Introduction
in spite of their unique characteristics of
heterogeneity, perishability, intangibility and simultaneity (of consumption
and production) , services also present problems similar to manufacturing. One
such problem is capacity management, however, in service sector it presents
additional problem.
On the one hand, firms in the services sector are
generally faced with a strong seasonality in demand, and on the other, the
presence of the customer when the service is actually being offered is
fundamental in the majority of services. This latter fact directly affects the
service since it involves personalized demand.
The aim of the present article is to carry out,
after a succinct review of the problem inherent in capacity management in
service enterprises, the development of a model that permits the calculation of
the minimum staff needed to carry out correctly all the functions within a
service to be determined, while guaranteeing an expected level of quality (for
some measures previously chosen). The spheres of application chosen as
implementation examples are the cases of the assignment of staff in hospital
nursing and in a catering company.
The capacity
management problem in services
As mentioned above, the number of service
enterprises is growing considerably in the majority of countries. This increase
in competitors means that firms in this sector need to increase their
efficiency, productivity and competition, which requires adequate management of
the available capacity, a not-at-all simple task.
When speaking of capacity management, the aim is to
minimize customer waiting time and to avoid idle capacity, with the goal of
attending to demand in time and in the most efficient way possible.
Lovelock (1992) defines the capacity of a service
as the highest possible amount of output that may be obtained in a specific
period of time with a predefined level of staff, installations and equipment.
There exists a certain amount of debate with respect to the identification of a
valid measurement of capacity (up until now, developed basically in the
industrial sphere, though currently of more and more interest in the services
sphere). Some of the causes of imprecision in the measurement of capacity were
summarized by Elmaghraby (1991) in six points: the problem of product mix; the
problem of setup time; the problem of varying efficiency; the problem of
semi-finished items (or subassemblies); the problem of scrap/dropout; and a
number of social/cultural/economic considerations.
In addition, other types of considerations have to
be taken into account when planning capacity. In the long term, capacity is
linked to installations and related to their expansion and contraction in the
organization. All this is intimately connected to the concepts of economies of
scale and scope. On the other hand, the major barrier for capacity in the short
term is to be able to deal with unexpected demands. To do so, Meredith (1992)
proposes different alternatives:
1) Increase resources:
a) use overtime;
b) add shifts;
c) employ “temporary
workers”;
d) use part-time workers;
e) hire resources;
f) sub-contract.
2) Improve the use of resources:
a) stagger shifts;
b) program appointments;
c) accumulate stock (if
this is feasible) prior to demand;
d) queue demand.
3) Modify the product:
a) standardize the
product;
b) make the recipient do
part of the work;
c) reduce quality;
d) transform service
operations into product operations that can be itemised.
4) Modify demand:
a) vary the price;
b) carry out promotions.
5) Not satisfy demand: do not supply the entire demand.
However, the present article proposes a different
alternative to these, based on a better planning of available resources (which
are considered as a constant).
In the particular case of services, capacity
management is made more difficult by the impossibility of making an inventory
of the service for its subsequent use, as occurs with the production of goods.
The impossibility of synchronizing supply and demand produces a loss in
opportunity to attend to certain customers when demand is higher, and supposes
high costs due to the loss in income when demand is insufficient and the fixed
available capacity is not put to good use In this sense, capacity management has a
considerable impact on the quality of the service perceived by customers
Another of the barriers that has to be resolved in
service activities is related to problems arising from seasonal demand. This
provokes the need to have models for forecasting the demand for certain periods
of time. The ideal situation for a service enterprise would be the possibility
of reducing its capacity in periods of low demand and increasing it in the high
season . In practice, imbalances are produced which may be managed in two
distinct ways: managing the supply for a fixed demand, or managing the demand
for a fixed supply
Human resources planning is related to the
assignment of the right number of people at the right place and time, in order
to perform efficiently the job to be done. Many are the steps in the planning
process (from forecasting or recruitment, to rostering definition)
integrated staffing problem for the Customs at the
Auckland International Airport.
“call
center” performance (where more than 50 per cent of total operations cost are
associated with staff). Is it possible to reduce the percentage of abandoned
calls and increase the percentage of calls answered “quickly”.
In the case of nursing staff (and also police and
fire departments in some cities) this problem has even become a political
issue. The Clinton administration proposed to set minimum staffing standards,
which implied spending $1 billion in federal funds (Harrington, 2001)
studied the
relationship between productivity and quality, remarking on the difficulty of
assigning staff based on productivity: in high-contact services (such as
nursing) looking for better productivity will usually imply a reduction of the
contact time and, therefore, lower quality indices.
This trade-off must be considered by the manager
when assigning resources to different departments. When a limited number of
resources must be distributed, our model gives an idea of the lower limits that
should be maintained.
A proposal of minimum
staff model
The goal of our model is to establish minimum
capacity levels below which quality may be affected, so as to be able to assign
short-term resources on the basis of the demands that arise. In this approach
we use historical data in order to disturb as little as possible the quality
indices used when making manpower decisions. Thus, having a model that permits
the identification of minimum staff levels in a particular service would mean a
better distribution of staff among the different units and thus a better use of
available resources.
Determination of the estimated times to execute the tasks of the service
The starting point of the model consists in
ascertaining as exactly as possible the real execution times, under normal
performance conditions, of each of the different tasks that are carried out on
a daily basis in a particular service, taking into consideration a standard
class of customer. Note that a unique type of homogeneous workers is considered
(of the same category), and not all the department will be staffed.
Calculation of the average number of activities per type of customer
Other basic data required are the ascertaining of
the average number of times that each of the different tasks is carried out in
a given period of time (Table I), as a function of
the type of customer. When the standard time defined in the previous step
varies highly according to different types of customers, this frequency is used
for correction, having in mind that the objective of these values is the theoretical
staffing calculation in the next step.
Calculation of theoretical staffing
For each time period for which there is registered
historical data, a calculation is made of what would have been the theoretical
staffing using a system for evaluating loads. This is the instrument that
allows those responsible for the unit to determine its capacity needs on the
basis of the activities that foreseeably have to be carried out.
Determination of the Δ ratio
Then, comparing the relative historical data with
the actual staffing levels that the service had, we calculate the Δ ratio: the
quotient of the real staffing and the theoretical staffing obtained (Table II), for each month
for which data are available.
Consolidation of quality data and calculation of minimum staff
The proposed model relates the available quality to
specific quality indices (those recorded by the company according to its
experience), thus evaluating the services offered (Table II). The quality
measures selected can be any of interest to the company. Results will be
obviously related to the meaning of the measures selected.
With the information obtained in the prior step for
a specific time period, together with the registered quality indices, I i , we obtain a
relationship such as that given in Table II. Grouping together prior data, we
obtain average quality values associated with each range of Δ ratios (Table III), being able to
represent different quality indices for each range of Δ values (Figure 2). From all of
them we shall obtain a global quality function.
Calculation of minimum staff for a mix of customers
Thus, given a specific service on any one day, the
theoretical number of employees on the staff may be calculated as a function of
the type of customers received (on the basis of standard times) as well as the
minimum admissible number of employees on the staff (multiplying this value by
the acceptable Δ ratio).
No comments:
Post a Comment