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Human Augmentation

Human Augmentation in Supply Chain Management

Jan. 24, 2022
Companies are increasingly using AI technologies to get the correct product to the desired place at the right time.

Across industries, there is a massive shift underway to use emerging artificial intelligence (AI) technologies to improve both processes and systems, especially in supply chain management to improve decision support and improve productivity. AI is the process of learning from existing data while supplementing human work to achieve desired goals more quickly, more efficiently and at lower costs. This practice is known as human augmentation—using AI technologies to increase our human capacities and capabilities.

Today, AI is augmenting many human activities across a wide range of activities, including software development, cloud-based services, data centers, manufacturing, supply chain management, distribution, industrial robots, automated voice synthesis, and more. In all of these areas, new technologies are used to support human work and optimize product development and delivery in a range of industries.

Human Augmentation Market

All this activity is leading to a fast-growing market for AI-developed technologies. The Global Human Augmentation Market could reach $17 billion by 2026, according to a 2021 report prepared by market researchers Global Industry Analysts Inc.

Specifically, in supply chain management, human augmentation will take an important sliver of that total, as it’s anticipated to grow to $1.3 billion in the next few years. Leading experts in artificial intelligence agree. Accenture’s head of applied intelligence Sanjeev Voohra recently noted he’s seeing a massive move toward companies using analytics, AI and automation practices to help companies make the digital transformation for their businesses.

A potentially massive market is in the making. I believe that digital transformation is underway, and human augmentation with AI is integral for the success of the supply chain. Modern supply chains involve a complex web of people, companies, data, transportation, and other resources to help move goods and services from suppliers to customers. Using AI and human augmentation in supply chain management could transform the existing paradigm through a mix of task replication or task supplementation.

Let’s look at some of the ways that human augmentation can propel this supply chain transformation forward.

Know the Purpose

AI is often brought into the supply chain for a specific purpose. However, AI doesn’t work on its own. It must have elements of human augmentation to allow for the AI model to function effectively. As part of the AI and digitalization process, it is imperative that human experts consistently interact throughout the learning process to enhance and further shape the AI model and then meet the unique needs of the business. Together, humans and the AI system work together to produce the highly specific and desired outcomes. This results in higher payback over the long term that organizations will discover as much more valuable to them.

In business, we must take responsibility to leverage AI models with correct information. When you know the purpose of processes and systems, and provide AI models accurately to help your supply chain management, then you can drive the desired outcomes.

Company teams working with the supply chain’s material handling and logistics must teach the AI model the correct information—the purpose—using machine learning. Doing so will help to safeguard the company against any misreads or errors moving ahead.

Transfer of Knowledge

Human interaction drives organizational planning and interaction. Knowledge transfer can help teach AI not only the right process but the learnings from past behaviors. Put these two areas together, and you can see how human augmentation can supplement the AI model for a more efficient and effective supply chain.

AI models are less flexible when it comes to adjustments involving prior background and experience. If AI is incorrectly taught, it may produce incorrect decisions and potentially negative outcomes. That can adversely impact the organization and its supply chain.

It’s incumbent on corporate teams to leverage their knowledge base for AI to work properly. When done right, these experiences will ensure the AI model is on the right path.

Top-Level Execution

Your senior leadership and departmental teams must execute properly. Your organization may have a great new AI system to help streamline the supply chain, but there needs to be organization-wide support and an internal understanding of your AI models.

For a well-oiled AI supply chain to work, it’s up to talented workers to build and maintain large, standardized datasets, remove existing data silos, and provide good communication to trading partners, including suppliers, carriers and customers. Employees should evaluate cloud-based AI solutions to ensure decision support in the supply chain.

If execution falters at any point, the promise of AI to boost supply chain effectiveness may also fall short. Companies may find that there is no net positive impact. A move to AI must use the best information available to ensure that top goals are being met. AI is put to work to better drive business strategies, such as reducing inventories or scaling the business to meet specific areas of demand more quickly. AI can use data and turn it into purposeful reaction points that impact areas including logistics, operations and finance.

When you can infuse your supply chain with these AI superpowers, your company will benefit in numerous ways:

• Reduce supply chain costs, while improving service levels.

• Get the correct product to the desired place at the right time.

• Lessen operational risk through more trusted, sustainable materials and supply management.

• Optimize maintenance, repair and operations inventory levels.

• Build trust in your data and harmonize your data sets in new ways.

• Provide robust decision support to boost employee efficiency and effectiveness.

Businesses that have struggled with supply chain issues since early 2020 must look ahead to embracing more advanced digital capabilities of their businesses. They must use AI models with good data to drive decision-making and find new ways for AI to amplify the supply chain. Only then will we empower the resilience and agility of the supply chain in the years ahead.

Paul J. Noble is the founder and CEO of Verusen, a provider of supply chain data, inventory and procurement technology.

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