Actuals, Alerts and AI: Synchronize Decision-Driven Planning with SAP IBP

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.?

Indeed, research from Gartner has shown that ill-informed decisions by financial business partners with disconnected views of operations cumulatively cost more than 3% of profits.?

Key Gartner analysts have questioned the impact of current planning processes on effective decision-making, proposing new concepts such as decision-centric planning.

This Gartner view is that the decision-centric approach should be powered by modern supply chain planning technology enabling companies to continuously monitor events, estimating the impact of these events and supporting informed decisions for organizational agility.

Can real-time data, custom alerting, and artificial intelligence (AI) support more effective and informed business decisions that reduce waste and improve profits?

Actuals: Real-time Data and Decisions

Decision-centric planning can be a key concept in moving from more process-driven planning to a more agile and driver-based approach.

To support these decisions, the right business data, rules, policies, and thresholds need to be in place to assess events’ impact and enable further automation of mitigation plans.

This becomes especially important as IDC research shows data dense products amounted to two-thirds ($17.3 trillion) of U.S. GDP in 2022.

Accessing the right segmented data at the right time with informed and in-the-moment decision frameworks and rules means teams can also start to trust advanced algorithms and increase what IDC refers to as decision velocity.

Digitally mature organizations, with end-to-end integrated data platforms and processes across supply chain, finance and procurement, can accelerate this decision velocity.

Indeed, IDC research has shown that such digitally mature companies have twice the focus on decision speed and control as less mature organizations.

Segmenting decisions into situational, scenario or portfolio decisions will help an organization focus on the right decision speed and control for the right decision segment.

This will also support further real-time automation, for example of situational decisions based on financial actuals, where many should be executed in seconds; or algorithm-driven planning or scheduling scenarios based on real-time actuals.

Modelling decision-gate processes, rules and responsibilities with the right input data mapped to the decision logic helps business users to establish the right frameworks for decision automation, and identify exceptions that can be linked to alerts.

Tailored alerts based on the business thresholds and rules support decision-makers with the volume and prioritization of decisions, to enable more exception-based decisions where they need to intervene, encouraging more efficient decision-making.

Alerts: Tailored Exceptions and Decisions

Trust has long been shown to be a key factor in effective decision making, both at an organizational and team level. Data and dashboard democratization has accelerated the impact and trust that business analysts can deliver.

However, the volume of data and dashboards can lead to data overload and decision distress for individuals and teams. Indeed, data complexity and the number of alerts can even add to the inertia in the face of inevitable VUCA (volatility, uncertainty, complexity, & ambiguity).

Custom alerts can steer teams and organizations to focus on the key decision priorities based on specified threshold values that will be used to determine the importance of issues.

Custom alerts are used to find important or critical supply chain issues such as inventory shortages, an imbalance of supply and demand, or any unexpected changes in the supply chain.

An overview of custom alerts, clustered by subscription, priority, category or by any attribute of the alert, can directly trigger cases for individuals or teams to swiftly solve and track decisions.

These alerts and cases can be paired with organizational procedure playbooks to show how similar issues were solved in other cases, to develop and deliver recommendations for decision speed and control.

Coupled with the proven value of a more driver-based planning approach, custom alerts can be set across certain organizational value drivers derived from the strategy to help steer specific supply chain drivers and lead to more objective decisions with less bias.

Alerts across the quantity or quality of risks and opportunities combined with external factors such as industry benchmarking and AI-driven scenarios enable companies to adjust demand forecasts and act with more agility, to go for profitable growth.

AI: Support and Scale Human Decisions

AI-enabled decision frameworks can support humans for more efficient decision-making. A recent Harvard Business Review article highlighted that AI tools can lighten the cognitive load for teams and improve decision-making effectiveness in many ways.

Organizations can already benefit from improved decision tracking and AI simulations, realistic practice in virtual settings, and real-time AI-powered decision advice. AI is driving informed decision democratization across all organizational levels.

With decision modelling tools, incremental AI can be empowered to support the further automation of situational decisions. AI-driven scenario decisions can be automatically approved based on range and risk thresholds.

Enterprise AI assistants, such as SAP Joule, can guide and collaborate with teams to scale the volume of decisions based on internal corporate data, and teams' information and files. Speed and control of decision making will increase trust in the decision outcomes.?

There is the impending potential for transformative AI to steer portfolio decisions based on assessed risks and opportunities, perhaps to create feasible proposals for new business divisions or products, or recommend detailed business plans for accessing new regions.

At the enterprise level, it’s imperative to trust in responsible business AI that avoids bias, or reliably avoids interpreting bias in the underlying relevant information, plans or data.

Embedded AI in SAP Integrated Business Planning (IBP) and SAP S/4HANA helps automate decision-making through pattern or outlier recognition and smoothing, interpreting and optimizing complex data sets and simplifying real-time insights.

Synchronizing decision-making across planning and fulfillment as per the image above will improve agility and alert overviews to steer situational and scenario decisions with informed AI-driven decision engines and workflows.

Generative AI such as Joule will assist teams across finance and supply chain to align on joint scenario and portfolio decisions and accelerate the assessment and selection of options.

Conclusion

SAP is supporting our customers’ success with the ability to take swift decisions and actions across organizational silos using actuals data, custom alerts, and AI at scale. Find out more here.

Securely synchronizing multiple systems and platforms with real-time data, while modelling the ability to automate decisions with alerts and AI, builds decision trust with relevant data, reliable scenarios, and responsible and secure access.