Data-driven planning for decision-making
Analysis of historical information and by the use of Machine Learning methodologies, executives can get a clear view of the entire supply chain and thus optimize for specific variables. Not only this, they can also model future scenarios and forecast cust
Consumer product goods (CPG) companies know that global supply chains are just not working as they should, leading to shortages and higher costs. At the same time, COVID-19 has changed India’s consumers; they are more digital, selective, and less loyal.
In response to these trends, many CPG companies are investing in Artificial Intelligence (AI) and Machine Learning (ML), but fall short of potential. According to a recent McKinsey research, 80 per cent of senior leaders from large Asian CPG manufacturers have only limited realtime decision-making. Even those who do incorporate data-driven planning methods optimize decisionmaking at local level, rather than globally, and cannot address potential disruptions in real time.
The approach should be to integrate the entire supply chain so that most processes and decisions can be run through autonomous planning, defined as the use of advanced analytics and AI to enable critical business processes. Autonomous planning covers everything— demand planning, dynamic production scheduling, inventory and replenishments, exceptions, and the integration of suppliers.
Through the analysis of historical information and the use of ML methodologies, executives can get a clear view of the supply chain and thus optimize for specific variables. They can also model future scenarios, predict customer behaviours more accurately, and meet demand faster and with a higher level of confidence. In our experience, autonomous supply-chain planning can increase revenues by up to 4 per cent, while inventory and supply chain costs can be reduced by as much as up to 20 and 10 per cent, respectively.
It can also play a role in environmental sustainability. Capturing these benefits is not just about buying the right technology. It entails a shift in the way that organisations work. There are three priorities.
INTEGRATE PROCESSES
The organisational design of the supply-chain function matters. Even if the right solution is in place, it would not work as intended if individual components are disconnected.
Companies should consider creating formal roles, such as demand-planning analysts, control tower planners, and sales and operations planning facilitators, to coordinate specific aspects of autonomous planning across different business units and functions all along the value chain.
It is also important to get everyone on the same page by defining companywide performance indicators with incentives to match. For companies that are used to setting targets at the function or business-unit level, this will represent a major change, but the value of an integrated performance management system is substantial. One Indian pharmaceutical company, for example, reduced inventory stockouts by two-thirds after it introduced autonomous planning capabilities.
Build capabilities: a CPG company is used to thinking in terms of beginnings and ends for specific processes. A demand forecast or a production capacity prediction is a separate consideration with its own timing. In autonomous planning, flexibility and cohesiveness replace rigidity. Instead of monitoring outcomes, operating executives manage for responsiveness; their task is to understand changing conditions and make real-time adjustments.
Companies must consider creating formal roles to coordinate specific aspects of autonomous planning across different business units and functions, all along the value chain