"agent-based service analysis, forecasting, simulation and optimisation - from origin to...

Agent-Based Service Analysis, Forecasting, Simulation and Optimisation – From Origin to Pioneering Industry Applications

Dr Yang Li

Research and Innovation

Technology, Service and Operation

British Telecom

Email: [email protected]

© British Telecommunications plc

Overview

Part 1: business landscape

Part 2: evolution of analytical methodologies

Part 3: five service analytical examples

Part 4: turning analytics into software applications

Part 5: a new curriculum on service analytics

Conclude


Part 1: Business Landscape


Why “Service”?

UK GDP Industry Weights (%)

1948 to 2012

People Employed in UK Industry Weight (%)

1948 to 2012

Top 2 GDP Countries (2012)

UK GDP (2012)


Telecommunications Industry, BT, and Field Service

BT quick facts

- Revenue: $26 billion (2012)

- Operates in 170 countries

- 88,500 employees

- Responsible for 20 million

telephone lines in the UK

Telecommunications Industry

- 4~5% of UK GDP

- One of the top 10 sectors

as “Critical National

Infrastructure”

A telecommunications

network consists of

- Telephone exchanges

- Trunk network

- Local / access network

- Mobile phone network

Types of engineering work

- New build

- New provision

- Routine maintenance

- Fault repair

Network + People

(25,000 field workers is a multi-billion-pound business)

Key questions

- What is the exact field service demand for future?

- How to best match up demand and resource?


Value Chain in Telecommunications Field and Customer Services

Faults

Productivity

Weather

Non-

Weather

Forecast

Actual

Accuracy Rules

Time

Horizon

Org

Hierarchy

Service API

& Web PortalBackend

Engines

• Propose and downstream innovations

• Research into new algorithms

• Pilot, trial and live deployment

• Timescale: Day 1 to

Day 14

• Accurate fault and

productivity forecast

leads to committable

provision books

• Timescale: Day 1 to Year 3

• Accurate demand forecast leads to sound

resourcing plan (location, skill, productivity)

• Timescale: on-the-day

and next day

• Accurate fault forecast

leads to lean

resourcing plan


• Reduced faults and

on-time delivery lead

to improved customer

care and satisfaction


• Predictive demand and

accurate scheduling

leads to less travel and

over time

Maximise

Revenue

Minimise

Cost

Field Forecast

/ Planners

Job

Controllers

Customer

Services

Field

DirectorsSOM

Managers

Research &

Innovation

Business

Specialists

Field

Engineers


Telecom Fault Prevention and Forecasting

Five Examples

Scenarios Both inside and outside BT Anonymised for illustration purpose

Cover Work force: location, skill, process Infrastructure: network, vehicle

Key question: what is the most

suitable analytical approach?

Strategic Workforce Co-Location

(1)

Tactical Field Force Re-Skilling

(2)

Complex Service Production Simulation and Management

(3)

Corporate Fleet Analysis and Deployment

(4)

(5)


Part 2: Evolution of Analytical Methodologies


Co-Evolution of Analytical Methodologies and Social-Economics

Economy

Technology

Society

Agriculture Manufacturing Service sector

PCsMainframe

ComputerInternet

Austereness / Collectivism Consumerism / Individualism

Simulation

Method

System dynamics

(strategic)

Discrete event simulation

(tactical) Agent-based modelling

(operational)

1950s 2000s1990s1980s1970s1960s1940s

Analytical

GranularityCoarse-grained

Fine-grained


System Dynamics

Definition: an approach to understanding the nonlinear behaviour of complex

systems over time using stocks, flows, internal feedback loops, and time delays.

Equations in Discrete Time:

1) Potential_Adopters(t) = Potential_Adopters(t-1) - New_Adopters(t)

2) Adopters(t) = Adopters(t-1) + New_Adopters(t)

3) New_Adopters(t) = AdoptionFromAd(t) + AdoptionFromWOM(t)

4) AdoptionFromAd(t) = Potential_Adopters(t-1) Χ AdEffectiveness

5) AdoptionFromWOM(t) = Adopters(t-1) Χ PotentialAdopters(t-1) Χ ContactRate Χ AdoptionFraction / TotalPopulation

Weather Forecast

Agricultural Throughput

Epidemic Disease Propagation

Chemical Process


Discrete Event Simulation

Definition: an approach to model the operation of a system as a discrete sequence of events in time.

