Effective Strategies for Aligning EBITDA Expectations with Reality

In our previous blog, “From Expectations to Reality: Understanding EBITDA Variations”, we delved into the intricacies of EBITDA variations. In this subsequent blog, we aim to equip you with the approaches to proactively manage your business’s profitability.

A recent survey from Deloitte suggests that 82% of companies said they missed their cost-reduction targets, up from 72% a year ago. This is the highest failure rate Deloitte has ever recorded since starting this series in 2008. (Source: Deloitte)

The Approach for Bridging the EBITDA Gap

This section will outline the strategies to align your business with EBITDA expectations and transform them into tangible realities.

83% of companies are looking to change how they run their business margin improvement efforts. (Source: Deloitte)

Businesses are adopting a holistic approach with robust optimization & cost management processes by integrating people, processes, and technology that integrates the following:

xP&A: The xP&A system integrates financial and operational activities across the entire organization to facilitate comprehensive planning. This next-generation xP&A solution encompasses various organizational functions such as budgeting, forecasting, modeling, profitability analysis, variance analysis, automated reporting, and Integrated Business Planning (IBP). Budgeting involves creating detailed financial plans for an organization's expected revenues, expenses, and capital allocations over a specific period, generally a fiscal year. Forecasting revises financial projections based on the latest data and advanced analytics for a current view of financial health and performance. xP&A financial modeling uses mathematical representations to create scenarios, assess outcomes, and aid decision-making. Profitability analysis in xP&A evaluates business segment profitability and identifies opportunities for growth. Variance analysis in xP&A compares actual financial performance with budgets or forecasts to identify discrepancies and understand their causes, helping management take corrective actions promptly. Through automated report generation and distribution, Decision AI ensures the delivery of accurate, timely, and pertinent reports derived from the latest data. Integrated Business Planning (IBP) connects financial planning with strategic and operational planning in xP&A, ensuring alignment with the broader business strategy and operational plans for improved coordination and goal achievement.

Decision AI: Integrating Decision AI into xP&A leverages advanced AI and machine learning to optimize decision-making processes and facilitate automated reporting within organizations. Decision AI encompasses Prediction and Scenario Simulation, Decision Orchestration, Data-Driven Insights, and Cause-and-Effect Decision Outcomes. In the realm of Decision Orchestration, Decision AI oversees decision workflows, facilitating stakeholder engagement at each stage to promote collaboration and accountability. Organizations can assess potential outcomes and make informed decisions by running multiple scenarios and simulations. Decision AI facilitates testing different assumptions and their implications, enabling organizations to make well-informed decisions. By harnessing historical data and machine learning, Decision AI has the capacity to forecast future trends and outcomes, allowing organizations to anticipate market changes and customer behavior. Additionally, Decision AI supports Scenario Simulation, enabling organizations to explore the impacts of different strategies, thus aiding in well-informed decision-making. Moreover, Decision AI seamlessly integrates with diverse data sources to derive Data-Driven Insights using advanced analytics techniques such as machine learning and natural language processing, thereby revealing patterns, correlations, and insights that may not be evident through traditional methods. Furthermore, Decision AI assists in identifying the root causes of issues or opportunities by analyzing large datasets and identifying underlying patterns for Cause-and-Effect decision-making.

Risk and Compliance Management: The alignment of Risk and Compliance Management with corporate governance and sustainability practices significantly enhances the overall efficacy and dependability of an organization’s planning and performance management processes. By integrating Decision AI and change management into Extended Planning and Analysis (xP&A), organizations can more effectively attain their performance management objectives. The integration of corporate governance frameworks ensures that decision-making processes adhere to legal requirements, ethical standards, and best practices, thereby enhancing transparency, accountability, and integrity within the organization. The inclusion of ESG criteria in risk management practices enables organizations to consider environmental and social impacts alongside financial performance, thereby promoting sustainable business practices and bolstering corporate reputation. Implementing sustainability practices involves efficient resource management, waste reduction, and minimizing environmental impact, contributing to long-term operational efficiency and resilience.

Evolution of xP&A and Decision AI Tools

85 percent of business leaders have suffered from decision distress, 93 percent believe having the right type of decision intelligence can make or break the success of an organization. (Source: Oracle)

Evolution of xP&A

• 2000 - FP&A was confined to finance departments. (Source: SAP)
• 2010 - Organizations began integrating financial planning with operational functions. (Source: Insightsoftware)
• 2015 - Cloud computing and big data analytics accelerated XP&A. (Source: SAP)
• 2020 - Gartner formally coined the term xP&A. (Source: Gartner Research)
• 2024 and beyond - Autonomous Finance using AI, Gen-AI driven Forecast and Budget Variance Explanation, AI-Augmented Human Decision-Making, Intuitive AI Experience Unified Real-Time Data Ecosystems. (Source: Financealliance)

