Perhaps some of the reluctance is because of a perceived inability to model organizations’ complex operational activities and especially how they impact the financial statements.

Procurement managers have long used S&OP as a tool for aligning supply and sales function, but these techniques, even when expanded to include finance, are of limited use in terms of financial optimization. While organizations that use Integrated Business Planning (IBP) are heading in the right direction, this is typically operationally-focused and not specifically concerned with financial optimization. To succeed and meet their financial targets in 2020, CFOs need something more.

A Definition of Financial Optimization for Businesses

Financial optimization for businesses can be defined as those business practices that generate the greatest revenue while minimizing input costs to achieve consistently higher profits in the short-, medium- and long-term, while simultaneously complying with relevant ethical, legal and financial constraints.

To optimize financials, it’s necessary to fully understand how this complex function interacts with the organization and to be able to measure and analyze performance. In turn, this means financial managers need tools that allow them to model the organization to understand how the organization functions. Such tools need to access relevant information and provide the ability to analyze operation performance to determine optimal financial decisions in line with agreed corporate objectives.

Examples Illustrating the Importance of Financial Optimization

There’s a significant difference between measuring and monitoring financial metrics in an organization and actively seeking to optimize them. Here are some examples of what represents financial optimization.

Understanding demand optimality

Forecasting demand is a crucial planning process, but it’s important to determine optimal demand rather than trying to maximize demand. Key to this is understanding the financial implications of the actions taken to influence demand through sales advertising, promotions and discounts. Rather than seeking to increase sales at any cost, it’s essential to measure the financial impact and tradeoffs of achieving increased sales because additional selling expenses may ultimately impact organizational returns.

Treating costs appropriately

Some costs are considered fixed in that they don’t vary, while others are variable related to sales. However, this is too simplistic an approach because fixed costs don’t remain fixed but can vary over a period of time. For example, a fixed cost such as plant overhead costs don’t change significantly in the short term, but they will vary in the longer term as the business grows and invests in additional capacity or shuts unprofitable lines. There’s a need to model these costs in such a way that they naturally reflect short- and long-term realities.

Effects of tradeoffs

Every organization faces numerous tradeoffs. There are balance sheet tradeoffs such as liquidity, leverage and working capital, and then there are practical tradeoffs such as production capacity, manning limitations and logistic capacity. These tradeoffs must be explicitly known and taken into consideration.

Calculating marginal profitability of constrained resources

To maximize shareholder value, it’s important to ensure the effective allocation of resources and capital, thus optimizing the balance sheet along with the P&L. This leads to the need to understand the marginal profitability of resources, especially constrained resources. By understanding the additional products that could be sold and netting the revenue against the additional supply costs, we can understand:

• The value of releasing that constraint
• Which product combination generates the highest marginal profit
• How to optimize profit

A History of Financial Optimization

It’s probable that the roots of financial optimization lie within two completely separate yet related processes:

• S&OP
• Portfolio Financial Analysis

S&OP was introduced to overcome the organizational silos that existed between sales, production and procurement. Initially, it excluded finance, but as it became apparent that organizations were measured by their financial performance, finance was incorporated within S&OP and metrics became measured in financial terms as well as in terms of units, numbers and sales.

At the same time, analysts were using sophisticated mathematical techniques in an attempt to understand, predict and optimize financial markets. These included various optimization techniques based on linear and nonlinear programming.

Initially, this work relied on clever mathematicians, but as computing capacity increased, it became possible to represent organizations as mathematical models and run computer simulations to determine optimal results. By combining these techniques, it was possible to model the organization and use linear and nonlinear programming methods to optimize organizational financial performance.

Barriers to Effective Financial Optimization

The most obvious barriers include the will and ability to perform financial optimization. In many instances, organizational silos resist it and standard financial reporting systems don’t offer this capability.

However, the successes of S&OP and IBP have shown it’s possible to break down organizational silos. Additionally, mathematical modeling software has opened up the way for business modeling, and optimization solver software provides the ability to evaluate different scenarios and determine optimal financial solutions.

Initially, business models were hard coded using modeling software, a laborious and expensive process which required advanced programming skills. This limitation restricted business modeling to organizations with deep pockets, especially as model preparation took considerable time. Another barrier to success was difficulty in accessing data. Many organizations had multiple software packages that couldn’t talk to each other, making it difficult to obtain a true picture of the organization. Finally, limited computing capabilities meant it was difficult, if not impossible, to analyze data in a reasonable time, effectively restricting modeling to larger businesses with major data centers.

These limitations no longer exist. It’s now possible to write financial models using relatively simple and intuitive modeling techniques without the need for hard coding. ERP solutions provide one version of the truth. There’s plenty of organizational and other data available to populate models, while cloud computing has removed the cost and computing capability barriers.

What Do You Need to Succeed in 2020?

CFOs need to focus on optimizing the organization’s financial performance and understanding the factors that drive financial optimization. This can be achieved through creating a comprehensive financial model of the business that includes all aspects of the financial function and how these functions interrelate with the day-to-day business. The model should be structured to include cashflow, capital allocations, revenue, costs and other constraints as well as purely financial factors such as bonds, treasuries, loans and bank accounts.

Then, using techniques such as predictive and prescriptive analytics, it’s possible to interrogate the model and determine how to improve profitability, cashflow, leverage and other important financial indicators. Using such a model, it’s possible to perform complex analysis and obtain deeper knowledge of how the organization functions, and utilize it to evaluate how to meet your business KPIs.

Transforming the Organization with Financial Optimization Modeling

Because the financial model would largely represent the organization in its entirety, there are many opportunities for collaborative financial optimization exercises between finance and other departments. While this may require adding additional features and functions to the model, this process is simplified with the latest generation intuitive modeling packages that don’t require hard coding.

An important benefit of financial optimization modeling is that it moves the focus away from historical past performance toward proactive, forward-looking, data-driven decision making.

Financial optimization based on prescriptive analytics allows executives to bridge the gap between managerial and financial accounting. This is important. While the CFO is naturally focused on ensuring financial accounting complies with GAAP requirements, the CEO is more concerned with management accounting information that supports decision-making. Although a financial optimization model must reconcile with the P&L statement, it’s possible to cut through the clutter and provide specific answers to the managerial questions. In this way, the financial implications of key business decisions are presented in terms that both the CEO and CFO understand.