Activity Based Management
Author:
Brian Plowman
Interest in Activity Based Management (ABM) is on the upsurge. ABM helps managers deliver greater profitability in the private sector; better utilisation of resources in the public and is now an essential part of the tool kit of analysis techniques used by finance, sales, commercial, operations and shared & central services.
Managers want to know:
• The true cost of making products, providing services and the costs of servicing customers
• Which products, services and customers are profitable and which are not
• Which resources can safely be cut, or re-deployed and made available for investment in new products and services
• The level of resource required in future periods to make predicted volumes of products and provide planned levels of customer service
In this article we start with the principles of ABM and the ways in which these principles can be used to provide valuable management information. Then we bring ABM to life with two case studies to illustrate the main types of benefit that it delivers.
Improve product and customer profitability
ABM identifies all the direct, indirect and overhead costs associated with making products, providing services and delivering these products or services to customers. Deducting these costs from revenue reveals the ‘ABM contribution’: the contribution made by the product or customer to pay for the remaining costs incurred by the business and thereafter to profit. The remaining costs provide for the future of the company, typically research into new products or the development of existing services.
The results that ABM delivers often surprise management. Received wisdom may be turned on its head: the ‘cash cow’ is revealed as a poor performer; a steady customer, long-ignored by management and the sales force, is a major source of profit. Why the surprise? In contrast to the ABM approach, the traditional split of ‘direct costs’ and ‘overheads’ masks the extent to which each product or customer uses overhead resources, so hiding real product and customer costs. The following example from the manufacturing sector demonstrates the point.
Product A required simple manufacturing technology, enjoys steady demand with few schedule changes and uses the same suppliers of raw material over a long period. Product B, on the other hand, with its complex technology, more exacting quality issues, material sourcing difficulties and volatile market conditions, drew much more heavily on overhead resources. In both cases the gross margins (revenue minus direct labour and material costs) were almost identical. However, once the difference in overheads for both products had been accurately determined the profitability of Product B was far lower than previously assumed.
Relationships with customers are no less variable. Customer X raised large numbers of low value orders, made numerous ordering errors, paid late and operates manual ordering. Customer Y ordered electronically, paid through BACS, and never made returns. The reduced demand on overhead costs made this customer a much more attractive proposition.
These subtleties of product and customer ‘behaviour’ are disguised by traditional accounting methods, that are at best an unreliable guide to profitability, at worst, a serious distortion that can lead to poor decision making concerning prices, product mix and the desirability of pursuing certain customers. Understanding how different products and customers make use of support resources is key to determining product and customer profitability. The information can be used in a variety of different ways: to lower costs; to change pricing policy; to enter or leave markets; to charge for particular customer services.
Improve resource planning
The link between resources and products/services can be run backwards, enabling managers to predict the resources required to supply forecast volumes of demand. This approach overcomes three big problems associated with the conventional approach to budgets.
Firstly, a conventional budget mirrors the organisation’s functional structure and fails to reflect the impact of cross-functional processes on resources.
Secondly, conventional budgets deal with input costs, for example the employment costs of staff. They ignore the nature and value of the outputs that staff and other resources produce. But forecasts are normally expressed as volumes of outputs, such as sales volumes or new product launches. Without proper links between inputs and outputs, there is often no rational basis for allocating resources.
Thirdly, a conventional budget draws heavily on the previous year’s budget and ‘actuals’, which, in a fast-moving business, are of limited use in predicting future resource needs.
The ABM approach provides a logical means of using forecast demand and avoids the senseless and time-consuming haggling that is the budgeting experience for many organisations.
Improve processes
The cost of providing a volume of outputs divided by the volumes of these outputs gives the unit cost. They can be used for benchmarking the performance of different groups doing similar tasks. In addition, the unit cost of critical outputs can be used as a key performance measure for operational managers and focuses attention where it’s needed for continuous process improvement.
ABM provides valuable insights about processes by identifying how much activity in each process is ‘diversionary’. As the name implies, diversionary activities are only performed because a process has failed. They typically comprise between 20% and 50% of all activity in an organisation. By improving processes, diversionary time can be significantly reduced and with some of the spare capacity created can then be redistributed to core work: doing the things that add value, rather than those that just add cost.
Case studies
In this section each case study illustrates a key facet of ABM and shows how the principles have been applied to generate major business improvement.
