Robot woman Image credit Pixbay/CommFreakYou have an appointment in 90 minutes. It’s a simple half-hour drive down the freeway, so you’re in no great rush. Then Google sends a notification to your phone: It’s time to leave. A traffic jam has left your route badly backed up.

Perhaps you’ll depart immediately, or assess Google’s recommendations for alternative routes. Either way, you’re making an informed, intelligent decision based on artificial intelligence. Google’s AI-based cognitive machines learn from your behavior and provide helpful recommendations that make your life easier.

Such innovations from Google and others, are fundamentally reshaping consumer lives. They cut out clutter and let us focus on what’s most important. We’re able to make smarter decisions and better use of our time.

Now lets put this in the context of a complex supply chain. Imagine, you have an important order you need to fulfill to a platinum customer. This is already in your plan and your supply chain is on track to make and deliver it in full. Then you get a notification on your phone: Move unrestricted stock from another distribution center to meet target service levels. Inventory are depleting below planned levels due to production disruptions — much as Google does with real-time traffic information and offer advice for your best route.

Such cognitive automation is already available today for the enterprise. Some large public companies are already deploying such capabilities in their supply chain and are driving faster and intelligent decisions while eliminating waste.

From Tedium to Strategy

In the enterprise realm, machine-driven innovation stands to profoundly impact the way people work. Take, for instance, the role of a supply chain planner. As it is, these professionals spend untold hours collecting data and crunching numbers. These are mental tasks, but they involve lower-level thinking — not a person’s full intellectual capacity.

In our AI-driven future, machines will take over data collection and number-crunching chores traditionally handled by planners. This scenario goes a step further with a cognitive operating system. This uses AI to think and learn over time and deliver specific recommendations to optimize supply chain operations.

The result: Supply chain planners no longer have to look for answers. Instead, they can ask better questions. Intelligent machines swiftly and decisively address those queries. Beyond that, full cognitive automation can be applied to specific scenarios where machines can make and execute decisions thereby driving velocity.

Consider three of the many ways this stands to transform the role of a supply chain planner:

Better & faster decisions. No more guesswork. No more futile debates over whose numbers are “correct.” Planners are able to put AI’s data-driven recommendations into action, or let the system act autonomously.

Greater strategic focus. Planners have new time to creatively contribute to strategic direction. They can focus on solving problems and bringing new operational innovations. They are able to focus on what matters most.

Improved morale and satisfaction. No one got a college degree to be a spreadsheet jockey. Relieving personnel of tedious manual work improves morale and satisfaction for a more effective workforce, and lower employee turnover.

Distinctions in Automation and AI

Of course, the elephant in the room is the impact on jobs. Does this mean that companies will need fewer supply chain planners? Possibly, but not necessarily. My belief is that the supply chain is so complex, and so critical to enterprise objectives of becoming more agile, cost-efficient and customer-centric, that roles in supply chain management will be safe for years to come.

It’s important to note a critical distinction when thinking of the future of work. Many forecasts from thought leaders and industry experts pertain to technologies such robotic process automation, or RPA, geared to automate relatively simple tasks like data entry and order processing.

In contrast, I’m talking about AI and machine learning within a cognitive operating system that can understand, predict, recommend and act. That’s very different than simple automation. With a cognitive operating system, AI augments human intelligence with its own capacity to learn and predict, paving the way for employees to be more knowledgeable, efficient and effective.

However, delivering this type of operating system is a no easy task for a large enterprise in industries such as CPG, pharmaceuticals, manufacturing and others.

Data and Guesswork Decisions

Global companies have dozens, even hundreds of applications in place to support the supply chain. These include ERP, CRM and systems for procurement, supplier and warehouse management and manufacturing. Operations are global. Terabytes or petabytes of data are in play and that data is changing every second of every day.

It almost seems unfair to expect that supply chain managers can make the best possible decisions in the face of such massive torrents of real-time data. To be sure, they do their level best. But it’s a Sisyphean task riddled with guesswork, and supply chain operations can never truly be optimized.

Fortunately, with the availability of highly elastic cloud compute, full scale machine learning and feature rich domain specific algorithms, the data problems can be put to rest and supply chains can be truly demand driven. As an example, we at Aera Technology are precisely leveraging these trends and applying our industry expertise to deliver that cognitive operating system. We are helping some of the largest supply chains in the planet to run faster and be more cost-efficient.

The Future of Work Is Now

For those customers, the future of work is now. We hear repeatedly how employees have new license to analyze, innovate and create. They’re pioneering new business enhancements and playing a far more significant strategic role in reshaping their businesses to thrive amid the many pressures of our digital age.

Thinking of the future of work, I’m reminded of an astute observation from the former chess grandmaster Garry Kasparov, who famously lost to IBM’s Deep Blue chess-playing computer in 1997. In his book Kasparov asserts:

“Machines that replace physical labor have allowed us to focus more on what makes us human: our minds. Intelligent machines will continue that process, taking over the more menial aspects of cognition and elevating our mental lives toward creativity, curiosity, beauty and joy.”

For a corporation, beauty and joy take the form of business agility, capital efficiency, customer satisfaction and strong profitability. Those objectives are within reach when we let machines take on certain aspects of mechanical thinking, so that managers and staff can focus on what truly matters.

Aera Technology Logo (c) 2018 Aera TechnologyAera Technology delivers the cognitive operating system that enables the Self-Driving Enterprise. Aera understands how businesses work; makes real-time recommendations; predicts outcomes; and acts autonomously. Using proprietary data crawling, industry models, machine learning and artificial intelligence, Aera is revolutionizing how people relate to data and how organizations function. Headquartered in Mountain View, California, Aera services some of the world’s largest enterprises from its global offices located in San Francisco, Bucharest, Cluj-Napoca, Paris, Pune and Sydney. For more information about Aera, please visit


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