The Role of AI in Building Efficient Supply Chain

 How Did It Start?

AI is not a recent invention; it has existed since the 1950s, but it wasn’t until the mid-20th century it began to take shape. A significant moment was when the term Artificial Intelligence was coined in 1956 at the Dartmouth Conference. Ever since, it has continuously evolved and developed dramatically. Today, it can simulate human-like capabilities and can respond to human language. Many industries have begun implementing this technology into their business model to optimize operations, assist decision-making, improve customer experience, boost efficiency, and more.

AI can most certainly make the supply chain more efficient, and it begins by enhancing visibility. With real-time data, it allows firms to view and track inventory to resolve disruptions. It gathers records and extracts data precisely, enabling firms to map out different supply chain tiers. It uses data and analytics to identify potential risks and provide effective mitigation solutions, assist in forecasting to ensure that the business runs smoothly, and provide valuable insights to compete globally and meet sustainable standards.

• Accurate inventory management: Managing inventory is the art of balancing supply and demand while keeping the cost in check. To keep this balance in check, technology can analyze historical datasets, seasonality, and current trends to predict accurate future demand forecasts.
• Improve warehouse management: Warehouses are the backbone of supply chain management as they keep the flow of goods running smoothly. AI can solve various warehouse issues and suggest floor layouts to assist in timely inventory travel from racking to packing and shipping.
• Optimize & reduce operation cost: Humans are prone to mistakes, but technology can reduce mathematical errors, identify risks, and provide solutions. It can help businesses complete tasks more accurately and on time and lower bottlenecks, ultimately reducing operating costs.
• On-time delivery: This is crucial for every supply chain to maintain reliability by meeting customer expectations. Use AI to identify efficient routes, predict potential disruptions, monitor shipments, optimize delivery schedules, and streamline transportation operations.

• Complex system: AI systems are complex and require massive computational resources, such as time and space, to execute tasks. The cost of implementation will be a huge initial investment.
• Cost of training: Like any other technology, implementation requires an interdisciplinary team with expertise in data science and supply chain. Companies will have to spend a significant amount of downtime training employees, which comes at a cost.
• Cost of implementation: Integration is costly; the price goes beyond procuring, hardware and software integration, and training using accurate historical data. Another aspect organizations should consider is the maintenance and replacement cost of the processors.

• Audit logistics network: Begin by documenting current processes to identify bottlenecks, common challenges, and fallible processes.  Ensure that the data collected is complete, relevant, and accurate for efficiency.
• Set objectives & create a roadmap: Having a clear idea of the pain points will give clarity to the goal, aiding in setting objectives and creating a roadmap.
• Assess platforms: Based on the set goals, access vendors and analyze the functionalities of the AI system they provide.