnover will be for the next year. As the figures have not fluctuated greatly over the given period, it looks as though it will continue to remain fairly steady. However, it is important to remember that this is only a forecast therefore this may not necessarily be how the figures will turn out.

Exponential Smoothing This technique is unique as it allocates different weightings to various parts of time series data from which the forecast will be calculated. The weightings are decided by the time period of which the data has been extracted from i. e. greater weighting is placed upon the most recent data provided whereas older data is given the least weighting. The weights are determined by selecting a value of smoothing constant which in known as alpha. This forecast was created using the exponential smoothing method.

This shows that for the seventeenth period, the forecast has estimated that 515 employees will be the minimum figure for the mean square error. From analysing the results presented in the graph, it would appear that in the seventeenth period, which will be the next quarterly period of 2002, the labour turnover will remain the same as the sixteenth period. When the raw data is divided into four quarters which also display fluctuations within each period, the moving average method can be used to produce values that create the trend.

After the trend has been generated, the values are then placed at mid points of the existing data’s quarterly intervals. Once the values for the trend have been established, they are then subtracted from the corresponding raw data values which can then be analysed in order to identify the seasonal variations or any other changes that may become apparent. The last step is a subtraction which will give the final values for residual variation. The advantage of this model is that it shows how the data has changed over a period of time in comparison to the raw data figures-within the same graph.

This Additive composition graph shows that in the seventeenth period, the forecast predicts that there will be a very slight upward trend in Dewhurst’s employee figures. More specifically, the unadjusted forecasted figure is 511. 9392 but is marginally higher for the adjusted figure-514. 3678. Multiplicative Model The Multiplicative model is exactly the same as the Additive model which uses moving averages. It is analysed in the same way however the only difference is that their total must be equal to the number of values used to calculate each trend value.

Then by dividing the given figures, it gives you values for the residual and seasonal variations within the forecasted data. The Multiplicative graph shows that there appears to be an upward trend forming in the first quarter of the next year. The unadjusted figure for the given period is 511. 9661 and the adjusted forecasted figure is 514. 3838. After looking at the various methods of forecasting available, I have decided that Exponential Smoothing would be the best method of forecasting for Dewhurst Plc’s Labour Turnover.

There were other methods which were nearly as useful however, I chose Exponential Smoothing as it takes into account the age of the data presented and apportions it wisely. With regards to Labour turnover, there are many factors that can change the figures such as recession or an increase in business which can require more staff. This method places more importance on the more recent data n less on the ‘older’ data therefore if there were significant changes within the past years, it will not have much of an effect on the forecast making it more reliable.

This data can benefit Dewhurst by giving them an idea of how many of their employees are joining or leaving their company on an annual basis. Dewhurst has a low Labour Turnover which is a good thing for a company as it means that their employees are happy within their work and so are staying for longer periods. This also benefits a company as many of them spend a lot of their budget on training for their employees, but if their workforces are consistently leaving the company, they will never see the benefit of the training.

Dewhurst is the market leader in keypad technology and is a leading world-wide supplier of high quality lift components. Therefore as it is a technological supplier, quantitative techniques is an important aspect of how the company functions on a daily basis. With the introduction of Time Series Forecasting within the company, it has proved to be a very useful piece of software for various areas within the company as it deals with a vast range of information that helps with the future planning and current project analysis.