Autonomous supply chain planning for consumer goods companies

To capitalize on analytics, consumer packaged goods organizations—especially in Asia—can build an integrated system with the power to oversee and control the entire supply chain from end to end.

AI and machine learning hold the potential to dramatically improve supply chain performance for consumer packaged goods (CPG) companies. Yet most companies are limited by their approach thus far: investing in a collection of point solutions that work well for individual processes but don’t talk to each other or integrate data. The problem with this approach is that it still requires COOs and ops teams to be directly involved in decision making and oversight in order to manage the junction points and interdependencies among individual applications.

To capitalize on the true potential from analytics, a better approach is for CPG companies to integrate the entire end-to-end supply chain so that they can run the majority of processes and decisions through real-time, autonomous planning. Forecast changes in demand can be automatically factored into all processes and decisions along the chain, back to inventory, production planning and scheduling, and raw-material procurement.

The experiences of a few major CPG companies show that autonomous supply chain planning can lead to an increase in revenue of up to 4 percent, a reduction in inventory of up to 20 percent, and a decrease in supply chain costs of up to 10 percent. But capturing these benefits is a journey, not a one-time transaction, and it entails thinking beyond technology to include process redesign, talent, performance management, and other aspects of operations.

Changes compounded by the COVID-19 pandemic

The CPG industry has long relied on traditional processes to manage supply chains and operational performance, but the pandemic has upended many (if not most) of these efforts. Consumer sentiment has changed dramatically, with a marked shift to value and a greater focus on essential products. In many markets, concerns about physical stores have accelerated growth in online shopping. Purchasing loyalty has diminished, as consumers have become more willing to try new brands. All of these changing consumer needs and market dynamics put significant pressure on CPG companies to find better ways of planning.

Most organizations aren’t there yet. McKinsey recently interviewed senior leaders from large CPG manufacturers in Asia about the state of their planning processes. The results show significant room to improve. In our sample, approximately 80 percent of companies still follow traditional or collaborative sales and operations planning (S&OP) processes, with limited real-time decision making or automation (Exhibit 1). Current processes often depend on unreliable sources of data and outdated IT systems, with coordination limited across functions. To be fair, certain CPG companies consistently follow data-based planning methods, but even they typically optimize decision making at the local level, rather than globally, and with limited ability to address potential disruptions in real time.

Few consumer packaged goods companies in Asia have adopted autonomous supply chain planning solutions.
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Given the rapid-fire shifts in demand due to the pandemic, there is a real risk that traditional supply chain planning processes will be insufficient. Companies run the risk of product shortages, increased costs from stock, inventory write-offs, and related inefficiencies up and down the value chain.

At the same time, a small number of leading CPG companies (7 percent in our sample) have started to adopt autonomous end-to-end planning—a more advanced approach to managing supply chains in volatile conditions, and one that will soon become the baseline for CPG organizations around the world.

What is autonomous planning?

Autonomous planning is a continuous, closed-loop planning approach built on a fully automated technology platform, designed to optimize S&OP processes in real time. For large, complex CPG companies, autonomous planning can help supply chains function more effectively in volatile environments, and with less direct human oversight and decision making required. It combines big data (internal, external, and customer information) and advanced analytics at every step of the supply chain planning process.

Autonomous planning can help supply chains function more effectively in volatile environments, and with less direct human oversight and decision making required.

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

  • Reduce human involvement by relying on automation to handle most processes end to end, with manual interventions required only to address exceptions and special circumstances.
  • Rely on integrated advanced analytics throughout the supply chain, moving beyond standard software functionality for individual processes to create an explicit link from demand forecasts and orders back through to the production schedule and plan.
  • Evolve from structured planning processes—which are typically slow, rigid, and reactive—to a more fluid approach, based on cross-functional and continuous touchpoints that can proactively integrate real-time information.
  • Hard-wire processes and KPIs directly into business units, rather than concentrating ownership and control over the supply chain to the operations function.
  • Build the organizational capacity to evolve over time, by piloting new uses cases, learning from experience, and developing data and analytics capabilities.

Because it is so comprehensive, autonomous supply chain planning leads can improve performance in a range of processes across the supply chain (see sidebar, “A CPG company’s initial success with autonomous planning”).

Sidebar

Increased service levels. As companies better understand and capture variability of future demand through forecasting, they can predict customer behaviors more accurately and meet their demand with a higher level of confidence—and with significantly reduced lead times from order to delivery. Demand is more granular and segmented, to satisfy differing fulfillment requirements in various categories and regional markets, while tolerating promotions and other variables that enhance volatility. The entire organization becomes more agile and customer-centric, leading to an increase in revenue of 3 to 4 percent.

