To Main Proceedings Document
Subject:
ABSTRACT: Gogish, Lev
Date:
Sat, 15 Jan 2000 10:33-0500
From:
Bart Burns <bart@ Basit.COM>
To:
sds@ cnsvax.albany.edu, SD2000program@ifi.uib.no
Paper submission for the 18th International Conference of the System Dynamics Society,
Bergen, Norway, August 6-10, 2000.
About the authors:
1. Lev Gogish, Dr.Sc., Ph.D., Central Institute of Aviation Motors,
Moscow, Russia; has over 30 years experience in complex system analysis
in the field of aero-space technology in the former Soviet Union.
For the last five years, Lev as worked in the telecommunications industry
in the U.S.A. at NY NEX and Bell Atlantic.
2. Bart Burns has worked at NY NEX/Bell Atlantic for the last 13 years,
in the AI Lab, the Human-computer communication Lab,
and now in the Network Systems Advanced Technology Lab.
3. John Lastowka worked in the field force at NY NEX/Bell Atlantic for over 33 years.
In 1998 he received the Leader in Excellence A ward,
said to be "the highest honor the company can bestow".
Paper Title: SY STEM DY NAMICS MODELING of the INSTALLATION and REPAIR FIELD FORCE
at BELL ATLANTIC CORPORATION
Lev Gogish, Bart Burns, John Lastowka
ABSTRACT
This paper discusses an initiative focused on improving
the quality of information used in decision-making processes in Bell
Atlantic Corporation. This effort was initiated by comments of
the Chairman and CEO of Bell Atlantic Ivan Seidenberg about the
significance of installation and repair problems as a core of
reliable customer service and the reputation of Bell Atlantic. In
general, the goal of this project is to help make clear the system of
causal interrelationships and interacting feedback loops inside Bell
Atlantic which determine profitability, sustainability, and prosperity of
the Corporation. The deliverables are explicit and testable models of
dynamic behavior of the Corporation under given circumstances and
policies.
Part 1 describes dynamic structures for modeling service
provisioning in a large telecommunication company. As a service
provider, such a company is under various pressures from markets,
competitors, technology and quality standards. These pressures are
increasing continuously. As a consequence, time pressure on employees
(management and union) and load on field technicians continuously rises,
which brings about negative effects. Those negative effects need to be
compensated by improving the field force skill and effectiveness,
by increasing operational management capacity, and by modernizing testing
tools. The current model contains more than 100 parameters and 25
equations. This model helps to explain company behavior over time.
This part describes the map and the main parameters of the model. The
map is implemented using the STELLA(*) software. Simulations based
on this model demonstrate customer growth under various
circumstances. Those circumstances include the impact of new
technological and service innovations, human factors, and network
capacity.
The map contains six sectors. Each sector is a submodel which
characterizes certain aspects of a telecommunication company. The
sectors are the capital sector, the corporate work force sector, the
customer sector, the technology sector, the corporate policy sector, the
operational culture sector, the operational work force sector and the
learning sector. All sectors are interrelated.
The main parameters of the model are accumulations, driving forces,
boundary conditions or some substantial things or concepts which affect
the company's behavior over a period of time.
Driving forces in the customer sector are the following:
1) quality force,
2) price force,
3) market force,
4) equipment force,
5) competitive force,
6) maintenance force.
The external variable influencing the customer sector is "customer
market". The main internal influential force in the customer sector is
the maintenance force. The maintenance force is a complex of field
technicians and operational management, their experience and training,
their skill and testing equipment, etc.
Each of the accumulations should be considered as an aggregation of many
subvariables. For instance, customers of a telecommunication company
are:
1) residential customers,
2) small business customers,
3) big business customers,
4) Internet customers,
5) data customers,
6) government customers, etc.
In the future, each customer could be considered as a discrete
parameter. Such a map will contain as many individual customer sectors
as there are types of customers. The same applies to the other
accumulations.
The present model provides a basis for further integration of company
data, business facts, and management experience. Using this basis,
increasingly more correct simulations of company behavior could be
developed in the future.
[* STELLA: Structural Thinking and Experimental Learning Laboratory, developed by
Barry Richmond for System Dynamic modeling on PCs.]
Part 2 describes dynamic structures for simulation of part of the
maintenance force in a large telecommunication company like Bell
Atlantic, namely the operational force. As a service provider, such a
company might have a problem meeting installation and repair
quality standards which are continuously increased. As a consequence of
continuous technological and service innovation -- time pressure on
employees (management and union) and load on field technician
continuously rises, which bring about some negative effects. On the
other hand, the rise of competition encourages the tumover of the most
experienced field techs and managers.
The negative effects should be compensated by intelligent company policy
which is embodied in the variable "coherence of corporate policy".
Intelligent policy is crucial for long-term prosperity and
sustainability of a telecommunication company.
The model of the operational force described in this part allows us to
simulate various scenarios over time under given conditions. Such
conditions include influences of competition, new service/technology
implementation, union contracts, early retirement offers, etc. The
further development of the model depends on specific information about
the company. Such an information might be elicited from the company's
data bases and from its management. The more the model contains
such specific information, the better it will simulate the behavior of
the field force.
