AI Frontiers Series: Supply Chain
<p>Recently, I’ve pondered how I can provide equal value to both technical and business-oriented professionals in my writings. Fortunately, my role as a data science consultant naturally offers a wealth of interesting topics. Beyond coding, we consistently review literature and articles detailing data-driven solutions across various sectors. This continuous exploration enables us to evaluate the feasibility, efficiency, and effectiveness of these solutions, leading us to either employ existing strategies or craft bespoke algorithms.</p>
<p>This article marks the beginning of a series aimed at illuminating the primary challenges, standard solutions, as well as emerging opportunities and risks in three pivotal sectors: supply chain, human resources, and retail. It is aimed at professionals within these industries looking for data-driven solutions, as well as fellow data scientists interested in broadening their scope. As the title suggests, our first stop is the complex world of supply chain management.</p>
<p>From managing intricate logistics networks to accurately forecasting demand and optimising inventory, the supply chain ecosystem presents numerous formidable challenges. One of the hardest ones to solve is the seamless coordination and optimisation of operations amidst an intricate network of diverse stakeholders such as suppliers, manufacturers, distributors, retailers, and customers. Furthermore, constant disrupters such as fluctuations in raw material availability, market demand, geopolitical tension, and environmental catastrophes pose additional difficulties.</p>
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