4 Use Cases of Machine Learning in Logistics and Supply Chain

Eugene Makieiev, BDM

Logistical science dates back to mankind's clay tablet days, and in many ways, the basic problems optimizing supply chain efficiency have remained unchanged: how can a group of people deliver goods to the source of demand at the best time, in the best condition, for the lowest cost, and with the least waste?

Yet few industries have undergone a more profound transformation than the logistics sector is experiencing now. Emerging technologies such as IoT, computer vision (CV), and advanced predictive analytics have driven a metamorphosis in supply chain management that Deloitte has called "the fourth industrial revolution."

Machine learning applications in the supply chain range from demand forecasting to shipping route optimization, and they provide businesses with a crucial competitive edge. Global enterprise is in a scramble for digital readiness, leading all other sectors in machine learning deployment. The automation potential and predictive power of these technologies free human workers to focus on innovation—a business essential in every industry.

In this post, we examine four use cases that are key to the future of intelligent supply chain management. Learn about the trends reshaping the logistics industry and the solutions machine learning offers to problems that have dollar figures in the trillions.

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The Importance of Machine Learning in Supply Chain Management

Traditional business intelligence has always been about producing insights into data about the past: last quarter's trends, last month's overtime, last week's sales. Those insights are still important, but they're no longer sufficient. A level of business agility that would have been headline-worthy twenty years ago is now, in many sectors, the minimum necessary to survive.

Supply chain management using machine learning's predictive capabilities, on the other hand, helps buyers and suppliers get out in front of changes and stay resilient in the face of major disruptions. Machine learning technology can process and analyze data from every layer of an enterprise to provide both high-level and incredibly granular forecasting for demand, delivery, customer behavior, and forward and reverse supply flow.

But let's back up a moment. What exactly is machine learning, and how is it distinct from other branches of artificial intelligence?

Machine learning fundamentals

For both humans and computers, learning is a process of receiving, evaluating, and applying information in order to improve performance on tasks. Whereas humans come preprogrammed for learning, however, computers have to be trained.

Machine learning (ML) specialists use a variety of approaches to create the algorithms that power the world's most successful enterprises. Broadly, these approaches can be broken down into three paradigms: supervised learning, unsupervised learning, and reinforcement learning. To learn more about the nuts and bolts of machine learning methodologies, check out our Data Science Specialist Max Liul's plain English primer on AI/ML.

Machine learning methodologies used in the supply chain vary according to both the type of problems to be solved and the data available to solve them with. Before an algorithm can be developed and trained, an organization's data—often staggering quantities of it—must be prepared for modeling.

Proper data preparation is the foundation of effective machine learning.

A recent study by Dimensional Research found that 96% of enterprises embarking on ML projects encountered difficulties with data quality.

Modern techniques allow data scientists to employ data sets that might otherwise be insufficient or imbalanced to create valid models that return incredibly accurate forecasting. Significant mathematical expertise is, however, required to apply these techniques.

Payoffs from using ML in the supply chain

In 2020, the worldwide predictive analytics software market was valued at over $5 billion. By 2028, that figure is expected to surge to $41.5 billion. Spending on intelligent process automation (IPA) topped $10 billion just last year, and 84% of businesses worldwide believe that investing in AI will give them a competitive edge. The global machine learning race is on.

Why are so many enterprises embarking on machine learning projects, particularly in supply chain management and the logistics industry? In a word, ROI. Solid supply chain forecasting and end-to-end visibility dramatically reduce operational overhead and risk. McKinsey has estimated the overall value of AI and machine learning's impact on global supply chain efficiency at between $1.2 and $2.0 trillion.

Key Challenges in Supply Chain Management

Every stage of the supply chain today faces volatile conditions, and brittleness anywhere in a supply or distribution network can cripple businesses relying on it when markets fluctuate. (When, not if.)

Suboptimal production processes, unplanned downtime, shifts in customer-supplier relationships, resource scarcity, and massively disruptive events—like a global pandemic or a boat stuck in the Suez Canal—can be logistical nightmares for the unprepared. Companies that still depend on outdated forecasting methods simply cannot get the insights they need to respond constructively.

