Planning is a simple concept to understand and yet can be difficult to perform. Part of the reason is its multifaceted nature that, within a business context, can come in a range of types (eg, strategic, operational, financial), functions (eg, sales, logistics, production) and techniques (eg, top-down, bottom-up, driver-based). Plans include different combinations of these areas, depending on the purpose being served. As a consequence, it is easy to lose sight of what each is designed to do, and organisations can end up with a mishmash of plans that have little or no connection to what they are trying to achieve.
As organisations become more complex and involve many people, planning must become better disciplined and organised. Every facet of the business needs to be considered, whether that means sales and operations, logistics, human resources, or tax. The company must be organised so that plans are complete and assembled in a logical sequence. There are dependencies. For example, there is no point in planning cash requirements unless sales forecasts and supplier orders are known with some degree of accuracy. To do this requires an analysis of market trends, competitor activity, and production capacity (and cost) of our own setup. Each part of a plan has the potential to impact another, and some method is needed to conduct planning in an orderly and efficient manner.
It is unlikely that an organisation can jump from where it is today into a full-fledged, continuous planning process. It will require a number of incremental steps that introduce changes over time. Because not everyone is at the same stage, we have developed the following maturity model that describes levels of planning exhibited within organisations today. These levels can then be used to assess the next steps in developing the planning process.
Planning and forecasting maturity levels
The maturity of planning and forecasting processes is driven by the level of model integration. As models become more integrated, they support faster processes that yield greater forward visibility and reduce uncertainty.
These approaches can be broken down into five stages of planning and forecasting maturity. A key feature that separates each of these stages is the type of driver-based planning approach that it employs. Being driver-based means that variables affecting performance or resources can be related to one another. The dependencies between the variables can be modelled. For example, production costs could be related to volume made, which in turn could be related to sales success, and so on. As driver-based planning becomes more mature, organisations can support more sophisticated scenario planning.
At this stage, organisations employ traditional bottom-up budgeting approaches that are augmented by very basic models. These models are typically based on financial relationships where a planning line is expressed as a percentage of another line item or period. The following are examples of this:
- Sales are expressed as a percentage increase or decrease over the prior year.
- Cost of sales is expressed as a percentage of sales.
- Expenses (eg, salaries and travel) are expressed using either approach.
- Cash flow is expressed as a percentage of receivables and payables.
The following are some of the classic characteristics of these models:
- Any analysis that supports them is often maintained in offline systems or spreadsheets.
- Operational reconciliation is done on an ad hoc basis, if it is done at all.
- There is a loose connection between objectives, targets, budgets, and forecasts.
The effectiveness of these models depends on the complexity of the business. As complexity rises, these models are not as useful because they are not accurate and they do not support consensus.
The financial integration stage is one where organisations use operationally based driver models that estimate how costs behave as volume and revenue changes. Key features of this approach include the following:
- The role of finance is to determine which drivers best quantify the impact of changes.
- This role often entails summarising operational planning models into simpler financial ones.
- The models are typically expressed in terms of cost per driver or per unit of output.
- Examples of drivers include the number of orders, customers, products, or shipments.
These models are typically developed for financial planning and cost-estimating purposes only. Activity-based costing (ABC) is the accepted method to proportionately trace the consumption of resource expenses such as wages, supplies, or power to outputs such as product costs using a cause-and-effect relationship. Although operations provide input into developing the models, the level of granularity of ABC’s activity-cost pools is typically adequate for strategic insights because of the high cost accuracy, but they may not be sufficiently detailed to enable operational managers to make operational decisions.
At this stage, many organisations start using balanced scorecards and other performance measurement approaches. However, target-setting, budgeting, forecasting, and scenario-planning processes are only loosely connected. This is primarily because the planning models are not sophisticated enough to connect key performance indicator (KPI) targets to resource requirements.
In organisations that experience little change or variability, these models can be very effective. However, more complex ones often experience the following:
- Maintaining models can be time-consuming, often resulting in models that are inaccurate.
- Embedded operational assumptions are often only valid across a narrow range of scenarios.
