The administrative department wanted to cut costs but staff felt overwhelmed. They lacked data on staffing needs and workflow. An expert developed a metrics system to collect data on key services, activities, employees and processes. Analysis found financial reporting consumed 20% of the budget. Automating reporting through an app called OTTO reduced costs by standardizing manual tasks and saving 250 hours per 200 reports. By year's end, OTTO automated 90% of reporting work and completed over 60% of reports.

1. The Story of OTTO
A case example*
*Based on an actual client project. Please inquire for more details.

2. An administrative department was facing a conundrum: there was
pressure from above to cut costs but staff were feeling overwhelmed
by current workloads. Management felt like they didn’t have enough
information to come up with an adequate response. They wanted
data they could turn to in order to evaluate trade-offs and answer
questions like:
 What are our true staffing needs?
 What’s the best way to allocate work among staff?
They also wanted to be able to track their metrics over time.
“We’re
overworked.
We need
more
resources”
“You need to
cut costs”
Staff
Central

3. A.) Articulate the Mission
B.) Define Supporting Metrics
C.) Design a Data Collection System
To develop a set of metrics that could help answer these questions, we
started with the mission of the department, which focused on delivering 5
key services effectively and efficiently. This became the foundation of our
metric system. We then collected hypotheses from management and staff
about workflow constraints and sources of variability and added in the
metrics we would need to test them. The final set of measures would let us
examine service costs and efficacy in relation to outputs, activities,
portfolios, customers, customer needs, individual employees, roles, teams,
productivity measures, seasonality, frequency requirements, and process
outcomes.
Key Services
4

4. Dashboards for displaying information
Our full metric ecosystem consisted of data collection tools and
processes customized for each role, integration of operational
data collected by other systems, a set of dynamic, drill-down
dashboards to allow in-depth explorations (particularly around
key hypotheses), and frequent data refresh mechanisms to keep
tabs on changing dynamics.
Tools & processes for
collecting measurements
Infrastructure for
compiling data

5. From watching the dashboards, we observed that certain processes
seemed to consume a greater share of effort and display greater
variability in what it took to complete them. To be able to pinpoint and
quantify the opportunities suggested by these trends, we turned to a
data analysis framed around the “Costs of Service” and the specific
drivers of those costs. Starting from an aggregated portrait of what it
took to deliver on each of the 5 mission-linked services, we used our
collected data to investigate each of our observations and
hypotheses.
A.) Evaluate Service Costs & Benefit Ratios
B.) Examine Sources of Variability
C.) Ask and Estimate “What If…”
Exec
Mgmt +
Internal
Processes
Non-
Labor
Costs
1
2
3
4
5
1 2
3
4
5
Department
Budget
$
Key
Services

6. Our biggest finding was that financial reporting was far and away the
most expensive service component, consuming nearly a fifth of the
total department budget. So we took a closer look at the cost and
quantities of report production by type to pinpoint the largest
opportunities. When the costs were examined by individual, it became
clear that the portfolio approach to reporting was far less efficient than
process specialization, which suggested a more efficient way to
allocate the work. However, a look at the overall cost per report
showed that process changes could potentially be of much greater
impact than reallocation.
What If…
Reduce variability in process X by Y……….….……..…$
Re-allocate all of process X to Employee A……..…$
Automate process X to reduce effort Z%..............$$$
Variability
by Employee
Report
Types
0.0
1.0
2.0
3.0
4.0
5.0
Type A
Type
B
Type C
14%
Type E
Type
D
Avg.
Hrs.
Each
Number of Reports of Each Type
REPORTING
~20%
Department Spending ($)
Type
F
Type
G
Type
H
Type
I
Report Costs & Quantities by Type

7. The next step was to build solutions. Through job shadowing and
interviews, we identified many processes that were manual,
inconsistent, time consuming, and arbitrary. We quickly realized that
automation could bring standardization, batch processing efficiencies,
greater accuracy, and significant time savings. A web portal would
centralize processes, information, and access as well as create
automatic audit trails. Working closely with the current pracitioners, we
set about documenting business processes, clarifying business
requirements, and mocking up an automated portal.
A.) Isolate Business Requirements
B.) Prototype and Test
C.) Devise the Best Long-Term Plan
Portfolio
Management
Batch
Production
Portfolio
A
Systems
data
Dept X
Report
Report
Dept Y
Report
Report
Report
Portfolio
C
Systems
data
Dept R
Report
Report
Dept P
Report
Report
Report
Automated
System Update
Self
Serve
Dept
Interface
Single
Data Feed
Batch
Report
Creation
Portfolio
B
Systems
data
Dept
Z
Report
Report
Dept
W
Report
Report
Report

8. After 3 months of development, “OTTO” was ready for rollout with the
majority of end-state functionality in place. Together with an operating
arrangement that kept OTTO from placing any burden on internal IT, the
first year development and maintenance costs were about the cost of 1
extra staff person. As one measure of success, by the time of rollout, the
time to create 200 invoices had gone from 250 Hours to less than 1. With
frequent feedback and development cycles, OTTO was able to take on
more and more complex scenarios, such as learning to recognize reports
that required additional input, directly soliciting that input from the correct
user, and then continuing with fully automated report production and
distribution.
Video
Tutorial and
Other
Rollout
Aides
Tracking of
Usage and
Other KPI’s
Rapid
Feedback +
Development
Cycles
Unique
Daily
Users

9. By the end of the year, OTTO was officially a success:
• Capable of automating 90% of the work associated with reporting
• At the end of year 1, >60% of all reports being completed by the app
• Over 400 end customers using the app to access personalized information
• Elimination of hours and hours of calculation checking and re-work
• Hugely simplified audit processes (with all the information now in 1 place)