Case study #1: Accurate costing of shared services
A multi-business-unit Financial Services company employed over 850 specialist personnel at its corporate headquarters and in its offices in seven other countries. However rapid growth was bringing its own problems. The central shared services functions that served all the business units had grown complex and expensive. The company needed to have accurate internal charging of its shared service areas in order to simulate the costs of future demands from its business units. This information could then be used to adjust product portfolios, service levels and prices.
Starting with the ledger, various costs were grouped into ‘Resource Groups’. Within each Resource Group, activity data was collected through staff interviews. There were around 5 to 10 activities in each Resource Group.
Resource Groups provided ‘Services’ both to other Resource Groups within Shared Services and to the Business Units. To create a particular service, activities were assigned from various Resource Groups. The level of the total cost of an activity that was assigned to a particular service receiver was based on the cost driver volumes relating to each service receiver. For example, the Stores Service archived boxes from many parts of Shared Services and the Business Units. The overall cost of the Stores Service was divided across the service receivers based on the number of boxes each had archived.
When all the activity costs had been assigned to services, and the services assigned finally to the Business Units, a clear difference arose between the previous highly dubious method of charging based on Business Unit sales volumes and the accurate charges found using the ABM model.
The data enabled a two pronged focus on those services that were perceived as ‘costly’. On the one hand Business Units now reduce Shared Service costs by reviewing the volume of demand for services and on the other hand managers of the Shared Services focus on process improvements that will reduce the unit costs of services so in turn reducing the costs charged to Business Units.
Case study #2: Uncovering the true costs of customer behaviour
With consumers allowed free choice, electricity companies knew they had to compete with one another to retain their customers and win new ones. With low average profitability per customer, the question they were all asking was: ‘which of our customers are profitable and which are not?’ Long histories as regional monopolies meant they had very little information on which to base an answer.
One utility company got to grips with the problem. It was particularly interested in exploring the costs caused by customers. The initial analysis found that Customer driven activities were 32% of all activity costs. A significant amount.
The company categorised customers in many different ways. For each category, it analysed the extent to which its internal processes were employed. It then used ABM to identify the cost of each process, and from that the profitability, of each customer category.
It found, for example, that the cost of handling
a customer’s change of address averaged £6.25. So customers who frequently move house aren’t good News. This behaviour was difficult to influence.
However, signing up new customers with a ‘behaviour’ attribute such as disappearing without paying was an action that led to unprofitable growth. Unfortunately the utility had hired a firm to get new customers to switch and paid that firm on the basis of number of switches. It now started to wonder if the firm really knew (or even cared) whether the utility was winning ‘good customers’!
Customer segments were created based on their behaviour patterns which in turn created sequences of costs that could be pulled together from the ABM database. In the illustration (see fig), two things were a surprise to the company.
Firstly how quickly the gross margin could be eroded by some simple activities taking place which weren’t necessarily indicators of ‘bad’ customers. Secondly, the largest increments of costs occurred whenever the customer interacted with the Call Centre. Avoiding any trigger that made a customer phone the Call Centre was seen as paramount to reduce costs. Previously management of the Call Centre thought ‘efficiency’ was about cutting short any conversations with customers!
A statistical analysis of its customer database was undertaken, linking customer categories as defined by their mix of activities that caused the company to incur high costs. From this information, the utility company was able to set robust marketing and service level strategies for attracting and retaining the more profitable customer categories.
Key benefits of ABM
• ABM provides reliable information on profitability, based on the accurate assignment of costs to products, services and customers
• ABM demonstrates how individual activities influence profitability for better or worse, enabling managers to assess the impact on the financial results of any plan for change
• ABM enables organisations to forecast sensibly the impact of projected business volumes on staffing levels, as well as on other resources.
In short, ABM places knowledge in the hands of the people tasked with enhancing the value and profitability of the business. It promotes a common language and a common approach to decision-making. It focuses everyone on delivering the future prosperity of the business.
About Develin & Partners
Develin & Partners was formed in 1988 and has experience of most types and size of business, and of most industry sectors. Together with Process Improvement, Activity Based Management is one of its core capabilities. The ABM approach has been implemented in over 120 organisations. Brian Plowman, Managing Director, is a presenter of a number of courses for ICAI on the subjects of ‘Managing Overhead Costs’ and ‘Activity Based Management’. The next ABM courses will be in Dublin on 26th September and in Belfast on the 27th.