1. Optimizing Work Distribution for NP Orders
Submitted to:
John Boosey
Metering Services
Indianapolis Power & Light
Prepared by:
Nate Dotzlaf
Senior Business Systems Analyst
BCforward
Brian Kaiser
Lead Engineer, Asset Management
Indianapolis Power & Light
September 11, 2013

2. Page 1
Executive Summary
Problem Statement
IPL implemented the Meter Mobile Data project in February of 2011, a part of which
included the move to Computer-Aided Dispatched (CAD) to schedule and track work crews. 18
months after implementation, five of the six types of work have stopped using the job routing
system. This project identifies several reasons for the causes behind inefficient job routing of the
CAD system and provides a number of solutions to improve the performance of the system.
Problem-Solving Approach
A cross-functional team was formed with representatives from the Operations Technology
and Metering departments, and led by Brian Kaiser and Nate Dotzlaf. Examination focuses on the
performance of the NP order processes since the implementation of Meter Mobile Data and utilizes
the Six Sigma DMAIC problem-solving approach to identify opportunities for improvement. First,
brainstorming sessions and Ishikawa diagrams were used to establish potential reasons for the
discontinued use of the job routing functionality. Next, using 12 months of historical data, Pareto
analysis was conducted to identify the largest contributor of errors and R-Charts were used to
measure process capability. Additional brainstorming sessions were conducted to identify key
process input variables from which we were able to identify several recommendations for
improvements.
Major Findings and Recommendations
A major finding of our project is that the majority of the problems associated with job
routing are attributable to legacy route management practices. Historically, mapping districts were
allocated based on population density and qualitative worker input. Every year, districts would be
reviewed and redistributed based on population changes. As the complexity of this process
increased, the redistricting process has been dropped for several years. In addition, the routing
system algorithms initially were set based solely on geographic distance whereas mapping districts
(and hence billing cycles) followed only loose geographic patterns and allowed only a small time
window for work completion. Furthermore, the CAD system was designed to only perform a single
optimization pass, rather than utilize more robust multi-pass routing algorithms. It is our position
that the inefficiencies inherent in the current system are due to a combination of factors and that no
single fix will solve the majority of the problems.
That said, changes have been ordered to include the mapping district code in the data given to the
routing system and preliminary analysis indicates positive results.
Other improvements planned:
1. Re-draw the billing districts using quantitative variables (population density, historical
workload, distance) to reduce variability in workload
2. Reduce the overall workload by implementing new technology to reduce the need for cutoffs
(phone calls, text alerts, smart meters)
3. Adjust wrench time to reflect historical data
4. Incorporate more robust routing algorithms
5. Re-design processes to encourage collection of process data to promote future improvements

3. Page 2
1.0 Improvement Opportunity: Define Phase
In February 2011, Indianapolis Power & Light (IPL) went live with an expansion of its
Computer-Aided Dispatch (CAD) system into the Metering department. A key part of the project
was to implement automated job creation and dispatching for high-frequency, low-variability jobs
such as disconnect and reconnect of customer service. The process was outlined in the SIPOC
diagram below.
Figure 1: SIPOC Diagram
The initial results were mixed; crew routes were seen to overlap each other, visit the same location
twice in a day, and other obvious inefficiencies. Within a couple months, the automated dispatching
had been disabled on all work types except for one: disconnect of customer service (cuts). As a
result, much of the expected gains in efficiency from the project were never realized.
The goal of this project was to design and develop a complete set of solutions to address root causes
behind the inefficiencies and problems in the cut job process. Success was defined as decreasing the
average cost of a cut, as well as increasing the accuracy of crew routing (projected number of jobs
completed in a day vs. actual). Another metric will be satisfaction and trust in the system by
dispatchers and crews, as measured by comments made in feedback sessions.
The project was limited to analyzing the NP-07 job process in CAD and related processes in other
systems. It was assumed that the recommendations would also help to make automated dispatching
an option for other work types. Analysis would involve not only the automated processes but the
manual steps involved in creating and dispatching work. The physical completion of the work was
excluded from this project.

