An Appraisal of the Implementation of Artificial Intelligence in Sales Forecasting: A Study of Retail Businesses in Plateau State
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- NGN 5000
Background of the Study
Artificial intelligence (AI) has the potential to revolutionize sales forecasting by analyzing large datasets, identifying patterns, and predicting future trends with greater accuracy (Smith & Rogers, 2024). Retail businesses in Plateau State, which often deal with fluctuating customer demands, can significantly benefit from AI applications in sales forecasting to optimize inventory, improve marketing strategies, and maximize revenue. This study will evaluate how AI is being implemented in sales forecasting by retail businesses in Plateau State.
Statement of the Problem
Many retail businesses in Plateau State continue to rely on traditional sales forecasting methods, which can be inaccurate and inefficient. With the growing availability of AI tools, it is crucial to examine how these tools are being integrated into sales forecasting practices and their impact on operational efficiency. This study aims to assess the effectiveness of AI-driven sales forecasting in improving business operations.
Objectives of the Study
- To evaluate the level of adoption of AI-based sales forecasting tools among retail businesses in Plateau State.
- To examine the impact of AI implementation on sales forecasting accuracy and business performance.
- To identify the challenges faced by retail businesses in implementing AI for sales forecasting and suggest solutions.
Research Questions
- How are retail businesses in Plateau State adopting AI-driven sales forecasting tools?
- What impact does the implementation of AI in sales forecasting have on business performance?
- What challenges do retail businesses face in implementing AI for sales forecasting?
Research Hypotheses
- The adoption of AI in sales forecasting leads to more accurate predictions and improved business performance.
- Retail businesses in Plateau State that implement AI for sales forecasting experience increased operational efficiency and reduced inventory costs.
- The challenges of implementing AI in sales forecasting are related to the lack of technical expertise and resistance to change.
Scope and Limitations of the Study
The study will focus on retail businesses in Plateau State that have implemented AI-driven sales forecasting tools. Limitations include the potential lack of transparency in business operations and the complexity of measuring AI’s direct impact on sales outcomes.
Definitions of Terms
Artificial Intelligence in Sales Forecasting: The use of machine learning algorithms and AI technologies to predict future sales trends based on historical data and other variables.
Sales Forecasting: The process of predicting future sales revenue based on historical data, market conditions, and trends.
Retail Businesses: Companies that sell goods or services directly to consumers, often in small to medium-sized operations.
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