Modified Trend and Seasonal Time Series Analysis for Operations: A Case Study of Soft Drink Production

Abstract

Many Production and Business Time Series Are Non-Stationary Time Series that Contain Trend and Seasonal Variations. Seasonality Is a Periodic and Recurrent Pattern Caused by Factors such as Weather, Holidays, or Repeating Promotions. this Paper Presents a Trend and Seasonal Time Series Analysis of Soft Drink Production over the Period 2003–2010, it Is Necessary to Know the Trend in Soft Drink Production to Elicit the Reasons why Demand of Soft Drink Is Increased or Decreased at Specific Periods. the Objectives of this Paper Are (i) to Study the Trends in the Production and Productivity of a Soft Drink Bottling Company, and (ii) Analyze the Demand of the Firm with a View to Identifying Trend that Exists in the Company Using Time Series Analysis. A Software Program Was Developed Based on Applicable Methodology to Facilitate Accurate and Faster Analysis of Data. Characterization of Demand Data Using Decomposition Was Done, which Reveal the Nature of Seasonality, Cyclical Activity, Trend and Noise. on the Whole, the Results of the Decomposition Analysis Clearly Show that there Is a Remarkable Linear Trend in Demand Pattern. the Study of Seasonality Shows that the Highest Peak in Demand of the Product Occurred at 12th, 24th, 36th, 48th 60th, 72nd, 84th and 96th Months which Turn Out to Coincide with Yuletide. the Study Further Indicated a Positively Increasing Trend in the Demand Rate of Company’s Product.