Evolution of Decision AI Tools

• 2000 - Decision Support System using If/Then/Else Rules. (Source: Lawrence University)
• 2010 - BI tools were static, focusing on descriptive analytics based on historical data. (Source: SAP)
• 2015 - Bl tools began incorporating predictive analytics capabilities, signalling the early stages of decision intelligence. (Source: Spiceworks)
• 2020 - AI integration revolutionized proactive decision platforms, enabling early signs of prescriptive analytics. (Source: Cubesoftware)
• 2024 and beyond educated projections- Pilots/Copilots for timely decision making, Explainable and Responsible AI, Human-AI Agent Collaboration, AI-Driven Data Management, Next Gen-AI Models for precision-level accuracy. (Source: Financealliance)

Example Scenario of addressing EBITDA variances

CEO to CFO: "Hi, do you have any recommendations on improving the gross margin on shoe sales for the next quarter?"

In addressing the CEO's question, the approaches of Company-A utilizing Next-Gen xP&A with Decision AI and Company-B leveraging ERP systems and spreadsheets would likely differ significantly due to their respective strengths and toolsets.

Company-A: xP&A with Decision AI

Company-A's approach would leverage value-driven data centralization, financial data quality & trust, profit & growth analysis, scenario simulation & planning and cross-functional collaboration :

1. Value-driven Data Centralization: Centralize the data based on the value rather than centralizing everything.
2. Financial Data Quality & Trust: Ensure financial data quality meets the standards to maintain trust and credibility.
3. Profit & Growth Analysis: Use predictive and diagnostic analytics to identify high-growth profit factors.
4. Scenario Simulation & Planning: Use driver-based planning to auto-simulate scenarios for optimal real-time decisions.
5. Cross-functional Collaboration: Learn decision impacts to collaborate and boost business margins.

Company-B: ERP with Spreadsheets

Company-B's approach would likely be conventional, emphasizing extensive financial analysis and meticulous manual data scrutiny, covering

1. Data Collection: Manually gather sales, cost, and margin information from various spreadsheet reports.
2. Organize Data: Combine multi-source data manually, maintaining version control for data integrity.
3. Gross Margin Analysis: Identify trends and patterns to determine why margins vary by product, channel, and region.
4. Scenario Modeling: Use spreadsheet functions to model pricing, cost, and product mix for optimal strategy.
5. Executive Collaboration: Provide a report suggesting areas where costs can be cut, prices can be adjusted, and products can be discounted.

Company-A leverages automated, data-driven insights and predictive models to quickly identify issues and suggest improvements, while Company-B uses traditional, thorough, and somewhat manual financial analysis to provide strategic recommendations. Company-A's approach may be faster and more adaptable to changing conditions, but it may be less rooted in the extensive, contextual business understanding that a traditional FP&A team might provide. On the other hand, Company-B's methods, while slower, are likely to produce detailed, actionable financial insights rooted in established analytical practices.

35 percent don't know which data or sources to trust and 70 percent have given up on making a decision because the data was overwhelming.?(Source: Oracle)

Call To Action

Initiate Margin Improvement Programs: Implement strategies for cost reduction, process optimization, and maximizing value across products and services. (Source: Beacon CFO plus)

Evolve the xP&A Function: Expand FP&A to strategic roles, collaborating with operations and focusing on business outcomes and financial metrics. (Source: FP&A Trends)

Establish an Enterprise-wide Data Strategy: FP&A leaders should develop a data strategy enhancing accessibility, quality, and governance enterprise wide. (Source: Infopulse)

Invest in Processes and People: Train staff in analytics and new technologies and streamline processes for efficient financial operations. (Source: Strategileadership)

Leverage AI and ML in Financial Practices: Use AI and machine learning for predictive insights, automating tasks, and dynamic scenario planning. (Source: Veritis)

Adopt Specialized Tools: Shift from spreadsheets to advanced xP&A tools for real-time data, integration, and enhanced analytics. (Source: IBM)

99% of surveyed executives are planning to implement some form of business margin improvement program. (Source: Deloitte)

Summary

In summary, organizations must accurately align their EBITDA expectations with reality to optimize their financial performance. Achieving this alignment requires implementing effective strategies that promote informed decision-making. By adopting these strategies, organizations can enhance their ability to make sustainable financial decisions and achieve long-term success.

In our upcoming blog post, we will explore the various types of artificial intelligence (AI) that can be leveraged to boost profitability, focusing on their applicability to business professionals. This discussion will delve into the practical applications of AI in different industries, highlighting how AI has been used to optimize business operations and enhance decision-making processes.?

References-

1. Rethinking business margin improvement strategies
2. Innovation Insight for Extended Planning and Analysis (xP&A)
3. Global Study: 70% of Business Leaders Would Prefer a Robot to Make Their Decisions