Formulae:

Source1 ::= ArrivalRate1 / Second

Source2 ::= ArrivalRate2 / Second

Service1 ::= (Triangular(ServiceTime11/2, ServiceTime11, ServiceTime11 Χ 2), 1/AbandonMeanTime1)

Service2 ::= (Triangular(ServiceTime21/2, ServiceTime21, ServiceTime21 Χ 2) |

Triangular(ServiceTime22/2, ServiceTime22, ServiceTime22 Χ 2),

1/AbandonMeanTime1 | 1/AbandonMeanTime2)

Operator1 ::= NOperators1

Operator2 ::= NOperators2

Manufacturing Factory Hospital theatre Network SimulatorCall Centre


Agent-Based Modelling and Simulation

Definition: an approach to simulating the actions and interactions of autonomous

agents with a view to accessing their effects on the system as a whole.

Formulae:

Ad ::= AdEffect / Day

WOM ::= ContactRate Χ AdoptionFraction

Biology Ecology Sociology

Key question: why ABM has not

been used for real-world service

operation domain?

ABM

Tools

Time

1990 1995 2000 2005 2010

StarLogo Swarm

NetLogoRepast

Anylogic

Gama


What Academic Experts Said?

PO Siebers (University of Nottingham), CM Macal (University of Chicago), J Gamett (University of West of

Scotland), D Buxton (dseConsulting), M Pidd (Lancaster University), “Discrete-Event Simulation is Dead,

Long Live Agent-Based Simulation!”, Journal of Simulation, 4(3) pp. 204-210, 2010.

“However, today, 10 years later, the adoption of the technique has not yet filtered into the mainstream,

either within the academic community, although evidence suggests that this is increasing, and certainly not

within industry”

“There is lots of interest in using ABS in academia and industry but most people don’t know how to apply it.

There are no established frameworks or methodologies to guide researchers and analysts through the ABM

and simulation process, there is no specific guidance on ABS output analysis, there are no easy to use

drag-and-drop ABM and simulation tools, and there are no text books focusing on practitioner needs. All of

this leads to ABS not getting a foot in the door in OR.”


What Best Vendor Said?

Responses from Anylogic support team on issues related to Oracle database adapter and visual

map:

“Different databases supports different SQL standards. So ‘Insert’ and ‘Query’ components can work

incorrectly in some cases. There are several bugs in our databases concerning this issue. I hope they will

be resolved in near future”

“We plan to release new Anylogic this autumn. Most likely it will include some kind of maps (Google,

OpenStreetMaps, Bing or others)”

“We’re trying to answer in 24 hours. But some questions/problems may require more time because we

need to check something or ask our Developing Team to do this if the source of the problem is in

AnyLogic source code”


What I Said?

Y. Li, “Agent-based service analytics”, Encyclopedia of

Business Analytics and Optimisation, 2014.

ABS for service and DES for manufacturing

Keep away from CS and OR agent definition debate

Use actual data instead of abstract data

ABSA as an end-to-end framework

ABSA as a software engineering project

Set up a new curriculum

John Wang, Editor-in-Chief, Encyclopedia of Business

Analytics and Optimisation

Double-blind review comment:

“I think the manuscript is very well written and clearly states

the point. The authors have good understanding of the

subject”


Part 3: Five Service Analytical Examples


Example 1: Tactical Field Force Re-Skilling

o Business context

o Traditional approach

o Architecture of an agent-based analytical toolkit

o Use case 1

o Use case 2

o Use case 3


Tactical Field Force Re-Skilling – Business Context

Fleet Map

Business scenario A national business organisation with a large field

service workforce

Available data

o Historical jobs

• Type of work, location, date and time

o People details

• Type of skill, work area, productivity

Business objectives1) Can we find optimal skill mix for a given number

of field engineers?

2) What is the benefit of optimising skill mix?

3) What is the impact on service performance, if a

field engineer is re-trained to a specific skill?


Tactical Field Force Re-Skilling – Traditional Approach

Skill 1 Skill 2 Skill 3 Skill 4

Area 1 (10, 5) (20, 10) (30, 15) (40, 20)

Area 2 (20, 10) (40, 20) (60, 30) (80, 40)

Area 3 (40, 20) (80, 40) (120, 60) (160, 80)

Demand Profile by Area and Skill

(Mean, Standard Deviation)

Skill 1 Skill 2 Skill 3 Skill 4

Area 1 10 20 30 40

Area 2 20 40 60 80

Area 3 40 80 120 160

Resource Profile by Area and Skill

(Actual)

Monte-Carlo-Based DES model ?

