TIME SERIES ANALYSIS AS A MEANS OF MANAGERIA DECISION MAKING IN MANUFACTURING INDUSTRY
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- Chrystal Dennis
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From this document you will learn the answers to the following questions:
How often are table water sold?
What is the acronym for the company that analyzes the sales?
In what year did the study on the sales of table water begin?
Transcription
1 TIME SERIES ANALYSIS AS A MEANS OF MANAGERIA DECISION MAKING IN MANUFACTURING INDUSTRY 1 Kuranga L.J, 2 Ishola James.A, and 3 Ibrahim Hamzat G. 1 Department of Statistics Kwara State Polytechnic Ilorin,Nigeria 2 Department of Marketing Kwara State Polytechnic Ilorin,Nigeria. 3 Department of Mathematics and Statistics Hassan Usman Polytechnic Katsina Nigeria. Abstract This research Time Series Analysis on monthly sales of table Water as a means of decision making in manufacturing industry was aimed to identify the nature of the sales if there exist trend or seasonality, and. also to eliminate the trend and seasonality in the sales fitting an appropriate model for the sales pattern and predict the future sales. Minitab was use to analyse the data using multiplicative model and from the analysis we discover that the sales of Table water will be increasing every month. And attempt were been made to identify the nature of fluctuation in monthly sales of Ola table. A model was proposed for the sales patterns and the future sales were forecasted based on the fitted model. Keywords: Industry, time, decision, sales series and analysis INTRODUCTION Analysis of sales has become a very important for producing or manufacturing company to see the growth or progress of the company. Sales can also be said to be strategic means by which products of a company are being moved to the channel of distribution[2],[4]. Generally, a sale is said to be the process of selling or rendering service. Furthermore, primary aim of any company is to minimize loses and maximize profits. Further values may be predicted or forecasted from past sales of previous years. Forecasting is projecting some possibilities in the future sales forecasting which an analysis of potential demand is an integral part of a company s policy to project what will or can be sold in future[1],[3]. Basically, the first step in sales forecasting is to apply statistics on sales records, Selling is a practical implementation of marketing, it often form a separate grouping in a corporate structure, employing separate specialists operatives known as sales person(s). The primary function of professional sales is to generate and close leads, educate prospects, fulfill needs and satisfy wants to customer appropriately and therefore turn prospective customers into actual ones. From the marketing point of view, selling is one of the methods of promotion used by marketers. Other promotional technique includes advertising, sale promotion, publicity and public relations. Various sales strategies exist such as tit for tat which is the best if ongoing deals and interactions are expected. This insight is behind so called consultative sales process which is used by Saturn to sell cars, as well as for some direct business sales. Types of sales that we have included were direct, consultative and complex sales. 103
2 METHODOLOGY Regression Analysis using Least Square method with minitab is used to analyse the data The model of a linear regression is given by Y i = dependent variable, X i = The independent variable β 1 = regression coefficient t and e i = error term DATA PRESENTATION TABLE 1: MONTHLY SALES OF OLA TABLE WATER MONTHS/Y EAR Y i = β o + β 1 xi + ei where January February March April May June July August September October November December SOURCE:OLA TABLE WATER below TIME PLOT In order to obtain the visual representation of the series, the observed values Y t is plotted against time t as shown Yt- number of bottles of Ola table water sold in millions at time t T = 1, 2, 3 60 i.e. monthly from January 2007 to December 2011 from the graph, the behaviour of the sales shown trend at the peak of the plot and seasonal variation which is also periodic. INTERPRETATION OF THE TIME PLOT From the time plot, we can see two components of time series which are: the trend and seasonal variation. At the peak of the plot, where we found the trend which shows high series and the variation in the sales which shows the seasonality. Multiplicative Model 104
3 This scheme assumes that presence of interactions among the various component and that the original series, Y t is the product of the various components. Algebraically, Y t = T t x S t x C t x I t Where Y t = the original series at time t T t = the effect of trend over time t S t = seasonal effect over time t C t = cyclical effect over time t I t = irregularity effect over time t DATA ANALYSIS Time Series Decomposition Data Actual value Length NMissing 0 Trend Line Equation Yt = *t Seasonal Indices Period Index Accuracy of Model MAPE: 56 MAD: MSD: 5.94E+10 Row Actual value (Yt) Trend Seasonal Detrend Deseason Model 105
4
5 Forecasts Row Period Index SUMMARY OF FINDINGS.In conclusion, there are variations in sales of table water from one year to another, there is higher sales in the following month: January, February, October, November and December. The original data trend value show the upward movement. The seasonal index and the prediction values show that there is higher sales of Table water during the months of January, February, October, November and December because we are in the dry season when people always look for cold water to refresh themselves. Also, the sales is low during the raining season..finally, we can observe that time also goes with sale of Table water because it has seasonal. That is, dry season and raining season. 107
6 REFERENCES [1].Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences, 3rd Ed. Mahwah, NJ: Lawrence Erlbaum Associates. [2]. Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26, [Simple rules of thumb based on empirical findings.] [3]. Maxwell, S. E. (2000). Sample size and multiple regression analysis. Psychological Methods, 5(4), [When predictors are correlated with each other, larger samples are needed.] [4]. Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4 th ed.). Mahwah, NJ: Lawrence Erlbaum Associates. [This inexpensive paperback is accessible, filled with examples and useful advice.] 108
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