Rein In Your Field Operations with Analytical Models

In the 1970s and 1980s, the problems associated with strategic planning and decision making in establishing, managing and controlling a field force operation were relatively straightforward. For example, a field service organization's operating budget might be developed by the marketing or product management organization based on a percentage of the planned sales revenue for the coming year. Staff levels and logistics budgets were similarly clear cut.

However, the growth in demand for service forced companies to run their field services as a line of business. The strategic planning and decision-making process now requires a complex analysis of the tradeoffs between service levels to be offered. The service executive and manager must consider response time, customer downtime and the cost of the service to be developed in the context of defining customer requirements and their willingness to pay. Alternative staffing and logistics support levels must also be considered. The ultimate objective is generally a trade-off between service profitability and revenue levels as well as between customer service satisfaction levels and customer willingness to pay.

Usually, service goals and cost goals conflict. Service response can improve with increased costs, and vice-versa. Market segmentation, in which different market segments will pay different amounts to achieve a given level of service, makes the calculation even more difficult. An optimized service portfolio involves service pricing by market segment, trading off service performance and response against the price of service as a function of customer requirements and willingness to pay on the one hand, and operating costs and expected service performance on the other.

In this situation, subjective planning based on rules of thumb or hit-and-miss methods is no longer satisfactory. The service team must carefully consider the complex trade-offs involved, as well as the integration of more than 30 variables, parameters and confidence levels to calculate the optimum solution.

Generally, there are three approaches that can be used in analyzing, evaluating and ultimately selecting an optimized strategic plan and tactical program in the field service market. These include the following:

• Spreadsheets and simple averages.

• Complex simulation models. These models represent the dynamics of service staffing and logistic support. These mechanisms generally require a higher investment of time for both software and analytical development, and it is difficult to obtain sufficiently detailed data and parameters.

• Closed form analytical models. The queuing theory model is a good example of this format. In particular, it can represent the key interactions between service personnel, staffing levels and logistic support budget on the one hand and service levels and response time on the other.

It is possible to establish a series of closed form equations (Table 1) that demonstrate the following:

• Overall service response and repair time as a function of call-answering time, mean travel time and mean time to repair.

• The demand for services as a function of the timeframe of use and the characteristic mean time between equipment failure.

• Service staff requirements--a function of the expected number of service calls per day and the service capacity as expressed in the number of calls that can be completed per day, considering travel and repair time.

• The explicit relationship that exists between service response levels and staff productivity or efficiency. This relationship can be expressed using queuing theory.

The key to this analytical approach is the basic queuing theory model that predicts a direct but nonlinear relationship between service utilization/efficiency and customer waiting time. This real-world dynamic can be observed at tollbooths on a busy bridge. The waiting/service time for customers will be a function of the cars' arrival rate (demands for service), the speed of toll collecting (mean time to service) and number of toll takers working. As the toll takers become busy, the service customer waits longer. If the toll takers are not busy (underutilized), the customer waiting time decreases. In a field service environment, service demand will be affected by density of the installed base and complexity of the equipment to be serviced. Repair time will be affected by equipment complexity and parts availability.

Based on these models (and others) reflecting logistics support efficiency, it is possible to implement field force planning systems that can improve service profitability and performance by 30 percent or more.