Incorporating machine learning in supply chain design redefines what's possible in terms of transparency, planning, and ultimately, minimizing losses while identifying opportunities.

4 Most Useful Cases of Machine Learning in Logistics and the Supply Chain

Machine learning use cases in the supply chain point to a path forward through many of the difficulties businesses face today. These include problems long-familiar as well as emergent issues that have shaken the foundations of enterprise in the last half decade.

The benefits of machine learning in the supply chain apply to sectors ranging from retail to humanitarian relief. Here, we'll examine four high-level use cases that illustrate what's at stake for companies contemplating a shift to ML-enhanced logistics.

  • Inventory management

    This is arguably the quintessential machine learning use case, both because efficient inventory management depends on accurate demand forecasting and because inefficient inventory management costs businesses dearly.

    Revenue losses from out-of-stock items have been called the trillion-dollar problem. A 2015 study by IHL Group estimated that out-of-stocks resulted in $984 billion in lost sales worldwide, and follow-up research in 2018 found that up to 24% of Amazon's North American retail revenues stemmed from customers encountering an out-of-stock scenario elsewhere first.

    That was pre-pandemic. Today, online and mobile shopping has become the go-to in many sectors that used to see the vast majority of their revenues from brick-and-mortar transactions—and consumers expect detailed, accurate information about product availability every time they place an order.

    Machine learning provides suppliers and retailers with a previously unimaginable degree of prediction and visibility from end to end in the supply chain. Expert machine learning models can give businesses real-time insight into products' performance at the brand-, store-, and SKU-level. They can also forecast demand and optimize pricing with the same granularity. Savings from improved inventory management can run to tens of millions of dollars annually.

  • Distribution node planning

    Your company is growing, and it's time to scale up. Should you build a new warehouse, or do you need a distribution center? Where should you put it? What will the typical staffing needs be?

    Traditional approaches to supply chain engineering were essentially linear, and poor information flow gave rise to a simple, crude rule: stockpile as much as possible of every product, every time, at every warehouse. Overstock rates are high for supply chain companies still following that rule, which means businesses swallow the costs not only of the items themselves but of storage and disposal.

    The emergence of modern technologies makes more intelligent distribution possible, instead. Advanced predictive analytics provide precise, dynamic forecasting. Computer vision (CV) aids rapid, automated inventory counting and defect detection. Integrated systems allow decision-makers to receive and act on that information instantly.

    As a new level of agility becomes the norm, consumer expectations rise in turn. Demand for customizable options on products is at an all-time high. COVID-19 lockdowns normalized utilitarian online shopping, and deliveries that might once have been segregated by product category now commonly put dental floss in the same box with a trendy blouse.

    In many industries, these changes are reflected in a shift away from warehousing toward distribution centers. Compared to warehouses, distribution centers provide a more customer-oriented link between suppliers and consumers, but their operations are significantly more complex. Machine learning is the technology empowering businesses to handle that complexity.

  • Shipping optimization

    No matter what industry you're in, providing accurate and up-to-date shipping and delivery estimates is absolutely essential to customer satisfaction and retention. The demand for faster delivery is always rising, but reliability is even more important than speed. Broken promises spell bad reviews.

    Conventional distribution models assume a straight line from supplier to customer, where any interruption along that line must necessarily result in delays or even complete loss of a sale. But the digital supply networks (DSNs) emerging today are increasingly robust, as complex flow modeling allows businesses to identify bottlenecks and alternative supply routes. That means they can more often meet consumers' expectations even in the face of serious disruptions.

    Machine learning also permits an unprecedented level of visibility throughout the shipping process even as delivery keeps getting faster. Technologies powered by machine learning for supply chain management and logistics aid in tracking shipments, autonomous dispatching, and route optimization to get more packages to their destination on time and intact. Last-mile package tracking helps minimize losses and delivery failures, which all adds up to happier and more loyal customers.

  • Returns and reverse logistics

    The rapid growth of online shopping has, like any revolution, given rise to new challenges. Reverse product flow is unquestionably among the biggest, as returns worldwide account for over $600 billion in lost sales each year.

