using less material with better planning and defect ... 2018... · using less material with better...

Using Less Material with better planning and

defect detection in Telecommunication and

Manufacturing.

Le Hong Viet

CTO, FPT Corporation

FPT Overview

22

Highest International Standards

SEI CMMI Level 5 | ISO 27001:2013 (BS 7799 -2:2002) | ISO/ TS 16949 |

ISO 9001:2008 | ISO 20000-1:2011

We are where our clients are: 33 global offices

Key partners of Major Technology Providers

#1 in IT

Services/ITO

& Systems Integration in

Vietnam

#1 Entertainment

Portal in

Southeast Asia #1

Private

University

in Vietnam22,000

Students

FPT - The leading ICT

Corporation in Vietnam

Founded: 1988

Public

listed:

Dec 2006 on VNSE

Revenue: USD 1.96 billion (2017)

Staff: 32,092 (Technology

Engineers and

Experts: 15,449)

Developme-

nt Centers:

Hanoi, Hochiminh City,

Danang, Can Tho

R&D investment:

5% profit before tax

Clients: 250+ clients with 40+

Fortune 500 s

© Copyright by FPT 2018

FPT – Leading ICT Company in Vietnam

3

1 | TECHNOLOGY SECTOR 2 | TELECOMMUNICATIONS SECTOR

3 | DISTRIBUTION AND RETAIL SECTOR 4 | EDUCATION SECTOR

Software Development

System Integration & IT Services

#1 in Revenue and Workforce;

#1 in IT services provision;

High level partner of Microsoft, AWS,

SAP, etc

TOP 100 IAOP’s Global Outsourcing;

~12,000 Technology experts & engineers;

#1 in Technology product distribution

1,500+ agents in Vietnam

Distributor for 30+ large technology

partners 450 retail stores in 63/63 cities

and provinces

Telecommunications

#2 in Fixed broadband internet access services;

North-South axis, 8,400km long;

280Gbps international bandwidth;

04 Data centers;

Digital content

34.5 mil. pageviews/day for VnExpress

Leading e-commerce marketplace -Sendo.vn

Top University in IT Education; 17,000

students at all levels (FPT University, FPT

Polytechnic…); 98% of graduates having

jobs within 06 months;

Using materials Smarter, Less and Longer

4© Copyright by FPT 2018

Use Case 01: Preventing Waste in by Eliminating Defects


Introduction

A printing company in Japan is wasting $30M a year due to defects (with the rate of 1%) in their printing products and wish to

use Big Data Analytics to reduce this number.


Source of Defects

(1) Design and Retouch

(2)Review and Approve

(3)Controlled Printing

(4)Maintenance

- Human Error when designing printing

products.

- Unattended Review, overload of review

tasks

- Machine or Material Defects.

- Control and Monitoring

- Defect parts- Down time


(1)

(2)

(3)

(4)

Solution


STREAM 1

Human Error

Reduction

STREAM 2

Monitoring &

Alert

• Defect detection sensors

• PLC data integration

• Just-in-time Alert

• Abnormally Detection

• Collect data design

office: (temperature,

humidity)

• Designer behaviors

(mouse trajectory, heart

beat…)

• Supervised Learning via

Review Data

STREAM 3

Predictive

Maintenance

• Usage Sensor

installation

• Predictive Maintenance

Analytic Platforms

Effects / Findings

• System is still in evaluation, however, few important finding has been concluded.

• Random Check is replaced by prioritized review.• Suspected products will be prioritized for review

• Alerts make reviewer be more focused on faulty products.

• Recommended break for Designers, regain focuses.

• Working condition improved.

• Just-in-time alert helps to reduce mass defects.


Use Case 02: Use less fiber optic cables in Telecommunication Industry


Background

• FPT Telecom owns a largest FTTH network in Vietnam, reaching to multi million households across 63 provinces.

• Wiring the network to households consume a lot of effort and resources.

• Unoptimized Routing and handling causes inefficiency in using fiber optic cables.


Process

• Process 1: Running L1 cables, connecting L1 and L2.

• Workers manually draw the cable paths on a map.• Cable are provided with length 115%*L for the

workers.• They then do the laying, with the provided cables,

roughly according to the drawing of step 1.

• Process 2: Running L2 cables to households.• Workers are provided with almost unlimited cable

length.• Workers manually lay the cable from L2 nodes to

customer nodes, actual length is recorded.• The cable length used is compared to distance on

the map, between customer nodes and L2 node, to check if there is excessive spent of cable.

• Randomly, some customer nodes are select for post examination, by independent teams of examiners, to check if the cable running is reasonable.


Node L1

Node L2

Customer Customer

Node L2

Customer

What are the problems?


Problem & Solution

• Predict Future Consumptions:• Problem: Cost for wiring is high => predict consumption is crucial for

the initial wiring.

• Solution:• Inputs:

• Using previous customer data on Coper Network.

• Using Demographic Survey from Government.

• On the field information about: residential characteristics, income estimation, …

• Solution: Machine Learning models to predict consumptions

• Capacity of network

• Network growth rate.

• Result:

• 3% improvement over experts design.



Prediction of consumption in 1 year

Pink nodes: need

upgrade to match

future demand

Blue nodes: can

survive the demand

in 1 year

Problem & Solution

• Routing Optimization and Anomaly Detection• Problem:

• Inefficiency in route planning especially in choosing POP location (L2 location)

• Terrain dependence (hanging on electrical popes or underground).

• Tiny roads (not on map).

• Solution:• Workers, besides main work, need to collect GPS, maps, pictures on routes and

meta data (e.g.: Road on map, underground, hanging high….)

• Machine learning algorithms are used to detect abnormal wiring.

• History of wiring (from multiple teams) are used to improve planning.

• Result:• Expecting 5-7% of improvement.



A

B

Cable route from A to B drawn manually,

based on generic map

A

B

Efficient route from A to B generated by

machine learning, from collected data

Small lane from GPS data, not shown on generic map

Cross-road hanging from metadata

Thank You

19

using less material with better planning and defect ... 2018... · using less material with better...

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