4. Page 3
2.0 Current State of the Process: Measure Phase
2.1 Current Performance Level
The team utilized SQL queries from the CAD database to collect all recorded NP-07 dispatches
spanning a single year from October 1, 2011 to September 30, 2012. Because the system was
implemented in February 2011, the first six months were omitted to avoid learning curve issues and
allow for point-to-point comparisons. Initial results yielded 137,613 records; however, it was
necessary to remove a large number of records to prevent skewing of the data analysis results.
Primary causes for removal:
1. Dispatcher updating of records (11,837): Dispatchers often update records for a number of
reasons including: worker request, worker error, and end-of-day system adjustments
2. Test-user account (36,014): A system account (test-user) is utilized for a number of processing
tasks and by a number of different users. There is no reliable way to either confirm that the
records itself represents a real job or to attribute the results to a particular user.
3. Zero drive time (53,628): The current tracking system allows workers to run a number of routes
and log all data entry after the fact. Accordingly, a large number of records showed drive-time
durations of zero. Even though plausible reasons exist for this result – for instance several routes
at a single apartment complex – the relative importance of this factor in the overall cost and
performance measurements necessitated removal.
After cleansing, 36,134 total records were deemed fit for further analysis, or 26.3% of the original
sample set.
2.2 Identification of Key Variables
Key product/Process Output variables:
1. Total Drive Time –
a. Measured from either:
i. Shop location to first stop
ii. Job completion to next stop
b. Affected by:
i. Distance
ii. Traffic Conditions
2. Total Wrench and Reporting Time- CAD routing assumes an average time of 8 minutes per
location.
a. Total Wrench Time – the total time starting when a cut-off man reaches his
destination, completes the cut-off, and return to his truck.
b. Total Reporting Time – The total time required for the cut-off man to log service
information and accept the next location.
3. Total Job Time – Total time required to complete a single job including drive, wrench, and
reporting time.
4. Cost
Key product/Process Input variables:
1. Distance to Location

5. Page 4
a. Distance can vary a great deal but the CAS system creates requests based on billing
districts (20 sections of the service territory). In general, this somewhat limits the
total daily driving distance, but because the cut-off crews service a large
geographical area, first and last segments can involve much longer distances. Other
factors that can affect driving distance are:
i. High risk accounts: preference is placed on completing these jobs despite
longer driving distances
ii. Socio-economic factors
iii. Housing density
iv. Road conditions/routing
2. Cut-off Man Type
a. Contractor – Contractors work on an as needed basis and consequently carry a much
lower cost per hour. Contractors are limited in what types of cutoffs they are allowed
to perform and are thought to be slower due to their unfamiliarity with the routes.
However, historically they are much more likely than experienced fulltime Cut-off
Men to follow the driving directions given by the CAD system and are permitted to
use consumer GPS devices to facilitate their work.
b. Fulltime Employee – Fulltime employees are always available to service cutoff
requests regardless of demand, resulting in a much higher cost per cutoff. Fulltime
employees face fewer restrictions in the type of jobs they can perform and are much
more familiar with the city and with the cut-off routes.
Figure 2: Cut-off Man Histogram
3. Service Type
a. Voltage – Service types range from 120V to 480V and dictate which Cut-off Man is
able to complete the job. Contractors are not permitted to service higher (and more
dangerous) voltages; these are instead reserved for fulltime employees. However,
120V services represent the majority of cutoff requests.
411
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Cut-off Man Histogram

6. Page 5
+
Figure 3: Voltage Type Histogram
4. Rate Code
a. Rate Code can also indicate the type of service. However, we saw that the large
majority (88%) were standard residential services (RH and RS).
Figure 4: Rate Code Histogram
2.3 Identification of Target Performance Levels or Project Goals.
• Introduction of MES routing will lower the average cost per cut for contractors and average
cost per cut for full time employees. (Employee ratios are beyond the scope of this project)
• Decrease the DPMs for driving time (estimated vs. actual time)
• Changing estimated wrench time will create more accurate routing – would allow for better
planning and future performance evaluation
528 426
35082
13 82 1 2
0
5000
10000
15000
20000
25000
30000
35000
40000
Voltage Type Histogram
2 3 4
3074
3 2
10225
4
21489
46 9 6 2 68
1197
0
5000
10000
15000
20000
25000
CB EH ES RC RD RE RH RI RS RT RU RV RW SH SS
Rate Code Histogram

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3.0 Analysis and Findings: The Analyze Phase
Workflow Analysis
To begin to analyze the cut-off process, a workflow diagram of the entire process was developed:
Figure 5: NP process diagram

8. Page 7
It revealed that while each job was handled almost identically, the dependencies upon various
computer systems to deliver accurate data were high.
Next, brainstorming sessions involving the project team and key stakeholders were held to identify
potential causes of cut jobs taking longer than estimated.
Figure 6: Cause and Effect

