AI Business takes a deep dive into predictive maintenance and demand forecasting in the supply chain
Warehouses, factories, freight operators, and legions of employees across the globe are already calling on machine learning-driven analysis to unlock efficiencies.
Gartner indicates that by 2023, around 50 percent of product-centric enterprises will have invested in real-time transportation visibility systems.
By 2024, roughly 50 percent of all supply chain organizations will have put money into some form of AI or advanced analytics platforms.
The question facing vendors is how to bring AI to the next wave of supply chain clients.
This isn’t a one-size fits-all sector. Global logistics giants coexist with modest, family-run warehouses.
This raises the question: Where are the quickest wins for AI in fulfilling supply chain objectives?
Predicting how much product needs to be shipped is a core objective of logistics operations.
Deep within a distributor’s data flows lie forecasting patterns indiscernible to the human eye, but which AI can teach itself to scrutinize.
External data sources like weather forecasts or social media reactions can also be incorporated.
By deploying machine learning algorithms, the distributor can aggregate and evaluate a holistic spectrum of demand factors.
Since conventional forecasts can be off by as much as 30 percent, improving demand forecasting is paramount to helping avert delays and chargebacks.
Fully-fledged ML-powered platforms such as Blue Yonder’s Luminate Control Tower build on demand forecasting by also highlighting any disruptions in the client’s distribution network.
The software is then able to suggest potential solutions, like switching up the distribution schedule to front-load cargo and stay ahead of any issues.
As with most enterprise AI technologies, it will be the manager who makes the final decision.
Still, a helping hand is always welcome
Artificial intelligence equips supply chain robots with advanced motor and navigation skills.
These are particularly suited for tasks requiring adroit mobility, such as moving boxes around the facility.
Robots can be expensive to implement, however, so clients might need to embark on a trial before making a large financial commitment; for this reason, increasing numbers of robotics vendors are now offering their products as-a-service.
When done right, the next generation robots can limit the burden on manual laborers, reducing injuries and fatigue.
Supply facilities equipped with state-of-the-art IoT networks might want to look at advanced AI applications such as predictive maintenance.
In predictive maintenance, AI models are used to monitor equipment and inform decisions on when repairs should be made.
This empowers maintenance staff by freeing up their capacity and could also reduce wage expenditure by limiting the need for temporary contractors.
A big caveat is that predictive maintenance may need robust IoT sensors to aggregate data from the ground – but artificial intelligence can also be used to secure distributed IoT networks from cyberattacks and system failures.
Visibility into customer orders
There’s a substantial market for solutions that help track shipments after dispatch.
Real-time visibility helps keep all parts of the chain in sync while also making sure that the end recipient is informed of potential disruptions.
Ultimately, this can help build transparency and trust across the entire ecosystem.
According to Gartner, dispatch visibility is a highly fragmented sector, and due diligence is needed to ensure products play well with the client’s existing operational technologies.
To read more about how AI can help predict and prevent disruption in the supply chain, check out our eBook: ‘AI in supply chains: Helping businesses respond to change’