An Assessment of Deep Learning Models for Demand Forecasting: A Case Study of FMCG Companies in Kano State
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- NGN 5000
Background of the Study
Demand forecasting is a critical function for fast-moving consumer goods (FMCG) companies, enabling them to predict future customer demand and make informed decisions about inventory, production, and supply chain management. Traditional forecasting methods often fall short in capturing complex and nonlinear patterns in demand data, leading to inaccuracies and inefficiencies (Bello & Ibrahim, 2023).
Deep learning models, a subset of artificial intelligence, have emerged as powerful tools for demand forecasting due to their ability to analyze large volumes of data and identify intricate patterns. Techniques such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are particularly effective in processing time-series data, making them ideal for demand forecasting applications (Eze & Musa, 2024).
FMCG companies in Kano State face unique challenges, including fluctuating consumer behavior, seasonal demand patterns, and supply chain disruptions. This study aims to assess the effectiveness of deep learning models in improving demand forecasting accuracy for FMCG companies in the region.
Statement of the Problem
Inaccurate demand forecasts can lead to overstocking, understocking, and significant financial losses for FMCG companies. Traditional statistical methods, while useful, are often unable to capture the complexities of real-world demand data. Despite the proven effectiveness of deep learning models in demand forecasting, their adoption among FMCG companies in Kano State remains limited due to barriers such as high implementation costs, lack of technical expertise, and insufficient infrastructure (Ahmed & Yusuf, 2023).
The lack of empirical studies on the application of deep learning models in the context of Nigerian FMCG companies, particularly in Kano State, necessitates further research. This study addresses this gap by evaluating the performance of deep learning models in forecasting demand and their potential impact on operational efficiency.
Objectives of the Study
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To evaluate the adoption of deep learning models for demand forecasting by FMCG companies in Kano State.
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To assess the accuracy of deep learning models in predicting customer demand.
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To identify challenges and propose strategies for improving the adoption of deep learning models in demand forecasting.
Research Questions
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How widely are deep learning models adopted for demand forecasting by FMCG companies in Kano State?
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How accurate are deep learning models in predicting customer demand for FMCG products?
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What challenges hinder the adoption of deep learning models, and what strategies can address these challenges?
Research Hypotheses
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Deep learning models have no significant impact on the accuracy of demand forecasting for FMCG companies.
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The adoption of deep learning models does not significantly improve operational efficiency in FMCG companies.
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Strategies for enhancing the adoption of deep learning models have no significant effect on forecasting outcomes.
Scope and Limitations of the Study
The study focuses on FMCG companies in Kano State, assessing their use of deep learning models for demand forecasting. Limitations include variability in data availability, differences in model implementation across companies, and potential biases in survey responses.
Definitions of Terms
Deep Learning Models: Artificial intelligence models that simulate the functioning of the human brain to process data and create patterns for decision-making.
Demand Forecasting: The process of estimating future customer demand for products or services.
FMCG Companies: Businesses that produce and sell fast-moving consumer goods, such as food, beverages, and personal care products.
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