The Future of the Automated Supply Chain

The future of the automated supply chain hinges greatly on artificial intelligence. AI is the top digital investment priority for more than one-quarter of North American supply chain professionals responding to a recent Gartner survey (see chart).

Behind this interest is the promise AI holds for many supply chain organizations. “AI-enabled automation shifts supply chains from reactive to proactive and predictive,” says Sudhir Balebail, product management leader, order management, IBM Sustainability Software.

Leveraging traditional AI technologies, such as machine learning, along with newer solutions like generative AI to process vast quantities of data can provide visibility, insight, and recommendations.

The result? “Better resiliency, increased agility, and optimized operations,” Balebail says. For example, AI can provide real-time inventory snapshots, enabling smarter replenishment and minimizing over- and under-stocking.

Avnet, a global technology distributor and solutions provider, has been using traditional AI for predictive modeling to improve the quality and visibility of its inventory management function for both suppliers and customers, says Doug Adams, senior vice president, global logistics and quality.

The company is now investigating multiple potential uses for AI to both improve the customer experience and to help suppliers with forecasting.

Within its global logistics function, Avnet has established an innovation and technology council that’s researching how AI can help all parties identify potential blind spots. The goal is to ensure Avnet is putting tools in place that can help its suppliers produce the right product, which Avnet can locate where it’s most needed, and in the optimal quantities.

Avnet is investigating potential uses for generative AI in transportation, freight, and supply chain functionality, such as modeling Avnet’s distribution center network.

For example, Avnet currently has four locations across Asia. Artificial intelligence could provide additional insight on these markets as they are today, as well as their expected growth, enhancing inventory allocation decisions.

While it might seem there’s little difference between traditional automation and AI, that’s not exactly so. Traditional automation is task oriented. “It’s ‘see this, do that,’”says Sujit Singh, COO of Arkieva, a supply chain solutions provider.

Artificial intelligence can handle tasks, while encompassing advanced algorithms that can understand, reason, learn, and exercise some level of creative decision making, says Remington Tonar, co-founder of Cart.com, which offers a unified commerce platform. Cart.com, in partnership with select clients, currently uses AI to predict some
customer demand.

BOOSTING PRODUCTIVITY

The capabilities artificial intelligence offers can improve supply chain operations in multiple ways. It can leverage varying sources of information, including real time data, to improve decision-making. Particularly in times of uncertainty, relying solely on historical data may lead to sub optimal decisions.

Early on, the technology probably will be leveraged more in execution actions than in planning. For instance, AI could help a supply chain professional determine whether to ship cargo today or tomorrow, and via one lane or another. While much of this is already within the realm of automation, AI would add a level of intelligence.

Warehouse automation could be another early use case. “Warehouses are complex, but well-controlled environments,” says Matthias Winkenbach, principal research scientist at the Massachusetts Institute of Technology.

For example, in order fulfillment, AI can analyze real-time data to predict the most efficient paths for picking items.

DEMAND FORECASTING

Applying AI to demand forecasting offers the “biggest lever,” says Ansgar Thiede, vice president, data science with Korber Supply Chain Software, now Infios. Improved forecasting can drive revenue growth by minimizing the risk of both over production and lost sales. In other areas, the usage of AI is more about driving cost efficiencies. These are important as well from a profitability perspective, but might be smaller at first sight, he adds.

In addition, the demand and supply planning and forecasting functions all involve tremendous amounts of data and numerous menial tasks. AI solutions can perform these tasks, freeing planners to focus on larger projects, says Richard Davis, chief executive officer with Demand Chain AI, a provider of supply chain and demand planning solutions.

MANAGING INVENTORY

When it comes to inventory management, AI can analyze seasonal trends and purchasing patterns, helping companies better anticipate demand and more intelligently position inventory closer to customers. This accelerates delivery, cuts costs and boosts customer satisfaction, says Sowmya Mullur Rajagopalan, vice president and head of travel, transportation and hospitality, Americas with Tata Consultancy Services.

AI Squared, a software company that enables businesses to integrate AI models into their existing software, worked with a client to leverage AI in order fulfillment, says Benjamin Harvey, Ph.D., founder, and CEO.

Before the order was executed, the AI solution would run a simulation identifying the percentage that could be actually filled, and showing which, if any, pieces of the order were missing. The company could then decide how to obtain the items. Only once an order was 95% filled would it be processed.

TRANSPORTATION

Streamlining transportation is another use for AI. Deciding between the tremendous number of available shipping options can quickly grow complex. While many companies already use straightforward algorithms for rate shopping, it’s possible to layer in intelligent algorithms.

For example, a traditional algorithm would simply query multiple sources to obtain real-time rates from various carriers and rate cards, and then assign the cheapest one within the desired class of service. A more intelligent algorithm could understand cost and time trade offs on each order, and present different options to shoppers at the point-of-sale, based on their delivery information.

If some customers knew they could cut shipping costs with a different class of service that added only 12 hours to the delivery time, some would take the trade-off.

More intelligent algorithms might also be able to account for accessorial charges on an address-by-address basis. Then, sellers could price and forecast shipping options accordingly.

Some logistics companies leverage AI to improve delivery density, Sowmya says. Suppose a logistics provider has two packages to deliver to a residence on Friday. Using AI, the logistics provider can offer its customers—the sellers—a discount if they ship additional packages to the same or nearby addresses within a set timeframe, reducing shipping costs for all.

With assistance from AI, supply chain professionals can more precisely target drivers’ arrival times to minimize downtime at warehouses and distribution centers, says Ann Marie Jonkman, global vice president, industry strategy with software solutions provider Blue Yonder. This is especially valuable when schedules are upended due to unforeseen events.

While this data has been available, it often wasn’t possible to access it quickly, Jonkman says. With AI automation, information can come together more quickly, accelerating decision-making.

