SlideShare une entreprise Scribd logo
1  sur  3
DEPARTAMIENTO CA
ASIGNAD
O POR EL
MTC
PORTABILIDAD (*) LÍNEAS
EN
SERVICIO
31.12.2017
PRE-
DISPONI
BLE
OTROS USOS
DISPONIBLE
PROYECCIÓN
Ene - Dic.2018
RESERVA NECESIDAD
GANADAS PERDIDAS
HIBERN
ACIÓN
ASIGNADO
A
CENTRALES
TUPS
AMAZONAS 41
9,620,000 346,150 534,629
51401 5,255 0 0 0
3’519,590
160,920
656,692 5,070,000
ANCASH 43 197020 22,545 0 0 0 322,062
APURIMAC 83 61310 6,485 0 0 0 95,094
AREQUIPA 54 194474 21,399 0 0 0 331,279
AYACUCHO 66 111835 11,818 0 0 0 159,995
CAJAMARCA 76 201009 18,988 0 0 0 206,552
CUSCO 84 273457 24,978 0 0 0 323,557
HUANCAVELICA 67 35596 4,184 0 0 0 55,611
HUANUCO 62 155798 15,484 0 0 0 210,077
ICA 56 116539 10,315 0 0 0 202,513
JUNIN 64 224040 21,009 0 0 0 284,236
LA LIBERTAD 44 270484 27,043 0 0 0 366,833
LAMBAYEQUE 74 210456 19,463 0 0 0 230,223
LIMA 1 1600043 163,567 0 100,000 0 2,988,989
LORETO 65 151794 13,083 0 0 0 157,138
MADRE DE DIOS 82 74099 5,601 0 0 0 102,113
MOQUEGUA 53 37942 3,817 0 0 0 28,969
PASCO 63 69004 6,022 0 0 0 64,979
PIURA 73 397333 31,963 0 0 0 497,149
PUNO 51 314273 28,546 0 0 0 351,772
SAN MARTIN 42 245295 19,993 0 0 0 314,936
TACNA 52 136113 11,818 0 0 0 153,213
TUMBES 72 68637 6,606 0 0 0 146,226
UCAYALI 61 103130 10,867 0 0 0 125,865
TOTAL 9’620,000 346,150 534,629 5’301,082 510,849 0 100,000 0 3’519,590 7’880,301 656,692 5’020,000
Tabla1: Uso del recurso numérico y necesidad
No DEPARTAMENTO ENERO FEBRERO MARZO ABRIL MAYO JUNIO JULIO AGOSTO SEPTIEMBRE OCTUBRE NOVIEMBRE DICIEMBRE
1 AMAZONAS 8727 10023 10445 8647 15642 15387 17517 10000 9005 9163 10812 11485
2 ANCASH 19979 19617 31229 21258 34510 31706 37067 23771 21403 23297 22917 28417
3 APURIMAC 5667 6658 7039 6468 10762 8602 11690 6913 6211 6844 6807 7964
4 AREQUIPA 20349 19790 25599 21805 32777 37095 41041 24221 23348 24167 24572 25672
5 AYACUCHO 9927 9193 12192 13127 21896 16977 19468 11774 10427 10489 9776 11516
6 CAJAMARCA 15923 14954 17175 15213 24869 22251 28441 16497 15883 15698 14449 17918
7 CUSCO 21729 19327 21173 21961 34762 33306 41854 23755 23451 25041 23734 27015
8 HUANCAVELICA 3689 3265 4264 2964 6053 5560 7315 3738 3355 3539 3513 4167
9 HUANUCO 15379 14935 14899 14288 25015 22146 27542 14390 13314 15232 15746 16348
10 ICA 11286 10357 11052 11858 23019 21000 25847 15702 15500 16541 15734 18529
11 JUNIN 19738 15902 18976 17184 29212 31519 38214 23064 20303 23432 23849 25654
12 LA LIBERTAD 23893 21799 23943 24720 42126 41247 47465 26646 25624 27766 28153 29699
13 LAMBAYEQUE 14657 14711 18921 12172 22076 26397 29709 16914 16317 18581 18333 20306
14 LIMA 107179 113716 124019 148052 225318 225764 271290 148034 140302 159315 152623 187714
15 LORETO 15542 12219 13052 12057 19342 15737 16168 8734 9490 11570 10828 13192
16 MADRE DE DIOS 6996 7286 7291 6470 11610 11535 13037 7413 6605 7279 6756 7145
17 MOQUEGUA 3582 3874 3608 3662 5217 4880 5811 3883 4002 3992 3180 3734
18 PASCO 4660 5244 4795 4081 7039 5573 7030 4282 4437 4962 4824 5494
19 PIURA 34008 33216 33284 32917 61471 60886 62773 35411 33094 37208 39646 44212
20 PUNO 24828 23170 27519 25089 40957 38349 44407 24122 23257 25973 24327 25771