<

<

Historical Job Details

Historical Engineer Details

Traditional statistical approach

(Drawbacks)

Loss of data fidelity

o Job (location, travel time,

task time, service level)

o People (patch, attendance

pattern, skill code)

Cannot scale up

o 800 skill codes

o 1800 work areas

o 90 days

Cannot give actionable insight

o To which specific skill

codes should one engineer

be re-trained?

o What will be the benefit

after re-skilling?

Step (a)

Step (b)

Step (c)


Tactical Field Force Re-Skilling – Architecture of Agent-Based Toolkit

Engineer State Chart

Filtered Data Set

Agent-Based

Simulator

Simulated Service

Performance Output

Raw Data Input

Skill Mix InputVerifier Optimiser


Tactical Field Force Re-Skilling – Use Case 1: Improving Service Performance

Action Balance

capital 0

data 0

e250 1

e500 -19

internal 15

power 0

radio 0

rits 3

evotam 0

Source Action Target Action Shift

e500 e250 1

e500 internal 15

e500 rits 3

Skill Balancing and Re-training Plan

Workflow1) Calibrate simulator via “Verifier”

2) Run “Skill Mix Optimiser”

3) Run “Skill Mix” simulator twice:

i. Use original skill mix

ii. Use optimal skill mix

4) Record benefit of skill

optimisation

5) Record recommended re-

skilling

Acceptable Errors

Simulation Verifier

1)

Skill Mix Optimiser

Optimal Skill Mix

2)

Original skill

mix

Optimal skill

mix

•Success rate: +2%

•Completed tasks:

+6.1%

Skill Mix

Simulator

3)

4)


Tactical Field Force Re-Skilling – Use Case 2: Head Count Reduction

Workflow1) Run “Skill Mix” simulator for

original skill mix and record

service performance

2) Run “Skill Mix Optimiser”

a) using original head

count and record service

performance

b) Using original head

count minus 1 and

record service

performance

c) And so on …

133 techs vs 128 techs

No significant

difference in

productivity and RFT

0

2000

4000

6000

8000

10000

12000

14000

Total Completed

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

RFT

Resulto Identify 5 FTEs reduction

opportunity


Tactical Field Force Re-Skilling – Use Case 3: Up-Skilling Opportunities

Workflow1) Generate and visualise

engineer utilisation profile

2) Recommend engineer re-

skilling opportunities

3) Confirm feasibility of re-skilling

4) Run “Raw Data Simulator” and

record improved service

performance

3)

Simulated Service

Performance Output

Agent-Based Simulator

Raw Data Input

1)

Engineer Utilisation Map

2)

Recommended Re-Skilling


Example 2: Strategic Workforce Co-Location

o Business context

o Analytical skill gap

o Agent-based analytical approach


Strategic Workforce Co-Location – Business Context

Work Location Map

Business scenario A national business organisation with a large

building portfolio

Available data

o Employee identity number

o Function unit

o Co-locatable function flag

o Home worker flag

o Work building address

o Home postcode

Business objectives Can we co-locate workers based on their

functions and locations to improve team culture

and productivity?

What are the quantifiable benefits?


Source Data

Strategic Work Co-Location – Analytical Skill Gap

“We have the data on spreadsheets but seeing

the wood for the tress is hard. If possible

having analyst with such a tool as part of that

could really help us move forward”

A genuine skill gap between OR

practitioner and CS practitioner!


Strategic Work Co-Location – Agent-Based Service Analytics

Identify Centre of Excellence

People Move Site Exit

Agent-based service analysis Prioritise work locations via machine-

assisted visualisation technology

o Centres of Excellence

o Satellite sites

o Other sites

Rule-based people moves

o Criteria: e.g. same function

o Constraint: e.g. 30-mile restriction

Key result Significant percentage of

people can be co-located

Significant percentage of

sites can be exited

Accepted business case

by executive board


Example 3: Complex Service Production Management

o Business context

o Limitation of DES Toolkit

o Agent-based process model


Complex Service Production Management – Business Context

Survey Design

JointingFit & Test

Road Work

Cabling

Business scenario- A key source of revenue

- Involve teams of diverse skills

- Both desk-based and field-based

- Different geographical structures

for each skill team

Key questions- Where is the bottleneck / under-

utilisation in the process?

- What is the optimal resourcing

plan?


