Embracing Autonomous Supply Chain Planning for CPG Industry

Introduction

CPG companies find themselves at a pivotal moment, requiring the evolution of their planning systems to adapt to changing circumstances.

The ongoing crisis has severely impacted many Consumer Packaged Goods (CPG) companies, driven by factors such as rising raw material costs, supplier uncertainties, increasing inventory levels, and unpredictable demand patterns. These challenges, compounded by global disruptions like geopolitical shifts, regulatory changes, and natural disasters, continue to threaten both top-line growth and profitability.

Traditional supply chain planning methods, which rely heavily on historical data, are proving inadequate in addressing these complexities. They often fail to account for forward-looking external factors, making it difficult for CPG companies to adapt swiftly to evolving consumer behavior, such as the shift from brick-and-mortar stores to online shopping or scaling production to meet sudden demand surges.

According to McKinsey’s CPG Asia Survey, about 80% of CPG companies still rely on traditional or collaborative Sales and Operations Planning (S&OP) processes, with limited real-time decision-making and automation. This approach requires direct involvement from COOs and operations teams to manage interdependencies between systems, often resulting in delays due to manual interventions. Autonomous supply chain planning offers a solution to these challenges by enabling faster, more efficient decision-making.

Driving force behind autonomous supply chains for CPGs?

Autonomous planning is a constant, closed-loop planning approach built on a fully automated technology platform to optimize real-time processes. For large, complex organizations, autonomous planning can help supply chain solutions function more effectively in volatile environments and require less direct human oversight and decision-making. It combines big data and advanced analytics at every step of the supply chain planning process.

The autonomous supply chain planning process leverages advanced technologies such as Artificial intelligence (AI) and Machine learning. It holds up the evaluation of millions of data points and the generation of insights from them.

Businesses will better understand current and historical supply and demand to determine the best course of action to keep pace with the customer requests, regardless of market conditions.

Although it is entirely based on technology, autonomous planning needs more than software and hardware. That's because it necessitates a shift in the way that organizations work based on a set of foundational principles:

Although it is entirely based on technology, autonomous planning needs more than software and hardware. That's because it necessitates a shift in the way that organizations work based on a set of foundational principles:

• Decreased human involvement by relying on automation to handle most processes, with manual interventions required only to address exceptions and special circumstances.
• Rely on advanced integrated CPG analytics throughout the supply chain, moving beyond standard software functionality for individual processes to curate an explicit link from demand forecasts and orders to the production schedule and plan.
• Evolve from structured planning processes typically slow, reactive, and rigid—to a more fluid approach based on continuous and cross-functional touchpoints that can proactively integrate real-time information.
• Build the organizational capacity to evolve by piloting new use cases, learning from experience, and developing data and analytics capabilities.

As it is so comprehensive, autonomous supply chain planning leads to improved performance in a range of processes across the global supply chain.

• Increased service levels- As organizations better understand and capture the variability of future demand via forecasting, they can predict consumer behaviors more aptly and meet their demand with a higher level of confidence.
• Optimized inventory- Inventory levels can decrease by 10 to 20 percent, often with a corresponding drop in inventory costs, while still meeting required service levels.
• Improved planning efficiency- Automated execution equips enterprises with a robust tool that allows demand planners to shift focus to more complex issues and enhance organizational efficiency.

Just like we've seen successful applications of autonomous systems in aircraft, automotive, and other transport mechanisms, we are seeing it successfully today in consumer goods. Why? Organizations have reached sheer maturity when it comes to connectivity, their capability to acquire precise data close to the source and store it, and the obtainability of machine learning and advanced modeling techniques to learn from this data.

Additionally, flexible compute architectures enable complex models to learn in high-performance cloud computing environments while being deployed at the "edge" resulting in quick system influence with low latency.

Take a Sneak Peek into the key use cases of autonomous CPG Supply Chainn

There are multiple autonomous supply chain use cases that CPGs can implement to improve efficiency, reduce costs, and increase flexibility. Here you go:

Final Thoughts

Therefore, in the current complex and volatile environment, the consumer packaged goods supply chains can no longer rely on the traditional planning processes. Instead, they should look for a clear opportunity to improve financial and operational performance by implementing autonomous planning across the end-to-end supply chain analytics.

We at Polestar Solutions embrace new technologies, where we help enterprises implement autonomous solutions to ensure that they can compete and thrive regardless of what the future holds to improve the CPG value chains. Book a demo today to understand more about our CPG supply chain solutions.