21 SAN MARTIN 16569 17335 20066 18369 33331 32658 35030 24105 24764 25152 25317 28345
22 TACNA 9069 9112 8622 11635 14742 13960 15207 8772 8037 8732 8824 10995
23 TUMBES 8755 8729 9387 8510 16482 16943 20467 11624 10368 11121 11334 12801
24 UCAYALI 7639 8759 10077 8862 14447 13763 17998 10201 7511 7957 8322 10114
TOTAL 429770 423191 478627 471369 772675 753241 882388 503966 476008 523051 514376 594207
Tabla2: Altas durante el periodo Enero – Diciembre 2017
No DEPARTAMENTO Ene-18 Feb-18 Mar-18 Abr-18 May-18 Jun-18 Jul-18 Ago-18 Sep-18 Oct-18 Nov-18 Dic-18
Proyección 12
meses
1 AMAZONAS 12,229 13,755 14,746 11,731 11,630 10,810 12,578 13,720 11,960 13,754 15,817 18,190 160,920
2 ANCASH 23,056 22,757 36,498 23,266 21,863 19,633 23,857 27,499 24,760 28,473 32,744 37,656 322,062
3 APURIMAC 6,717 8,009 8,637 7,054 6,968 5,611 7,654 8,152 7,268 8,358 9,612 11,054 95,094
4 AREQUIPA 24,024 23,545 30,232 22,028 20,648 23,400 26,372 27,801 26,681 30,683 35,286 40,579 331,279
5 AYACUCHO 11,613 10,861 14,354 13,418 13,843 10,639 12,432 13,458 11,891 13,675 15,726 18,085 159,995
6 CAJAMARCA 16,435 15,264 17,667 14,194 13,795 12,358 16,070 16,998 16,776 19,293 22,187 25,515 206,552
7 CUSCO 25,519 22,700 24,701 22,172 21,934 20,840 26,830 26,918 26,424 30,387 34,945 40,187 323,557
8 HUANCAVELICA 4,477 3,851 4,957 3,595 3,802 3,646 5,150 5,001 4,232 4,867 5,597 6,436 55,611
9 HUANUCO 17,811 17,359 17,378 15,515 15,612 13,655 17,436 16,675 15,748 18,110 20,827 23,951 210,077
10 ICA 13,231 12,198 13,003 13,303 14,452 13,002 16,429 18,117 17,779 20,446 23,513 27,040 202,513
11 JUNIN 22,830 18,567 22,351 18,206 18,326 19,249 23,513 26,031 23,063 26,523 30,501 35,076 284,236
12 LA LIBERTAD 27,672 25,295 27,942 27,553 26,180 25,259 29,877 30,819 29,286 33,679 38,731 44,540 366,833
13 LAMBAYEQUE 16,922 16,984 21,874 13,442 13,618 15,976 18,466 19,478 18,717 21,525 24,754 28,467 230,223
14 LIMA 188,418 193,889 216,212 216,522 224,427 237,339 285,660 229,231 239,776 275,742 317,104 364,669 2,988,989
15 LORETO 18,008 14,112 15,173 13,055 12,221 9,607 10,105 10,161 10,954 12,597 14,486 16,659 157,138
16 MADRE DE DIOS 8,172 8,471 8,559 7,147 7,252 7,064 8,293 8,601 7,721 8,879 10,211 11,743 102,113
17 MOQUEGUA 2456 2491 2353 2151 2090 1943 2188 2321 2198 2528 2907 3343 28,969
18 PASCO 5,463 6,182 5,665 4,286 4,672 3,650 4,632 4,990 5,095 5,859 6,737 7,748 64,979
19 PIURA 39,187 38,309 38,442 36,990 37,053 36,763 38,413 41,086 38,232 43,967 50,561 58,146 497,149
20 PUNO 29,132 27,142 32,151 26,548 25,322 23,515 27,802 27,792 26,509 30,485 35,058 40,316 351,772
21 SAN MARTIN 19,450 20,217 23,541 20,039 20,662 20,071 22,128 27,945 28,214 32,446 37,313 42,910 314,936
22 TACNA 12,996 13,172 12,445 11,376 11,053 10,276 11,574 12,274 11,625 13,368 15,374 17,680 153,213
23 TUMBES 10,123 10,115 10,882 9,460 10,036 10,189 12,644 13,412 11,889 13,672 15,723 18,081 146,226
24 UCAYALI 8,868 10,191 11,793 9,367 9,217 8,743 11,469 12,018 8,852 10,179 11,706 13,462 125,865
Total 564,809 555,436 631,556 562,418 566,676 563,238 671,572 640,498 625,650 719,495 827,420 951,533 7,880,301
Tabla4. Previsiones de utilización, en el tiempo, del recurso

