Date of Award

5-2024

Document Type

Project

Degree Name

Master of Science in Information Systems and Technology

Department

Information and Decision Sciences

First Reader/Committee Chair

Dr.Conrad Shayo

Abstract

This study explores the impact of seasonality on SKU (Stock Keeping Unit) demand forecasting in small-scale food retailers through the analysis of historical sales data, focusing on four key products, labelled: Product A, Product B, Product C & Product D. The research aims to assess the accuracy of different forecasting models in capturing seasonal fluctuations. The research questions are: (Q1) What is the impact of seasonality on SKU demand forecasting in case of small-scale food retailers. (Q2) How does the efficacy of Traditional model compare to alternative technologically driven forecasting techniques? (Q3) To what extent can the impact of seasonality on SKU demand forecasting be differentiated between perishable and non-perishable goods? The data collected is from a local food retailer of LA county from the year 2021-2023. The collected dataset was broken down to weekly forecast sets to match our methodologies pick of small-time intervals as opposed to yearly or quarterly forecasts. The findings are:(Q1) There is a significant influence of seasonality on demand forecasting accuracy. Models incorporating seasonal components outperform those that do not. Specifically, the Winter-Holt's model, integrating seasonal components, yields more accurate forecasts for both Product A and Product B compared to the traditional Holt's model. (Q2) SARIMA (Seasonal Autoregressive Integrated Moving Average) emerges as the preferred forecasting model, outperforming techniques like Moving Average, Simple Exponential Smoothing, and ARIMA (Autoregressive Integrated Moving Average) for Product A. SARIMA's superiority lies in its adept handling of seasonal dynamics and identification of stationarity within the data. (Q3) further differentiation between perishable and non-perishable goods reveals SARIMA's superiority in capturing seasonal demand patterns for non-perishable products like Product A. This study acknowledges limitations, such as reliance on past data and the exclusion of external factors impacting demand patterns. Future research should adopt a holistic approach, integrating historical sales data with external variables like market trends and consumer behavior. By embracing a multidimensional perspective and harnessing advancements in technology, future studies can pave the way for more sophisticated forecasting methodologies, enhancing overall performance and customer satisfaction in small-scale food retail environment.

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