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Top 12 AI Use Cases for Supply Chain Optimization in 2023
Only the right combination of AI and supply chain can help companies tide over this crisis. To help you achieve this, you need to understand use cases and go through some well-established supply chain case studies. An in-depth reading of at least one modern supply chain case study may prepare you better to use AI and ML in the supply chain. “Many organizations might not have the level of the volume of data needed at the right level of granularity to be able to get value out of the AI outputs,” Amber Salley, senior director analyst at Gartner, tells CGT. Nineteen percent of consumer goods executives in the 2023 Retail and Consumer Goods Analytics Study said logistics optimization was one of their top AI/ML use cases.
Last-mile delivery is a critical aspect of the entire supply chain as its efficacy can have a direct impact on multiple verticals, including customer experience and product quality. Data also suggests that the last mile delivery in supply chain constitutes 28% of all delivery costs. Machine learning and artificial intelligence can offer useful insights into supplier data and can help supply chain companies make real-time decisions. Artificial intelligence in supply chain industry has great potential with the promise of accurate freight rate prediction, facilitated invoice processing, improved efficiency and cost reduction.
Supply chain & operations
At the same time, its distribution and customer service teams were able to be more proactive. All told, the investment helped Kimberly-Clark decrease variability daily by 40%, particularly in locations where production plants are shipping to its distribution centers. AI in the supply chain isn’t magic, but it can be an extremely powerful tool for an SCM team. Thanks to its ability to quickly gather massive amounts of data from different sources and swiftly analyze it to provide insights, AI can make itself a convenient companion with a variety of activities. As businesses continue to leverage these capabilities, we can expect a more streamlined, efficient, and innovative supply chain landscape. AI can be used to automate routine logistics operations such as data entry, labeling, and packaging tasks.
In addition, based on AI analysis and factors you have previously determined, machine learning can make critical decisions for you. In conclusion, the in intertwining of AI with the sensitive nature of supply chain management empowers organizations to not only adapt but thrive in the face of unprecedented challenges. AI mastery in supply chain dynamics is not merely an option but a strategic imperative for those seeking to chart the course toward a future of innovation, resilience, and sustained success. It is more vital than ever for manufacturers to have total visibility of their entire supply chain.
The Concept of Supply Chain Management
Obviously LLMs shine in use cases with large amounts of data — massive amounts of unstructured text data, to be more precise. Here’s the thing — you need to be able to execute on both moonshot and mundane Generative AI use cases. But even more immediately, you must start augmenting processes throughout your organization, finding the right balance between autonomy and control.
When it comes to the ultimate cost-saving, AI and analytics solutions are the most effective options to negotiate better shipping and procurement rates, pinpoint changes in the supply chain profit process and manage courier contracts. You can assess a centralized database that takes virtually every aspect of the supply chain to deliver financial decision-making. This data-rich modeling method is by far the best use case of data science and AI for the supply chain forecasts that empower warehouse employees to make more informed decisions on inventory stocking. This application of AI in supply chain can automatically allow your business to pursue breakthrough ideas and provide better customer needs and demands. If you still haven’t decided on embracing the use of analytics in the supply chain for your business, our next point of discussion is for you.
Top 12 AI Use Cases for Supply Chain Optimization in 2023
For example, Roambee’s AI-powered platform combines real-time IoT sensor information with data streams from carriers, ports, airport operations, rail lines, traffic reports, and weather forecasts. Chatbots reduce the costs of employing human customer service advisors and also lead to faster response times for customer queries. In response to these challenges, the integration of Artificial Intelligence (AI) into supply chain management has become more prevalent.
What is the most used generative AI?
- GPT-4. GPT-4 is the most recent version of OpenAI's Large Language Model (LLM), developed after GPT-3 and GPT-3.5.
- ChatGPT.
- AlphaCode.
- GitHub Copilot.
- Bard.
- Cohere Generate.
- Claude.
- Synthesia.
Machine learning enabled techniques allow for automated analysis of defects equipment and to check for damages via image recognition. The benefit of these power automated quality inspections translates to reduced chances of delivering defective or faulty goods to customers. Machine learning is a subset of artificial intelligence that allows an algorithm, software or a system to learn and adjust without being specifically programmed to do so.
Improving customer service.
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Will supply chain be automated?
While modern supply chains utilize automation frequently, not all supply chains are fully automatable. Supply chains will become increasingly automated as time goes on, but will likely always require human attention and focus in certain areas.