Beyond Predictive Analytics?predictiveanalyticsandforecasting.com/wp-content/uploads/2017/10/... ·...

© 2017 Sven F. Crone. All rights reserved.

Beyond Predictive Analytics?Delivering value on in business forecasting

for Virgin Atlantic Cargo

Dr. Sven F. CroneIIF Workshop on Predictive Analytics & Forecasting, Munich, 14.09.2017

http://www.bt.com/index.jsp?BV_SessionID=@@@@1743980602.1107967121@@@@&BV_EngineID=ccccadddkmgekeecflgcefkdffndfli.0&obsOID=30777&obsPage=/index.jsp&vStore=1128&obsType=LINK&obsNoSee=true

http://www.bt.com/index.jsp?BV_SessionID=@@@@1743980602.1107967121@@@@&BV_EngineID=ccccadddkmgekeecflgcefkdffndfli.0&obsOID=30777&obsPage=/index.jsp&vStore=1128&obsType=LINK&obsNoSee=true

http://www.tesco.com/r/?from=HomepageLogo&url=http://www.tesco.com/

http://www.tesco.com/r/?from=HomepageLogo&url=http://www.tesco.com/

http://www.burtonsfoods.com/home.asp

http://www.burtonsfoods.com/home.asp

http://www.daler-rowney.com/index.asp

http://www.daler-rowney.com/index.asp

http://www.barclaycard.co.uk/?previous_page_id=1&link_type=TTL

http://www.barclaycard.co.uk/?previous_page_id=1&link_type=TTL

http://www.uk.experian.com/index.html

http://www.uk.experian.com/index.html

http://www.ams.mod.uk/ams/content/docs/sadir/597.htm

http://www.ams.mod.uk/ams/content/docs/sadir/597.htm

http://www.dwp.gov.uk/

http://www.dwp.gov.uk/

http://www.infor.com/infor/index.htm

http://www.infor.com/infor/index.htm

http://www.cowandgate.co.uk/en/home.asp

http://www.cowandgate.co.uk/en/home.asp

http://www.janssen-cilag.de/;jsessionid=GRVDTMW4UJXTYCUCERDBXCQ?product=none

http://www.janssen-cilag.de/;jsessionid=GRVDTMW4UJXTYCUCERDBXCQ?product=none

http://www.plymriver.co.uk/images/nhs_logo.jpg

http://www.plymriver.co.uk/images/nhs_logo.jpg

http://www.google.de/url?source=imgres&ct=tbn&q=http://upload.wikimedia.org/wikipedia/de/thumb/4/4f/Capita_Logo.svg/800px-Capita_Logo.svg.png&usg=AFQjCNFY0sruCJy0B_6Rx3efI_ZneXvIPA

http://www.google.de/url?source=imgres&ct=tbn&q=http://upload.wikimedia.org/wikipedia/de/thumb/4/4f/Capita_Logo.svg/800px-Capita_Logo.svg.png&usg=AFQjCNFY0sruCJy0B_6Rx3efI_ZneXvIPA

http://www.astrazeneca.co.uk/

http://www.astrazeneca.co.uk/

http://www.daler-rowney.com/

http://www.daler-rowney.com/

http://www.renaissancefoundation.org.uk/supporters/co-op.jpg

http://www.renaissancefoundation.org.uk/supporters/co-op.jpg

http://ungermeatco.com/

http://ungermeatco.com/

http://www.google.co.uk/imgres?imgurl=http://www.jupitercreative.co.uk/blog/wp-content/uploads/2008/11/brand_logo.jpg&imgrefurl=http://www.jupitercreative.co.uk/blog/2008/12/12/the-home-of-family-value-a-new-look-brand-unveiled-for-wilkinson/&usg=__lyFqa0n2OQSTyA4kG7O7CVoZUwI=&h=280&w=395&sz=12&hl=en&start=4&zoom=1&tbnid=5t0riPua-P7PMM:&tbnh=88&tbnw=124&ei=_2PWTsiDCIaX8QOcvOiSAg&prev=/search?q=Wilkinson,+shop&um=1&hl=en&sa=N&gbv=2&tbm=isch&um=1&itbs=1

http://www.google.co.uk/imgres?imgurl=http://www.jupitercreative.co.uk/blog/wp-content/uploads/2008/11/brand_logo.jpg&imgrefurl=http://www.jupitercreative.co.uk/blog/2008/12/12/the-home-of-family-value-a-new-look-brand-unveiled-for-wilkinson/&usg=__lyFqa0n2OQSTyA4kG7O7CVoZUwI=&h=280&w=395&sz=12&hl=en&start=4&zoom=1&tbnid=5t0riPua-P7PMM:&tbnh=88&tbnw=124&ei=_2PWTsiDCIaX8QOcvOiSAg&prev=/search?q=Wilkinson,+shop&um=1&hl=en&sa=N&gbv=2&tbm=isch&um=1&itbs=1

http://www.dh.gov.uk/health

http://www.dh.gov.uk/health

http://www.hagemeyer.com/

http://www.hagemeyer.com/

http://ovum.com/

http://ovum.com/

http://www.jdwilliams.co.uk/shop/dispatcher.servlet?pageTo=home

http://www.jdwilliams.co.uk/shop/dispatcher.servlet?pageTo=home

http://www.lancord.com/index.htm

http://www.lancord.com/index.htm


• The Hype?• From Forecasting to Analytics?• Gartner’s Hype Cycle over time• The Gap in Forecasting

• Analytics at Virgin Cargo!• Descriptive Analytics• Predictive Analytics• (towards) Prescriptive Analytics

• Feedback?

