Driver-Based Budget Planning

國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

up-to-date data, and the ability to obtain information concerning project costs and milestone progress with a modicum of computer-related experience and effort.

The research also discovered any formalized budgeting system which evaluate the cost and benefit trade-offs over the set of available projects is not utilized. Many use the standard project control sheets. These documents are used to help justify the proposed budget and allow tracking of costs and milestones. Because of the diversity of project types, resources and criteria within the budgeting unit, math programming or financial model are generally not used of the budgeting and resource allocation process. Gantt charts are principally used for project control, and techniques and methods develop have made some inroads into R&D project management, such as Program Evaluation Review Technique/Critical Path Method (PERT/CRM) (Liberatore

& Titus, 1983). With evolution of IT technology, sophisticated R&D project management becomes achievable. Company X hence implemented a completed planning and controlling system to manage project management. It will be described in the later section in detail with impacts to R&D projects, seen as core drivers to grow of Company X business.

2.2.3 Driver-Based Budget Planning

As mentioned in previous sessions, in the majority of organizations, the budget is the most important tool used to control performance. Executives might have spent time off-site working at clarifying their strategies. The resulting strategies might be very sound and take account of all the likely external and internal issues that impact the organization’s financial performance both in the short term and in the foreseeable future. It might have been translated into a success map with appropriate measures and targets being cascaded down to individual managers. It is the budgeting process that takes precedent. Figure 2.6 shows detailed enterprise-wide budget planning process by different layers and components.

‧

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

Figure 2.6 Enterprise Budget Planning Model

(Trend Micro Inc. & Deloitte Touche Tohmatsu Ltd., 2011)

However, the budgeting process has received an increasing amount of criticism in recent years. It takes too long and therefore costs too much. Because of the rate of change in many markets, the annual budget is out of date almost before it is completed. That is why the ability to re-forecast more frequently is of such importance. Organizations need to routinely reassess the future and realign their operational plan and resources accordingly.

With the rise of “management by objectives (MBO)” and individual accountability, accounting results such as income, return on capital employed and return on investment cam to be used as targets for everyone from board members right down to departmental managers. This led to the budget becoming a critical determinant of many people’s benefit. Linking rewards to relative measures such as improvements over the previous year or outperforming industry peers will encourage organizations to become more dynamic and responsive to internal and exchange changes and to continually seek out opportunities that create value. However, unless the integration of planning and budgeting into a seamless process, organization are unlikely to be able to re-forecast with the frequency they desire, no matter how they want to reward their staff.

The traditional budgeting process is hierarchical and focuses on collecting and consolidating individual contributions to produce the enterprise profit and loss account. But when managers generate their departmental budgets, they are modelling the operational drivers

‧

and causal relationships that run horizontally across an organization. When asked to produce a budget or a re-forecast, the managers’ first concern is that the department upstream of them provides them with a reliable forecast of future demand. In fact, until they have received this, they cannot start their own departmental planning.

Driver-based budgeting uses both non-financial and financial driver data to model line item expense. Drivers will differ by industry and even by company. It is a piece of non-financial or financial data which when changed directly impacts either revenues or expenses, ultimately changing the forecast profit and loss account, cash flow and balance sheet.

The term “driver” is used for assigning expenses to activities costs to cost objects in Activity-based cost (ABC). Many of the drivers an organization would use for planning and budgeting are identical to those they use for cost assignments in ABC. Many drivers would also be important metrics to monitor in CPM. Since the definition limits drivers to those things, which directly impact either revenues or expense, they can be included in a rule formula that will directly calculate either a revenue figure or a line item expense.

There are many different types of drivers that are used in planning and budgeting. These including the following:

 Quantitative measures of demand: including both the forecast level of demand for the products or services sold customers and the level of demand faced by individual departments. For example: Market size and market share, the number of sales units of a product, the number of inbound telephone calls, the number of active customers, etc.

 Consumption rates, productivity rates or cycle times: measuring the amount of resource required to satisfy demand or produce a unit of output. For example: the average duration of a call; the amount of space needed by each full-time equivalent, etc.

 Unit resource cost: the average cost of a unit of resource during a period. For example:

the cost of a litre of fuel, the average salary cost of a particular grade of staff, etc.

 Unit selling prices: the average selling price of a product or service. For example: the average premium of a particular type of insurance policy, the anticipated fee for each consulting engagement, the anticipated selling price for a particular product, etc.

Some drivers are only important in one department. For instance, the drivers involved in

‧

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

forecasting staffing requirements and salary expenses in a contact centre are only important to the department manager and their superior. In a driver-based budgeting model, this will be a simple rule that restricted to this department, but applies to all periods and versions.

Many drivers run horizontally across organizations, spanning departments just like the business processes they are part of. The output of one department become the input of other departments downstream from them. In certain instances, this may be a one-to-one relationship.

