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Measure and Evaluate Customer Profitability Management
In order to fully evaluate the impact of ABC and ABM initiatives it is necessary to undertake a quantitative assessment of their impact and assign calculable values.
The following describes some of the key measures used in ABC/M (Anonymous, 2005):
- Activity Drivers:during ABM planning stages the sources of all activity driver data should be identified. Some of the data may be gathered from current transaction files with the rest being derived from other sources.
- Volume Cost Drivers: are expressed in basic throughput terms, e.g. gallons bottled, invoices processed.
- Structural Cost Drivers: uncover the segmentation that exists within the business, e.g. range of components, suppliers, product types.
- Change Cost Drivers: uncover how changes to the status quo impact the business, e.g. new product introduction and customer churn.
- Introduction Cost Drivers: relate to the activities associated with future products and services, e.g. design and development.
- Cost Driver Variability: provides a test concerning the strength of suspected pro-rata relationships between an activity and the chosen driver. Variability is said to exist if more than one driver is acting on the activity. In this case it is normal to divide activities into smaller ones with each having separate cost drivers.
- Unit costs:these comprise total activity costs divided by the driver volume, and can form a powerful means of displaying the data. Unit cost data also provides a means to benchmark parts of the process, either internally, by comparing various geographic regions, or externally with comparable organisations or published best-practice data. Unit cost data forms the basis for improved budgeting.
- Materiality of Costs: a key issue when determining how to employ the various costs found in the ledger is materiality, i.e. if a certain cost represents only a very small percentage of total costs it is unlikely that it would have any material effect upon the model and therefore may be ignored. (Anonymous, 2005)
The president of the Australian Chartered Institute of Management Accountants, Malcolm Smith, wrote that non-financial indicators are an important tool used in (a) management accounting, (b) total quality management, and (c) ABM. Some of the goals associated with these management methodologies are to minimise costs, raise quality, improve response times, and to generate greater innovation. The classification of quality costs can facilitate a closer examination of the drivers of quality which is part of ABM. The following non-financial indicators provide a basic idea of how ABC/M efforts might be assessed: (Smith, 1997)
Non financial indicators for ABC/M
- Quantity of raw material input e.g. actual versus target number or amount. This measure provides information concerning the cost of materials and waste.
- Equipment productivity e.g. actual units produced versus standard units. This measure provides and indication of the efficiency of the plant and its management.
- Maintenance efforts e.g. number of production units lost through maintenance, or number of production units lost through failure, or number of failures prior to scheduled overtime hours/total hours, or breakdown maintenance/ total maintenance. These measures provide an indication of the costs of prevention, appraisal and failure which all may be attributed to the cost of quality.
- Overtime hours e.g. overtime hours/total hours, or % of total hours, or average weekly overtime hours. These measures provide an indication of productivity and cost of failure or cost of upkeep.
- Product complexity e.g. the number of component parts produced. This measure provides an indication of areas for possible productivity and design improvements.
- Quantity of output e.g. actual output quantity versus target completion quantity. This measure provides information concerning possible productivity improvement.
- Employee productivity e.g. direct labour hours per unit. This measure may provide an opportunity for productivity improvement.
- Quality of output e.g. % yield. This provides a measure of both quality and productivity.
- Reliability e.g. % warranty claims. This provides a measure of quality and productivity
- Quality commitment e.g. % dependence on post inspection, or % conformance to quality standards. This measure provides and indication of cost of quality
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