2012

Akira Oyama, Principal Consultant

5/6/2012

A Strategy for Optimizing Networks

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Introduction

A leased network planner is responsible for a number of activities including interconnections, capacity

management, least cost network design and network optimizations. A company with large network operating

expenses tasks its leased network planners to achieve a certain level of network optimization. There is an

optimization target set each year to measure the plan's success.

Because a Key Performance Indicator (KPI) is based on the current year run rate savings for many organizations,

the planner’s network decision is generally based on the monthly cost reduction that can be measured.

Unfortunately, making an optimization decision solely on the measurable monthly savings will lead to mixed

results because the planner failed to address hidden costs that cannot be easily measured, cash flow beyond the

current year and risk factors incurred with the short-term level of cost savings

This paper's intent is to show additional elements needed in the planning decision process that enables the

planner to be able to make the better financial and risk management decisions.

Run Rate Savings

To illustrate a better planning process, I created a hypothetical arrangement where the planner is looking to

extend leased network hubs to minimize the individual circuit loop costs. These circuits are assumed to be under

the AT&T Managed Shared Network Service (MSNS) plan. I assumed the DS1 equivalent average cost is $100

with total DS1 equivalent count of 1,000 in this market. Because the planner, Ellen, is very good and diligent, she

was able to identify 20% of the total circuits with average per cost of $120 that for the extended Competitive

Local Exchange Carrier (CLEC) hubs. She expects per circuit savings of $60. She must also pay $3,000 a month to

establish the new CLEC hubs (10) for this project. To simplify the illustration, I assumed no termination liability

or non-recurring charges associated with this project. Under this scenario, Ellen is able to realize $108K

annualized net savings:

Gross monthly savings: $60 x 200 = $12,000

Net monthly savings: $12,000 - $3,000 = $9,000

Annualized net savings: $9,000 x 12 months = $108,000

Ellen thought that this is not a bad optimization project. The project is initiated and the run rate savings

eventually realized. Case closed. Unfortunately, the actually savings is not as great as Ellen originally thought.

Hidden cost

Ellen's first mistake is failing to understand the financial impact to the MSNS plan and overall cash flow as a

result of initiating the optimization project.

MSNS is a service in which the customer assigns to AT&T the responsibility for facility design and engineering and

routing of point-to-point circuits (DS0 through OC192) from serving wire centers to the aggregation point. 90%

of point-to-point circuits must be committed under this plan (total 900 DS1 equivalent circuits in this scenario).

Because establishing a CLEC transport will lead to the establishment of new demarcation points, 20% of 1,000

DS1 equivalent circuits in this market will result in a MSNS short fall of 100 DS1 equivalent circuits.

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Ellen now realizes that the optimization project will incur a MSNS shortfall penalty of $90,000 ($900 x 100 DS1

equivalent). The adjusted savings will only be $18,000 a year ($108K - $90K), hardly a good project to take on

when there will be additional resources required to extend the circuits and a new CLEC commitment must be

established with turning up the new hubs. If she had known the relationships between the AT&T embedded

plan and her optimization project, she likely would not have initiated the project.

Although it’s not always easy for any planner to incorporate the commitment based carrier plans in their overall

optimization analysis, it’s vital that they address the explicit as well as implicit financial impacts from making any

optimization and planning decisions. Thus, Ellen should

Always have a good understanding of embedded contracts and tariff plans as well as have an idea of how

these plans will be financially impacted as a result of optimization and planning decisions.

Out year Risk Assessment

Let’s assume that the AT&T MSNS plan has 3 years remaining. Ellen must assess the overall cash flow impact to

the MSNS plan for its remaining life as a result of initiating the optimization project. She will also need to

evaluate the potential cash flow risk based on the circuit demand and scale of the optimization project.

Although cash flow evaluation and risk assessment may sound like too much work for Ellen, she doesn’t need a

sophisticated tool to evaluate the multi-year cash flow under different forecast and optimization assumptions.

A simple forecasting tool, like regression analysis, can be applied to predict the future baseline demand. Once

the planner is able to capture the baseline forecast, she can project the multi-year cash flow impact as a result of

optimization decisions.

To illustrate this, I created a simple three-year cash flow projection based on different levels of optimization

project scope and forecast in Table 1. Although extending circuits by 20% will result in the highest financial

return over three year period when circuit growth is at 10% a year (+$397,800), this strategy will also lead to the

biggest financial loss if circuit growth decreases by -10% a year (-$415,800).

For example, establishing new CLEC hubs to minimize the mileage cost is a strategy based optimizing Time

Division Multiplexing (TDM). The AT&T MSNS plan is also a commitment plan based on point-to-point circuits.

Ellen will be taking a huge financial risk, if the customers decide to purchase Ethernet loops instead of TDM

circuits to support their data requirements. If she believes the circuit demand will be flat for the next 3 years as

a result of technology migration and after reviewing trends, scaling down to groom just 10% of circuits instead of

20% will result in the best return ($162,000).

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Annual Growth Projection (table 1)

% of Extension -10% -5% 0% 5% 10%

0% $ (270,000) $ (45,000) $ - $ - $ -

5% $ (313,200) $ (50,850) $ 81,000 $ 89,100 $ 97,200

10% $ (356,400) $ (97,200) $ 162,000 $ 178,200 $ 194,400

15% $ (399,600) $ (145,800) $ 108,000 $ 260,550 $ 291,600

20% $ (415,800) $ (167,400) $ 81,000 $ 311,400 $ 397,800

Ellen must understand the overall demand of the network and be able to select the right planning strategy to

maximize short and long-term cash flow while minimizing risk. She can also introduce another project with

different risk and cash flow characteristics to help reduce the overall project risk while achieving the desired cash

flow. Thus, Ellen should remember that

Trying to achieve the highest short-term monthly savings may not always be a good strategy and may be

taking on too much risk for additional incremental savings. It’s very important to understand the planning

strategy options and impact to the long-term cash flow and risk. Just as in an investment selection, Ellen must

select a project with appropriate risk levels for the desired return and counter the project risk by considering

the right mix of projects that lower the overall risk while maintaining cash flow.

Conclusion

I have seen many cases where planners are asked to initiate additional optimization projects to achieve a run

rate savings goal. Unfortunately, the planning and optimization decisions based on the monthly circuit savings

figure often ends up in creating long-term issues that must be addressed in subsequent years. In addition,

planners may treat these issues in the future as another optimization savings opportunity even though these

issues were created by poor planning decisions made in prior years.

Planning beyond short-term savings through the effective use of forecast and risk analysis in addition to a holistic

network view will help uncover long-term cash flow effects and the sensitivity to change in the cash flow. This

strategy allows the network planner to truly optimize the network.