9. Page 8
This level of analysis showed the scale of the requirements to complete a cut job. For this project,
emphasis was placed on the Methods, System, and Job Routing branches of the diagram.
Driving and Wrench Time Analysis
Pursuing the System and Job Routing areas, the first area targeted for analysis was the accuracy of
the computer-generated routing in the CAD system. If data like estimated driving time, estimated
wrench time, etc. are inaccurate, then the routing will be inaccurate even if the other aspects of the
process are correct.
Sample data was analyzed in several directions: union vs. non-union personnel; by crew; by area of
service territory; by day; and against common mapping products such as Google Maps and Bing
Maps.
The first stage of analysis verified driving times generated by the CAD system against those
generated by Bing Maps. The assumption was made that a professional tool, Bing Maps, was the
most accurate driving time estimate system available due to the resources and scale of Microsoft.
Results showed that CAD-generated driving times were sufficiently accurate (less than 1%
variance) when compared to Bing Maps and there were no identifiable factors of error found.
Therefore, the team concluded that drive times assigned by the CAD system when dispatching work
to crews should be an accurate estimate of the time needed to complete and would not contribute
significantly to total drive-time errors.
The second stage of analysis was determining the average wrench time. An estimate of 8 minutes
was set at the initial configuration of the system. After over two years of using the system, the data
pulled should be an accurate representation of actual wrench time needed to complete cut jobs.
Figure 7: Xbar-R Analysis of Reported Wrench Time
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Sample
SampleMean
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UCL=12.68
LCL=-6.67
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R=3.64
UCL=11.88
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Xbar-R Chart of ReportedWrenchTime
Tests performed with unequal sample sizes

10. Page 9
Through the analysis, we determined that an average of 3.00 minutes was required to complete a
cut-off job. However, with an R value of 3.64, it’s clear that the process measured through wrench
time has a very low capability. Therefore, the recommended estimated wrench time will need to
take this variability into account.
Job Routing
Through interviews with crew members and administrators, the team learned that much of the
frustration around inaccurate job routing stemmed from the switch from MES-based routing in the
paper method to hub-and-spoke routing in the computerized method. MES is a system whereby
each service address is given a unique routing number. The unique number is a combination of
billing district, route number, and address. This system was developed to route the meter readers as
efficiently as possible in the era of paper.
Interviews with stakeholders indicated a general consensus that MES-based routing prevented route
overlap and distributed job more evenly – and was consequently superior to the CAD system. To
test this theory, simulations were run comparing each method with historical samples of jobs.
Figure 8: Job Distribution using hub-and-spoke routing method

11. Page 10
Figure 9: Job Distribution using MES-based routing
The MES-based routing yielded much better grouping of jobs on each crew than the hub-and-spoke
routing method, with a result of less total driving time. In addition, the jobs selected for dispatch
were closer together, also helping to lower the total driving time. All of our test cases suggested that
the MES-based routing will return either the same or more efficient routing than the hub-and-spoke
routing.
Workload Variability
A key data variable – that wasn’t apparent during early analysis – that emerged out of subsequent
analysis was the number of customers in each billing district. Historically, when there were physical
meter readers who visited each residence every month, billing districts were adjusted each year to
help eliminate variance caused by population changes. Since the introduction of Automated
Metering Reading in the early 1990s balancing the billing districts became less important and yearly
adjustments were forgotten. Further review shows that after 20 years, the number of customers in
each billing district has changed dramatically:

12. Page 11
Figure 10: Customers per Billing District
Because the balance of the billing districts, in terms of population density, geographic location, and
socio-economic factors is no longer consistent with the rigid schedule set by monthly billing cycles
the system will inherently have a high degree of process variability. Furthermore, unstable workload
cycles place a burden on managers who must constantly balance workload and available labor on a
daily or weekly basis, the result is a surplus of labor on some days and a shortage on others.
19000
21000
23000
25000
27000
29000
31000
33000
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Customers
Billing District

