Global enterprises are coming to the realization that there’s no such thing as “normal,” and that when it comes to meeting customer expectations in this digital world, the supply chain is at the center of a series of critical decisions that can make or break their business.

Geopolitical unease, extreme weather disruptions and even a global pandemic are no longer isolated events. Rather, more and more leaders are turning to emerging technology platforms to provide the kind of decision-making intelligence that helps enterprises respond more quickly as these events occur.

At least 31% of businesses have fully automated at least one function, according to a 2020 survey by McKinsey & Co. But the challenges we’re facing in 2022 and beyond require a more holistic approach.

We’re seeing a host of unpredictable events collide — making it harder and harder to make intelligent decisions. The result is a global supply chain crisis that threatens not only the health of enterprises, but the wheels of society as a whole.

Leaning In to the New Normal

Ready or not, a reckoning has arrived. Supply chain management demands a level of automation that many leaders have shied away from, for fear of interrupting employee engagement, shouldering a heavy financial investment or getting tangled in a years-long IT implementation.

But the data is clear: Adopting artificial intelligence to solve supply chain problems is a winning strategy. Applying the technology in supply chain management increases revenues by about 28%, according to McKinsey.

The only solution to current challenges and the inevitable disruptions of the future is agility. Supply chain planning in the future must be able to predict potential issues, pivot and adapt to address them, and learn from every deviation how to optimize future operations. It’s not just about having the “right” data, but having that data at the right time and being able to respond to it quickly.

Supply chains capable of these adaptations will prove resilient and self-healing.

The Self-Healing Supply Chain

Threats to a healthy supply chain run the gamut within and outside of the enterprise. From cyberattacks to worldwide digital outages and internal operational silos, the data needed to inform intelligent decisions is nearly impossible to amass and analyze using existing transactional systems and the human brain alone.

Cognitive automation technology can harness all of these inputs — including past decisions — and use AI, machine learning and human intelligence to better respond to almost any scenario.

A self-healing supply chain starts with the collection of data, including internal factors (“Where are our shipping trucks right now?”) and outside factors (“How congested is the port at the destination?”).

Cognitive automation platforms organize and enrich these inputs into a harmonized data model that serves as a real-time representation of the supply chain. By enabling this model, data scientists no longer have to spend half their time — or more — wrangling data to train and re-train their AI and machine learning tools.

IT developers and business analysts have access to the latest and greatest from the data science teams and the most up-to-date data available at their fingertips. They can now quickly build, deploy and manage composable cognitive skills that generate predictions, make recommendations and act on the decisions made in the underlying transactional systems.

It’s important to close the loop when it comes to augmenting and automating decisions. By retaining memory of the decisions made, the context in which they were made and the resulting impact, cognitive automation platforms enable organizations to learn from past decisions to make better ones in the future.

This kind of technology will become increasingly crucial to surviving supply chain disruptions.

The Human Piece

Those unfamiliar with AI and automation technologies assume they supplant human involvement in the management process, and this assumption tends to come with a level of wariness.

It’s important to note that institutional knowledge is a critical implementation component. Computers are excellent at crunching data and running algorithms, but it’s the human veterans of your business who have the tribal knowledge of how and why decisions are made, determine which data sources are relevant, provide past statistics on supply chain management successes and failures, and set the parameters for acceptable operations.

The decision experts inform how the platform is configured to best collect, examine and act upon relevant information.

Cognitive automation also works in a continuous learning feedback loop. As decisions are made, humans will — at first — review and either accept or reject suggested adjustments.

Those decisions and the associated outcomes are then fed back into the data and modeling to improve accuracy. While a touchless system may be the eventual outcome, it can only be achieved with expert input along the way. Eventually the supply chain can self-correct when issues arise, based upon available data, historical knowledge and proven ability to solve issues.

Once some decisions are automated, human workers are freed from daily triage and crisis management to engage in the sort of creative and strategic innovation that humans do best. Agility isn’t just about an adaptive supply chain, it’s also about future planning and building new horizons. Operational agility is further expanded when organizations have the time and space to evolve rather than struggling just to maintain current workflow.

Here are some successful, real-world examples of how cognitive automation is helping organizations build self-healing supply chains:

Multinational Petrochemical Company

Consider a large petrochemical company with the mission to provide sustainable products that support improved living standards around the globe. With the worldwide demand for chemicals projected to rise 45% by the end of the decade, the need for greater visibility into their supply chain is only growing.

The company used a cognitive automation platform to harmonize data from order to delivery, and incorporate actual and planned data from third-party partners. The team developed a custom skill that prioritized orders and recommended corrective action to ensure on-time delivery or communication to customers.

Finally, the company also redesigned their shipping process to account for issues that were causing visibility gaps and offline, ad-hoc communication with partners. The result was a shipping process with more resilience, flexibility and agility.

With this end-to-end visibility, the customer is monitoring shipments from order entry to transportation planning, tendering and shipment creation, over to slot booking, actual loading and the delivery of goods to the customer. The recommendations implemented are driving the overall status and performance against the initial plan, continually identifying critical cases to be managed.

Results have included increased customer satisfaction, increased on-time delivery, better communication with third-party partners and closer alignment of planned vs. executed action.

Multinational Science and Technology Company

Limited by its traditional planning system, the company’s technology did not offer actionable reporting and was not able to blend data sets. Manually combining information was time consuming and error-prone, causing costly excess inventory or shortfalls.

The team sought other solutions that could harmonize data from disparate systems first, then used machine learning to forecast demand using external data, which led to the ability to augment and automate decisions about demand and supply.

This enabled an immediate improvement in the organization’s forecasting abilities: the forecast could consider more information, more quickly, as multiple datasets were harmonized to provide greater insights. This centralization of data also brought products to market faster, bringing together previously siloed groups within the organization for better communication and understanding.

The onset of the global COVID-19 pandemic made the benefits of the new technology clear:

• Reliability and service levels stayed high.
• Forecast accuracy improved 11 percent, reducing costs and generating new revenue.
• Operating costs were reduced.
• Decision-making processes and automation were improved.
• This company was able to thrive, even in the difficult landscape of COVID-19, while similar companies in their industry struggled.

Only the agile will survive this new, fraught landscape of disruption and innovation. Adopting new ways of working, like using Cognitive Automation, offers actionable solutions to the modern world’s ever-changing realities, building self-healing and resilient supply chains.

Fred Fontes is general manager for customer success at Aera Technology.