Three Decision Maker Perspectives

Financial management as a decision science has developed a number of frameworks to support these various perspectives in the way most appropriate to each. If left to sift through reams of low level accounting figures, none of these decision makers would get very far. However no single metric or decision framework around a metric could ever be used to serve all decision makers when it comes to financial analysis. This multiple viewpoint and decision support requirement aspect is another important point of comparison between the science of financial analysis, and the emerging science of decision support in HCM.

Consider a large diversified public company and three different decision makers who are engaged in choosing among alternative actions based, to some extent, on financial information. The first decision maker is external to the organization and is deciding whether or not to purchase shares of common stock in the company. As a sophisticated investor, this individual is interested in a variety of important measures related to the financial stability of the company he is interested in investing in. There are literally dozens of metrics that this investor could examine, both at their current values and in the form of trends over time. Many of these metrics would involve ratios of asset and debt figures from the firm’s balance sheet and profit and loss statement (the well known “quick ratio” would be a good example). These fairly simple ratios combine two or more accounting level figures, which by themselves have little value. It is critical to know what they were six months ago; two years ago. What is a “good” value for a quick ratio? What are the values for the firm’s competitors, in similar but noncompeting industries? The framework of decision support around determining the stability of the financial health of a firm from an asset investment perspective involves more than just the numbers; it includes guiding principles for interpretation and comparison on an intra- and inter-company basis. Once the calculations are defined and understood, the mechanics of the math are relatively simple. The methodology for interpretation, the core of any financial decision framework, is where the complexity and true value inherent in the process emerge.

Let us examine another perspective, this time an internal one; that of the company’s Director of Fleet Vehicle Management. It is this person’s job to acquire and maintain all of the company’s vehicles. In deciding which vehicles (assets) to acquire, this decision maker must examine a large number of metrics. What is the vehicle’s capacity, its expected useful life? What mileage does it get? What is the acquisition cost? What are the typical maintenance costs? Once the assets (vehicles) are acquired, how often should they be maintained? Will changing the oil twice as often as the manufacturer suggests lead to longer vehicle life above and beyond the additional costs of the increased maintenance? Here again the decision science of financial management offers a range of metrics and tools to be used along with some guiding principles in making decisions about acquiring assets and optimizing their value (in this case, the vehicle maintenance schedule would have a decided effect on the value of the assets).

An important realization is that neither of these decision makers has been in the Finance department; one is not even part of the firm! Finance and Accounting have provided the tools, metrics, methodologies, and the framework for others to make critical decisions. Thirty years ago it was likely that the decision as to which vehicles to acquire would have been made by someone in Finance. Over the past several decades, the trend has been for Finance to become a supporting role to other departments, disseminating the tools and information to those who should be making the decisions: the ones closest to the action.

Now let’s turn to a third perspective, that of the Director of Call Center Operations. The firm currently has an in-house call center that provides phone support for users of the firm’s products (their principal business is consumer electronics manufacture, distribution, and sales). The call center employs more than 500 employees in about a dozen locations. Employee turnover is high in this group (as it is in most call centers), and more than a thousand employees come and go in a typical year’s time. The firm’s product family is large and complex and these products are sold around the globe. Running this call center is a complex task involving critical decision making.

Decisions that the director makes have a significant effect on the operation of the center and, as one of the more significant cost components, on the financial bottom line of the entire company. The director has considerable experience using highly sophisticated tools for making a variety of these decisions. The field of Operations Research, through the use of Queuing Theory, has provided cutting edge tools that allow the director to tune the mechanical aspects of the operation (the number of agents necessary to handle given peak volumes of calls with a predetermined maximum on-hold time as an example) and plan for various contingencies with a fairly high degree of precision. As such the director is not shy when it comes to complexity and the use of tools and models.

Recently, the director has been talking to the Manager of Training, who oversees all personnel training in the organization, including that of employees in the call center. The Manager of Training is suggesting that the current training class, which all new call center employees go through before they start working, be upgraded and expanded from 24 to 32 hours. The hope is that the additional training will provide added value above and beyond the direct costs (training staff and materials) and indirect costs (eight hours less potential productive work from each new employee). The Manager of Training has been investigating this issue based on some information received from Human Resources on exit surveys. It appears that many of the employees, those who leave within the first 90 days of employment, cite a lack of training as one of the reasons they are leaving. Without enough up-front training before they start their new job, many are overwhelmed by the complexity and never feel comfortable.

The director also knows that recent statistics measuring customer satisfaction have shown a marked slip. As part of its customer satisfaction efforts, the firm has been conducting satisfaction surveys within a variety of customer groups, including those who have called in for support. The company has added several new products, one of which is an MP3 player that requires more knowledge on the part of users as well as any support staff with whom they communicate. From their own measures, the director also knows that call handling times appear to be increasing and that the quality scores based on random test call-ins have been declining. On the surface it sounds like additional training may be one component of a solution. However, this will be a fairly expensive proposition. What value will it bring? Will the staff in the call center provide better support? Will they stay on the job longer? If so, what is the value of having them stay longer? How long are they staying now?

These questions sound similar to those that many decision makers face when making choices among alternatives that have differing financial implications in that they relate to investment and return. Unfortunately, the team in Finance does not have a model for helping the director to make this decision. Why not? This seems to fundamentally be an investment optimization problem. The employees in question are certainly critical assets to the company, requiring an up-front investment to acquire and ongoing investments to retain. The knowledge and skills that these employees possess define the very concept of human capital, but where is the framework for understanding the value of this critical component of a firm’s asset base? The decisions that affect the optimization of human capital occur throughout the organization. The example above involves only the call center and the training department.