Complex Service Production Management – Limitation of DES Toolkit

SimEvents from MathWorks- Able to model discrete process

with fixed resource type

- Unable to handle dynamic agent-

based resource


Demand

Resource

Resource

Utilisation

Job

Completions

Process

Bottlenecks

Process

Model

Agent

Model

Complex Service Production Management – Agent-Based Process Model

New solutions- Anylogic as agent-based process

modelling platform

- Oracle database and APEX as data

manipulation and visualisation platform

- Better configurability and scalability,

ease of re-orgs, seamless operation

New findings- Significant cost saving opportunity from under-utilised resource

- Three worst process bottlenecks activities could be improved

by either injecting new resource or re-aligning resource by area

- The throughput of two process activities could be best

improved via increasing productivity rather than injecting new

resource


Example 4: Corporate Fleet Optimisation

o Business context

o Traditional statistical approach

o Agent-based analytical approach


National Fleet Optimisation – Business Context

Business scenario Anonymous organisation with large national fleet

Available data

o Trip bookings from 2014 to 2018

Transport request number

Start date

End date

Expected duration

Source postcode

Destination postcode

Vehicle type

Number of passengers, bags

o Current vehicle deployment

Source postcode

Vehicle type

Number of vehicles

Fleet Map

Business objectives What is the bid opportunity?

How much could it be worth?


Corporate Fleet Optimisation – Traditional Approach

Classic Analytical Result via Pure Statistical Approach

Drawback of pure statistical approach

- Coarse-grained

- Not verifiable

- Not trustable

- Not actionable

Typical questions on the analysis

- Are these genuine opportunities of

business optimisation?

- To what extent can we increase

utilisation time for these vehicles?

Need finer-grained analysis across

- Geographical dimension

- Time dimension


Corporate Fleet Optimisation – Agent-Based Analytical Approach

Refined Geography

Refined Time

Optimised Vehicle Profile

National Vehicle Balance

Optimise

From local

to national

Bottom-up approach

Agent-based service analytics to

provide convincing evidence

o Verifiable

o Trustable

o Actionable

Real-time data processing,

visualisation, intelligent optimisation

Key result

Significant cost saving opportunity

from reducing under-utilised

vehicles

Accepted input to a bid project


Example 5: Telecom Fault Prevention and Forecasting

o Business context

o Traditional statistical approach

o Rule-based analytical approach


Telecom Fault Prevention and Forecasting – Business Context

Weather impact- Wind

- Rain

- Humidity

- Temperature

- Thunder, etc.

Access network faults- Overhead cable

- Underground cable

- Fibre network

- Broadband, etc.

Core network faults- Switch

- Transmission

- Radio

- Power

Key questions- How to measure impact of weather on faults?

- How to prevent and forecast weather-

impacted faults?

Access Network Core Network


Telecom Fault Prevention and Forecasting – Statistics-Based Approach

Traditional regression models Explain normal weekly and

daily variation in fault counts

quite well

But difficult to

o get clear correlation

between weather and

faults

o get most extreme peaks

right

weather

faults

Because it is too broad a brush

that cannot reveal subtle details!


Telecom Fault Prevention and Forecasting – Rule-Based Approach

Rule-based approach

- Identify root cause of network faults by weather

- Extract rules for both fault prevention and forecasting

Initial result

- New discovery on humidity as one of the key drivers

- Significant improvement in forecast accuracy

0

20

40

60

80

100

120

140

3-Summer Baseline Fault for CAL in Southampton

Source_Actual Avg_Actual

Rule-Based Forecasting

Seasonal Weather-Fault PatternsRoot Cause Analysis for Network Fault

Example rule: IF X in (B1, B2) during Season Y, THEN

Raise fault volume by Z% above baseline forecast


Part 4: Turning Analytics into Software Applications

o Operational applications

o Strategic applications


Operational Applications

(a) Operational Forecasting Dashboard

(c) Override Engine Forecast

(b) Diagnose Historical Forecast

(d) Adjust Model Parameters

General process1) Develop / enhance model in Testbed

2) Trial by business users

3) Adopt the new model in Live platform

4) Go back to 1)

Engineering attributes Transparency

Responsiveness

Scalability

Interpretability

Controllability

Agility


Strategic Applications

Identify Centre of

Excellence

Complex Service Production Simulator

(c) Model Tuning for

Future Temperature

(d) Multi-Factor Fault

Volume Forecast

(a) Strategic Forecasting

Workflow

(b) Multiple Regression for

Historical data

Strategic Fault Forecasting

Optimised Vehicle ProfileNational Vehicle Balance

Strategic Vehicle Balancer

Identify Engineer

Utilisation

Engineering attributes Transparency

Responsiveness

Scalability

Interpretability

Controllability

Agility

Seamlessness

Actionability

Replacability

General process1) Develop / enhance

model in Testbed

2) Trial by business users

3) Adopt the new model

in Live platform

4) Go back to 1)


