How SAP AI can enhance impact-driven extended Planning and Analysis (xP&A).

Recent Gartner research shows that organizations that can change plans fast are nearly 200% more likely to capture the full potential of opportunities created by external events.

For companies to take advantage of agility and adapt to external events or market trends, they need to plan across traditional silos, with a view of their ecosystem, in what Gartner terms extended Planning and Analysis (xP&A) .

Understanding a range of impacts of external events across silos and their ecosystems enables companies to (re)act faster with more informed decisions and actions for improved commercial excellence and to fulfil demand.

According to a McKinsey survey of more than 1,200 global business leaders , inefficient decision-making costs a typical Fortune 500 company 530,000 days of managers’ time each year, equivalent to about $250 million in annual wages.

How can companies use AI to accelerate automated planning processes with impact-driven xP&A for connected decisions that drive margins and growth?

AI-enhanced xP&A

Generative AI could add trillions of dollars of value in the global economy, including up to $660 billion a year for the retail and consumer packaged goods (CPG) industries , partly by faster understanding and actions on changing consumer behaviors and market volatility.

AI-enabled processes, people, and platforms can accelerate and orchestrate better scenarios and solutions with increased time-to-value, while identifying new opportunities quicker with driver-based planning .?

Driver-based planning is a framework used to plan business performance and scenarios based on company-specific value drivers and KPIs developed from strategic objectives.

Driver frameworks can include both quantitative and qualitative data with specific weightings across multiple planning levels, and can be aligned in financial and supply chain planning.

Each internal or external driver input or factor, like sales volume, customer acquisition cost, or production capacity, is assigned an impact indicator based on high-to-low assessment scales and automated with accurate and real-time data.

By cutting across data silos with consistent drivers, data, and factors, companies can apply more automation and AI to their xP&A processes, and simulate combined impact across commercial, workforce, operations, and finance.

Streamlining data management can drive more accurate ranges, impact analysis and decisions . Business AI can enable more effective xP&A organizational change by identifying likely obstacles, and the talent or skilled colleagues to align with to solve them.

According to McKinsey research , generative AI tools can identify and synthesize trends, key drivers, and market and product opportunities from unstructured data such as social media, news, academic research, and customer feedback.

Generative AI tools can also act as virtual experts and collaborators to accelerate xP&A across functions and ecosystems, improving flow and decisions.

SAP has launched Joule , the generative AI assistant to collaborate with our customers in SAP processes and tasks while helping find new value in their data.

Faster xP&A with AI

COVID-19 fundamentally changed the way that CFOs and Finance teams ran their budgeting processes, with a new focus on being able to react more quickly and efficiently to disruptions .

By moving to a shorter budgeting cycle more closely synchronized with commercial and supply chain plans, finance teams are increasingly seen as an agile enabler of growth and margins.

Combined with a more dynamic speed and quality of decisions with AI support, customers can look to enable new revenue streams and bring innovation to market faster. For CPG companies, organizational intelligence, simplification and focus enables further agility .

Indeed, McKinsey’s recent survey of AI top performers showed that 50% of respondents use AI to go for growth or new business, compared to 33% from laggards.

The recent survey of over 550 risk managers in the Federation of European Risk Management Associations (FERMA) showed that in the next two years, changing customer behavior and uncertain growth will be top risks.

Trying to fix ‘one number’ for these challenges and drivers is less effective than being able to react faster with a range of drivers, inventory, and distribution options to capture growth and optimized margins as consumer behaviors alter .

Firms that lead in financial planning are likelier to adopt more dynamic and externally facing practices , and xP&A extends data-driven decisions across silos and help identify risk maps across procurement and supply chains.

SAP’s planning partner Cosmo Tech runs AI vulnerability scans across customers’ supply chain networks to simulate risks with the recommended reconfigurations to mitigate these risks.?

Business and generative AI can revolutionize internal knowledge management systems guidance on these risk factors and data to help more informed scenarios and decisions. Immediate AI insights into the data can build trust in the decisions. ?

BCG research points to upside of AI in planning processes but the risk of disparate data, with an example of separate commercial, financial and supply chain planning systems and data costing over $100 million for one customer .

So, there is not necessarily a one-size-fits-all approach, but there are benefits of a natively integrated xP&A platform and data fabric for customers deploying AI decision support at pace.

Deploy xP&A swiftly with SAP

To enable swift steering of xP&A value and volume drivers , SAP offers standard multilevel value-driver trees with planning models to generate predictive scenarios. These drivers can be applied across commercial, financial and supply chain planning.

Customers can overlay these drivers over predictive planning and scenarios in classification, regression, time-series planning models – with statistical confidence intervals that use volatility or time periods as dimension parameters.

Disruptions can be simulated and planned for with teams of cross-functional experts. Business users can rapidly configure new models and predictions with the smart insights and immediate drill-downs to validate decisions and actions.

SAP Planning smart insights can easily be extended with generative AI and natural language models to swiftly back up decisions. With SAP’s business data fabric, data is consistent and real-time, building trust in outcomes and query results.

Accenture has developed an always-on enterprise planning and decision-centric driver framework with SAP Integrated Business Planning (IBP) and Analytics Cloud (SAC) Planning that now also includes Commercial Planning capabilities . We are also partnering to help businesses adopt generative AI .

Using SAP Signavio to guide xP&A process alignment, decision makers and data, impact analysis is aligned across volumes and values with predictive scenarios, with analytical stories to guide agile management business reviews and decisions.

Conclusion

Extracting key insights from a variety of data sources to support xP&A used to mean having to hire in data scientists and apply AI to disparate data sources.

Now SAP embedded and generative AI, which is secure and reliable, is opening new opportunities for companies to access intelligent enterprise business data faster and deliver more accurate and fully costed decisions in near real-time.

Please get in touch if you would like to see how an AI-assisted driver-based xP&A process can support faster and more feasible plans for your organization.

?