In other instances, such as when a new sales forecast is produced, it will be a one-to-many relationship with virtually every department needing to re-forecast. The following figure 2.7 takes technical support service cost as an example and shows an inter-departmental driver-based planning process to a software house.

Figure 2.7 Driver-based Planning Process in a Software House

(Trend Micro Inc. & Deloitte Touche Tohmatsu Ltd., 2011)

The rule needed to do this is no more complex than that needed for an intra-department rule. It is just that the output of the rule becomes the input for a number of other departments.

What is important is that these downstream departments are quickly alerted that they need to re-forecast themselves and that they have immediate access to the new data.

Most driver-based planning and budgeting models start from some measure of demand. In consumer markets, this might be a market-based model with market size, market growth and

‧

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

market share being the drivers of sales volumes and demand across the entire model.

Organization competing in business-to-business markets might start by using the amount of sales and marketing activity as the primary driver of demand for their model. However, in certain manufacturing and supply industries, plant and assets have to be in continuous use around the clock if the organization is to be commercially viable. In such situations, production capacity has to be the primary input any driver-based model with most other resources being driven by the need to produce and sell the output for the highest possible price. Most will have iteratively worked out the way their business works and will already be using an appropriate methodology to model revenues and resource requirements. All finance need to do is integrate these models into the budgeting process.

Figure 2.8 Types of Driver-based planning and budgeting model

(Barrett, 2007)

Knowing that the expenses for a responsibility centre are above or below plan is incomplete information. Until we know more we cannot take any action. However, if we have access to information about the level of demand facing that responsibility centre during the

‧

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

period, the amount of resource required to satisfy that level of demand and the amount of resource actually provided, we know exactly what action to take. We can immediately see where excess capacity exists and take action to bring it into line with what is actually provided, we know exactly what action to take. We can immediately see where excess capacity exists and take action to bring it into line with what is actually required. At a time when profitable revenue growth is increasingly difficult to achieve, keeping resources tightly aligned with trading is a problem common to many sectors. Sometimes called “consumption-based” planning and budgeting or “resource consumption analysis”, the above approach demonstrates the key characteristics of driver-based planning and budgeting: it is all about building a dynamic budget where drivers are used to model revenues and line item expenses within the planning and budgeting application (Barrett, 2007). The following figure 2.9 shows a high-level planning model overview for the software industry in common practice.

Figure 2.9 Planning Model Overview of Software Industry

(Trend Micro Inc. & Deloitte Touche Tohmatsu Ltd., 2011)

‧

Much organization theory argues that efficiency requires bureaucracy, that bureaucracy impedes flexibility, and that organizations therefore confront a trade-off between efficiency and flexibility. Managers must choose between organization designs suited to routine, repetitive tasks and those suited to nonroutine, innovative tasks. However, all forms of flexibility present a common challenges: efficiency requires a bureaucratic form of organization with high levels of standardization, formalization, specialization, hierarchy, and staffs, but these features of bureaucracy impede the fluid process of mutual adjustment required for flexibility; and organizations therefore confront a trade-off between efficiency and flexibility (Knott, 1996) (Kurke, 1988). Specifically, organizations should adopt a mechanistic form if their task is simple and stable and their goal is efficiency, and they should adopt an organic form if their task is complex and changing and their goal is therefore flexibility (Burns & Stalker, 1961).

Organizational theory presents a string of contrasts reflecting this mechanistic/organic polarity:

machine bureaucracies vs. adhocracies (Mintzberg, 1979); adaptive learning based on formal rules and hierarchical controls versus generative learning relying on shared values, teams, and lateral communication (McGill, Slocum, & Lei, 1992).

The trade-off view has been echoed in other disciplines. Standard economic theory postulates a trade-off between flexibility and average cost. However, strategy researchers argue that firms must choose between a strategy of dynamic effectiveness through flexibility and static efficiency through more rigid discipline. In general the optimal choice is at one end or the other of the spectrum, since a firm pursuing both goals simultaneously would have to mix organizational elements appropriate to each strategy and thus lose the benefit of complementarities that typically obtain between the various elements of each type of organization. They would thus be “stuck in the middle” (Porter, 1980).

Hypercompetitive environment force firms to compete on several dimensions at once, and flexible technology enable firms to shift the trade-off curve just as quickly as they could move to a different point on the existing trade-off curve. While the trade-off can be shifted, much of what we observe when firms make notable improvement in several dimensions at once represents catching up to best practice (Skinner, 1985); (Hayes & Gary, 1996); (Clark, 1996).

Pushing the best practice frontier is a far more difficult task, since trade-offs are inevitable when organizations must make difficult-to-reserve commitments in plants, equipment, and