2024 Tech and Analytics Trends: AI-Driven Supply Chain Resilience

resilient supply chain
resilient supply chain

The global supply chain has been in a constant state of flux over the past years. Sociopolitical conflicts within and across national borders, rising costs of material and human resources — all these entail businesses to adapt and innovate. Predictive and prescriptive analytics will empower businesses to remain resilient amid such challenges, with AI techniques enhancing capabilities for forecasting and planning.

In this next part of our series on technology and analytics trends for 2024, industry experts at Lingaro’s supply chain analytics (SCA) practice share their insights on how supply chains will be transformed by current contexts and technologies and how Lingaro can enable leaders and managers to achieve this transformation.


Predictive analytics for resilient supply chains

The global market has remained volatile since the pandemic as economic and sociopolitical landscapes experienced upheavals year on year, upending the flow of products from manufacturers to stores. Inflation continues to put pressure on consumers and companies such that only 36% of supply chain managers believe that inventories will return to normal by the end of 2023. The same percentage expected normal activity by 2024.

Staggering headwinds across industries increased the impetus to create supply chain systems that are anticipatory and responsive to disruptions, from health crises to climate-induced changes. Businesses need to be resilient so as not to break. This is where predictive analytics will shine.

Lingaro Group Supply Chain Analytics Practice Senior Director Jacek Warchoł observed, “Supply chain risk and resilience are now much more tangible than theoretical. Lots of companies started to use advanced analytics to create and analyze different scenarios in logistics operations.”  

Warchoł further emphasized, “Companies are looking to create more effective, resilient, sustainable, and intelligent supply chains. Resilience is a key requirement.” In fact, for the first time, agility and resilience were included in the Association for Supply Chain Management’s (ASCM) top 10 supply chain trends for 2024.

In this context, resilience demonstrates the capacity and capability of the organization to realize the best possible outcomes for its supply chain while proactively adapting to changes. For instance, the ability to collect and analyze vast amounts of data on product demand, shipment patterns, customer preferences, and historical performance allows businesses to prepare for disruptions and reduce risks.  Predictive and prescriptive analytics provide these capabilities to CPG and manufacturing companies. In turn, third-party logistics (3PL) and shippers must also align with these shifting tides in supply chain management.

The integration of artificial intelligence (AI) techniques like deep learning, computer vision, and natural language processing uncovers concealed trends and knowledge that will enhance the power of predictive analytics. AI can uncover insights from historical data and market trends and factor in external variables to predict. Using these insights, businesses can generate better forecasts and adjust which products to stock up on, how much to produce, and what prices to set. They can also widen the scenarios to explore, test, and validate more alternatives, and therefore develop and implement well-informed plans and strategies for manufacturing and logistics operations, including warehousing, transportation, and inventory fulfillment.

Of course, AI and machine learning (ML) models will only be as good as the data they are provided. That’s why supply chain visibility is pivotal to high-quality analytics and insights. In a recent Deloitte survey, 76% of manufacturers stated that they are adopting digital tools for enhanced transparency into their supply chain. Although it is now a solid capability, more sources and types of data are needed to create advanced visibility platforms and connect with predictive analytics.

Warchoł did note that there will be a reality check particularly for the nascent generative AI technology when it comes to use cases in the supply chain. Companies are focusing on forecasting and planning, where they found the most benefits for data, analytics, and intelligence according to the 2024 3PL Study.

“Supply chain professionals must create a meaningful road map to plan the investment into technologies that can be used to help improve supply chains,” Warchoł shared. Organizations should be ready to address multiple dimensions, including sustainability reporting and effective daily operations.


Data intelligence use cases in supply chain management

Lingaro’s SCA practice, for example, has been working with enterprises in consumer goods, manufacturing, transportation, and warehousing to reinforce their digital supply chains’ resilience against a volatile global market and ever-evolving technological landscape. Warchoł mentioned that many of the enterprises that they collaborate with expressed an urgent need for solutions that use modern and analytics-enabled technologies. These simulate “what-if” scenarios from which they bring one into fruition depending on their probability of happening or their strategy or business need, such as improving customer service/experience, reducing costs, and streamlining processes.

Additionally, Warchoł emphasized the significance of incorporating external data, risks, events, market changes, and industry benchmarks in the accuracy and effectiveness of these what-ifs. In fact, the SCA practice has collaborated with a multinational beverage company to develop a data intelligence solution that integrates company-specific data with current market trends and industry benchmarks. This solution, Warchoł said, adeptly anticipates disruptions from within and outside the organization, providing well-defined scenarios that reduce risk and optimize value.

Their practice has also built solutions that compare transportation prices using internal and external variables (i.e., internal costs vs. market benchmarks), which are then correlated with shipment forecasts. The SCA practice also provides end-to-end data intelligence across the supply chain. In warehousing, for example, their practice can and is developing solutions for optimizing the picking process, which, according to Warchoł, is accountable for at least 40% of operational costs in the warehouse. With analytics, decision-makers can prioritize what pallet to bring in the picking zone and thus improve efficiencies. Another proof point is in manufacturing, where solutions could be built to precisely allocate capital expenditures. Through the analysis and prediction of machine breakdowns, resources can be strategically allocated to assets that, when experiencing downtime, will have the most significant impact on operations.

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