In recent months, we have witnessed two major supply chain disruption events. First, commercial vessels traversing the Red Sea to reach the Suez Canal—accounting for nearly 15% of global shipping traffic—have suffered from the aftermath of the Israel-Palestine conflict. Second, the Panama Canal, responsible for approximately 40% of all U.S. container traffic, is experiencing severe interruptions due to an unprecedented drought and has forced canal authorities to cut daily traffic by over 36%1. These geopolitical and weather-related disruptions exemplify the vulnerabilities of global supply chains, which have also been strained by pandemics, labor strikes, and similar events in the past. In 2021 and 2022 alone, these disruptions cost businesses an estimated $1.6 trillion annually in missed revenue opportunities.
The complexity of the supply chain ecosystem demands innovative solutions to identify alternative ways to address these bottlenecks. Without the right analytical and technological interventions, it is impossible to manage disruptions, irrespective of whether they are geopolitical conflicts, weather-related issues, or operational in nature, such as excess inventory, uneven distribution across geographies, and stockouts. At each point of the CPG supply chain, an enormous amount of data is generated by both online and offline channels, but much of this data remains unutilized for analytics. Harnessing this data is crucial not only for solving traditional demand forecasting challenges but also for managing customer expectations and tackling supply chain disruptions.
Leading CPG firms are employing the following strategies to integrate disparate data sources, enhance resilience and agility, and create opportunities for growth while optimizing costs and service levels.
Implementing a virtual control tower
Managing logistics effectively requires real-time data alignment and timely tracking of shipments. A control tower provides visibility into end-to-end global operations for CPG companies. By integrating data from ships, containers, ports, warehouses, distribution centers, and other sources, it offers real-time insights into the movement of goods. A prime example is Amazon, where the control tower enables real-time tracking for same-day delivery, optimized inventory management, route efficiency, and scalable operations, ensuring optimal customer satisfaction.
The predictive capability of control towers helps anticipate potential disruptions, such as adverse weather conditions or port congestion and allows proactive measures to mitigate risks. A critical component of control towers is their ability to alert and notify about critical events, such as delays or disruptions, empowering decision makers to take immediate corrective actions.
Control towers have become a must-have for CPG companies aiming to advance toward an optimized and self-orchestrating supply chain network. The next generation of Control Towers will integrate advanced technologies like predictive analytics and GenAI, offer autonomous decision-making capabilities, and provide even greater transparency and trust across the supply chain.
Incorporating digital twin intelligence
Digital twins create a digital replica of a company’s global supply chain operations, encompassing manufacturing facilities, freight and cargo operations, third-party contractors, and distribution centers. This technology allows CPG companies to test multiple stress scenarios daily, examining potential outcomes without real-life disruptions. Digital twins are essential for stress-testing supply chains, setting up contingency plans, and identifying alternate transportation routes.
What sets them apart for decision-making in evolving scenarios is their ability to leverage deep reinforcement learning, cloud technology, and near real-time data sets. CPG firms are deploying digital twins to enhance operations across manufacturing facilities, increase machine uptime, and reduce waste. For example, one leading retail company used digital twins to achieve a 7% reduction in carbon emissions and a 5% improvement in customer orders received on time.
Focusing on predictive modeling
A coherent data management system is crucial for CPG companies, allowing data extraction at every phase of the product journey from factory to consumer. Implementing predictive modeling at each stage of the product journey unlocks insights that enhance efficiency, improve customer experience, and provide a competitive edge. Analyzing vast datasets of historical sales, promotions, and external factors like weather and social media trends helps companies accurately forecast demand. The accuracy of the models ensures reduced inventory overstock and understock, and keeping products on shelves when consumers want them. Predictive analytics can also help build resilience in supply chains by developing risk control capabilities in the face of disruptions.
End-to-end data integration, linking internal factors like sales trends and external factors like macroeconomic trends, into a centralized data lake can break data silos and support various predictive modeling initiatives. This integration provides insights into inventory levels, production schedules, and real-time demand signals.
Autonomous planning through AI and machine learning
AI has traditionally been used to optimize logistics by improving delivery routes, managing inventory, and streamlining warehouse operations. It has also been employed to automate repetitive tasks, optimize transport route planning, and perform quality control through AI-enabled vision systems. Post COVID, there was an increasing drive to embed AI and Machine Learning (ML) more than ever to automate decision-making and make supply chains autonomous, enabling real-time adjustments to changes in supply and demand. According to McKinsey, autonomous supply chain planning can increase revenue by up to 4%, reduce inventory by up to 20%, and lower supply chain costs by up to 10%.
Recently some CPG firms have also started utilizing unstructured data on shipments, suppliers, and external sources to analyze trends, identify anomalies, and extract actionable insights. By clustering and classifying supply chain conditions, events, products, and customers, ML helps manage complexity through differentiated responses and enables strategic adjustments based on real-time data. However, to leverage ML effectively, it’s crucial to gather, aggregate, clean, and manipulate vast amounts of data, ensuring the accuracy and reliability of the insights derived. A data-centric approach is required to effectively utilize AI and ML to optimize supply chain operations and respond to uncertainties with agility and precision.
Managing evolving consumer behavior
Consumer behaviors are rapidly shifting. Across the board, there is diminishing customer loyalty, a shift to direct-to-consumer channels, shrinking delivery times, increasing demand volatility, and the rise of subscription models. These changes, driven by digitalization and evolving customer expectations, require CPG companies to adopt a new mindset to remain competitive.
CPG companies must adopt a data-centric mindset, integrating diverse data sources, including unstructured data, with traditional data in a near real-time, cloud-based ecosystem. This unified approach is essential for leveraging insights and maintaining a competitive edge. During and after COVID-19, firms that had cloud infrastructure and predictive analytics capabilities in place were better able to respond to the supply chain challenges. Real-time data analytics provide deeper insights into consumer preferences, enabling companies to adjust their supply chains accordingly. Leveraging AI and cloud-based data ecosystems will continue to be crucial for anticipating and proactively responding to changes.
Embracing new modes of transport
Emerging transport technologies, such as autonomous vehicles, drones, and hyperloop, promise to revolutionize logistics. These innovations offer faster, more reliable, and cost-effective transportation options delivering goods efficiently and minimizing reliance on fossil fuels, enhancing sustainability in delivery operations.
As these technologies mature and become more widely adopted, they will profoundly impact global supply chains, enhancing efficiency and resilience. Pilot projects like Tesla’s autonomous trucks and Hyperloop initiatives are already receiving a lot of attention. In the future, they may address the shortfall of commercial drivers, save on fuel costs, and reduce driving costs to 70% of those incurred by human drivers, demonstrating the potential benefits for the CPG industry. Regulatory hurdles, technological maturity, and infrastructure development are key challenges that need to be addressed.
What’s next for CPG supply chains?
The COVID-19 pandemic accelerated online shopping, presenting growth opportunities but also exposing bottlenecks in legacy supply chain systems and outdated processes. Supply chains face ongoing disruptions from evolving consumer behavior, geopolitical factors, and emerging transport technologies. The future of supply chains in the CPG industry depends on embracing technological advancements and adapting to new trends. By implementing control towers, incorporating digital twin intelligence, leveraging predictive modeling, and adopting AI and ML, CPG companies can build resilient, agile, and efficient supply chains and unlock significant growth opportunities.
To learn more about EXL and how we can help your business navigate CPG supply chains, visit us here.
About the author
Sangeetha Chandru is senior vice president and global practice head for CPG and retail at EXL, a leading data analytics and digital operations and solutions company.
