How AI Can Be Helpful for Future Supply Chains
Many experts liken artificial intelligence to a supply chain manager’s co-pilot — a helpful assistant that stands to make work more effieicne and accurate. Thanks to the versatility of its benefits — what business function wouldn’t want to have their mountains of data quickly analyzed? — AI can be applied to supply chain activities in numerous ways, and the future of AI in the supply chain is promising.
In fact, almost every part of the supply chain could potentially make use of AI, if deployed correctly. According to IDC’s FutureScape report, 50% of supply chain forecasts will be automated using AI by 2023, improving accuracy by 5 percentage points.
How can AI be applied to supply chain activities?
Here are just a few examples of where supply chain leaders are applying AI in the supply chain:
- Risk Management: AI-fueled systems can quickly analyze and crunch vast amounts of data to navigate and avoid the impacts of risks such as weather delays and product quality issues.
- Demand forecasting and inventory: Analyzing historical data to generate forecasts on what is needed when and where.
- Supplier selection: AI can analyze past performance and reliability of particular suppliers and make recommendations based on findings.
Examples of artificial intelligence in supply chain management include retailer and manufacturer Karl Lagerfeld. The luxury brand is tapping AI to automate allocation across its network to expedite the planning process, reduce inaccuracies, and fine-tune forecasts to optimize stock placement.
How will AI be helpful in the future?
The future of artificial intelligence is bright as adoption increases across the industry. In a November 2022 EnsembleIQ study, 15% of retailers and consumer goods manufacturers said that they had implemented AI or ML to support their supply chain functions. An additional 16% said they planned to add it in the next one to two years.
With this widespread adoption already taking place, AI will only continue to revolutionize the supply chain in numerous ways – demand forecasting, quality control, inventory management, customer experience, quality control, and more.
However, these technologies will arguably have the most impact on the day-to-day lives of the humans within the supply chain. As silos continue to break down within organizations, the benefits of AI in the supply chain will include automating repetitive tasks and freeing people up for the more valuable, innovative work that drives the business forwards.
“[I]n the end, supply chains are human networks. Ultimately supply [chains] are made of people who make, store, move, contract, communicate — all augmented by increasingly powerful technologies. And technology is an augmenting force for many of the uniquely human qualities, not a replacement force,” commented MIT Professor Yossi Sheffi in his recent book, The Magic Conveyor Belt.
What is the future of AI in supply chain?
Generative or conversational AI is another hot topic within supply chain technology trends, particularly as we look ahead to the future. While still in the emergent stage, these investments are already happening with 84% of business leaders planning to use generative AI by 2024, according to Samsara.
“Every part of the supply chain has the potential to be reinvented, as humans working with AI co-pilots becomes the norm, dramatically amplifying what people can achieve. Generative AI will impact tasks, not occupations. Some of those tasks will be automated, some will be transformed through AI assistance, and some will be unaffected,” says Adhere Bahulkar, global supply chain lead of Accenture’s Consumer Goods & Industry practice.
In this regard, the future of AI in the supply chain may mean better connectivity, breaking down silos, and using data to inform decision-making across the entire enterprise. By automating away rote tasks within supply chain, leaders in the space will be able to devote time to more valuable endeavors. “In our view, generative AI is not just automation—it’s about augmentation and acceleration,” shares McKinsey.
How is AI and machine learning changing the way we manage the supply chain?
Machine learning uses AI to identify patterns and generates predictions based on analyzing large data sets. This process involves providing use cases, building models, then training the algorithm to use it. Gartner identifies three major subdisciplines involved with these types of observations:
- Supervised learning – where observations contain input/output pairs
- Unsupervised learning – where labels are omitted
- Reinforcement learning – where evaluations are given on how good or bad a situation is
In this respect, machine learning will continue to change the way we manage the supply chain by analyzing vast quantities of information at speed and at scale. This will have widespread implications and influence over the management of demand forecasting, inventory management, logistics planning, and risk management.
Levi’s is currently using machine learning to overcome inventory roadblocks, launching its BOOST e-commerce solution last year. The internally-developed ML platform makes independent, informed decisions based on shipping, packing, and labor processes.
Last year, the company’s chief global strategy and AI officer, Katia Walsh, summed up the brand’s enthusiasm for the widespread adoption of these technologies, sharing a vision of the future in which “every single thing we do [would be] AI-enabled.”