Supply chain digitalization has advanced rapidly in the last four years. Digitalization projects now touch virtually every aspect of the supply chain and cover a broad range of technologies, including robotics, sortation and packing technologies, and intelligent process automation. Many of these projects are already delivering significant enhancements in productivity, speed and visibility to supply chain operations. Yet, the supply chain digitalization journey is far from complete.
The industry can expect to see additional advances in the coming year as three major technology trends—data analytics orchestration and generative AI—come together to continue to advance digitalization and create a solid foundation for building on and amplifying those benefits. This will enable companies to further gain real-time insights, optimize operations and make data-driven decisions across the entire supply chain.
Data analytics vital to successful orchestration
Advanced data analytics have already established a foothold in supply chain management thanks largely to third-party logistics (3PL) providers who have integrated systems across the supply chain while also employing powerful business intelligence and visualization tools in their solutions.
The ascendancy of advanced analytics in logistics and supply chain organizations signifies a notable shift in the paradigm of data utilization within the industry. From the early days of accumulating vast datasets and undertaking basic analysis, the industry now employs complex analytical methods to unearth insights and enable data-driven decisions along and across segments of supply chains.
Rather than merely having access to large volumes of data (data quantity), logistics professionals can intelligently use and analyze this data for strategic advantage (data quality), which is essential to answer the increasingly complex and specific questions asked by businesses today.
Examples of this can be found in forecasting. For instance, machine learning can be used to analyze historical order patterns to improve peak demand management. The result is more accurate forecasts by workstream that enable companies to better prepare for demand fluctuation and efficiently manage extreme peaks without compromising service levels.
As companies look to embark on orchestration, it is important to understand that the strength of the data analytics program is vital to the success of any orchestration strategy — it is the building block that allows orchestration to occur. Data analytics is the sheet music that guides each piece of technology to perform its role in the synchronized orchestration of warehouse operations. It provides the visibility needed to efficiently manage and coordinate warehouse activities to maximize the productivity of people and technology.
Orchestration unlocks value of supply chain data
Today’s supply chains are subject to a high degree of variability on any given day. Trucks arriving late, employees calling in sick, order volumes exceeding projections, and equipment failures are just some of the issues that can impact daily operational plans. Warehouse and transportation managers regularly adapt to these disruptions as best they can to minimize their impact on customers, but the consequence is often inefficient resource utilization.
The goal of orchestration is to eliminate these inefficiencies by dynamically balancing supply chain resources so that the value of every resource is maximized, and orders can be filled faster, more efficiently and at lower cost. A fully orchestrated supply chain would rely on a range of digitalization technologies to create near-100% accurate inbound forecasts and then develop and execute a plan to ensure all supply chain resources — people, systems and automation — are balanced and used optimally to execute on that plan.
As orchestration continues to evolve and mature, it is bringing a new level of sophistication to supply chain management that minimizes the costs and maximizes the speed of supply chain operations. By synchronizing operations through the connection of disparate systems, such as inventory, management, procurement, production and logistics, orchestration is helping ensure that data from these various sources is unified and easily accessible.
Generative AI amplifies forecasting and optimization
Generative AI, particularly deep learning models, is poised to drive significant advances in supply chain digitalization. Whereas traditional AI systems typically focus on optimizing specific tasks (e.g., demand forecasting or route planning), generative AI goes further by generating new solutions, scenarios and strategies based on the available data.
Generative AI can create more accurate demand forecasts by analyzing historical data, market conditions and external factors like weather patterns, geopolitical events or macroeconomic trends. It can also design optimized supply chain strategies in response to changing business environments, ensuring that companies remain flexible and proactive.
For example, generative AI algorithms can be used in logistics to optimize route planning for deliveries. By considering various factors such as traffic patterns, weather conditions and delivery priorities, AI-powered systems can generate efficient routes while providing transparent explanations for the decisions made. In logistics, which relies heavily on location services, generative AI may also be used to accurately convert satellite imagery to map views, enabling the exploration of yet uninvestigated locations.
So how might data analytics, orchestration and generative AI work together to accelerate the digitalization of the supply chain? An orchestration platform sends a real-time alert about a delay in a shipment. Data analytics identifies the likely cause of the disruption (e.g., weather conditions). Generative AI suggests alternative routes or production adjustments to mitigate the impact of the disruption. Finally, the orchestration platform implements the changes automatically.
The past few years have seen a wave of technological innovations across the supply chain, especially in the warehouse that have fueled supply chain digitalization efforts. However, the combined might of data analytics, orchestration and generative AI promises to make supply chains more agile, transparent and capable of meeting the demands of the digital age.
