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How Humans and Robots Will Work Side-by-Side in the Supply Chain

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Humans and robots can work in harmony to create a safer, more efficient working world, here’s what that world might look like.

Robots and Humans Working Together
In Robots in the Supply Chain: The Perfect Employee? Merril Douglas paints a picture of a time in the near future when robots and humans will work side-by-side to help companies gain speed, increase accuracy, cut costs, and handle the grunt work.

“We’re sitting in the middle of a perfect storm for robots in the supply chain. E-commerce sales continue to climb, forcing retailers to pick up the pace in their fulfillment and distribution centers,” Douglas writes.

“But these days, it’s hard to find workers to keep product moving in any kind of warehouse e-commerce or otherwise.”

We’re already seeing examples of robots being designed to take over the supply chain’s least attractive tasks. “In some cases, robotic systems do this work entirely on their own, freeing humans for more complex functions,” Douglas points out.

“In other instances, bots collaborate with humans. Whatever the scenario, proponents say that these automated solutions provide a big productivity boost.”

Some companies are deploying robots to perform repetitive, simple job tasks and allowing human laborers to focus on tasks that require deeper thinking and strategizing.

The new term for this collaboration, “cobot,” allows each type of worker to focus on the tasks they do best.

For example, bots can be used to deliver products from place-to-place in the warehouse, DC, or yard; autonomous drones can perform mundane and repetitive inventory management tasks (as well as tasks that are dangerous for humans, such as flying up to view inventory on high shelves); and robots can lift shelving units from densely-packed storage areas and then transport those goods to a picking station.

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How Robotics Take the Supply Chain to the Next Level

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We all expected advanced robots to have a disruptive effect on industry — and now robotics has entered the supply chain, too. Some of the ways robotics will advance and reinvent supply chain operations and management are fairly straightforward, while others have been a little more unexpected. Below are four major ways robotics are already taking the supply chain to the next level — complete with specific technologies and implications for each one.

Robots Assume Customer-Facing Roles

Sometimes talking about supply chain operations makes it sound like something that happens away from the public eye. That’s far from the truth, because there are two major points along the average product journey where robots are poised to make a dramatic entrance.

Selective Automation Reduces Injury and Error Risks

One of the greatest supply chain robotics trends to come about so far is selective automation. Far from replacing human jobs outright, selective automation is helping us organize our efforts more effectively by getting people out of dangerous or risky environments, or out of the pilot’s seat of heavy equipment, or away from tedious and error-prone tasks.

Bolt-on Autonomy for Vehicles

There’s an emerging and appealing middle-ground between replacing machinery outright with automated versions and retrofitting your existing equipment, including vehicles, with technology that allows it to operate autonomously.

New Types of Human Jobs

This is not a specific technology. Instead, it’s a cumulative benefit of the technologies we’ve discussed here, as well as many others that are coming of age. All of them come with some welcome reassurance: We’re not obsolete quite yet.

The Supply Chain’s Bright Future

Each one of the benefits of robotics we’ve talked about is helping our global supply chains and all their operators realize a future where machines don’t reduce our quality of life, but rather help us better manage our resources and time. The intelligent application of robotics is one major piece of that puzzle.

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Artificial Intelligence: The next big thing in Supply Chain Management

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Imagine the endless possibilities of learning from 2.5 quintillion bytes of data generated every day. Artificial intelligence (AI), which began its journey 60 years ago is well on its course to make this implausible scenario a reality. Artificial Intelligence, is slowly taking over our lives.

From personal assistants like Siri in Apple products to stock trading to medical diagnosis, AI is able to learn from seemingly unstructured data, take decisions and perform actions in a way previously unimagined.

Businesses too are undergoing digitization rapidly. They are using AI – capable of performing tasks normally requiring human intelligence – to create a significant impact in the way businesses operate. In an increasingly dynamic environment comprising demanding customers and the need for speed, it was only a matter of time before the businesses embraced AI to obtain much needed agility. According to Accenture’s Technology Vision 2016 survey spanning 11 countries and 12 industries, 70 percent of corporate executives said they are significantly increasing investments in AI.

Artificial Intelligence in Supply Chain

Organizations are increasingly digitizing their supply chains to differentiate and drive revenue growth. According to Accenture’s digital operations survey 85 percent of organizations have adopted/ will adopt digital technologies in their supply chain within 1 year.

The key implication of this change is that the supply chains are generating massive amounts of data. AI is helping organizations analyze this data, gain a better understanding of the variables in the supply chain and helping them anticipate future scenarios. Thus, the use of AI in supply chains is helping businesses innovate rapidly by reducing the time to market and evolve by establishing an agile supply chain capable of foreseeing and dealing with uncertainties.

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

“We view the Data Science Machine as a natural complement to human intelligence,” says James Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine.

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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A new and unique computer system from MIT has outperformed human intuition using its algorithms, and it’s amazing, and perhaps a little frightening: the Data Science Machine beat out over 600 human teams in finding predictive analysis.

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

Read more at Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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