The evolution of artificial intelligence (AI) and machine learning (ML) is redefining the supply chain.
Until recently, digital technologies reduced repetitive tasks and eliminated waste at each link in the supply chain — clearing an obvious path to ROI. But supply chains aren’t so straightforward anymore. Yes, supply chains still move parts to factories and goods to consumers. But they also use data and advanced algorithms to anticipate future demand, correlate with supply challenges and adapt to emerging competitive threats.
That’s a whole new kind of supply chain.
AI/ML projects have to take these new realities into account to drive value and avoid wasteful missteps. Keeping these seven factors in mind will help you do that:
- Next-level complexity. The quantity and direction of moving parts in supply chains is exploding. Automation in the form of learning algorithms provides the only hope of corralling everything. Our next six AI/ML issues illustrate the sources of supply chain complexity.
- Platforms vs. pipelines. Platform companies like Apple and Airbnb create many-to-many supply chains where buyers and sellers switch roles constantly. That’s a profound shift from the conventional, one-to-one supply chain that fed resources from mines and ports to production lines and distribution centers.
- Regulation and taxation. We can’t automate governments out of existence. But AI/ML can feed regulations, statutes and tax laws into algorithms and help companies ensure all the rules get followed and everybody pays the taxes they owe. Overlooking compliance issues in AI projects can produce expensive headaches that ruin ROI.
- Robotic process automation (RPA). We still need conventional supply chains to move products from producers to consumers. Learning algorithms can drive innovations in robotic processes that reduce errors and improve throughput in factories and distribution n centers.
- Natural language processing (NLP). Voice technologies can help produce better chatbots that streamline and personalize customer service environments. In the contact center, NLP also can scan audio files from every customer service rep to assess their success and recommend improvements. That can help boost compliance and elevate the customer experience.
- Recommendation engines. Algorithms can help consumers find what they’re looking for by scanning previous purchases and correlating them with their current circumstances. AI can recommend the product or service most likely to please the customer. This kind of technology used to be the sole province of giants like Amazon, but it’s rapidly becoming available to the rest of the retail sector.
- Implementation. With all these complications, AI projects live or die on the strength of their implementation. Missteps at the beginning of an AI/ML initiative can pollute everything that follows. To succeed, you have to tie everything into your business, industry, marketplace and customer expectations. That’s too much for any organization to figure out on their own.
DMI: A Consulting Partner for AI in Supply Chains
Just knowing where to start an AI project can be confusing and frustrating. At DMI, we recommend small pilot projects you can scale and evolve quickly once you figure out what succeeds.
But how can you tell where to start in a large enterprise? The best route is to join forces with a consulting partner who has a proven track record and a strong reputation for success in your industry.
That’s how DMI drives ROI with AI/ML in the supply chain.
Our Agile methodologies deliver fast prototypes and accelerated time to value. Our deep focus on areas like edge computing, 5G, data science and AI/ML helps our clients deploy the right mix of talent and tools for their unique needs.
With supply chains moving in new and unexpected directions, you need that kind of expertise to succeed with AI/ML.
— Varun Ganapathy, director, commercial/consumer: digital technology office