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Decision Profit Optimisation.

The goal is for a telco to use its backbone network to route inbound traffic to a final destination at the optimum cost to ensure a stable cost base at predetermined service level agreements for quality of service. Then simultaneously pricing to customers to win traffic that would maximise profit per route.

Related to critical path analysis.


  • Traffic
  • Route Capacity
  • Product Quality
  • Trade Deals
  • Time of Day
  • Routing Cases
  • World Number Plan
  • Legal Compliance
Routing Algorithm

World Number Plan

No definitive resource so source of opportunity for arbitrage.

Traffic Profiles

Influences on traffic volume

  • Pricing relative to the market
  • Special events such as Christmas, Olympics, etc

Available Capacity

Physical cables from one switch to another. Where the other switch could be

  • International backbone
  • Domestic backbone
  • Another telco

Quality of Service

Product type examples are Retail and Wholesale.

Each product type must have a way to designate what level of service the customer had paid for either by route or A number matching.

Each Routing Case (Destination) had acceptable target KPIs. If a supplier falls below thresholds for a particular Destination and Product, add them to a blocklist.

  • Answer Seizure Ratio
  • Post Dial Delay

Trade Deals

Obligated Minutes

  • Bilateral Agreements

Open-ended minutes

  • Price Sheets
  • Invoices



  • Routes per switch
  • Routing cases per switch


  • Overflow from first-choice selection is minimised
  • Use suppliers most valuable routes, then exclude
  • Backbone by customer type
  • No circular looping
  • Minimal overflow
  • Blocked destination suppliers
  • Trade Agreements
  • Dial Code Destination Matching
  • Dial Code Cost Blending

Routing Algorithm

In the dream scenario, the network would be full but not overflowing capacity, with every destination perfectly priced to customers for maximum trading profits.

All traffic for each product type and destination, terminates with the first-choice selection with no routing overflow and all traffic terminated above benchmark quality

Pricing at the highest possible price to attract maximum customer volume without cause overflow as per point one.


Predicted Traffic Profile

Machine Learning Input: Projected Minutes by Routing Case code per Hour

  • Growth Trend
  • Historical Trend
  • Global Events
  • Pricing
  • Time of Day

Blended Rates

Load Blended Rate Profile for each routing case by mapping offering price, code overlap for each Time of Day

Optimum Margin Routing

Get the order

  1. Lock in bilateral agreement deals with any special rules
  2. Find the order for each destination
    • run in order from highest volume destinations to least
    • do not consider blocked providers
    • get the first and second routing providers
  3. For each node on the network, against anticipated overflow setting, calculate the blended cost for each node in the backbone network add egress cost.
  4. Recalculate the best routing options for destination including backbone nodes
  5. Ensure no option for circular routing

Run the routing algorithm in iterations, after each pass of the routing order logic;

  1. Subtract the second choice cost from the first choice cost
  2. Order dataset by greatest saving
  3. Loop through to sum anticipated volume per provider for each destination/product profile based on volume demand (as)
  4. If a provider's safe outbound capacity is reached;
    • Remove provider with full capacity as an option
    • Add the provider and route to a list to be ignored as a routing option in the next routing iteration
    • Move next provider option forward

Iterate until the desired variation is achieved, or for a set number of iterations.


Find out the extremes of what each provider will pay by analysing their price sheet.


Problems to solve

  • Reduce manual data entry to a minimum
  • Eliminate data replication errors