13. Page 12
4.0 Recommendations: The Improve Phase
Through several group meetings, a total of six options were identified as possibilities to improve the
cut process.
A. Route using MES address from CAS
This recommendation, involving minor modifications in CAS and CAD, will switch from
the hub-and-spoke dispatching to a sort using the MES address. The expected result will be
jobs dispatched in the same manner as before CAD was implemented. While we don’t
anticipate large performance improvements, the business requirement of completing as
many jobs as possible in a billing district before moving to the next district will be better
met.
B. Use ladder and access data from CAS to differentiate jobs
While ladder and access information is currently available for some premises, they are not
keyed upon in the current dispatch process. This recommendation will create a new work
order type in CAD based on known ladder access issues. Jobs requiring a two person ladder
crew will only be dispatched when an appropriate crew is available. In addition, a new work
order type in CAD will indicate when a job requires special access from a customer, making
it more apparent that the customer will need to be contacted prior to beginning the job. This
recommendation will require more significant development and testing time on both the
CAS and CAD sides.
C. Route using independent vehicle routing program
This recommendation will involve adding a third-party Vehicle Routing program to the
work order creation process. This program will replace the hub-and-spoke dispatching in
CAD with a more efficient, multiple-pass algorithm that will minimize driving time and
maximize work time for each day’s jobs. Significant improvements on the current system
are expected, but adding another system to the process will increase development, testing,
and maintenance needs.
D. Adjust expected times according to historical data
With over two years of data now available, analysis has shown that the wrench and reporting
times estimated by the CAD system could be more accurate. This is a low-risk, low-gain
recommendation, but will result in dispatching a more accurate number of jobs to each crew.
E. Integrate communication processes with cut process
While the initial focus of this project stemmed from inefficiencies with the cutoff process, it
is our opinion that preventing the dispatch of a work crew in the first place offers a higher
ROI. In fact, several programs such as TeleVox notifications and early adoption of remote
cutoff meters are proceeding and show promise. Additionally, web based texting services
such as Twilio offer a low cost way to increase customer interaction. Overall, the group feels
that a more holistic analysis of customer delinquency and the steps leading to cutting service
is needed. However, these approaches could require significant analysis and possible
investment.

14. Page 13
F. Re-implement regular balancing of the billing districts
Balancing of the billing districts, in terms of population density, geographic location, and
socio-economic factors would reduce a large amount of variability in the process, resulting
in a more consistent workload. Further benefits could include increased regularity in labor
forecasts and cash flow cycle improvement.
Since recommendation F was already implemented by the Metering group after discovery, it was
not evaluated. The remaining recommendations were evaluated using six weighted categories:
1. Easy to Implement (100%)
2. Easy to Maintain (50%)
3. Significant Impact (75%)
4. Minimal Cost and Time (50%)
5. Senior Management Support (100%)
6. Solution will be Automated (75%)
The results are as follows:
Figure 11: Solution Ratings
As shown, the two recommendations are to pursue routing using MES order (16.75) and to adjust
wrench time according to historical data (17.125), in addition to restarting the billing district
adjustment practice.
5.0 Monitoring and Control: The Control Phase
The first recommendation, routing using MES order, was implemented in Production on August 9,
2013. After a week of data, it is suggested that the second change, adjusting the estimated wrench
time, go into effect. Re-instating the billing district adjustment process is ongoing, but requires
involvement of other groups and thus will take several weeks to accomplish.
Control of the process will be maintained through weekly and monthly reporting. The metrics of
this project were defined as the average cost to complete a cut job, and the accuracy of crew routing
(number of jobs scheduled vs. actual number of jobs completed). The expected results are:
1. Reduce average cost of a cut by 2%
2. Achieve 90% accuracy for crew routing
A format for the suggested reports can be found in Appendix A.

15. Page 14
To continue the process of improvement, complete analysis should be scheduled in six months. In
addition to reviewing the effectiveness of the implemented improvements, the other options
identified above should be re-evaluated at that point.
6.0 Conclusion
This project was initiated to address problems in the automated routing of service cut jobs at
Indianapolis Power and Light. Inefficiencies in routing such as route overlap were undermining the
improvements expected with the new automated dispatching system.
Each step of the process was analyzed using Six Sigma tools and methodology. Most effort was
focused on the Methods, System, and Job Routing categories of issues identified in the cause and
effect diagram. Out of a potential five options identified, two were chosen to improve the process:
use of the MES order in job routing; and adjustment of estimated wrench time according to
historical data. Expected results of (1) a 2% reduction in average cost per cut, and (2) 90% accuracy
for crew routing were set and will be monitored through weekly and monthly reports.
The final result was a combination of small improvements to the process with detailed identification
of further advancement opportunities. As a vital process to IPL’s business, even minor refinements
can yield a significant gain in the bottom line.

16. Page 15
Appendix A: Reporting samples
Average Cost of Cut by Week Ending
Rolling over previous 9 weeks
Accuracy of Driving Estimates
Rolling over previous 9 weeks
$19.00
$19.20
$19.40
$19.60
$19.80
$20.00
$20.20
$20.40
$20.60
$20.80
$21.00
Average Cost per Cut
80%
82%
84%
86%
88%
90%
92%
94%
96%
98%
100%
Accuracy