    Online retailers who want to remain competitive must be able to simplify the returns process for customers and also have the capacity to plan for returns. That includes financial planning, which depends on accurate forecasting along several axes, as well as logistical planning for transit, storage, and restocking.

    Supply chain management is highly complex even accounting only for forward flow. Reckoning with reverse flow scenarios doubles that complexity. Machine learning can help mitigate losses and extract order out of chaos.

Our Implementation of Machine Learning in the Supply Chain

Machine learning is a complex, highly specialized field. Scarcely more than half of the businesses surveyed by Dimensional Research had put an AI/ML project into production, and 71% said they ultimately outsourced their machine learning activities to experts.

Integrio Systems is an industry leader in artificial intelligence and machine learning. One in ten of our team members holds a PhD in mathematics, and we specialize in prediction, automation, and personalization. But the challenges of machine learning implementation lie not only in developing effective models, but in operationalizing the new software. The best algorithm in the industry is worth nothing if your business can't use it.

Our machine learning solutions are created with real people in mind. We don't just build models; we build software that integrates with your unique systems and processes to deliver standout ROI with minimal disruption.

Sophistication, simplified

The business analytics tools we develop can extract powerful insights from a tremendous volume of data. Just as importantly, we make those insights easy for end-users to understand with dynamic analytics dashboards such as the ones we created for CareOregon. The custom charts and graphs that form the cornerstone of CareOregon's solution help their management team identify new opportunities, reduce costs, and boost customer satisfaction.

Read full case-study

Transparent insights

Trust requires transparency. If a machine learning algorithm recommends that a company cut production of a product that's always sold well, demand forecasters need to be able to tell decision-makers why.

Cam Tran, Canada's largest full-line distribution oil-filled transformer company and a key player in national energy infrastructure, knows the importance of transparency better than most. That's why Integrio's business process optimization solution for them includes not only production and financial forecasting but also real-time access to project statuses, KPIs, and critical customer information across the organization.

Read full case-study

Continuous improvement

All software requires updates over its lifetime to stay performant, and machine learning applications are no exception. In addition to ordinary maintenance requirements, machine learning algorithms will confront concept drift as the statistical properties of their target variables evolve over time, and that drift must be detected and corrected in order for models to remain valid.

In 2018, Mobiry Technologies conceived an ambitious plan for a machine learning marketing automation platform that would analyze customer journeys, predict behavior changes, and take autonomous action to maximize engagement. They partnered with Integrio to develop the AI that powers the platform's A/B testing, recommendation engine, and predictive capabilities.

Our cooperation with Mobiry continues to this day, as Integrio's machine learning specialists ensure continuous improvement to the core product that brands including Disney and ABC rely on to maximize sales and marketing ROI.

Read full case-study

Final Thoughts

Supply chains today operate in an astonishingly data-rich ecosystem. In addition to historical data that for some companies may span decades, worldwide digital processes now generate roughly 2.5 quintillion bytes of data every day. Implementing machine learning in logistics and supply chain solutions can turn that data into tools that help make distribution networks more agile, resilient, and transparent.

It's often said that opportunity awaits, but in today's highly dynamic markets, it rarely waits long. Investing in machine learning for supply chain management puts enterprises in the strongest position to meet opportunity with preparedness.

FAQ: Machine Learning in Logistics

ML can improve supply chain management by:

  • improving customer experience
  • tracking packages almost real-time
  • assuring error-free delivery
  • optimizing inventory planning
  • reducing manual work
  • cutting costs

Companies like Tusimple, DHL, Echo Global Logistics, Amazon, and Neuro use AI technologies for improving logistics.

Machine Learning helps understand where a package is in the entire logistics cycle.


Four Use Cases of Machine Learning in the Supply Chain for Industry Logistics The Importance of Machine Learning in Supply Chain Management Payoffs from using ML in the supply chainKey Challenges in Supply Chain Management4 Most Useful Cases of Machine Learning in Logistics and the Supply ChainOur Implementation of Machine Learning in the Supply ChainFinal thoughtsFAQ: Machine Learning in Logistics

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