Three elements of building an integrated plan-to-cash platform
To address these problems head-on, consumer goods companies need to tackle three major challenges that are hindering their ability to optimize the end-to-end process of demand planning through to cash collection.
Closer functional alignment
Business functions frequently have competing or misaligned goals and incentives. Consider the sales and production groups in a diversified food manufacturer. The sales team is less worried about the mix of goods it must sell to reach its $100 million revenue target. But for the production team, the process of producing bottled water is vastly different from that of producing ice cream.
The pain may not be obvious during the normal course of business. But any kind of shock to the system – say, a natural disaster that creates a surge in demand for that bottled water – will stress test the operational plan and expose any weaknesses.
Now multiply this scenario over thousands of SKUs. Add the impact of trade promotions, regional variations in consumer demand, and channel structures. It's no wonder there's a level of mistrust – or at least misunderstanding – between functions. This often leads to subpar outcomes in operational planning, with inconsistent participation and a focus on reporting rather than action-oriented problem-solving.
Aiming for perfect alignment is not the goal here – nor is it a realistic expectation. However, it's possible to strike an effective balance by establishing an integrated plan-to-cash process with a framework of guardrails that allows functions to exercise some degree of autonomy within predetermined limits. In doing so, the chaos of operational planning can be better controlled.
Visibility – and ownership – of demand drivers
In most organizations, planners have limited visibility into what actually drives the business forecast and the flow of information across silos is inefficient. As a result, they're unable to connect demand forecasts back to cross-functional inputs – making it virtually impossible to pivot quickly if those inputs and assumptions change. This challenge is only compounded by the fact that the number of variables is constantly shifting, without any systematic way to capture them and orchestrate an effective operational plan.
Internal functions must strive to build a connected forecast that ties outputs to inputs. They must establish clear business rules for incorporating near-real-time data that enables agile and rapid decision-making when disruptions occur or new information becomes available. Everyone needs to understand and follow this framework and use a system of record to track drivers and assumptions.
Guardrails are also critical here. It's essential that each driver has an assigned owner for end-to-end accountability when one function makes a decision that impacts the overall plan. If this impact is above predefined limits, it's flagged straight away.
By implementing a consistent framework of business drivers – and holding functions responsible – businesses can propel a more cohesive plan-to-cash process to achieve enterprise-level goals while also having the flexibility to address emerging opportunities or risks. This also helps to move conversations away from discussing numbers and instead puts the spotlight firmly on making the right decisions to succeed in a dynamic market.
Automation with a human touch
Even in today's digital world, many organizations rely on manual forecasting processes. Employees spend an inordinate amount of time reconciling numbers and manipulating data to extract some degree of useful insight from them.
A lack of automation necessitates this manual work, but it's ultimately of little value and misses the opportunity to perform useful analysis, identify opportunities, and craft strategic responses. It's also unrewarding for the employees themselves.
Companies need to shift from focusing on data collection and manipulation to making smart decisions. This means providing real-time data for an accurate view of performance so they can make quick and decisive course corrections when opportunities or challenges arise. Applying advanced scenario analysis capabilities – powered by artificial intelligence and machine learning – adds greater speed and agility in responding to these kinds of changes.
When the enterprise digitizes the labor-intensive tasks of data collection, it can capitalize on the power of augmented intelligence, by which humans and machines work together. Employees are then free to access reliable, real-time data and apply their experience and judgment to what they do best and enjoy most: making critical business decisions. The enterprise must reimagine the organizational model to actively build augmented intelligence into the plan-to-cash process, rethinking not only organizational structures, but also the skills employees need to ensure they're effective in their new roles.