Analytics at Virgin Cargo

Agenda


Analytics Value Escalator


Inventory

Optimization

Demand Sensing

Demand Shaping

Demand Shifting

Outside-in

Focused

Proactive

Process

Lean Consensus

Forecasting

FVA

Market Supplier

Demand

Driven

Inside-out

Focused

Reactive

Process

Supply Sensing

Supply Shaping

Sales Marketing & Operations Planning

Market

Driven

Selling through the channel (pull) Selling into the channel (push)

Supply

Driven

15-30%

5-7%

Lean

Manufacturing

Lean

Forecasting

FVA

ShipmentsSales OrdersPOS

The “Market-Driven” Supply Chain Journey

© Charlie Chase @ SAS! talk to him to get his presentations


Is most value-added by

PrescriptiveAnalytics aka

Demand Shaping?

33%

13%

3%

[Gartner Magic Quadrant for BI platforms, 2012]

100% BI Implementations


Which level of„predictive“?

univaritate ETS vs. Causal Neural

Networks?Data Exploration

Demand Forecasting

Demand Shaping

???

???%

?%

??%

Hype-Cycle for Emerging Technologies

New Technologies make bold promises

how to discern the hype

from what's commercially viable?Should you make an early move?

Take a moderate approach?

Wait for further maturation?

Hype-Cycle for Emerging Technologies

We are on

the plateau

of productivity!

Are we all on

the plateau

of productivity?

In 2015 Gartner dropped Big Data,

Analytics, and Data Science!




• Feedback?


Agenda


Virgin

Destinations: 31

Fleet size: 39 (+ 23 orders)

Revenue: £2.93 billion (2014)

Passengers: 5,5 Millionen (2012)

The Virgin Atlantic Story

• Inaugural flight from London to Newark took place on 22nd June 1984.

• Grown in the 27 years since to become the UK’s 2nd largest long haul airline

• Set up an in-house department to handle its cargo product shortly afterwards.


Virgin CargoAirbus 330/340 and Boeing

747/777/787 series aircraft.

3,200 LD3 units


Original Foreasting @ Virgin?

ARIMA black-box( VB-plug-in,developed by another University)


Desriptive Analytics


ABC-XYZ Segmentation


Flight Rollup-Hierarchical Structure

Region

Sub-region

Area

Sector

Does not identify inbound/outbound nodes which are bottlenecks


Gozinto-Graph to focus meetings


Decomposition

Detrended time series


APAC

Americas

EMEA

Monthly Chargeable Weight


Weekly Chargeable Weight

APAC

Americas

EMEA


APAC

Americas

EMEA

Daily Chargeable Weight


Seasonality at different frequencies?

Total Network Quarterly Seasonality

Total Network Weekly Seasonality

Total Network Yearly Seasonality


Interactive Hierarchical Analysis


Time Series Clustering (with SOM)


Limited Research


Visualisation fo Forecasts?




• Feedback?


Agenda


Predictive Analytics

Normal Forecasting with Exponential Smoothing

Smoothes out everything, including Christmas, Easter …


Conventional Forecasting

Seasonality can handle some effects: Christmas etc.

Seasonality cannot handle Easter & Chinese New Year moves!


Calendar EventsBrazil Independence from PortugalAlso known as: Dia da Independência (Portugese)A national holiday observed in Brazil on September 7 of every year.The date celebrates Brazil's Declaration of Independence from Portugal on September 7, 1822.Chinese New Year (Day)Encodes only the 2nd week of CNY (if event spans two weeks) together with a small lead effect in the previous weekChristmas 26Gregorian New YearNew Year on January 1st.Korea Traditional New YearAlso known as: Gujeong (Korean), Seol-nal (Korean)Traditional Korean New Year festival celebrated on the first day of the lunar calendarThailand Traditional New YearAlso known as: Wan Songkran (Thai)The Songkran festival is celebrated in Thailand as the traditional New Year's Day from 13 to 15 April.USA ThanksgivingCelebrated on the 4th Thursday in November. Traditional start of the holiday season.Traditionally celebrates the giving of thanks for the autumn harvest.

Detected Events can be modelled as binary dummy variables

Extra time series of 0 and 1 where an event occurs


detected events in past & future captures holidays & season

NARX(p) Neural Network with Calendar Events


Neural Network Demo


External (causal?) Drivers


External (causal?) Drivers

Source: IATA, Seabury

Note: FTK stands for Freight Tonne Kilometres, the industry standard demand metric. The chart shows how this metric

has moved over the 2000-2012 period.


