Predicting the next supply chain disruption
I recently had the opportunity to deliver the keynote at Demand Driven Technologies’ Global Conference 2021, which focused on the technologies speeding up our supply chains, and yes, we are indeed entering a new era of enlightenment in this field. At the time, in late March, a large freighter ship was bottling up the Suez Canal, and I wondered out loud whether supply chains have enough intelligence built into them to alert and predict the impact on corporate operations.
Advanced analytics and artificial intelligence can play a key role here, of course, but these are still the early days for super-intelligent supply chains. The latest survey just released by the American Center for Productivity and Quality, for one, finds only 13% of executives foresee a major impact from artificial intelligence or cognitive computing over the coming year. Another 17% predict a moderate impact. A survey by Capgemini also found somewhat lukewarm progress in introducing AI into supply chain management. Only 11% of executives in that survey say they had implemented advanced analytics and AI within their supply chains.
The event of events over the past year has been the Covid crisis, which tested supply chains to their max. In the process, the learnings coming from this crisis and the convergence with analytics means a new wave of innovation and startups, industry leaders and thinkers agree.
“The pandemic is testing supply chains in a manner few have seen in our lifetimes, with businesses struggling to predict demand and keep factory lines moving,” says Sudheesh Nair, CEO of ThoughtSpot. “Businesses need better visibility and the ability to pivot quickly when a crisis arises. AI and data analytics are central to the new model, allowing organizations to spot disruption sooner, accurately gauge its impact, and make intelligent decisions about alternative sources of supply.”
Nair cites geospatial analytics that use satellite imagery, cell phone pings, and other data sources “to detect activity on the earth, such as when plants are closing or cargo ships are held up at ports.” Companies can apply AI to this data “to determine if its suppliers will be able to keep up with demand, so it knows quickly if it needs to look for alternatives.”
The Covid crisis also sped up the urgency of building more intelligence into supply chains. “We have been surprised by the acceleration of innovation, especially in the supply chain, despite all the ways the pandemic is making it difficult to predict what’s going to happen next,” says Melanie Nuce, senior vice president, corporate development at GS1 US. “However, those businesses that are exploring emerging technology today are the ones recognizing that the pandemic exposed major challenges with moving products to the right place at the right time. They can’t waste another minute using antiquated systems that do not allow for flexibility to keep up with the consumer’s shifting behavior.”
Of course, building an intelligent global supply chain requires the exchange of data, which needs to consistent and trustworthy across all continents. “Collaboration based on global data standards can engender greater levels of trust in data by acting as a reliable anchor,” Nuce advocates. “This will be a major focus as brands, retailers, restaurants, and other partners prepare for the predicted influx of spending and traveling after Covid restrictions ease. One of the greatest opportunities moving forward is to use emerging technology to better understand, sense, and respond to consumer behavior. With a combination of technologies, such as analytics, AI and machine learning, so much can be achieved to proactively form relationships with consumers, personalize experiences, and expedite how we fulfill orders.”
Businesses “need visibility into their supply chains — and the global factors that influence them — to compete effectively,” says Dr. James Crawford, CEO and founder of Orbital Insight. “Supply chain visibility remains largely a manual process based on incomplete, unreliable data. Companies piece together information from news services, social media and word of mouth.” Crawford sees the opportunities within the applying of AI to multiple sources of geospatial data. “With answers to critical questions about production, pricing and distribution, businesses can anticipate change sooner and take informed action.”
A company can employ AI technology “to improve the traceability of raw materials along its supply chain,” Crawford adds. “By analyzing delivery patterns, Unilever monitors signs that suppliers are struggling to keep up with demand and checks their financial health to quickly look for alternatives.”
AI “is the most impactful technology in this equation,” agrees Nair. “Applying machine learning to data produces real-time insights, which are critical when reacting to fast-moving events. For example, applying AI to data from social networks and global newswires can help to quickly identify where and how fast a disease is spreading, the severity of local political unrest, or the impact of a climate event such as flooding. These real-time insights buy valuable time in which companies can make critical sourcing decisions to offset supply chain disruption.”
Nair also sees applications of AI “to model prices, which has been a particularly tough process during this pandemic, though an intelligent pricing strategy that accounts for real-world conditions is essential even outside of a disruptive event. In a pandemic, a trade war, or another anomalous event, using AI and data to accurately model pricing is even more critical.”
Another competitive tool emerging in the post-Covid boom is stronger industry collaboration. “I believe this has to do with the increasing emphasis on trust,” says Nuce. “For example, CTOs, CIOs, and other innovation executives are shifting from centralized data models to distributed data. This leads to questions like, ‘who can see my data?’ ‘How accurate is the data I receive?’ ‘What protocols will be in place to authenticate data when more automation is introduced?’”
The Covid crisis “is a widespread shared experience and it’s become very apparent that consumers want to be informed and they want to be in control,” adds Nuce. “Innovators will be focused on leveraging technology and structuring data to facilitate these options.”
Lean inventory has been a mantra for decades, but the Covid crisis “has overturned this structure and exposed its weakness against major disruption,” says Nuce. “Even beyond the pandemic, rapid changes in the environment and economy are increasing the frequency and magnitude of supply chain disruptions, necessitating a crucial overhaul to how they are structured. To be able to quickly recover from issues like material and resource scarcity and limited visibility and traceability is key to long-term survival in an increasingly competitive next normal. To ignore the need for greater flexibility would be detrimental to a company’s performance and ability to connect with consumers.”