Many Consumer Packaged Goods (CPG) companies were hit hard during the pandemic by various factors such as raw material cost increases, supplier uncertainties, rising inventory levels and volatile demand patterns. These factors continue to pose a threat to the top and bottom line due to dynamic global scenarios such as geo-political changes, regulatory constraints and natural calamities.
Overcoming these challenges with traditional supply chain planning techniques is not very effective since these techniques focus only on historical data rather than taking forward-looking external data into consideration. That’s why many CPG companies failed to quickly adapt to changing consumer habits by moving from brick-and-mortar stores to online or scaling up production to meet demand spikes.
As per McKinsey’s CPG Asia Survey 2021, approximately 80% of CPG companies still follow traditional or collaborative sales and operations planning (S&OP) processes, with limited real-time decision-making or automation. The problem with this approach is that it still requires direct involvement of COOs and operations teams in decision-making and managing the interdependencies among individual applications. This also leads to delays in decision-making due to manual interventions. Autonomous supply chain planning addresses these very issues.
Autonomous supply chain planning is a closed-loop planning approach, designed to optimize S&OP processes in real time by leveraging AI, Machine Learning (ML), cloud capabilities and automated technology with human interventions meant only for managing exceptions.
In autonomous planning, demand planning is automated to consider historical data, NPD impact, promotions and real-time demand sensing. Supply planning is automated to consider visibility into supplier risk, value at-risk estimation, AI-powered guided buying and advanced scenario planning. If a factory runs out of a certain material, rather than shutting down the production line, should it make another product, and if yes, which product? The best course of action is recommended using predictive and prescriptive data analytics. Dynamic planning is automatically executed by converting planned orders into production planning, stock transfer and raw material procurement orders. ML helps the platform become better over time based on the analysis of its recommendations, expected outcomes and actual outcomes.
Autonomous supply chain planning solutions have been proven to help companies achieve a 2% increase in revenue, 30% reduction in stock-outs and 10% increase in inventory turns.
Now is the time for CPG companies to start adopting autonomous supply chain planning.