Technology has shaken up how the supply chain industry works today. From warehouse robots to autonomous vehicles and drones, every aspect of logistics is moving forward in leaps and bounds.
According to Gartner’s report, supply chain automation will reach the plateau of productivity in the next five years, when the automation hype will slow down and AI technology will be broadly adopted.
But what happens when people hand over supply chain control to machines? Is the technology mature enough to replace people's power? We’ll explore these questions in this blog post about supply chain automation and its impact.
The Promise of Automation in Supply Chain Industry Management
Automation is no longer just a fad. To stay competitive, chief supply chain officers have to create an automation roadmap with clear objectives and invest in workforce training.
Let’s look at what automation can do for supply chain companies and what opportunities it brings to the table.
Optimize processes and reduce errors with smart warehousing
Smart warehousing is expected to increase by 14.5% annually from 2024 to 2033. The first reason for this is the growing need to manage the inventory better, process large volumes of products faster, and deliver orders on time.
Secondly, working at a warehouse is physically demanding, energy-draining work that leads to high employee turnover. With smart warehousing, computers and machines assist humans and make them more productive.
Giant retailers like Kroger’s already invest in warehouse automation. They have a robot-filled fulfillment center in Ohio with 1,000 robots that work together with 400 human employees to pick, sort, and move items. Autonomous robots can not only handle items faster and more safely than humans, but they can also analyze available space and inventory levels in real time to improve warehouse space allocation and increase storage.
At DHL, workers use smart glasses to automatically read barcodes and instructions that optimize storage, inventory tracking, and order fulfillment.
Improve inventory management with demand forecasting
Consumer demand changes, sometimes rapidly. Businesses that don’t align their inventory levels with real-time needs will experience stock shortages and risk losing revenue. But AI-driven learning systems can analyze regional differences in buying habits and customer needs to adjust inventory requirements in real time.
For example, to avoid understocking, Walmart builds AI/ML frameworks and fine-tunes models with historical data on past sales, online searches, and product views. They also feed the AI algorithm ‘future data’ such as predicted climate patterns, economic trends, and anticipated changes in local demographics to forecast demand and potential fulfillment disturbances.
Speed up delivery time by optimizing routes
Using predictive analytics, companies can optimize delivery routes based on traffic, weather, and delivery schedules. Predictive analytics analyzes these parameters to adjust routing in real time and optimize the last-mile delivery.
DHL uses AI forecasting models to set up route sequences for different types of products (such as urgent medical delivery) and provide customers with an accurate delivery time.
Improve sustainability with track and trace technology
With the help of track and trace technology, companies can create transparency in the supply chain and build trust with consumers.
In his TED speech, Markus Mutz, CEO of OpenSC, shared how technology can create solutions to verify sustainability claims. OpenSC uses blockchain technology to track the full journey of other companies’ products throughout supply chains.
Using AI/ML, RFID chips, space tech (such as satellites), and sensors with GPS data, they verify if food production is ethical at various manufacturers and share this information with consumers. This is a great example of how technology brings transparency to the supply chain and makes it easy to validate the claims of manufacturers.
Optimize production with digital twins
A digital twin is a virtual replica of a physical entity (such as a factory, machine, or supply chain component). The physical entity uses sensors and IoT devices to transmit data to its digital counterpart in real time. This allows running simulations of manufacturing processes to predict any flaws and risks that may occur. For example, Emirates Team New Zealand uses digital twins to test boat designs and simulate their performance without building them in real life.
With the help of digital twins, companies can also track the journey of their assets from the supplier to the production floor, manage their inventory levels in real time, monitor the performance of equipment, and schedule maintenance proactively.
Provide better customer service with generative AI
AI-powered chatbots can handle customer inquiries and provide updates on order status and delivery time. These chatbots free up human workers to focus on building relationships with clients. Generative AI can also be used to develop production schedules and create product concepts.
Use generative AI to access a wider pool of suppliers
AI can analyze your current supplier base and discover additional sourcing options by scraping the internet and creating a list of new potential suppliers based on specific criteria. For example, Scoutbee is an AI-powered scouting solution that gives access to an unlimited number of suppliers based on your needs and provides data on sustainability, certifications, and supplier revenue.
While supply chain companies can take many approaches to automation, there are a few risks to consider. Let’s see why supply chain automation still requires human input to manage these risks.
AI Algorithms and Risk: Can AI Handle Nuanced Decision-Making?
AI can process large amounts of data in minutes, predict trends, and perform complex tasks. But what about the human role in these functions? Despite its rapid growth, AI comes with risks that only humans can manage. Let’s take a closer look at them now.
Inaccurate data
AI-powered supply chain automation requires human monitoring to ensure that data input and output are accurate. In addition, decision-making models must be transparent.
Let’s say you want to predict customer demand with machine learning algorithms. In the supply chain industry, this demand affects various stakeholders, like finance managers and the human resources team. To integrate customer demand information across stakeholders and make decisions, internal planning systems rely on human workers.
Although AI can make predictions based on the large amounts of data it analyzes, humans still have to verify that data and involve their team members in the decision-making process.