Part 5: A New Curriculum on Agent-Based Service Analytics

o Comparison between current curriculum and new curriculum

o Selected papers

o Recent recognitions


Comparison between Current Curriculum and New Curriculum

Mancester Business School

MSc Business AnalyticsNew Agent-Based Service Analytics R&D Advantage Examplar Tools

Draw on approach from Operational Research and Statistics Operational Research and Computer Science Service-Oriented, i.e. Individuality

Mathematical OptimisationLinear, Non-Linear, Dynamic Programming

(Excel / Solver)

Agent-Based Analysis, Simulation &

Optimisation

(Anylogic + Oracle PL/SQL/APEX)

Insightable, Actionable, Scalable

Business ForecastingMultivariate statistics

(Excel)

Rule-Based Approach

(Oracle PL/SQL/APEX)Insightable, Actionable, Scalable

Simulation and Risk AnalysisDiscrete-Event, System Dynamics

(Excel)

Agent-Based Simulation

(Anylogic + Oracle PL/SQL/APEX)Service-Oriented, i.e. Individuality

Data Analytics

Classification, Clustering, Predictive Modelling, Text

Mining, Visual Analytics

(SAS)

Integrated human intelligence / decision with

programmable machine intelligence

(Oracle PL/SQL/APEX + Java + HTML)

Flexible, Agile, Scalable, Engagable


Selected Papers

Y. Li, H. Yang, W. Chu, “A Concept-Oriented Belief Revision Approach to Domain Knowledge Recovery from

Source Code”, Journal of Software Maintenance and Evolution: Research and Practice, 13(1), pp. 31-52,

Wiley, 2001.

Y. Li, Z. Cui, H. Yang, H. Jiau, “Tolerating Changes in A Design Psychology Based Webpage Wrapper”, in

Proceedings of the 26th IEEE Annual Computer Software and Application Conference, IEEE CS Press, 2002.

Y. Li, S. Thompson, Z. Tan, N. Giles, H. Gharib, “Beyond Ontology Construction; Ontology Services as

Online Ontology Sharing Community”, in Proceedings of the 2nd International Semantic Web Conference, pp.

469-483, Springer, 2003.

Y. Li, H. Yang, X. Cheng, X. Zhu, “Programming Style Based Program Partition”, International Journal of

Software Engineering and Knowledge Engineering, 15(6), pp. 1027-1062, World Scientific Pub., 2006.

Y. Li, C. Voudouris, S. Thompson, G. Owusu, G. Anim-Anash, A. Liret, H. Lee, M. Kern, “Self-Service

Reservation in the Fieldforce”, BT Technology Journal, 24(1), pp. 40-47, Springer, 2006.

S. Thompson, N. Giles, Y. Li, H. Gharib, T. Nguyen, “Using AI and Semantic Web Technologies to Attack

Process Complexity in Open Systems”, Knowledge-Based Systems, v. 20, n. 2, pp. 152-159, Elsevier, 2007.

Y. Li, “Service Productivity Improvement and Software Technology Support”, in Proceedings of the 1st IEEE

International Workshop on Barriers towards Internet-Driven Information Services, IEEE CS Press, 2008.

Y. Li, B. He, “Optimising Lead Time and Resource Utilisation for Service Enterprises”, Journal of Service

Oriented Computing & Applications, 2(2-3), pp. 65-78, Springer, 2008.

Y. Li, “Managing Enterprise Service Level Agreement”, International Journal of Applied Logistics, 2010.

Y. Li, “Agent-Based Service Analytics”, Encyclopedia of Business Analytics and Optimisation, 2014.


First Paper on

Agent-Based

Service Analytics

2014

Chapter Invitation

from Executive

Editor of IGI Global

2016

USA

Recognitions

UK IT Industry

Business Analyst of The Year

UK IT Industry

Medal

Special

Commendation

2011

2013, 2016

2012

2015

European

Recognitions

2012 2015Two published books on advanced design

approaches and green service engineering

Books

Founder and Lead Chair

2008 - 2015

Workshops

Recent Recognitions


Finally


Summary

Service sector is continuously dominating world economy.

Existing statistics-based analytical approach is too coarse-grained to solve service problems.

A new curriculum overlapping between computer science and operational research could boost next-generation service business analysts.

Agent-based modelling and simulation could not set foot in operational research.

Agent-based service analytics was then coined and pioneered to solve a wide spectrum of real-world service analytical problems.


Final Thought Are consumerism and individualism good?

https://www.theologyofwork.org

https://www.theologyofwork.org/


Thank you

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