Contenu connexe

En vedette

Social Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie InsightsSocial Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie Insights
Kurio // The Social Media Age(ncy)
 
Good Stuff Happens in 1:1 Meetings: Why you need them and how to do them well
Good Stuff Happens in 1:1 Meetings: Why you need them and how to do them wellGood Stuff Happens in 1:1 Meetings: Why you need them and how to do them well
Good Stuff Happens in 1:1 Meetings: Why you need them and how to do them well
Saba Software
 
Introduction to C Programming Language
Introduction to C Programming LanguageIntroduction to C Programming Language
Introduction to C Programming Language
Simplilearn
 

En vedette (20)

How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024
 
Social Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie InsightsSocial Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie Insights
 
Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024
 
5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary
 
ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd
 
Getting into the tech field. what next
Getting into the tech field. what next Getting into the tech field. what next
Getting into the tech field. what next
 
Google's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search IntentGoogle's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search Intent
 
How to have difficult conversations
How to have difficult conversations How to have difficult conversations
How to have difficult conversations
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Time Management & Productivity - Best Practices
Time Management & Productivity -  Best PracticesTime Management & Productivity -  Best Practices
Time Management & Productivity - Best Practices
 
The six step guide to practical project management
The six step guide to practical project managementThe six step guide to practical project management
The six step guide to practical project management
 
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
 
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
 
12 Ways to Increase Your Influence at Work
12 Ways to Increase Your Influence at Work12 Ways to Increase Your Influence at Work
12 Ways to Increase Your Influence at Work
 
ChatGPT webinar slides
ChatGPT webinar slidesChatGPT webinar slides
ChatGPT webinar slides
 
More than Just Lines on a Map: Best Practices for U.S Bike Routes
More than Just Lines on a Map: Best Practices for U.S Bike RoutesMore than Just Lines on a Map: Best Practices for U.S Bike Routes
More than Just Lines on a Map: Best Practices for U.S Bike Routes
 
Ride the Storm: Navigating Through Unstable Periods / Katerina Rudko (Belka G...
Ride the Storm: Navigating Through Unstable Periods / Katerina Rudko (Belka G...Ride the Storm: Navigating Through Unstable Periods / Katerina Rudko (Belka G...
Ride the Storm: Navigating Through Unstable Periods / Katerina Rudko (Belka G...
 
Barbie - Brand Strategy Presentation
Barbie - Brand Strategy PresentationBarbie - Brand Strategy Presentation
Barbie - Brand Strategy Presentation
 
Good Stuff Happens in 1:1 Meetings: Why you need them and how to do them well
Good Stuff Happens in 1:1 Meetings: Why you need them and how to do them wellGood Stuff Happens in 1:1 Meetings: Why you need them and how to do them well
Good Stuff Happens in 1:1 Meetings: Why you need them and how to do them well
 
Introduction to C Programming Language
Introduction to C Programming LanguageIntroduction to C Programming Language
Introduction to C Programming Language
 