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 an organization with a powerful tool that allows demand planners to shift focus to more complex issues and improve organizational efficiency. There is an explicit link from forecast demand signals back to the production schedule and plan, ensuring that sufficient raw materials are in place. Overall operations become more cost- and resource-efficient, resulting in a reduction in supply chain costs of 5 to 10 percent, freeing resources of time and capital to support investment and fuel growth.

Four elements to deploy autonomous planning

Autonomous planning is a journey. Companies have found that implementation is most successful when supported by four key elements (Exhibit 2).

Four elements of autonomous planning help deliver at-scale impact.
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Streamline processes

Autonomous planning focuses on enabling critical business processes with advanced analytics and artificial intelligence. That includes S&OP, demand planning, dynamic production scheduling, inventory and replenishments, exceptions management for expedited orders or other outliers, and the integration of suppliers.

All of these processes use historical information and machine-learning methodologies to create a clear view of the entire supply chain, so that COOs can optimize for specific variables. For example, an ideal solution would maximize product availability and production capacity, while also lowering the total cost to serve. In addition, it would be able to model potential future scenarios, with predictive planning to simulate the impact on the supply chain, along with the specific implications of various mitigation measures.

Rewire the organization and performance-management processes

The organizational design of the supply chain function can have a critical impact on overall performance; even with the right solution in place, execution can be nearly impossible if individual components of the system are not aligned. To accomplish that, CPG organizations can create formal, dedicated roles—including demand-planning analysts, control tower planners, S&OP facilitators, and agile coaches, among others—to coordinate specific aspects of autonomous planning across different business units, functions, and sites along the end-to-end value chain.

In addition, KPIs will likely need to be defined for the entire supply chain organization, with everyone incentivized to strive for the right target behaviors. This will represent a major change at many companies, a large number of which still set performance targets within individual functions or business units. Accordingly, companies may need to redesign their performance-management systems to be more integrated and cohesive.

Build up people capabilities to support the change

CPG companies often have deeply entrenched ways of working built around specific processes with clear beginnings and ends: a highly deterministic parameter, such as a demand forecast or a production-capacity prediction, creates a discrete output—a manufacturing or fulfillment plan—by a specific date. In autonomous planning, these sorts of rigid processes are less relevant. Agility and responsiveness become more important for COOs, pressed to better understand changing conditions and dynamically adjust in real time.

Planning will likely always require some level of human involvement, but increasingly it can be limited to managing rare exceptions, with artificial intelligence and machine learning handling the bulk of standard processes in an autonomous manner. But this evolution involves a different way of thinking that calls for a different set of capabilities, including the following:

  • Cross-functional coordination and project-management capabilities to loop in stakeholders from a range of functions to arrive at a consensus.
  • A mindset of accountability, with squads empowered to make decisions and held responsible for delivering results.
  • Familiarity with enterprise-level software applications—not just enterprise resource-planning (ERP) systems but workflow management and transportation management solutions, along with advanced analytics and artificial intelligence tools, including machine and deep learning. (Operations leaders do not need deep, highly technical knowledge of how these solutions work; rather, they need to understand big-picture implications of how they work, how they create transparency and improve performance, and—critically—where they might have blind spots and require human intervention.)
  • An ability to segment products and customers and understand concepts such as the total cost to serve.

Deploy technology—intelligently

The final component concerns technology. Autonomous planning rests on a technology platform with a centralized data model. Some organizations believe they need to build a new tech stack to make this happen, but that can slow down the process; we believe that companies can make faster progress by leveraging their existing stack.

More important are sensing and prediction capabilities. Organizations increasingly need to pull data across the value chain from intelligent sensors, programmed to identify critical events, assess their impact, and adjust planning and control variables. Similarly, software capable of modeling the implications of various disruptions is also vital. Today’s algorithms can analyze a company’s network of suppliers and determine the total impact if a specific supplier goes down. Similar technologies can conduct the same analysis for internal assets, such as production facilities or even individual pieces of manufacturing equipment—reviewing historical performance to model future risks, including the overall impact on service levels if a particular asset fails.


In a complex and volatile environment, CPG manufacturers can no longer rely on the supply chain planning processes of the past. Instead, they have a clear opportunity to improve financial and operational performance by implementing autonomous planning across the entire end-to-end supply chain. Capturing this potential will not be easy, particularly given that many companies have long legacies and deeply entrenched ways of working. Yet by embracing new technology, shifting their mindsets about what’s possible, and being willing to test and learn over time, companies can implement autonomous solutions and ensure that they can compete—and thrive—regardless of what the future holds.

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