How to Use AI Tools to Optimize Your Supply Chain

No industry in 2023 is complete without incorporating AI. Supply chain management must recognize this revolutionary asset because its versatility can raise revenues, increase productivity, and create cohesion in a sector desperate for optimization. How can administrators in supply chains leverage AI to achieve all these benefits while exceeding customer expectations and balancing B2B relationships?

One of the most volatile aspects of a supply chain is predicting seemingly unpredictable shifts in demand. Customers’ needs are ever-changing, and material availability, supply chain disruptions, and countless other factors can determine how much of what product goes through a supply chain.

AI could use predictive analytics to relay more accurate demand forecasting based on incoming and historical data. Estimating quantities is only one benefit — consider how much warehouses will appreciate more accurate stock estimates, less concern with backlogs collecting dust, and optimized inventory management. Storage and transportation costs decrease because overstocking is less likely to occur. Therefore, fewer unnecessary boxes fill trucks that companies can allocate to more valuable resources.

The way AI can predict demand could become even more hyperspecific as they collect more information. They could pull from regional social media trends, past customer records, and numerous other avenues to clarify the most niche products. Use AI to predict how specific items will fluctuate during holidays or seasonal changes and how TikTok trends could boost something for a short time. These insights provide greater consistency throughout the supply chain, ignoring the bullwhip effect that most chains dread.

Digital twins are one of the most productive ways to experiment with process changes throughout a supply chain. Equipment can be expensive, and it’s difficult to know if a pricey piece of tech will improve efficiency or other metrics as much as they claim. An expansive AI data set could combine with the power of predictive analytics to simulate how a more agile supply chain operates.

AI empowers these assets because they provide more than a preprogrammed simulation. They can show how stimuli interact with the scene in real time and use sensors to measure analytics like temperature or productivity to let teams know where they should allocate resources or if an investment is worth it. The more the digital twin runs, the more data the AI can learn about how these operational changes could affect the long term.

Transportation, waste, and energy use are some supply chain sustainability concerns. Determining how to decrease fuel consumption and emissions are only a few ways AI could improve supply chain fleets, especially as it constantly gathers traffic, weather, and employee driving data.

AI can also work with Internet of Things (IoT) sensors to monitor green analytics throughout the chain. Warehouses could reduce waste, administrative offices could be more aware of air quality, and manufacturing lines could improve energy efficiency with continued maintenance. These efforts could amount to a carbon-neutral, more circular business model that simultaneously helps the planet and a company’s bottom line.

It can all lead to more sustainable benchmarks and certifications, as transparent analytics guide companies to more actionable progress and customer communications about corporate social responsibility.

Whether it’s a procurement team or line workers reaching out to contractors, AI can improve every line of communication through automation and speed. Humans are familiar with the most popular AI communication trends like customer service chatbots and scheduling services that alleviate stress from workforces.

The more proficient AI gets at natural language processing (NLP), the more humanlike discussions become. It will automate necessary communications once reliant upon human memory and action to hit send and communicate essential information to teams.

Are there compliance updates or time to perform forgettable yearly maintenance on a machine? Is the stock in warehouses getting low, and because the AI knows that particular supplier historically runs behind, they can order stock months in advance?

Opening and improving lines of communication between third parties and internal staff are crucial for strengthening logistics performance. Supply chains thrive off effective logistics, and responsiveness is necessary to communicate unexpected obstacles or process changes because they could impact every subsequent phase. Assessing risk is one of the most influential assets to supply chain management as businesses navigate a post-COVID materials climate full of shortages and delays.

AI could automate orders based on sales analytics. This would help cross-departmental priorities stay on track because everyone understands what operations and process orders will focus on in the future, including fleets and marketing teams.

Additionally, administrative teams can inform stakeholders with more valuable information, increasing their brand loyalty and investments. Everyone, including investors, will appreciate the growth in transparency throughout logistics because of what AI can provide.

Optimizing a supply chain is as streamlined as installing AI. AI’s capabilities will only exceed expectations as technology develops, potentially bringing more benefits than the sector can conceive. Eventually, AI could more powerfully collaborate with workforces and provide customer satisfaction while overcoming some of the most significant industry hurdles.

Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him onTwitterorLinkedInfor more of his work.