AUTOMATED COURSE ROSTERING

2

Fleet (as of 01 August 2013)

– 101 aircraft

– Average age of 6 years 9 months

Network

– More than 60 destinations in over 30 countries

Crew strength

– 7800 Cabin Crew

– 2500 Tech Crew

BACKGROUND

3

Background & Objectives

The Enhancement in a Nutshell

– Preparation

– How it works

Progress and Results

Working with the Jeppesen Team

Limitations and Future Steps

AGENDA

BACKGROUND

Existing System with

Good Potential

SIA’s Training

Requirements

Why implement Automated Course Rostering?

• By seniority

• By rank

• By qualification

• Etc.

1. Many training courses

2. Some with complex criteria

Examples:

1. Capable Optimiser

2. Flexible business logic

5

OBJECTIVES

Model courses according to their structure 1

Identify and roster relevant courses to crew

Based on a set of pre-defined criteria for each course,

and working around existing pre-assignments

2

Perform trainer rostering 3

We wanted a model that allows us to

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PREPARATION

What was needed to achieve Automated Course Rostering?

Standard Crew Information

Crew Training History

Courses available for rostering each

month

Criteria for rostering these courses

Assign new tasks

Modify current manual processes

all necessary data had to

be retrievable within JCR

workflows had to be refined DATA WORKFLOWS

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HOW IT WORKS

1 2 3 4

Course modelling

& criteria setup

Create

course trips

Sync learning

history

Roster

Optimisation

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Reduction of manual effort

Reduction in time required for course rostering

~ 2.5% reduction in no. of man-days in a roster cycle

Only need one staff to handle what we needed a

team of 7 to do

New reporting and error-checking functionality

PROGRESS and EXPECTED RESULTS

Where we are now

• Course assignments as a pre-run

• Implemented trainer rostering

• Trainee rostering only for limited courses

• Process of fine-tuning

Where we want to be

• Course assignments to run in with flights

• Most, if not all courses (trainers and

trainees) to be rostered automatically

with minimal manual intervention

1

2

Estimated Benefits from Full Implementation

Progress

9

WORKING WITH THE JEPPESEN TEAM

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Challenges

• Geographical barriers

• Novelty of project

A Good Learning Experience

• Professional, quality work from the Jeppesen Montreal team, given the

challenging circumstances

• Requirements were understood well and required minimal clarification

• Prompt support for handling issues

Areas for Improvement

• On-site presence would help greatly

• More pre-development discussion/clarification

WORKING WITH THE JEPPESEN TEAM

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Not all courses can be

modelled with complete

accuracy

Only basic course rostering

ruleset available presently

LIMITATIONS AND FUTURE STEPS

Expand criteria and rostering rule-sets

In order to handle a wider variety of

rostering scenarios, course assignment

criteria will have to be expanded

1

Ongoing process of fine-tuning for

improved rostering efficiency

A work in progress, based on lessons

learned from Production

Limitations Future Steps

2

12

QUESTIONS