Ethical concerns and biases
Data and algorithmic bias are core challenges in supply chain automation that can lead organizations astray. For example, AI systems that use machine learning algorithms to recommend suppliers may show bias towards a specific type of supplier. Usually, this happens due to the lack of diversity in data and limits the company’s choice.
Amazon seized its AI hiring tool after it caught the algorithm picking candidates' resumes with words like “executed” or “captured,” which is more common for men.
Companies must carefully examine the data used to train AI algorithms as well as the algorithms themselves and their output to identify and minimize potential biases. Businesses can hire a team of data scientists to examine their datasets and remove information that can result in damaging outcomes, like gender biases or racial profiling.
In addition, companies need to encourage collaboration between humans and machines. For example, human workers can give AI direct feedback about the output of AI algorithms and achieve a balance between technology and human expertise.
Lack of nuanced communication and relationship-building
AI can’t replace communication between the front-line workers who speak directly with the customers and the broader supply chain team. Businesses need the connections only humans can nurture by listening to customers with empathy, discussing new ideas, and providing nuanced feedback to help workers improve. While AI can generate solutions based on patterns found in datasets, it can’t maintain relationships and read human emotions.
To overcome these risks, companies that automate their work processes will need to audit their data to detect and fix biases in AI models. Also, companies must reshape their workforce and create training programs to ensure their employees can operate in tandem with technology.
However, as automation matures, some workers’ skills will become obsolete. Let’s see how automation could change the labor market.
Deskilling vs. Reskilling: The Potential Loss of Critical Skills vs. the Opportunity to Upskill Workers
As the World Economic Forum’s 2023 Future Of Jobs Report reveals, the labor market in the supply chain and transportation sector will experience a 25% change in the types of jobs supported by the industry by 2027. The main reason for the expected job movement is the rise of new technologies and market digitalization.
With the development of robotic solutions, warehouses will cut blue-collar jobs and open new positions that require creative and analytical thinking, technological literacy, and experience in big data and AI.
Good news: most companies automating their work processes also plan to invest in their employees’ professional growth.
What does that mean for the labor market? It means reskilling and upskilling logistics professionals so they can combine their industry knowledge with technology, data analytics, and AI.
New positions, like AI system trainers, AI maintenance technicians, and data scientists who specialize in supply chain optimization will emerge.
Here are some of the new roles humans will perform:
Trainer
Trainers are people who actually train AI to meet the goals of the company, making sure the AI algorithms comply with industry regulations. This is not only about tagging data for supervised learning but also about sophisticated training to ensure that robots imitate human behavior.
Explainers
Explainers are people who can explain the implications of AI to non-technical professionals and end users. These people help companies decide when it’s appropriate to use black-box AI algorithms and when you need an explanation of how AI reached a certain decision.
Sustainers
Sustainers maintain AI systems to ensure they function properly. For example, developers of supply chain robots need to ensure that the robots can co-exist with humans and won’t harm the environment. Sustainers also include specialists who ensure that large language models (LLMs) and other AI applications aren’t biased, harmful, or unethical.
Trainers are responsible for technology implementation, whereas sustainers and explainers ensure appropriate, transparent use of the technology.
Automation will not only create demand for new specialists but will also change the routines of current workers. For example, in the future economy, workers who used to lift heavy boxes might instead fix picking robots in case of malfunction.
As companies strive to attain better results and the supply chain leans into automation, humans will need to share their work space with robots and collaborate with them. Let’s take a look at the future of human control in supply chains.
The Future of Human Control in Supply Chains
Companies such as DHL and CEVA Logistics already use human and robot collaboration systems called “cobots” that can handle up to 1,000 tasks per hour.
Additionally, Amazon is introducing new autonomous mobile robots that can take on highly repetitive tasks, such as removing empty containers from their packing line after items have been distributed. Amazon also uses highly mobile humanoid robots with legs for movement to complete multiple tasks across warehouses and navigate complex environments.
So, what is the role of humans in a world of machines?
A recent experiment analyzed the performance of fully robotized order fulfillment systems at leading companies around the world. It found that warehouse systems are more productive and efficient when people take charge. Humans and robots working in tandem are 8.3% more productive when humans are in charge compared to when robots are.
Amazon has over 750,000 robots that free employees from repetitive tasks and help them focus on customer service instead. However, Amazon’s main mission is to ensure that humans are still at the center of production. So, over the last ten years, Amazon has created 700 new job types related to robotics and trained their workers in the skills necessary for maintaining, repairing, and controlling robots.
As you can see, logistics and supply chain technologies are creating new job opportunities in which humans and robots work together. Companies don’t have to choose between robots and human power. They can strike a balance between both. No matter how advanced automation is, humans are responsible for interpreting data and making strategic decisions.
Conclusion
The rise of AI-powered automation is transforming every aspect of logistics, which raises a lot of questions about the future of the human workforce. However, the future of logistics is all about the synergy of humans with AI.
Humans understand context and AI understands patterns, which makes them a perfect match. Humans’ ability to build relationships and make insightful decisions, combined with AI’s speed and scalability, will advance the supply chain industry in novel ways.