Next steps? Prescriptive Analytics!

Expand Models with more variables?• Internal factors, eg. Capacity (different hullspace)

• External factors, eg. GDP growth, industry leading indicators, Global PMI, OECD Confidence index etc.


Analytics Value escalator

Fo

recast A

ccu

racy

Naive

Averages

ETSTriple ETS Neural Nets

simple k-NN

Diminishing returnsin accuracy on

growing complexityof algorithms




• Feedback?


Agenda


A Multinomial logit optimization model

75%

75%

75%

Fractional Logit

1a Total TV Model

1b TV Market Share Model

2 Optimization Model

1 Aggregate Demand

Objective Function:

q: Unit Sales

t: Week

pl: Price Level

i: Brandπ: Market share

p: Price

α: Attraction

Parameter

β: Price

Parameter

Simple Regression

Total price level = weighted by market shares

c: Cost

g: Profit

Partially following González-Benito et al. (2010)

no consideration of the

Heteroskedasticity (yet!)

Komplex approach …

How to price Weekly Chargable Weight per Route? (for maximum profit)?



PrescriptiveAnalytics

DescriptiveAnalytics

PredictiveAnalytics

Is most value-add

generated byPrescriptiveAnalytics???

Limited

capability

to execute perceived

value

actual

value

potential

value

training!

Newer (& better?)

algorithms exits!

… but so does

data exploration!

Extend your

Tool Box!Descriptive Tasks

using Data Visualisation

Predictive Models including

Artificial Neural Networks,

Support Vector Regression

C4.5, k-Means, SVM, Apriori, EM,

PageRank, AdaBoost, kNN,

Naive Bayes, and CART

Prescriptive Tasks in

Inventory Managment,

Revenue Management etc.

Artificial Intelligencemeets Forecasting.

Mulitlayer Perceptrons, RecurrentNeural Nets, Extremem Learning Machines Decision Trees, k-nearest NeighboursSupport Vector Machines, MARS/EARTH + statistical benchmarks in

NOW HIRING:

•PhD-Thesis

•Master-Thesis

•Werkstudenten

•Summer Internships

•Data sponsoring for PhDs

•Data Scientists!

•PhD students (with Lancs)

Take aways

• The hype is real– … but it‘s just a hype you can use!

• The gap is substantial. – Companies are slow adopters to algorithms– (not all) Software companies are innovators

• The potential is substantial– Calendar effects can easily be foreasted

• Important on an agregate level of regions / subregions• Improves accuracy significantly for important days

– Requires more complex algorithms• Introduces causal models of regression or neural networks• Could also incoporate prices, number of flights, aircraft sizes …• Available in standard software (at approx. 5000.-)

– Potential for best practices in logistics forecasts– Try new algorithms!

• Neural Networks• Support Vector Regression• Decision Trees• K-Nearest Neighbours• …


© 2017 Sven F. Crone. ALL RIGHTS RESERVEDUnless otherwise indicated, all material in this presentation is copyrighted by Sven F. Crone. No part of these pages, either text or images may be used for any purpose other than personal use, unless explicit authorisation is given by the author.

Reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means – electronic or otherwise, for reasons other than personal use, is strictly prohibited without prior permission.

1. The content of the pages of this presentation is for your general information and use only. It is subject to change without notice.

2. Neither we nor any third parties provide any warranty or guarantee as to the accuracy, timeliness, performance, completeness or suitability of the information and materials found or offered in this presentation for any particular purpose. You acknowledge that such information and materials may contain inaccuracies or errors and we expressly exclude liability for any such inaccuracies or errors to the fullest extent permitted by law.

3. Your use of any information or materials in this presentation is entirely at your own risk, for which we shall not be liable. It shall be your own responsibility to ensure that any products, services or information identified in this presentation meet your specific requirements.

4. This presentation contains material which is owned by or licensed to us. This material includes, but is not limited to, the design, layout, look, appearance and graphics. Reproduction is prohibited other than in accordance with the copyright notice, which forms part of these terms and conditions.

5. All trade marks reproduced in this website which are not the property of, or licensed to, the operator are acknowledged in the presentation.

6. Unauthorised use of this presentation may give rise to a claim for damages and/or be a criminal offence.

7. From time to time this presentation may also include links to other presentations or websites. These links are provided for your convenience to provide further information. They do not signify that we endorse the other presentations or website(s). We have no responsibility for the content of the linked presentations or website(s).

8. Your use of this presentation and any dispute arising out of such use of the presentation is subject to the laws of Hamburg, Germany.

9. No images rendered can be used for mechanical, electronic or printed reproduction without prior written permission by thee author. Images generated are for previewing purposes only.

Beyond Predictive Analytics?predictiveanalyticsandforecasting.com/wp-content/uploads/2017/10/... ·...

Documents

Transcript of Beyond Predictive Analytics?predictiveanalyticsandforecasting.com/wp-content/uploads/2017/10/... ·...