- Models often have to be manually updated to examine the impact of outlying scenarios.
- As a result, organisations can run only a limited number of scenarios, thereby exposing them to potentially unidentified risks.
- Scenario planning can be costly, as significant time and effort goes into validating and reconciling financial and operational scenario-planning efforts.
At this stage, finance and operations start sharing models and processes in parts of the organisation. Other parts continue to operate processes at levels 1 and 2. They do this because it:
- Reduces the cost of processes and systems;
- Results in greater clarity because there is only one plan;
- Supports more effective scenario planning in these parts of the business;
- Provides greater forward visibility into risk as a result of scenario planning; and
- Improves process speed by eliminating non-value-added activities.
This stage of planning maturity can take many forms. Demand planning is a classic example of where a single model can be used to support more effective processes. The manufacturing sector has led the way in leveraging such integrated models that, at a basic level, provide the means to connect the number of units sold to revenue and average selling price. Beyond that, they enable organisations to:
- Establish more collaborative demand and revenue planning processes;
- Automate the analysis of volume and mix variances on revenue and average selling price;
- Support a more effective rolling forecast process that adapts faster to change; and
- Co-ordinate new product development and promotion planning into demand and revenue forecasting.
Manufacturing has also led the way in developing models that integrate planning of direct costs or cost of sales. Bills of materials are used to define the commodities and components of the products they sell and how they are made. This results in a process that simultaneously forecasts:
Cost of goods manufactured and sold, together with inventory balances;
Production capacity requirements, together with capacity constraints;
Commodity purchase requirements and related cash flow impact; and
Purchase price and production cost variances from standard.
From an indirect perspective, integrated models translate KPI targets, along with departmental volume, into the following:
- Staffing requirements;
- Departmental budgets and forecasts; and
- Productivity (cost per outcome) targets.
Multiple planning models and systems still exist for organisations at this stage of maturity.
At this stage of maturity, organisations shift from traditional functionally based planning approaches to more horizontal cross-functional and outcome-based approaches. To support this, finance and operations share models and processes across the organisation. The following are key features of this stage:
- Profit and cash flow forecasting is explicitly linked to KPI and revenue targets.
- Plans are expressed from both functional and process perspectives.
- Budgets and forecasts are expressed in relative terms (cost per output).
- Planning and target-setting cuts across functions and business units.
One of the key reasons that organisations shift to such an approach is to optimise performance across functions and business units. In so doing, they are also recognising the limitations of traditional budgeting processes because they reinforce functional silos and thereby sub-optimise performance.
Organisations at this level of the planning maturity scale employ highly sophisticated models that integrate all aspects of planning and forecasting. One of the primary motives for doing so is to enable organisations to more effectively manage complexity, uncertainty, and risk, a KPI component of which is more effective scenario planning.
These organisations also conduct integrated scenario planning. This includes the ability to simultaneously evaluate the impact of different scenarios on all aspects of performance.
Four specific capabilities arise at this level of maturity:
- Dynamic models that self-adjust to changes in volume and mix;
- Forward-looking (activity-based) product and customer profitability and cash flows;
- Capacity-constrained cash flows, whereby models forecast capacity constraints, along with their impact on cash; and
- Project and portfolio return on investment, whereby models quantify the impact of operational changes on the cash flow of project portfolios.
As mentioned earlier, the levels of planning maturity described can be used to assess where an organisation is today and the level at which it needs to be.
Editor's note: This is an adapted excerpt of the CGMA book Budgeting, Planning, and Forecasting in Uncertain Times, by Gary Cokins and Michael Coveney.
Gary Cokins (email@example.com) is the founder of Analytics-Based Performance Management, an advisory firm in Cary, North Carolina. He was a consultant with Deloitte and KPMG and is the former head of the National Cost Management Consulting Services for Electronic Data Systems (EDS). He also worked in business development with SAS, a leading provider of enterprise performance management and business analytics and intelligence software.
Michael Coveney (firstname.lastname@example.org) is an experienced consultant and course leader in corporate performance management. He has been involved in product direction with software vendor GEAC/Comshare and has written other books on managing strategy.