Spatially Enabled Solutions: A Modern Approach to an Old Problem
A Modern Approach
Advanced modeling tools and techniques that were developed and refined in other competitive,
service-oriented industries can be adapted to incorporate the unique operational characteristics of
the utility industry. This provides a modern approach for assisting management with answering
fundamental questions regarding service delivery network configurations.
Objective
The objective of an SDN reconfiguration effort is to minimize the total long-term related costs
(labor, facility, and transportation) for a specified amount of work and desired level of service.
Given this objective the key questions to be answered include the following.
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What is the optimum number and location of service centers?
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What is the optimum assigned territory for each service center?
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How many field technicians and crews are then needed at each service center?
In the process of answering these key questions, much can be learned about the effect windshield
time has on productivity, customer/emergency responsiveness, reliability, and overall network
service costs. Related questions that can be explored include the following.
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Can the same field service level be provided with fewer resources?
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What are the estimated costs or savings associated with changing performance by X percent?
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From an external service provider perspective, what SDN footprint will be needed to provide the
desired level of field service for the work being outsourced?
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From an internal service provider perspective, what SDN footprint will be needed for the work
not being outsourced? For example, which service centers should be kept and which ones should
be sold or leased to external service providers?
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If we combine electric and gas asset services, what should the service delivery footprint look
like?
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After merging with a neighboring utility, will all of the service centers be needed? If not, which
ones are kept or consolidated, and what are the resulting service center territories and staffing?
Benefits
The benefits of an optimally configured service-delivery network include:
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Improved Customer Responsiveness and Service Flexibility
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Decreased average travel time from service centers to job-sites/customers
- Decreased average travel time between job-sites/customers
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Increased Operational Efficiency
- Increased “delivery density” – complete more work requests per day
- Minimized travel for given amount of required work
- Optimized number of service centers and required staffing
- Minimized total service-delivery network cost (travel, labor, and facilities)
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Minimized Environmental Impact
- Minimized vehicle exhaust/emissions
- Minimized service-delivery network footprint
Enhanced Benefits of Related Initiatives
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- Mobile workforce management
- Automated/Optimized scheduling, dispatching, and routing
Base-Case Model
To assist with determining the best long-term configuration, a model of the existing servicedelivery
network and corresponding work demands is created using a logistics application and
company specific data. The base-case serves as a reference point and validation that the model’s
methods and assumptions accurately portray the existing SDN. Validating the base-case scenario
helps to ensure that change-case scenarios—which depict network configurations not currently
experienced by management— reasonably represent the cost and service levels in practice.
Data Requirements
One complete year of data is typically used--current year data and projections. Data required
for developing the model can generally be categorized by its relation to service supply, service
demand, and service delivery. Service supply includes data related to the physical supply or
“inventory” and location of field technicians or crews. This would include service center
locations and cost as well as current staffing levels and related labor cost. Service demand
includes data related to the current and projected fieldwork demands expressed geographically.
Service delivery includes data related to the road network and vehicles that connect service
supply to service demand. (See Figure 2.)
Figure 2 Model Data
