AI in Supply Chain: Practical Warehouse Applications
Author: Jennifer Hall
May 15th | 4 min read
Practical Applications for AI in Supply Chains
I frequently get asked for my “take” on AI in the world of supply chain and logistics – at conferences, tradeshows, or by a client or partner. As a natural-born cynic, my tendency is to chalk up a lot of it to overhyped marketing buzz, putting a new spin on what has been around for quite some time. But even I can’t help myself being intrigued by the practical applications of AI, that drive truly tangible outcomes. From managing reports to capturing feedback and proactively preventing setbacks, here are some other ways to incorporate AI into your thinking.
Point 1 – Streamlining Custom Reporting
AI in Supply Chains Use Case: Quick Access to Custom Reports with Search Function Integration
Efficiency in a warehouse isn’t just about optimizing direct labor functions – it’s also about managing the administrative tasks that keep operations running smoothly. Warehouse Management Systems (WMS) or Enterprise Resource Planning (ERP)systems are treasure troves of data, containing valuable information about inventory levels, order fulfillment, and more. However, extracting actionable insights from this data can be a challenge.
- Data within these systems often have complex structures, including multiple tables, fields, and relationships.
- The systems also integrate data from various sources and modules such as inventory management, order processing, and supply chain logistics. Ensuring data consistency and accuracy across these modules requires technical knowledge of data integration processes, which IT specialists possess.
- Generating custom reports tailored to specific business requirements involves querying the database, selecting relevant data fields, applying filters, and formatting the output which requires programming skills and knowledge that typically falls within the realm of IT professionals.
While business intelligence (BI) tools have been used to bridge this gap, they often require constant back-and-forth with IT departments to create and maintain custom reports. This process can be time-consuming and inflexible, limiting the ability of warehouse managers or supply chain leaders to make timely decisions.
AI serves as an efficient solution to bypass this bottleneck and access actionable insights easily. By leveraging advanced search functions powered by AI, warehouse managers can access the information they need with just a few keystrokes. Want to know the current inventory levels of a particular product? Simply type in a question, and AI will comb through the data to provide real-time insights. Need to adjust the parameters of a report on the fly? No problem. AI enables warehouse managers to be nimbler in their data requests, allowing them to customize reports to suit their specific needs without relying on IT support.
Point 2 – AI-Driven Feedback Compilation
Use Case: Leveraging Voice Pick System Data
When it comes to warehousing, every step, every pick, every movement matters. Yet, relying solely on sporadic feedback can leave blind spots in improvement efforts.
But what if there was a way to not just rely on the occasional feedback from the squeaky wheel but to systematically capture and analyze feedback from the entire workforce to drive continuous improvement?
This is where the practical value of AI, particularly Natural Language Processing, in compiling commentary and feedback shines. While warehouses have been cautious in embracing AI-driven technologies—often due to flashy yet impractical solutions—incorporating AI in feedback processes feels like a natural and advantageous progression.
Let’s say a warehouse worker encounters a potential issue during their daily tasks—a classic pick line issue, perhaps. The products in adjacent slots look eerily similar, increasing the likelihood of a mistake. In the past, addressing such concerns might have involved cumbersome suggestion boxes or end-of-shift surveys, yielding minimal actionable insights. However, with the advent of voice pick systems, the process takes a transformative turn.
With a simple voice command, a worker can report observations in real-time, expressing concerns or suggesting improvements as they navigate their tasks. This not only streamlines the feedback process but also captures insights at the point of occurrence, ensuring accuracy and relevance.
With all the feedback and commentary compiled, AI processing can work through all this information, identifying patterns, themes, and critical issues buried within. No longer do warehouse managers need to sift through voice memos or handwritten notes. Instead, they receive concise reports highlighting areas ripe for improvement. The benefits of a tool like this are plenty. From suggesting smarter product placements to flagging tasks that could lead to strain, AI enhances both worker safety and productivity.
Plus, it levels the playing field – providing an equal voice to all and capturing insights from every corner of the warehouse floor.
Point 3 – Negative Event Prevention
Disruptions are an unfortunate reality in our line of business. Whether it’s due to global events or local challenges, supply chain managers and logistics executives are constantly seeking ways to mitigate risks and ensure smooth operations.
One of the strong, if not the strongest, advantages of using AI in supply chain and logistics is the ability to help warehouse managers and supply chain leaders prevent problems before they happen. While the concept of using data to anticipate issues is not new, AI takes it a step further by enabling proactive intervention. Instead of merely predicting potential issues, AI allows warehouses to take decisive action to prevent negative events altogether.
Negative events can range from stockouts and empty slots to delayed trucks and disruptions in supply chains. These events not only impact operational efficiency but also affect customer satisfaction and revenue streams. Traditional approaches often involve reactive measures, such as expediting orders or reallocating resources, after the occurrence of such events. However, we can better leverage this advanced technology to predict and prevent negative events altogether.
For example, by analyzing vast volumes of data—including picking records, replenishment trends, and external factors like weather patterns and supplier performance—AI can anticipate impending negative event such as a stockout with remarkable accuracy. This enables warehouses to pro-actively issue replenishment tasks, ensuring shelves remain stocked and customer demands are met. Instead of waiting for the replenishment trigger, AI systems can automatically task, or modify the trigger, minimizing downtime and optimizing inventory levels in real-time.
AI in Supply Chains – Final Thoughts
While the current AI focus often gravitates towards improved demand forecasting and supply chain management at a macro level, the integration of AI technologies into material handling systems and the utilization of NLP presents additional opportunities to streamline warehouse operations. With software providers and ERP companies honing their tools, there will be many more ways to think about the integration of AI as a transformative force within warehouses.
In other words, there is no doubt that AI is set to transform supply chain operations in more than just the flashy ways we often hear about.
For more information on the topic, reach out to Jennifer Hall directly [email protected] or click the link below.
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