Artificial Intelligence: The Quiet Workhorse of Decision-Making
Leverage AI with the right data, right network, right provider - e2open
Every day, you could read a dozen articles acclaiming artificial intelligence (AI) as the cure to supply chain complexity. While there’s truth to this narrative, it tends to focus on the destination rather than the journey.
Let’s break through the buzz and discuss some of the popular misconceptions about AI, and explore why businesses with complex, global supply chains should consider leveraging its transformative power.
Misunderstandings about AI
To many people, AI is a supernatural force that will either solve half the world’s problems or take over the planet and enslave humanity. The truth is much more mundane. AI is a powerful tool for change, but some common misconceptions exist about its role in supply chain transformation.
Not all AI is created equal
If we stop thinking of AI as a singular solution, we can see that the true power of AI doesn’t lie in complicated algorithms. Instead, it lies in how those algorithms work together with technology and data to provide transformative insights. Business leaders must do their due diligence and challenge vendors to provide the proof—not just on the type of AI they are offering, but also on how that AI will integrate deep, rich data to deliver real value.
Without data, AI is virtually useless
AI wholly depends on deep, real-time operational and historical data, and lots of it. Furthermore, quality AI decisions that empower your business and make it more agile require data from beyond your own four walls, including every partner at every tier. All the flashy marketing and advanced algorithms in the world won’t change that fact. This gets lost in the shuffle because it behooves vendors to sell the romantic version of AI rather than the pragmatic one.
To learn more about the type of AI needed to drive transformational change within your supply chain – not to mention the dependency, scope, and quality of data it will require – download our AI Buyer’s Guide using the link below.
There are four types of AI currently in use:
Supervised AI
Trained pattern recognition that uses labeled input and output datasets to accurately predict outcomes.
Unsupervised AI
Untrained exploration of unlabeled datasets to discover hidden clusters or patterns on its own.
Reinforcement AI
Repeated trial and error that explores different options on its own without predefined data and learns based on the best outcome.
Generative AI
“Human chat-like interaction” with large language models (LLM) for unstructured data queries and exploring large data sets.
The four prerequisites for successful AI
In enterprise AI deployments, the sheer volumes of data and the speed and frequency of the complex decisions being made are staggering. It would take armies of people years to do what AI can accomplish in minutes, which is what makes it so attractive to businesses with large supply chains. However, there are four major considerations that must be met before AI can be properly implemented into business operations.
1. Data is the currency
Types of Data
Extended supply chain data
Extended supply chain data contains demand and supply signals that influence growth opportunities or risks—such as shifting consumer behaviors, material constraints, logistics capacity shortages, and changing trade regulations.
Internal operations & ecosystem partners
There is vital information from internal operations and the thousands of ecosystem partners—including downstream distributor and reseller partners, upstream suppliers, and contract manufacturers, not to mention the logistics and broker partners to move goods along the way.
3rd party sources
Causal data from 3rd party sources can provide information related to risk, financial factors, weather, and news that might impact demand or supply.
2. Privacy and security
In the age of data, privacy and security have become hot-button topics and are especially important considerations for business applications that require robust data governance and policy. There are several recent and high-profile examples that provide us with a cautionary tale illustrating the risks to businesses. To ensure the security of all parties involved, AI must have enterprise-class data governance and policy to safeguard against I.P. leakage and hallucinations.
3. AI transparency
It doesn’t matter how good your AI is if you don’t trust the results. This is especially true in decision automation, which removes human review from the process. Trusting AI requires faith in the outcomes, and using attribution science to help teams understand why AI makes certain decisions is a crucial part of building that trust. Without trust, people override AI, revert to the old ways of doing things, and fail to realize value from new technology investments.
4. Closed-loop orchestration
The value of AI is only fully realized once decisions are turned into action, with internal operations teams as well as multiple tiers of ecosystem partners throughout your extended value chain (see chart on the left). E2open’s supply chain business network enables you to access data from these partners and also lowers the barrier to coordinate actions with them once those decisions have been made.
However, action alone isn’t enough. Corrective actions must be performed within a certain window of time for them to be effective. Once the action has been taken, continuous, real-time feedback is collected to determine whether the action was successful or if any additional corrections are required. Enabling continuous closed-loop feedback is vital to unlocking the value of decision automation and a prerequisite for strategic programs like autonomous planning and execution.
How AI manages layers of complexity
The complexity of running a global business has grown exponentially in recent years. Because of this, key data-driven decisions must be made almost instantly. Human manpower is simply not capable of processing information and taking action as quickly as AI, due largely to the following three layers of complexity.
Data complexity
Data must now come from the furthest reaches of the value chain. Said data must also be delivered, cleansed, and processed in real time.
Math complexity
Supply chain automation requires complex mathematics that go beyond human capabilities. AI can scale and continuously improve its models in a way human planners cannot.
Decision complexity
Decision-making can no longer be siloed. Decisions must now be cross-enterprise and cross-ecosystem and must be made and executed faster than human planners are capable of working.
Supply chain use cases for AI
The complexity of running a global business has grown exponentially in recent years. Because of this, key data-driven decisions must be made almost instantly. Human manpower is simply not capable of processing information and taking action as quickly as AI, due largely to the following three layers of complexity.
Planning
E2open’s Demand Sensing application uses AI with supervised learning algorithms to analyze real-time demand signals, helping our clients identify patterns and accurately forecast daily sales for up to 13 weeks in advance.
Logistics
In logistics, supervised learning finds patterns hidden in reams of transport data, such as routes, loads, and equipment types, to predict and compare freight rates to industry averages. AI is also used to predict future transportation capacity requirements by lane and mode, allowing shippers to proactively identify gaps in capacity and secure transport with preferred carriers at the lowest cost.
Global trade
Artificial intelligence (AI) is useful in several global trade tasks, including due diligence screening, product classifications, and identifying duty reduction opportunities.
Harness the power of AI to level up your planning, logistics, and global trade operations
If you’re interested in how your business can benefit from the transformative power of AI, reach out to us to schedule a consultation with one of our experts.
