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Autonomous planning for customer centricity



CPG producers must be prepared for change in today’s complex, uncertain business climate, say McKinsey & Company’s K. Ganesh, Associate Partner, Chennai, and Soumyadeep Ganguly, Partner, Gurugram.

Consumer product goods (CPG) companies, in India and elsewhere, know that global supply chains are just not working as well as they should, leading to shortages and higher costs. At the same time, the pandemic has changed India’s consumers; they are more digital, more selective, and less loyal now.

In response to these trends, many CPG companies are investing in artificial intelligence (AI) and machine learning (ML) — but woefully fall short of potential. According to a recent McKinsey research, 80 per cent of senior leaders from large Asian CPG manufacturers have only limited real-time decision making or automation capabilities. Even those who do incorporate data-based planning methods typically optimize decision-making at the local level, rather than globally, and cannot address potential disruptions in real time.

The better approach is 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 artificial intelligence 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 entire supply chain and thus optimise 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 up to 20 and 10 per cent, respectively. And 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 and all along the value chain.

It is also important to get everyone on the same page by defining company-wide performance indicators with incentives to match. For firms 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 pharma company, for example, reduced inventory stock-outs 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 adjustments in real time.

Under autonomous planning, AI and ML technologies deal with most standard processes; the human touch is less constant but perhaps more important to deal quickly with exceptional circumstances. That requires a new set of capabilities. Managers need to build teams that feel empowered to make decisions and are accountable for the results. They also need to have a sense of the organisation as a whole, so that they can coordinate across functions to make timely decisions. Finally, they have familiarity with the technology. They do not need to be experts, but they do need to understand how autonomous planning solutions work, where there are blind spots, and when human intervention is required.

Deploy technology, intelligently: Autonomous planning rests on a technology platform with a centralised data model. In some cases, a new tech stack may be needed, but installing one can slow down the process. Adapting to an existing stack, where possible, is faster and less costly.

Sensing and prediction capabilities are also important. Organisations 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 in response. Software capable of modelling the implications of various disruptions is vital. Algorithms can analyse a company’s network of suppliers and determine what could happen if one goes down. Similar technologies can do the same for internal assets, such as production facilities or even individual pieces of manufacturing equipment.

When an Indian player in the automotive industry implemented autonomous planning technology to get more visibility into its spare parts management, it led to a 10 per cent increase in service levels, a 40 per cent reduction in response times, and a 10 per cent increase in forecast accuracy.

Given today’s complex and volatile business environment, CPG manufacturers have to be ready for change. Autonomous planning offers a clear opportunity to improve performance throughout the supply chain and to get ahead of the competitive pack. Capturing this potential will not be easy, particularly given that many companies are used to doing things a certain way, and upending entrenched practises can be a struggle. Like it or not, though, the future is coming. Autonomous planning is one way to get ready for it.

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