I nop brochure_2017
- 1. iNOP presentation 20 June 2017
- 3. iNOP optimization software launched KOONSYS founded First contract with MNOs First international contract All Hungarian wireless MNOs contracted iNOP strategy accepted 2004 2015 2016 First iNOP service completed 2005 2006 2009 2010 2012 iNOP in media International launch Koonsys timeline - History
- 4. Traditional services Innovative services WirelessNetwork Operators β High Level Network design β Detailed RAN & TRM Network Planning β Measurements (drive test, statistical) β RAN Optimization (statistical & physical) β Planning tool development & support β Consultancy & Expert pool β iNOP Intelligent Network Optimization β Smart City planning β Revenue boosting solutions β GIS and OAM support tools Wired telecommunication β High Level Network design β Consultancy β FO network design β Expert pool β Optical hub site optimization Product and service portfolio
- 6. TRENDS
- 7. By 2025 900 billion USD CAPEX infrastructure investment will be done by mobile operators in the next 5 years to roll out faster networks. 1 2 3 4 Capacity 1 2 3 4 Lifecycle CAPEX Spending: Tech Capacity vs Lifecycle β’ 4 billion new broadband users β’ Data traffic per subscriber will increase by over 500-fold β’ Over 100 billion devices will be connected
- 8. MNOs are under double pressure Cost savings β’ ARPU decrease β’ Margin erodes due to competition β’ No decrease in EBIDTA is accepted β’ Technology life cycle shortens β as well payback time β’ Customer acquisition cost increase Network expansion β’ New technologies deployed β’ Mobile data boom β’ Smartphones enable data intensive services β’ 5G, IoT is behind the corner
- 9. iNOP is the only state of the art network optimization solution on the market addressing both cost savings and network enhancement problems.
- 10. What is iNOP and why it is unique? β’ iNOP is a new and unique concept on the market for transmission networks β’ iNOP is an E2E Solution as a Service for telecom carriers β’ iNOP is a techno-economic model combining both technical and economic aspects of transmission network optimization β’ iNOP is a software integrating engineering know-how, complex mathematical algorithms and financial measures
- 11. Same optimization methodology has been successfully used by Fortune top companies iNOP uses industrial best practices iNOP is the only telecom solution which implements best optimization practices of other industries like o Supply chain management o Logistics o Production o Workforce management o Energy management etc.
- 12. iNOP solves transmission problems at all network lifecycle stages β’ Ensure TRM capacity for all BS β’ Future proof TRM network β’ Optimal spending of CAPEX β’ Minimized OPEX LaunchnewRANtechnology RANexpansion β’ Minimum # of new HUB sites β’ Optimal spending of CAPEX β’ TRM capacity for all BS β’ Optimized $/Mbps TRMcapacityupgrade β’ TRM capacity bottlenecks β’ Overloaded HUB sites β’ Minimize CAPEX spending β’ Optimized $/Mbps Maturenetwork β’ Reduce OPEX β’ Increase spectrum efficiency β’ Reduce network complexity
- 13. iNOP helps to find substantial savings Typical CEE MNO budget $ 568,700,000 per year Subscribers Base 3-5M ARPU $12-15 Technology 2G/3G/4G Infrastructure ~4000 BTS Staff ~1000 If iNOP finds 10% savings it values over 2 million USD per year for the operator EBID 20% CAPEX 9% OPEX $403,777,000 71% Support and Overhead 13% IT 20% CustomerMgmt 16%Marketing/ Prod. Dev. 7% Sales 17% Network $109,019,790 27% Civil infrastructure 15% RAN 16% NW overhead 10% NW Mgmt 6% VAS 17% Core 15% TRM $22,894,156 21%
- 14. HOW iNOP WORKS?
- 15. Traditional engineering vs. iNOP optimization Traditional Engineering Optimization with iNOP Optimization on link level Technical network parameters are affected Only minor topology changes Best technical solution for Each link Limitations to handle complex network Optimization on network level Best network based on technical & financial targets set by the operator Even major changes in topology are possible No limits in complexity TRM Network Daily Challenges Planning new Links Increased Bandwidth Needs Cost Pressure Optimized Topology
- 16. How iNOP optimizes the network? Technical and financial network data is extracted from network Millions of variations are analyzed by advanced mathematical algorithms Network is translated into a mathematical problem Best solution is selected by iNOP based on pre-defined KPIs Mathematical solution is translated back to an executable network plan 1 2 3 4 5
- 17. Typical iNOP project results β’ Simplified network topology β’ Increased link capacity β’ Less overloaded HUB sites β’ Higher network availability β’ Lower OPEX β’ Lower cost / Mbit β’ Increased spectrum efficiency β’ Optimized capital expenditures
- 18. iNOP deliverables Network Status before and after iNOP Optimization TCO Status before and after iNOP Optimization Recommendations and detailed comparison of original and new network Before & After Comparison Optimized Network Characteristics ROI, CAPEX Needs & KPI Fulfillment Reports Detailed figures of potential CAPEX & OPEX Savings iNOP Optimization plan and program based on customerβs targets and needs Ready to Execute implementation program
- 19. iNOP Final Report samples
- 21. Customerβs demand arise for NW Optimization Extracting data from Customer's systems Data preparation and cleaning Setting optimization targets Running iNOP Iteratively evaluation and refining the plan Implementatio n of network plan 1 to 2 weeks 2 to 4 weeks 1 day 0.5 to 6 hours 2 to4 weeks Iteration if needed Typical iNOP Optimization Project iNOP project duration 5-10 weeks
- 22. iNOP input data requirements Technical data β’ Site and network information β’ LoS matrix or map data β’ Technology, capacity requirements β’ Spectrum availability and preference β’ Restrictions, redundancy requirements Financial data β’ Equipment and implementation costs β’ Frequency fee β’ O&M costs β’ Vendor support fee β’ Rental fees etc.
