Introduction
Artificial intelligence (AI) has always driven new insights, especially with automation. However, when considering supply chain management, there is always a way to improve existing practices. This is where the application of AI in supply chain comes into play. Hence, let us explore just how this digital technology can be applied, and why.
AI in the supply chain – use cases
AI, along with machine learning (ML), can be implemented in myriad ways to support supply chains. AI in supply chain management has many benefits, including:
– AI in supply chain planning: forecasting events related to demand & supply coordination
– Application of AI in the supply chain: reinforcing gaps in supply & logistical vulnerabilities
– Benefits of AI in the supply chain: drives accuracy, efficiency & productivity
– Role of AI in supply chain management: enhance informed decision making & visibility
– Boosts resilience & optimizes cost management for better resource utilization
– Augments employee workforce retention & workplace satisfaction
– Diversifies supplier management & drives sustainability with strategic transformation
– Demand forecasting: predictive analytics using statistical trend extrapolation
– Live algorithmic demand-related insights to drive continuous improvement & context
– Enhanced demand-supply channels with greater inventory availability closer to consumers
– Route & fleet management is optimized too for greener, quicker & sharper journeys
– Opens possibilities for last-mile delivery via drones or autonomous vehicles
– Hybrid settings for ultimate efficiency
– Multichannel inventory & order management for enriched tallying of entries for data hygiene
– Mitigation of risk & disruption handling via prediction
– Syncs & synergizes digital twins between manufacturing & storage
Implementation roadmap
– Create a business model & consider AI adoption option for quicker digitization & ROI
– Seek an optimal vendor who can cater to your specific needs
– Supervise developing & integrating solutions, prioritizing value creation opportunities
– Scale the seamless adoption of solutions by promoting awareness via training
– Manage data via tools, audit information for integrity, predict customer trends & accuracy
– Drive customer service with intelligent storage solutions
– Convolution Neural Networks identify visual patterns & features by deploying Computer Vision
– Greater efficiency with fewer parameter usage demand metrics
– Image/object detection, classification & segmentation
– Autonomous mobile robots powered by ML for route classification, navigation & accessibility
– Recurrent Neural networks detect sequential data processing patterns
– NLP (Natural Language Processing) performs customer sentiment analysis (feedback)
– Chatbots benefit from deep learning for added intelligence
– Connected information via dashboards & added customer experience
The future of AI Supply Chains
Supply chains are already expected to ebb and flow in terms of facing challenges; however, one thing is sure – they will always exist. For this very reason, they must always be powered by the latest technology to remain competitive & relevant in our rapidly evolving contemporary markets. Without this, supply chains are susceptible to various issues, including inefficiencies such as demand-supply mismanagement, stock discrepancies & sluggish order processing. AI will only mitigate these issues by automating many self-check tasks & ensuring ultimate accuracy. The result? Faster, more precise & productive workflows that all industries can benefit from. Moreover, the basis of live data will ensure continual global accountability for the betterment of all domains.
This will undoubtedly unify demand from end consumers to the supply by manufacturers. The convention of bottlenecks, communication gaps, avoidable delays, stock depletion & snags will all become things of the past. Yet it doesn’t stop there. If you consider the full potential of AI, then consumers should be able to directly order from suppliers rather than retailers, distributors, or wholesalers. This could ultimately alter the entire dynamic of supply chain management. A welcome change for consumers, of course. However, what about the middlemen who have managed to earn a buck from their current role, connecting demand with supply? With time comes change, and often, this is to the extreme detriment of some existing stakeholders.
Conclusion
Well, there you have it. AI is already driving supply chains into sustainable avenues by driving intelligent pathways powered via data analysis. The future appears promising with the advent of AI and its affiliated digital partner technologies, including ML, to augment its path. A lack of infrastructural unification to support such compatibility is the main hindering factor here. The universal adoption of AI in supply chain management can finally occur upon addressing this issue. The problem lies in ensuring that AI can be continually updated to meet evolving demand trends, without which, only further discrepancies will ensue.
Solutions need to be viable & sustainable to work in the long run. They should be able to sustain unexpected economic & geopolitical disruption, still emerging victorious in their mission to deliver supplies where & when they need. By driving such technological workflows, it is only a matter of time before fully automated supply chain operations can be monitored virtually globally – from anywhere worldwide. This would be in real-time and is already existent in many logistics firms. So, let’s all embrace the upcoming powerful storm that AI will bring to the supply chain management table.