REQ_New numbering Jan2018

  • 1. DEPARTAMIENTO CA ASIGNAD O POR EL MTC PORTABILIDAD (*) LÍNEAS EN SERVICIO 31.12.2017 PRE- DISPONI BLE OTROS USOS DISPONIBLE PROYECCIÓN Ene - Dic.2018 RESERVA NECESIDAD GANADAS PERDIDAS HIBERN ACIÓN ASIGNADO A CENTRALES TUPS AMAZONAS 41 9,620,000 346,150 534,629 51401 5,255 0 0 0 3’519,590 160,920 656,692 5,070,000 ANCASH 43 197020 22,545 0 0 0 322,062 APURIMAC 83 61310 6,485 0 0 0 95,094 AREQUIPA 54 194474 21,399 0 0 0 331,279 AYACUCHO 66 111835 11,818 0 0 0 159,995 CAJAMARCA 76 201009 18,988 0 0 0 206,552 CUSCO 84 273457 24,978 0 0 0 323,557 HUANCAVELICA 67 35596 4,184 0 0 0 55,611 HUANUCO 62 155798 15,484 0 0 0 210,077 ICA 56 116539 10,315 0 0 0 202,513 JUNIN 64 224040 21,009 0 0 0 284,236 LA LIBERTAD 44 270484 27,043 0 0 0 366,833 LAMBAYEQUE 74 210456 19,463 0 0 0 230,223 LIMA 1 1600043 163,567 0 100,000 0 2,988,989 LORETO 65 151794 13,083 0 0 0 157,138 MADRE DE DIOS 82 74099 5,601 0 0 0 102,113 MOQUEGUA 53 37942 3,817 0 0 0 28,969 PASCO 63 69004 6,022 0 0 0 64,979 PIURA 73 397333 31,963 0 0 0 497,149 PUNO 51 314273 28,546 0 0 0 351,772 SAN MARTIN 42 245295 19,993 0 0 0 314,936 TACNA 52 136113 11,818 0 0 0 153,213 TUMBES 72 68637 6,606 0 0 0 146,226 UCAYALI 61 103130 10,867 0 0 0 125,865 TOTAL 9’620,000 346,150 534,629 5’301,082 510,849 0 100,000 0 3’519,590 7’880,301 656,692 5’020,000 Tabla1: Uso del recurso numérico y necesidad
  • 2. No DEPARTAMENTO ENERO FEBRERO MARZO ABRIL MAYO JUNIO JULIO AGOSTO SEPTIEMBRE OCTUBRE NOVIEMBRE DICIEMBRE 1 AMAZONAS 8727 10023 10445 8647 15642 15387 17517 10000 9005 9163 10812 11485 2 ANCASH 19979 19617 31229 21258 34510 31706 37067 23771 21403 23297 22917 28417 3 APURIMAC 5667 6658 7039 6468 10762 8602 11690 6913 6211 6844 6807 7964 4 AREQUIPA 20349 19790 25599 21805 32777 37095 41041 24221 23348 24167 24572 25672 5 AYACUCHO 9927 9193 12192 13127 21896 16977 19468 11774 10427 10489 9776 11516 6 CAJAMARCA 15923 14954 17175 15213 24869 22251 28441 16497 15883 15698 14449 17918 7 CUSCO 21729 19327 21173 21961 34762 33306 41854 23755 23451 25041 23734 27015 8 HUANCAVELICA 3689 3265 4264 2964 6053 5560 7315 3738 3355 3539 3513 4167 9 HUANUCO 15379 14935 14899 14288 25015 22146 27542 14390 13314 15232 15746 16348 10 ICA 11286 10357 11052 11858 23019 21000 25847 15702 15500 16541 15734 18529 11 JUNIN 19738 15902 18976 17184 29212 31519 38214 23064 20303 23432 23849 25654 12 LA LIBERTAD 23893 21799 23943 24720 42126 41247 47465 26646 25624 27766 28153 29699 13 LAMBAYEQUE 14657 14711 18921 12172 22076 26397 29709 16914 16317 18581 18333 20306 14 LIMA 107179 113716 124019 148052 225318 225764 271290 148034 140302 159315 152623 187714 15 LORETO 15542 12219 13052 12057 19342 15737 16168 8734 9490 11570 10828 13192 16 MADRE DE DIOS 6996 7286 7291 6470 11610 11535 13037 7413 6605 7279 6756 7145 17 MOQUEGUA 3582 3874 3608 3662 5217 4880 5811 3883 4002 3992 3180 3734 18 PASCO 4660 5244 4795 4081 7039 5573 7030 4282 4437 4962 4824 5494 19 PIURA 34008 33216 33284 32917 61471 60886 62773 35411 33094 37208 39646 44212 20 PUNO 24828 23170 27519 25089 40957 38349 44407 24122 23257 25973 24327 25771 21 SAN MARTIN 16569 17335 20066 18369 33331 32658 35030 24105 24764 25152 25317 28345 22 TACNA 9069 9112 8622 11635 14742 13960 15207 8772 8037 8732 8824 10995 23 TUMBES 8755 8729 9387 8510 16482 16943 20467 11624 10368 11121 11334 12801 24 UCAYALI 7639 8759 10077 8862 14447 13763 17998 10201 7511 7957 8322 10114 TOTAL 429770 423191 478627 471369 772675 753241 882388 503966 476008 523051 514376 594207 Tabla2: Altas durante el periodo Enero – Diciembre 2017
