Since the release of ChatGPT in November 2022, there has been growing excitement about the transformative potential of Artificial Intelligence (AI) across many industries. The supply chain sector, in particular, is on the brink of significant change, with over 65% of executives predicting widescale AI adoption by 2025. Yet, many are still uncertain about what this adoption actually entails, leading some to wonder if AI is merely a passing trend. In this piece, we’ll clarify what AI really is, explore its various forms, and demonstrate how it is already enhancing core business functions in supply chain management—and what this could mean for your business.
While tools like OpenAI’s ChatGPT, Microsoft’s CoPilot, and Google’s Gemini are groundbreaking, they represent only one type of AI: Generative AI, which generates new content based on existing information. However, in the world of supply chain management, a broader spectrum of AI technologies is at play. These technologies are driving cost efficiencies, improving operational visibility, and supporting resilience in daily operations. Here’s a look at some of the ways AI is currently applied in the supply chain industry:
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Demand Forecasting Through Discriminative AI:
Discriminative AI differs from Generative AI in that it analyses historical data to predict future outcomes, making it particularly useful for demand forecasting. For example, a retailer preparing for Black Friday can use this technology to analyse past sales trends, regional preferences, and current consumer behaviour. This enables businesses to pre-emptively stock high demand items while avoiding the overstocking of slower-moving inventory. By utilising discriminative AI to optimise inventory levels and prepare for seasonal demand spikes, businesses can minimise costs associated with excess stock whilst effectively preventing stockouts during critical sales periods.
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Predictive Maintenance Through Machine Learning:
Machine Learning, a subset of AI, focuses on identifying patterns in data to perform specific tasks. In supply chain management, one of its most impactful uses is predictive maintenance. By analysing equipment performance data, machine learning can anticipate failures before they occur, helping businesses minimise unexpected downtime and maintenance costs. This proactive approach to maintenance strengthens supply chain resilience by reducing the likelihood of disruptions for manufacturers of all sizes.
- Warehouse Optimisation Through Reinforcement Learning:
While route optimisation often takes the spotlight for Reinforcement Learning in logistics, warehouse optimisation is critical in supply chain management, as it directly impacts delivery times and operational costs. Reinforcement Learning, another type of AI, is often utilised within warehouses to optimise the layout and positions of stock to packing stations for maximum efficiency. For example, during winter, a company selling sports goods can reposition their snow gear closer to the front to reduce pick times and improve efficiency. This dynamic adaption, down to individual items, enhances productivity, ensuring faster order fulfilment.
- Quality Control Through Computer Vision:
Computer Vision, a fast-growing field in AI, is finding a prominent place within the logistics and supply chain industry. This technology uses image recognition to automatically detect product defects or quality issues in real-time. Computer vision can quickly analyse images of product coming off the production line, alerting operators of any blemishes or manufacturing defects before the items are packed and shipped. By integrating this technology, businesses can enhance quality control, reduce returns, and ensure customer satisfaction.
- Information Extraction Through Natural Language Processing:
Another powerful AI tool in supply chains is Natural Language Processing (NLP), often misunderstood as simply generating text like Generative AI. NLP automates the extraction of key information from unstructured documents—such as commercial invoices—and converts it into structured data that can be directly uploaded to an ERP system. By automating this traditionally manual task, businesses can increase efficiency, reduce human error, and ensure accuracy in data entry, streamlining operations.
The Bigger Picture
AI in supply chain management extends far beyond Generative AI, encompassing a diverse array of technologies that drive efficiency, accuracy, and resilience. At Whale Logistics, we are committed to staying at the forefront of these advancements. This commitment initially began with finding a bridge between the traditionally paper-based freight industry and the paperless modern world through the implementation of a variety of innovative systems.
With these foundations firmly in place, Whale Logistics is now taking the next step by utilising Natural Language Processing (NLP) to automate the extraction and integration of critical information. This dramatically reduces the time between document creation and when its data becomes actionable, enabling faster, more accurate insights delivered in real time.
Looking ahead, we are actively working on a future where our systems evolve even further, leveraging AI to provide instant access to freight data. As NLP becomes integrated into more facets of the supply chain, immediate insights into an order’s journey and any delays will become possible. This kind of seamless, AI-driven interaction will replace the heavy reliance on emails and searching through spreadsheets, offering a more intuitive and efficient experience.
At Whale Logistics, we’re committed to staying at the forefront of these innovations, continually enhancing our services to create smarter, faster, and more connected supply chain solutions. To explore how our AI capabilities can elevate your business, contact us today.
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