How Profit is Maximized

The four abiding realities which make OIS work are the following:

  1. A model is built of the firm’s operations.
  2. The model relaxes the assumptions of both a fixed forecast and a fixed supply chain
  3. These assumptions are relaxed, respectively, with predictive analytics: viz, response functions and cost functions.  These functions describe the operations, the transitive verbs (e.g., procure, make, pick/pack, install) that model what the firm does for a living.  They are the causes that drive the effects of the line item expense detail in the CoA from which the traditional, budget income statement is created.
  4.  The model, so constructed, is then optimized with prescriptive math programming techniques (What is the best possible X?) and not, as is currently done, with decriptive techniques (What will happen if is do X?).  See solver comparison.

For an illustration of how the two companies partnered to create the sw and data required to create an OIS, see Integrate the Planning Capabilities of Two Advanced Analytics Companies to Create an Operational Income Statement (OIS) .  It illustrates the three points above.

What follows, then, are the factors these two companies incorporated, for the first time ever, that must must be present in any model built for developing an operational income statement (OIS).  They are all significant departures fr m how income statements and supply chains are traditionally developed and are the reasons an OIS is truly maximally profitable.  No other modeling product exists with all these factors; OIS is a completely unique financial planning optimization product and, therefore, of unique interest to the CFO.

1.Model must be demand-driven. S costs of SG&A are the independent variable that drives the dependent variable, forecasted units. This elevates the sales and marketing leadership to a preeminent role in the planning process as they are responsible for sizing and allocating the S expenditures by developing enterprise response functions.  These describe, quantitatively, the relationship between the dependent variables and the independent variables.  The advanced analytical technique employed is predictive analytics. See Enterprise Response Functions for an overview of how they are developed.

2. Model must be operations-driven. G&A and COGS costs are a dependent variable and the units are the independent variables creating costs as they flow through the operations in the model.  These relations relating unit flows to activity costs are called cost functions. See Cost Functions for an overview of how they are developed and the analytics are also predictive.  Importantly, if activity-based costing data already exists, the time and cost required to develop OIS‘ cost functions are significantly reduced.  For details, see  Wiley’s Journal of Corporate Accounting and Finance, article titled “Enterprise Master Plan: Next Generation Planning with Activity-Based Costing,” May/June 2014.

3.Objective function (i.e., what you’re trying to optimize) must be profit

4. Solver must be prescriptive (“what is the best possible outcome ( X)?”) and not scenario analysis (what will happen if we do “X”?)  The advanced analytical technique employed by OIS is a mix of integer and linear math programming (MILP). See more detail

5.  All the variables must be solved for at the same time. This is because in any system, the sum of the partial optimum benefits is less than the system optimum benefit.  An OIS optimizes the entire projected income statement with all of its functional silos simultaneously.  Thus, even if individual functions within the enterprise used prescriptive techniques to develop their portion of the total enterprise-wide projected income statement (e.g., marketing, sales, manufacturing)., the sum is necessarily sub-optimal.  See Principle of Sub-optimization.

For a description of the product, INSIGHT Enterprise Optimizer which creates an OIS, see IEO Description on the INSIGHT web site: How to Maximize Corporate Profitability.