Background of the Study
Software automation has revolutionized business processes by reducing manual effort, minimizing errors, and enhancing operational efficiency. Logistics firms, in particular, benefit from automation tools that streamline inventory management, order processing, and delivery scheduling (PwC, 2023). In Nasarawa State, logistics firms face challenges such as increasing customer demands, limited infrastructure, and high operational costs, making software automation a crucial tool for improving efficiency and competitiveness.
Automation technologies such as enterprise resource planning (ERP) systems and warehouse management systems (WMS) have enabled logistics firms to optimize workflows and improve service delivery. However, the adoption of these technologies varies across firms, influenced by factors such as cost, technical expertise, and organizational readiness (Adebayo & Yusuf, 2024). This study assesses the effect of software automation on business efficiency in logistics firms in Nasarawa State.
Statement of the Problem
Despite the recognized benefits of software automation, many logistics firms in Nasarawa State still rely on manual processes, leading to inefficiencies, delays, and increased costs. Factors such as limited awareness, resistance to change, and high implementation costs hinder the adoption of automation technologies (Mustapha et al., 2024). As a result, these firms struggle to meet the demands of a rapidly evolving logistics landscape.
While existing research explores the impact of automation on logistics efficiency globally, limited studies focus on its application in Nasarawa State. This study aims to fill this gap by analyzing the effect of software automation on business efficiency in the region.
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
This study focuses on logistics firms in Nasarawa State, assessing the effect of software automation on business efficiency. The research is limited to firms that have implemented or are considering the adoption of automation technologies. Constraints include access to proprietary data and varying levels of automation across firms.
Definitions of Terms
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