The Role of AI in Supply Chain Optimization

Supply chain operations can make or break a business. It impacts everything from the customer experience to the quality of a business's products. As technology continues to evolve, many corporations turn to artificial intelligence (AI) to optimize their operations.

What Is Supply Chain Optimization?

Supply chain optimization techniques often combine methodologies to speed up supply chain operations while reducing associated costs. Businesses can utilize blockchain, the internet of things (IoT), and AI in the supply chain. Of the various technologies available, artificial intelligence is the most promising because it can design optimized supply chain procedures and partake in them firsthand.

Procurement to Delivery: Importance of Supply Chain Optimization

Supply chains come with various costs, including storage, transportation, server management, and work hours. Improperly managed supply chains lead to more hours spent picking products, longer travel times between the product and its destination, and more server space to store erroneous data. Poor supply chain management can also hamper quality control efforts, leading to more returns and dissatisfied customers.

Imagining an AI-Driven Supply Chain

AI can optimize supply chain operations at nearly every level. Its potential use cases begin with the planning stage. These technologies can use a company's structured and unstructured data to shed light on inbound supply, customer location profiles, and more. Artificial intelligencetools can then generate a planning analytics dashboard or draft a supply chain planning document.

AI programs help in the execution phase as well, as these machine-learning algorithms can help make decisions about warehouse and inventory management, transportation management, and global trade management.

Benefits of AI in the Supply Chain

Enhanced Visibility, Predictive Analytics, and Risk Management

Artificial intelligence tools can operate without downtime, monitoring real-time supply chain metrics 24/7. Businesses can combine these technologies with IoT devices to monitor warehouse temperatures, gas mileage, and other vital metrics. More importantly, AI can structure these data points as they're collected and use them to predict problems that may arise.

Suppose a pharmaceutical company developed a vaccine that must be stored at a specific temperature. IoT tools could feed delivery truck temperature data to an AI tool that predicts whether adjustments must be made based on upcoming weather conditions.

Real-Time Decision Making

As an artificial intelligence tool is fed supply chain data, it learns more about the company's operations. These tools use this operational knowledge to identify inefficiencies and recommend corrective actions. For example, an AI tool may recognize a gradual increase in California-based customers and recommend storing more inventory in the warehouse nearest California.

Improved Efficiency and Cost Savings

It would take a large team of employees to accomplish everything you can do with a single AI program. Even if a company hired such a team, it would be impossible for them to optimize supply chain processes as quickly as an artificial intelligence tool can.

Artificial intelligence can be used to identify process gaps in real-time or predict them based on unstructured data. Once these process gaps are identified, the tool can recommend corrective actions, increasing ROI.

AI-Powered Technologies in Supply Chain Optimization

Streamlining the Supply Chain with Machine Learning

Machine-learning algorithms provide accurate inventory management processes while predicting demand. Inventory planners can use these data points to avoid shortages. As the program learns more about a company's supply chain, it can determine whether transportation service levels are being met and identify potential root causes.

AI-Driven Robotics and Process Automation

Artificial intelligenceis being used to let robots learn from each other in real time. For example, Covariant, a California-based tech company, is building "a universal AI to give robots the ability to see, reason and act on the world around them." These AI-driven robots act as a hivemind; when one robot learns a new skill, the others acquire that skill as well. These ever-improving robots can automate supply chain operations, improving accuracy and efficiency while reducing costs.

Reducing Friction with Natural Language Processing

Natural Language Processing (NLP) technology can monitor internal and external data in real time. For example, a company using a social media-monitoring NLP tool could monitor external trends, such as traffic accidents or wildfires, that may impact its transportation operations. These tools can also reduce friction caused by language barriers, as they can be trained to collect and analyze data in various languages.

Computer Vision

Computer vision uses AI to enable machines to interpret and understand visual information. This technology can be used to inspect products and components for defects, identify safety hazards in inventory warehouses, or guide robots in picking and packing.
Blockchain and Smart Contracts for Enhanced Transparency
Blockchain technology has been a hot topic over the last few years, primarily because of its role in cryptocurrency. However, this technology, and the automated smart contracts built on top of it, can optimize supply chain processes. For example, blockchain oracles, programs that feed real-world data to a blockchain, can inform AI-driven smart contracts when a contractor completes a job. The smart contract can then remit payment for the job automatically.

4 Real-World Examples of AI for the Supply Chain

1.    C3 AI | Optimizing Inventory with Machine Learning

C3 AI is a machine-learning software company specializing in tools that use predictive analysis for inventory management. According to its website, its tools enable "companies to minimize inventory levels of parts, raw materials, and finished goods while maintaining confidence that they will have sufficient inventory available to meet customer service level agreements."

2.    H2O AI | Demand Forecasting

H2O AI uses machine learning algorithms to forecast customer demand, enabling businesses to stock up when needed. This company also provides driverless artificial intelligence vehicles to help businesses meet demand. Industry behemoths, such as ADP, AT&T, PayPal, and Nationwide, use H2O AI tools.

3.    Echo Global Transportation | Transportation Optimization

Echo Global Transportation uses AI-driven tools to outsource transportation while lowering costs. According to Echo Global, its technology "leverages the latest in AI, machine learning, and advanced load matching algorithms." Companies can use its self-service portal to automate transportation procurement and build real-time transportation reports.

4.    IBM Supply Chain Intelligence Suite | Supplier Management

Many businesses have tens or hundreds of companies they partner with for their supply chain needs. Communicating with these partners often means sending emails back and forth, making phone calls, and attending virtual meetings. The IBM Supply Chain Intelligence Suite aims to reduce the time spent communicating with partners by automating routine decisions and triggering exception workflows automatically.

AI in Supply Chains: Optimization Roadblocks

Data Quality and Management

Many artificial intelligence tools use NLP technology to understand text and audio inputs. These tools break words down into their root forms to understand context and meaning. However, they aren't entirely accurate; typos and slang can lead them down the wrong path. Misinterpreted input leads to incorrect output, creating a domino effect in which a company's supply chain strategy may miss the mark.

Combining the Old With the New: Integration With Legacy Systems

Legacy systems may not have the necessary APIs and protocols to communicate with modern artificial intelligence tools, resulting in data silos and limited functionality. Additionally, older systems may not be scalable, making it difficult to support large datasets required for AI models. For the integration to be successful, the business must have a development team possessing the necessary skills. These limitations can result in a complete overhaul of a company's supply chain management architecture or employee base.

Ethical Considerations

No matter how you slice it, it's unlikely a company's employee base will remain unchanged throughout artificial intelligence integration. In all likelihood, the use of artificial intelligence will result in layoffs, as the business will require fewer employees than before. Although many AI advocates believe the long-term gains outweigh the short-term consequences, some believe this progress shouldn't come at the expense of human beings.

University of the Cumberlands Helps Drive the AI Revolution
Those looking to impact the AI supply chain ecosystem must possess the skills to create real change. University of the Cumberlands' online master’s in artificial intelligence program equips students with the knowledge to develop and manage artificial intelligence tools for corporate supply chains. AI may indeed impact the job security of many, but those creating the programs will be prepared for the changing landscape.