Penn State University

Supply chains are becoming incredibly complex due to global networks of interdependent organizations, rising consumer expectations, and volatile business environments. Complex supply chains bring a wealth of data created by people, systems, and machines from a variety of sources, including new types of data such as social media and IoT. The potential value of this data is enormous for supply chain management (SCM), but simply having rich data does not realize the potential for it.

To make data truly valuable, companies need the ability to analyze large amounts of data and convert them into actionable insights. In the search for an engine for Big Data analysis, companies will find answers in artificial intelligence (AI). With its algorithmic advancements and powerful computing capabilities, today’s AI systems can process large amounts of data in profound depth, in an extremely short time, and with autonomous learning capability. AI capabilities are well suited to SCM and the key processes of plan, buy, make, and flow.

AI in Supply Chain Planning

Supply chain planning is a data-driven and analytical intensive process. Companies invest in expensive enterprise resource planning (ERP) systems, but they typically do not come fully-equipped with appropriate algorithmic technologies and capabilities. Thus, some of the actual planning efforts are left to spreadsheet analyses to develop needed insight. Companies are turning to AI-powered analytics to perceive patterns of demand for products/services, across geographic and socioeconomic segments, while simultaneously considering factors like economic cycles, political developments, and weather conditions. More accurate forecasts are achieved at varying hierarchies (e.g. product, store, warehouse levels) and timeframes (e.g. daily, weekly, monthly). Planning for other processes like raw material sourcing, inventory management, and production are also improved as a result.

AI in Sourcing

Sourcing intelligence and opportunities for improvement can be facilitated through spend analysis. Prior to the AI revolution, commonly used tools for spend analysis included on-line analytical processing, data warehouses, and spreadsheets. While these tools certainly can be helpful, they typically lack classification capabilities and robust mathematical functions, and so are more focused on static instead of dynamic reporting. In contrast, AI-powered spend analytics automate the entire process and optimize the value of spend-related data through comprehensive dashboards and reporting capabilities. Companies can interact and view data in multiple ways as they continually delve into data exploration, thus enabling informed decision making.

Additionally, AI is making inroads in supplier risk monitoring, using readily available data (e.g. credit scores, legal filings, government data, customer reviews). Existing approaches are limited in their need to manually update relevant information, whereas with AI-powered data cleansing and classifying technologies, supplier data can be uploaded and analyzed in real time. Also, there is a growing popularity of embedding AI into predictive supplier risk monitoring systems that provide companies with multidimensional, dynamic risk scoring, real-time dashboards, and alerts and recommended responses when conditions change.

AI in Production and Distribution

While industrial robots are not new, it was not until recently that the new generation of more sophisticated, AI-powered robots emerged. In supply chains, the integration of intelligent robotics is becoming prominent in manufacturing and warehousing operations. Historically, assembly-line robots were designed for a single task, required hours to reprogram, and configured with a clear division of labor between humans and robots. Today, intelligent robots are able to learn from and work collaboratively with humans, and to significantly extend their capabilities.

Currently, the uses of intelligent robots in warehouses are accelerated by the challenges of ecommerce order fulfillment. Traditional operations that largely rely on manual picking systems have become ineffective amidst changing warehouse demand profile, from full case handling for store replenishment to single SKU items for unique online orders. Recent development of autonomous mobile robots features the embedded intelligence and application software that are the key differentiating characteristics of these systems, rendering picking process more productive, accurate, and efficient.

AI in Logistics

For companies that operate fleets and logistics facilities, the use of AI and predictive analytics can produce significant efficiencies. An example is the application of these tools to predict when maintenance may be needed for various types of assets. Whereas the scheduling of maintenance activities was traditionally done in advance, or when failures occurred, predictive maintenance helps by forecasting when asset failures are likely to occur. Benefits include prevention of failures before they occur, reduction of loss of asset downtime to remedy the failure, and decrease in overall cost reduction.

Meanwhile, more intelligent robotics are becoming increasingly effective in facilitating autonomous vehicle applications. One example is that of line-haul transportation that frequently involves long journeys overnight to support drivers’ health and safety. On the delivery end, and impacted significantly by the growth of ecommerce, intelligent robotics are increasingly benefiting the efficiency and effectiveness of last-mile deliveries.

The use of AI also helps to create great benefits in freight billing and payment processes that commonly rely on manual-entry accounting systems. AI-based systems create automation for these repetitive tasks, offering capabilities to identify shippers and consignees, read forms and bills of lading submitted in non-traditional formats, while learning to look for specific data as it scans bills. Results are more efficient, accurate, and transparent processes. Software firms are working to expand these AI-enabled capabilities to other logistics processes such as carrier selection and freight tendering that still largely involve manual approaches like emails, phones, and faxes.

What’s Next

Revolutionary AI represents a tremendous opportunity for companies to harness the value of data to better manage ever more complex supply chains. To date, emerging AI impact themes are more continuous and concurrent data analysis, more interactive data exploration, and more intelligent automation. Thanks to these AI-enabled improvements, supply chain managers are now able to adapt strategies with far more proficiency than before. In the next three to five years, we expect that AI will become more integrated in broader aspects of supply chain management, bringing repetitive tasks automation and intelligence to supply chain systems to the next level.

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