We all experience various levels of automation in our everyday lives. However, automating supply network processes and related decision-making across the partner ecosystem has been difficult to achieve. That is, until modern technologies came along and changed the game. Digitization of the supply chain network combined with AI technologies are delivering the foundation required to enable autonomous agents — which can deliver tremendous value when operating automatically across the ecosystem of suppliers, customers, transportation providers, and extended enterprises.

 

Recognized Potential in Autonomous Agents

Today, corporations are taking a serious look at bringing AI into their everyday business processes. This is because the early mover advantage is significant and nobody wants to repeat the mistakes that were made during the internet evolution when many took a cautious, go-slow approach in their online endeavors, allowing companies like Amazon and Ali Baba to dominate the channel. While there is widely recognized potential, companies are struggling to identify which AI technologies and methods will prove most effective based on their particular set of opportunities and business problems.

Let’s think about what is really happening in the supply network. Overall, the ecosystem is rewarded when an end consumer purchases a product or service. Let’s call this time zero or the “moment of truth.” Now let’s travel backward in time from this moment through the supply network – hours prior to purchase, days, weeks, and months.

Yesterday’s systems will certainly enable monthly, weekly, or even daily planning and execution but this is no longer enough. Real-time, “always on” planning and execution capabilities that eliminate the information lead times between unplanned shifts in consumer demand or supply capability are required. Businesses need a demand driven value network that enables a single version of the truth across their network of trading partners and becomes the foundation that the autonomous agents rely upon to get the job done. Every hour of information latency which is removed from a company’s business network operations can result in significant increases in customer satisfaction, sales, inventory improvements, and working capital reductions.

On top of that, given the scale of today’s global supply chains and the proliferation of consumer choice, autonomous agents are increasingly seen as a “must have” capability — since today’s business velocity has far outstripped human ability to identify and resolve exceptions in real time.

 

Scope of AI in Supply Chain Networks

With clarity across the network, supported by real-time data and collaboration, automated decision-making can finally hit center stage. Within the network organizations can apply two levels of AI capabilities. The first is robotic process automation (RPA) for the automation of routine, well defined, or repetitive tasks. The second would be various forms of math or statistical algorithms used in decision-making. This includes using rule-based engines to make decisions around alternate sourcing or substitute parts; heuristics for use in supply/demand netting; algorithms for use in optimizing objectives like revenue, cost, or profit; machine learning that extends a data model to include new vectors such as weather and traffic patterns; and finally deep learning for true pattern recognition. These autonomous agents combine elements of both RPA and advanced algorithms along with best-in-class business processes.

Machine learning (ML) and deep learning (DL) are specific cases of AI designed to predict outcomes, understand language, analyze patterns, and prescribe solutions. The primary difference between ML/DL and other math approaches is that with a traditional method you need to design a set of algorithms that will validate to the data and outcomes you have available to model the system. With ML it is the data itself that determines the relationship between the data and outcomes, which means that over time the predictions will continue to improve in accuracy as the model is fed more data. This means that in the future, it will be able to better predict who might buy a particular product today, what type of shopper is most likely to buy, and the amount of money someone will spend within certain timeframes. Similar to the Netflix recommendation engine, it will also help the business to understand the personalities and preferences of their prospective customers.

 

The Critical Success Factor: Take a Multi-Enterprise Approach

So how can this become a reality? Well, we’ve heard it before; “It’s all about the data.” Companies have struggled with the notion of applying autonomous agents across supply chain networks. The general consensus has been this is due to the lack of high-quality data across network trading partners along with the human bias contained in some of the available data.

Looking deeper, what became apparent is the data problem has been somewhat self-inflicted. While organizations have strived to create a collaborative ecosystem across their trading network, they have unfortunately been trying to do this by extending their own enterprise-centric view of the world. Full collaboration across the partner ecosystems requires a data infrastructure which flows seamlessly from planning to execution, both within the enterprise and across all network tiers vertically and horizontally. That means you have to include suppliers and their suppliers. Manufacturers, their contract manufacturers, and their suppliers. And all their transportation partners.

By doing so, information about a surge in demand at one end can quickly ripple all the way back through multiple tiers of supply and multiple parties, eliminating the need for big inventory buffers between them. This unlocks enormous value by boosting service levels and reducing inventories at the same time.

The combination of a demand driven real time network of trading partners coupled with the predictive capability of AI/ML/DL brings us to where businesses can now operate their supply network autonomously and at enormous scale. They can set parameters that are designed to make certain decisions automatically. Alternatively, and if a process is expected to generate an outcome which is outside the automated decision-making guardrails that have been established, a secondary workflow moves the decision to a workbench with decision support and analytics for a human touch — where further collaboration can take place across network partners.

 

Act to Capture the Early Mover Advantage

In summary, automation now extends beyond its traditional applications in manufacturing and logistics. Automation and autonomous agents now have the ability to become the brain behind your supply network and partner trading ecosystem, basically becoming the autopilot capitalizing on demand and resolving issues related to fulfillment, supply, logistics, and manufacturing.

In addition, dashboards, analytics, and workbenches are available when certain issues require human intervention based on hitting certain decision-making parameters. This will include not only prediction, but prescription in terms of choices to resolve issues and each one’s impact on economics, service levels, and other KPI’s. To maintain leadership, businesses should begin now to identify and prioritize the highest impact areas for the application of AI in their supply chain network operations. In most industries, there’s still time to capture that early mover advantage.

 

About the Author

Joe Bellini is COO at One Network Enterprises, provider of an AI-enabled business network platform that enables all trading partners to manage, optimize and automate complex business processes in real time. To learn more, visit www.onenetwork.com