Part 3 discusses a model of new service implementation in a
large telecommunication company. New service implementation is a
complicated and stressful process which depends on many factors. This
part describes the feedback loop structure and dynamic model of such
a process. The model simulations demonstrate generic scenarios over
virtual time. Such simulations show the nonlinear behavior of customer
growth, market behavior, and so forth - and the relationship of these
variables to the amount of system bugs, the experience of the
operational work force, and other conditions. This model contains
minimal parameters, accumulations and flows. As such, it allows us to
clarify the structure of main feedback loops which determine the dynamic
properties of the implementation process. Thus, the model facilitates
and encourages systems thinking as well. In the future, this model
could be used to adapt company financial strategy by optimization of
expenses among system testing, personnel training, and marketing.
Part 4 describes emergent learning centers in Bell Atlantic.
Learning centers emerge under conditions of continuous technological
innovations, aggressive competition, accumulation of capital resources,
and the presence of authentic leaders. Learning centers occur as a
response to the real needs of improving installation and repair
services. During the last three years, experience at Bell Atlantic
has shown that learning centers are effective in many ways:
1) they carry out advanced training of field technicians,
2) they evaluate testing tools and working conditions, and
3) they assume a leading role in solving marginal problems.
The learning center is an informal network, catalyzed by authentic
leaders. Such a network includes field technicians, management,
technical support specialists, vendors, and even business customers.
This network of skills gives rise to the capacity to solve marginal
problems - the inevitable but unpredictable, difficult, and anomalous
problems which constantly occur in an industry of exponential
innovations - telecommunications.
As an informal entity, the learning center is of great importance,
because it enhances cooperation and mutual trust between employees
and improves the social capital of a company.
Part 5. The variety and complexity of installation and repair
problems, with which the field force of a telecommunication company
deal, have been increasing dramatically for the last decade.
This is the result of the continual growth of technological complexity
and innovation flow in telecommunications. The complexity of field
operations are specifically high during the implementation of new
services.
As such, the field force should become cross-trained and get skilled
in a variety of problems. The continuously increasing training time
inevitably brings about significant expenses because of the reduction of
available service time. This problem is becoming considerable.
To enhance the productivity of the field force and simultaneously
decrease training time, a specific training strategy is proposed in
this part. This strategy is based on two concepts:
1) field force differentiation, and
2) intensive cooperation among its parts.
Such differentiation is not the result of some bureaucratic process, but
instead arises around support for voluntary learning.
This learning process could be tuned to the learning capacities of
individuals. Thus, field techs should find their own places in informal
structure, which further promote their learning, training, and skill
enhancement.
Following their learning capacities, field techs are subdivided on two
groups: a basic group and a small advanced group. These groups, spread
over an operational area, form a kind of informal structure, an
informal network. This network consists of local units or teams.
Each team consists of a number of field techs from the basic group
around their advisers or mentors from the advanced group.
Such an informal network creates many opportunities: Training time can
be shortened. Work force skill enhanced. Training expenses decreased.
However, high social capital and coherence of corporate culture are
prerequisites.
To make clear the advantages of such a strategy, various approaches were
used. These include an operational model of workforce training
strategy and some simple mathematical micro models of cooperation.
These models help give insight into how to optimize the effectiveness of
the workforce and catalyze the evolution of cooperation under a
given load of service problems.
All models lead to the same conclusions:
1) Creation of a learning environment for those who want to learn more
and to help the others -- should be the goal of corporate policy.
2) Differentiation, based on distinguishing learning capacity of
individuals, and cooperation between them is the proper way to get a
more efficient and productive field force.
The idea being to recognizing and support authentic leaders, while
providing fundamental support for learning and cooperation across
the field force.
3) Asa first step of cooperation, field techs could be subdivided into
two groups, based on their learning capacities: a basic group and a
small advanced group.
4) The informal network (based on local teams of field techs from the
basic group together with their mentor from the advanced group),
provided active cooperation inside of teams and between them, could
enhance the effectiveness of customer service.
5) The rate of cooperation, based on mutual trust and personal will to
cooperate, is more significant for solving the installation and repair
problems than training time.
6) Further development of the informal structure of the field workforce
would promote further enhancement of customer service.
Part 6 discusses, through the eyes of a seasoned expert field technician,
what the field force really needs to handle installation and repair problems
effectively in terms of technology, training, and management.
It makes clear what the information dilemma
means for field techs, what kind of jobs they perform, what testing
tools and applications are the most helpful, what kind of training they
have and need to have, and what conditions are necessary for intelligent
management of field-force.
This project helps to improve the understanding of the
interrelationships between the main variables such as customers, quality
of service, maintenance force, profitability, and so on. In addition,
this project gives the opportunity to start a process of multifunctional
correlation of data from various corporate data bases. Such databases
contain the data which could provide insight into customer dynamics,
trouble dynamics, technological/service innovations, testing
tools/systems, field force experience/training, human factors, company
policy, expenses and revenues, etc. The complexity of such a project is
real, but this project can provide real tools for decision-makers.
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