- 24. Case study 1 β Tier1 EU based mobile operator Client Background β’ 20+ years operating fixed and multi-technology wireless networks β’ Core network β Fiber; Last mile β P2P Microwave β’ # microwave hops: Total 4000 : For pilot: 400 urban / 350 rural Pain β’ Organically grown and increasingly complex network β’ Leading to excessive frequency fees to National Infocommunications Authority β’ EBIDTA pressure from shareholders Client Goals β’ Fast and easy to implement OPEX reductions β’ Frequency fee target reduction of 20% β’ Network optimization plan for long term Findings β’ Microwave hops β reduction of 23% possible β’ Frequency fee β reduction of 40% possible β’ Capacity / hop β increase of 28% possible β’ Pilot Study - ROI of 440% within 3 months
- 25. Original Change Extrapolated to entire network Number of microwave hops 779 516 -33 % -23 % Frequency fee $35,000 USD / per month $7,200 USD / per month 79.5 % 39.8 % Average capacity per hop 176 Mbps 223 Mbps 338 Mbps 277 Mbps Avg. 54 % 27.4 % Average length of connections (urban) Average length of connections (rural) 3,56 km 7,75 km 2,1 km 6,5 km 41 % 16 % Not relevant Case study 1 - figures
- 26. Case study 2 β Tier2 EU based public operator Client Background β’ Fiber optic and multi-technology wireless networks β’ Core TRM network β Fiber and P2P Microwave; Last mile β P2P Microwave β’ # microwave hops: Total 1500 : For pilot: 253 rural Pain β’ Network expansion bottleneck, cannot meet market demand β’ How to spend CAPEX, tower infra/technology change/re-build of the network? β’ EBIDTA and Time to Market pressure Client Goals β’ Frequency fee target reduction of 15% β’ HOP minimization and freeing up tower infrastructure/antenna space β’ Network optimization plan for short/mid/long term network development Findings β’ Microwave hops β reduction of 8% possible β’ Frequency fee β reduction of 33% possible β’ Capacity / hop β increase of 50% possible β’ Pilot Study - ROI of 440% within 3 months
- 27. Original Change Extrapolated to entire network Number of microwave hops 253 231 8,7 % 6,4% Frequency fee $109,000 $73,000 33 % 24,3% Average capacity per hop (rural) 73 Mbps 148 Mbps 50,7 % 42.5 % Average length of connections (rural) 14,6 km 13,2 km 10 % N/A Case study 2 - figures
- 28. Case study 3 β Tier1 MNO in Middle-East Client Background β’ Fiber optic and multi-technology wireless networks β’ Core TRM network β Fiber and P2P Microwave; Last mile β P2P Microwave β’ # microwave hops: Total >11000 : For pilot: 244 dense urban Pain β’ Network expansion capacity bottleneck, cannot meet market demand β’ Overloaded FO HUB sites β’ EBIDTA and Time to Market pressure Client Goals β’ Provide required transmission capacity on all BS sites β’ Have future proof transmission network for later (4.5G) expansion. β’ Relative OPEX savings with low additional investment Findings β’ Capacity / hop β increase of 260% possible β’ Relative OPEX reduction (cost/Mbps) β 51% is possible β’ HUB overload reduction β on most critical HUBs 25% reduction is possible
- 29. Original Change Number of microwave hops 244 244 0 % Relative OPEX (cost/Mbps) 100% 59% -41% Average capacity per hop 152 Mbps 399 Mbps 262 % Overloaded HUB sites 7 2 -72% Case study 3 - figures
- 30. Case study 4 β Tier1 MNO in Europe Client Background β’ Fiber optic and multi-technology wireless networks β’ Core TRM network β Fiber and P2P Microwave; Last mile β P2P Microwave β’ # microwave hops: Total >20000 : For pilot: 407 urban and hilly rural Pain β’ Network expansion to 4.5G has capacity bottlenecks β’ EBIDTA pressure β’ Complicated network structure, long chains Client Goals β’ Provide required capacity on all BS sites for 4.5G expansion. β’ Simplify network topology β’ Relative OPEX savings ($/Mbps) with low additional investment Findings β’ Capacity / link β35% increase is possible β’ Relative OPEX reduction ($/Mbps) β 16% is possible β’ Simplified networkβ 5% less links, 19% shorter hops
- 31. Original Change Number of microwave hops 407 387 -5 % Relative OPEX (cost/Mbps) 100% 84% -16% Average capacity per hop 268 Mbps 363 Mbps 35% BS connected to FO HUB in 1 or 2 HOPS 54% 80% 48% Case study 4 - figures