In recent months, we have seen two supply chain disruptions. First, commercial ships crossing the Red Sea to reach the Suez Canal, which carries about 15% of global maritime traffic, have been affected by the Israeli-Palestinian conflict. Second, the Panama Canal, which carries about 40% of U.S. container traffic, has been severely disrupted by an unprecedented drought, forcing the canal authority to reduce daily traffic by more than 36%.1 These geopolitical and weather-related disruptions illustrate the vulnerability of global supply chains that have been strained by the pandemic, labor strikes, and similar events in the past. In 2021 and 2022 alone, these disruptions are estimated to have cost companies $1.6 trillion annually in lost revenue.
The complexity of the supply chain ecosystem calls for innovative solutions to identify alternatives to address these bottlenecks. Without proper analytics and technological intervention, it is impossible to manage disruptions, regardless of geopolitical conflicts, weather-related issues, excess inventory, uneven distribution across regions, out-of-stocks, and other operational issues. Each point in the CPG supply chain generates vast amounts of data across both online and offline channels, yet much of this data remains untapped for analytics. Leveraging this data is critical not only to solving traditional demand forecasting challenges, but also to managing customer expectations and addressing supply chain disruptions.
Leading CPG companies are adopting the following strategies to integrate disparate data sources, become more resilient and agile, and create opportunities for growth while optimizing costs and service levels:
Introducing the Virtual Control Tower
Effectively managing logistics requires real-time data coordination and timely tracking of shipments. Control Tower provides end-to-end visibility into CPG companies’ global operations. By integrating data from ships, containers, ports, warehouses, distribution centers, and other sources, it provides real-time insight into the movement of goods. A prime example is Amazon. Control Tower enables real-time tracking of same-day deliveries, optimized inventory management, more efficient routes, and scalable operations to ensure optimal customer satisfaction.
A control tower’s predictive capabilities can help forecast potential disruptions, such as bad weather or port congestion, so that preventative measures can be taken to mitigate risk. A key element of a control tower is the ability to warn and notify of significant events, such as delays or disruptions, so decision makers can take immediate corrective action.
Control towers have become essential for CPG companies looking to achieve an optimized, self-adjusting supply chain network. According to Accenture, a supply chain control tower typically improves revenue by up to 1% through missed sales opportunities, reduces logistics costs by 3-5%, and reduces inventory by 5-15%. Next-generation control towers will integrate advanced technologies such as predictive analytics and GenAI to provide autonomous decision-making capabilities and enable greater transparency and trust across the supply chain.
Embedding digital twin intelligence
Digital twins create a digital replica of a company’s global supply chain operations, including manufacturing facilities, freight operations, third-party contractors, and distribution centers. This technology allows CPG companies to test multiple stress scenarios daily and validate potential outcomes without causing an actual disruption. Digital twins are essential for stress testing supply chains, setting up contingency plans, and identifying alternative transportation routes.
What sets digital twins apart is their ability to leverage deep reinforcement learning, cloud technology, and near real-time data sets to make decisions in evolving scenarios. CPG companies are adopting digital twins to enhance operations across manufacturing facilities, increase machine uptime, and reduce waste. For example, one large retailer used digital twins to reduce carbon emissions by 7% and improve on-time delivery of customer orders by 5%.
Focus on predictive modeling
A consistent data management system is essential for CPG companies. It allows them to extract data at every stage of product distribution, from factory to consumer. Implementing predictive modeling at each stage of product distribution provides insights that increase efficiency, improve customer experience, and provide a competitive advantage. Analyzing vast data sets of past sales, promotions, and external factors such as weather and social media trends allows companies to accurately forecast demand. The accuracy of the models reduces over- and under-stocking of inventory, ensuring products are on shelves when consumers want them. Predictive analytics also helps make supply chains more resilient by developing risk management capabilities in the face of disruptions.
End-to-end data integration can eliminate data silos and support various predictive modeling initiatives by linking internal factors, such as sales trends, and external factors, such as macroeconomic trends, into a centralized data lake. This integration provides insights into inventory levels, production schedules, and real-time demand signals.
Autonomous Planning with AI and ML
AI has traditionally been used to optimize logistics, including improving delivery routes, managing inventory, and streamlining warehouse operations. It has also been used to automate repetitive tasks, optimize transportation route planning, and control quality through AI-enabled vision systems. Since the COVID-19 pandemic, there has been more of a push to incorporate AI and ML to automate decisions and make supply chains autonomous, responding in real time to changes in demand and supply. According to McKinsey, autonomous supply chain planning can increase revenue by up to 4%, reduce inventory by up to 20%, and cut supply chain costs by up to 10%.
Recently, some CPG companies have begun to leverage unstructured data on shipments, suppliers, and external sources to analyze trends, identify anomalies, and extract actionable insights. By clustering and classifying supply chain situations, events, products, and customers, ML can manage complexity through differentiated responses and enable strategic adjustments based on real-time data. However, to effectively leverage ML, it is critical to collect, aggregate, clean, and act on vast amounts of data to ensure the accuracy and reliability of the insights derived. Effectively leveraging AI and ML to optimize supply chain operations and respond to uncertainty with agility and precision requires a data-centric approach.
Managing changing consumer behavior
Consumer behavior is changing rapidly. Overall, we are seeing declining customer loyalty, a shift to direct-to-consumer channels, shorter delivery times, greater demand volatility, and a rise in subscription models. These changes, brought about by digitalization and shifting customer expectations, require CPG companies to adopt a new mindset to stay competitive.
CPG companies must adopt a data-centric mindset and integrate diverse data sources, including unstructured data, with traditional data in a near real-time, cloud-based ecosystem. This integrated approach is essential to leverage insights and remain competitive. During and after the COVID-19 outbreak, companies that had cloud infrastructure and predictive analytics capabilities in place were better able to respond to supply chain challenges. Real-time data analytics provide deeper insights into consumer preferences, allowing companies to adjust their supply chains accordingly. Leveraging AI and a cloud-based data ecosystem remains critical to anticipate change and respond proactively.
Introducing new modes of transportation
New transportation technologies such as autonomous vehicles, drones and hyperloops are expected to revolutionize logistics. These innovations offer faster, more reliable and more cost-effective transport options to deliver goods efficiently and minimize reliance on fossil fuels, making delivery operations more sustainable.
As these technologies mature and become more widely adopted, they will have a significant impact on global supply chains, making them more efficient and resilient. Pilot projects such as Tesla’s self-driving trucks and Hyperloop efforts have already garnered significant attention. In the future, they could address the commercial driver shortage, save on fuel costs, and reduce driving costs to 70% of those of human drivers, with potential benefits for the CPG industry. Regulatory hurdles, technology maturity, and infrastructure development are the key challenges that need to be addressed.
What does the future hold for the CPG supply chain?
The COVID-19 pandemic has accelerated online shopping and presented opportunities for growth, but it has also exposed bottlenecks in traditional supply chain systems and outdated processes. Supply chains face ongoing disruptions from changing consumer behavior, geopolitical factors, and new transportation technologies. The future of the CPG industry supply chain depends on embracing technological advancements and adapting to new trends. By implementing a control tower, incorporating digital twin intelligence, leveraging predictive modeling, and embracing AI and ML, CPG companies can build resilient, agile, and efficient supply chains to unlock significant growth opportunities.
Learn more about EXL and how we can help you navigate the CPG supply chain here.
About the Author
Sangeetha Chandru is Senior Vice President and Global Practice Head of CPG and Retail at EXL, a leading data analytics and digital operations and solutions company.
