AI in the Enterprise: The future of the supply chain

The modern supply chain industry is made up by a complex and disparate array of moving parts all over the world. Sourcing, production, transportation, and other components are separated by significant gaps, both geographical and technological. As a result, getting those components to function properly and in alignment is often a brutally manual task. Delays and disruptions are simultaneously inevitable and unpredictable. Labor strikes in a factory half a world away, sudden military conflict, or even a global pandemic can come along and impact your ability to do business literally overnight.?

Through the course of the COVID-19 pandemic, the supply chain leapt to the top of political, economic and personal agendas. Some industries saw a massive increase in purchasing and overall demand while significant portions of the supply chain shut down. We saw demand for specific products spike unexpectedly, at levels that suppliers, shippers and vendors simply could not match. These logistical hiccups created an enormous backlog across myriad industries, impacting everything from the availability of key technologies to hand sanitizer, with delays and shortages felt all over the world. A collective inability to shorten timelines or strategically work around supply issues meant clearing the backlog was dependent on dock workers and truckers simply working longer, hard hours, rushing to manually compensate for compounding delays. It exposed the cracks in an industry that, at its core, has not experienced any significant overhaul in the last 50 years.?

There is an opportunity for AI to optimize aspects of the supply chain across a number of areas that impact the bottom line, which should ultimately lead to a better global supply chain strategy. In this installment of AI in the Enterprise, we will be examining those opportunities.???

Supplier Relationship Management. Using AI to streamline customer relationship management is nothing new. By contrast, the status quo for supplier relationship management is painfully manual. There is no Salesforce equivalent to manage this critical function, which presents an enormous opportunity for AI-powered advancements. If you have a hundred vendors, you can’t expect to facilitate a hundred meetings every week, and yet, that’s essentially what the current system demands. Using AI to ingest data from those suppliers could greatly increase productivity. Negotiations, benchmarks, pricing history, and budget comparisons can all be managed by an AI solution that auto-generates meeting notes and automatically updates your system of record.?

Procurement. Networks for procurement extend all over the world. From product components to office supplies to trade show swag, sourcing requires procurement teams to be “in the know” as to where and how to source what their company needs. A breakdown in the supply chain, such as an inventory shortage or a ship failing to leave port at the scheduled time, leaves these procurement teams holding the bag. If they lack the tribal knowledge needed to bridge the gap and find an alternate method of sourcing or shipping, that breakdown becomes more costly in the long run. There is an opportunity here to apply AI to the process of sourcing and procurement so that the system knows in advance how to flag any potential breakdowns in the supply chain and automatically resolve them, or even automatically contact secondary and tertiary suppliers when required.???

Inventory management. A key component in streamlining procurement is the ability to have actionable visibility into inventory at both the local and network levels. Knowing what you already have and what your suppliers have available is fundamental, but requires a tremendous amount of manual oversight. Automation is already common for inventory tracking, but any problems caught still have to be resolved through human intervention. AI has the ability not only to keep accurate inventory, but to intelligently ingest that data and forecast changes in supply or demand– and ultimately, do something about it. Imagine a system that can not only track inventory, but one that can automatically place orders for more if you’re running low in a key area. There are few outcomes more impactful than giving your operations team the information and lead time to better anticipate customer needs.??

Demand planning. Accurate forecasting and demand planning requires very clear decisions about what must be in place from a materials standpoint, and a clear plan for how to fulfill that need. Doing this properly hinges on cohesion between procurement, inventory and production teams so that the right amount of material gets sourced in time for production to meet completion deadlines. On the back end, customer success resources must be in place to follow through. It’s a task that requires ingesting an enormous amount of incoming customer data and sorting through for relevance to determine future patterns. Needless to say, there is a lot of room for error. AI could be used to control not only the ingestion of data, but the orchestration between departments, setting deadlines and monitoring task completion. Applying AI to forecasting could also allow a company to act more predictively, while simultaneously reducing the risk of doing business.?

Warehouse and production safety. The transportation and warehousing industry was ranked by the NSC as one of the top four most dangerous, with an injury rate of 220 per 10,000 workers and a weekly cost related to injury of $84 million. Functions to reduce injury rates exist, but they are almost entirely manual. At best, safety consultants walk the floors and take notes that are then fed into a system that monitors for hazardous situations. Computer Vision is poised to revolutionize the next generation of this process, using AI models connected to camera feeds that can flag safety concerns automatically, communicate them to a human counterpart, or even provide an automated fix that alleviates the potential danger.