  • 3. No DEPARTAMENTO Ene-18 Feb-18 Mar-18 Abr-18 May-18 Jun-18 Jul-18 Ago-18 Sep-18 Oct-18 Nov-18 Dic-18 Proyección 12 meses 1 AMAZONAS 12,229 13,755 14,746 11,731 11,630 10,810 12,578 13,720 11,960 13,754 15,817 18,190 160,920 2 ANCASH 23,056 22,757 36,498 23,266 21,863 19,633 23,857 27,499 24,760 28,473 32,744 37,656 322,062 3 APURIMAC 6,717 8,009 8,637 7,054 6,968 5,611 7,654 8,152 7,268 8,358 9,612 11,054 95,094 4 AREQUIPA 24,024 23,545 30,232 22,028 20,648 23,400 26,372 27,801 26,681 30,683 35,286 40,579 331,279 5 AYACUCHO 11,613 10,861 14,354 13,418 13,843 10,639 12,432 13,458 11,891 13,675 15,726 18,085 159,995 6 CAJAMARCA 16,435 15,264 17,667 14,194 13,795 12,358 16,070 16,998 16,776 19,293 22,187 25,515 206,552 7 CUSCO 25,519 22,700 24,701 22,172 21,934 20,840 26,830 26,918 26,424 30,387 34,945 40,187 323,557 8 HUANCAVELICA 4,477 3,851 4,957 3,595 3,802 3,646 5,150 5,001 4,232 4,867 5,597 6,436 55,611 9 HUANUCO 17,811 17,359 17,378 15,515 15,612 13,655 17,436 16,675 15,748 18,110 20,827 23,951 210,077 10 ICA 13,231 12,198 13,003 13,303 14,452 13,002 16,429 18,117 17,779 20,446 23,513 27,040 202,513 11 JUNIN 22,830 18,567 22,351 18,206 18,326 19,249 23,513 26,031 23,063 26,523 30,501 35,076 284,236 12 LA LIBERTAD 27,672 25,295 27,942 27,553 26,180 25,259 29,877 30,819 29,286 33,679 38,731 44,540 366,833 13 LAMBAYEQUE 16,922 16,984 21,874 13,442 13,618 15,976 18,466 19,478 18,717 21,525 24,754 28,467 230,223 14 LIMA 188,418 193,889 216,212 216,522 224,427 237,339 285,660 229,231 239,776 275,742 317,104 364,669 2,988,989 15 LORETO 18,008 14,112 15,173 13,055 12,221 9,607 10,105 10,161 10,954 12,597 14,486 16,659 157,138 16 MADRE DE DIOS 8,172 8,471 8,559 7,147 7,252 7,064 8,293 8,601 7,721 8,879 10,211 11,743 102,113 17 MOQUEGUA 2456 2491 2353 2151 2090 1943 2188 2321 2198 2528 2907 3343 28,969 18 PASCO 5,463 6,182 5,665 4,286 4,672 3,650 4,632 4,990 5,095 5,859 6,737 7,748 64,979 19 PIURA 39,187 38,309 38,442 36,990 37,053 36,763 38,413 41,086 38,232 43,967 50,561 58,146 497,149 20 PUNO 29,132 27,142 32,151 26,548 25,322 23,515 27,802 27,792 26,509 30,485 35,058 40,316 351,772 21 SAN MARTIN 19,450 20,217 23,541 20,039 20,662 20,071 22,128 27,945 28,214 32,446 37,313 42,910 314,936 22 TACNA 12,996 13,172 12,445 11,376 11,053 10,276 11,574 12,274 11,625 13,368 15,374 17,680 153,213 23 TUMBES 10,123 10,115 10,882 9,460 10,036 10,189 12,644 13,412 11,889 13,672 15,723 18,081 146,226 24 UCAYALI 8,868 10,191 11,793 9,367 9,217 8,743 11,469 12,018 8,852 10,179 11,706 13,462 125,865 Total 564,809 555,436 631,556 562,418 566,676 563,238 671,572 640,498 625,650 719,495 827,420 951,533 7,880,301 Tabla4. Previsiones de utilización, en el tiempo, del recurso