Artificial intelligence could also play a role in detecting cargo theft. Theft rings have become quite sophisticated, says Darin Miller, national director, marine, at Sedgwick, a global claims administrator.

Thieves often identify themselves as legitimate truckers then enter warehouses to pick up loads, which they transport to other locations and likely resell.

Artificial intelligence could be used to track vessels and send alerts when a truck detours from the expected route. It also could identify shipments at greater risk of theft, whether due to their location, type of cargo, or other factors. Security efforts could then focus on these shipments.

POTENTIAL RISKS

Along with its promise, AI carries risks. One is, somewhat ironically, the lure of its promise. “When you have a new hammer, everything is a nail,” Thiede says. The temptation is to address every challenge with AI.

But for some simple decisions, heuristics like “If A, then B” will do the job and be quicker to implement and easier to understand. If a warehouse has limited space, making it impossible to fill all demand all the time, a replenishment rule like “Reorder once inventory drops to 10 units” may suffice, Thiede says.

AI solutions tend to be more complex to set up and may require more maintenance. As a result, they often make more sense when there’s enough margin to cover the additional costs.

Some organizations are getting pushed to implement AI so quickly that they risk overlooking the need for guardrails, security, and governance. “Data science and AI is the wild, wild west,” Harvey says.

Outliers or biases in a dataset or model could lead the algorithm to generate insights that don’t reflect reality. Without solid governance and human oversight, any decisions made based on the model could be sub-optimal.

A starting point for addressing these risks is a thoughtful business case. “Don’t just sprinkle AI across everything,” Davis says. Prioritize and develop a roadmap to move toward the organization’s goals.

Although it may seem counter intuitive, most organizations will benefit by starting with a process that’s understood and yet will improve with automation. If the organization has never solved a particular problem before, it probably doesn’t have the data needed to train AI on it, and the organization would lack the ability to assess whether the AI solution is doing a good job. “Don’t start with the big hairy thing in the distance,” Winkenbach says.

Given that AI solutions interpret data, the quality of that data is key, Theide says. Say a retailer is trying to analyze consumers’ reactions to price changes and promotion information in order to optimize its demand forecast. If the company hasn’t been tracking the impact of price changes or promotional information, it can’t expect the model to provide solid predictions of consumers’ future actions.

The AI compliance and governance model should provide alerts when the results go beyond an acceptable realm, Davis says.

Humans should be kept in the loop, as they can decide how to handle a particular AI insight when their expertise indicates the solution is incorrect, Harvey says. For instance, if a solution is evaluating transportation routes and some routes were used in the past only because others weren’t available, that will influence the results. An experienced employee can pick up on this.

SMALL OR BIG?

Unlike many advances in technology that start with established enterprises and trickle down to smaller firms, some say smaller businesses, and particularly newer ones, may have an advantage when it comes to artificial intelligence. Newer companies can create native environments in which AI can thrive, says Talal Abu-Issa, chief executive and founder of Beebolt, a supply chain technology company. In contrast, bigger companies often are structured around certain processes and ways of doing things. “They’re not necessarily optimized for these models to really shine,” Issa says.

DISMANTLING SILOES

One overarching benefit of deploying artificial intelligence in supply chain and logistics is its ability to break down divisions between strategic, tactical, and operational decision-making, Winkenbach says. Traditionally, companies have tackled these separately because the issues are so complex that it hasn’t been feasible to assess them at the same time.

Artificial intelligence and machine learning, however, can address multiple big problems at the same time, such as helping companies determine where to build their distribution centers, place inventory and decide on transportation modes, Winkenbach says.

Ultimately, AI’s biggest bang for the buck may come from making complex decisions simultaneously.

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DISPENSING SMART TECHNOLOGY

Fastenal, an industrial supply chain company, supports business-to business products and services. Through its vendor managed inventory service, it can observe customers’ inventory status and determine when it’s time to restock.

To accomplish this, Fastenal leverages technology. The company provides technology-enhanced coil vending machines that offer general maintenance, repair and operations products and safety supplies such as gloves and safety glasses. Because employees access the items electronically, a company can collect data on how various products are being used—say, tying a product to a department or project.

The system also leverages AI to enable companies to bring together data from different technology product offerings, so it can be analyzed. This is key, because if a company for instance, buys fasteners for a production line from five providers, that means it has five places where data is stored and organized, and five different ways to share data.

Fastenal consolidates this information into one available repository, improving data quality for decision-making. The solution then uses AI to speed data analysis throughout the supply chain. “This drives better decisions because of the amount of information AI can process versus a human,” says Jeff Hicks, the company’s vice president. For instance, the solution can assess whether changing inventory levels would lead to more stockouts.

By leveraging AI, Fastenal can also help clients understand how their inventory is moving. If 100 items were used during the month, the AI solution can determine if the items were used at once or in sets of 10. The answer might influence purchasing.

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MIT, MECALUX ACCELERATE WAREHOUSE AUTOMATION

A new five-year collaboration between MIT’s Center for Transportation & Logistics and intralogistics leader Mecalux aims to drive groundbreaking advancements in warehouse automation.

Through MIT’s Intelligent Logistics Systems Lab, researchers are focusing on two goals: enhancing the productivity of autonomous warehouse robots and optimizing order distribution systems.

Robotic collaboration. The first area of research will develop a “swarm intelligence” system, enabling autonomous warehouse robots to work collectively, making smarter, coordinated decisions. The aim? To create robots that learn from human behavior for better efficiency and collaboration in dynamic warehouse environments.

Predictive distribution. The second research focus is on training AI models to anticipate customer demand patterns. This approach will help companies operating extensive warehouse and distribution networks determine the most efficient order fulfillment strategies in real time.

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