Operations & Digital Business
Nicolas van ZeebroeckMaster in Business Engineering – 2014-2015
Competing on Analytics
Competing on�Analytics
How�a�belowͲaverage baseball�team�can makeit several years in�a�row into the�playoffs
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 3
How�Oakland�A’s competed on�analytics
• Oakland�A's�took�advantage�of�analysis�of�player�performance�to�field�a�team�that�could�better�compete�against�richer�competitors�in�Major�League�Baseball�(MLB)
• Came�up�with�new�(easy�to�obtain)�measures�of�player�performance:�onͲbase�percentage�and�slugging�percentage
• Statistics�used�in�2�ways:• For�recruitment:�identify�undervalued�players�on�the�market• On�the�field:�to�select�the�best�players�to�play�at�any�point�in�a�game
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 4
Lewis�(2003)
How�Boston�Red Sox competed on�analytics
• Boston�Red�Sox�invested�in�analytics�after�Oakland�A’s• In�5th game�v.�NY�Yankees�(ALCS�2003),�something�happened
• Pedro�Martinez�was�pitching• Analytics�had�shown�Martinez�much�easier�to�hit�after�7�innings• Coach�Grady�Little�refused�to�hear�about�the�statistics• After�7th inning,�Martinez�got�shelled�by�the�Yankees• Yankees�won�the�game�and�the�series• Little�lost�his�job
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 5
Davenport�&�Harris�(2007)
Two key�lessons
• Analytics�can�be�a�powerful�way�to�outperform�the�competition
• But�need�to�spread�everywhere�within�an�organisation
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 6
Analytics defined
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Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 8
Why competing on�analytics?
A�necessity
• Classical sources�of�differenciation are�more�difficult to�sustain:• Product�and�service�differenciation vanishes• Technology is commoditized• Protective�regulation is gone• Geographical advantage is irrelevant with
Internet
• What’s left to�compete on?• Business�process performance:
• Execute your business�with maximum�efficiency• Make smartest business�decisions possible
An�opportunity
• Technology is now there to�provide• Granular (transaction)�data,�available in�quasiͲ
real�time�from ERP,�PoS and�Web�systems• Algorithms to�extract insights�from data• Computing power�to�run them
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GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 9Davenport�&�Harris�(2007)
LargeͲscale ambition
• Aspiration�to�achieve�largeͲscale�results
LargeͲscale ambition
• Aspiration�to�achieve�largeͲscale�results
EnterpriseͲlevel approach
• Data�management�&�analytics�managed�at�organisation level
EnterpriseͲlevel approach
• Data�management�&�analytics�managed�at�organisation level
Support�of�strategic,�distinctive�capability
• Explore�&�exploit�new�measures
Support�of�strategic,�distinctive�capability
• Explore�&�exploit�new�measures
Senior�Management�Commitment
• Organisational change�is�huge
Senior�Management�Commitment
• Organisational change�is�huge
ANALYTICALCOMPETITION
10
• Royal�Bank of�Canada:�customer data�centralizedin�the�1970’s
• Harrah’s:�CEO�first�had all�property managers�report�directly to�him
• Beracha (CEO Sara�Lee):�In�God we trust,�all�others bring data• Loveman (CEO�Harrah’s):�Do�we think or�do�we know?• Barclay’s:�5�years to�put�informationͲbased customer strategy in�place�Î Adjust every aspect�of�consumer�business:Clean�and�integrate data�on�13�million�customers,�How�to�charge�interest rates,�How�to�underwrite risk and�set�credit limits,�How�to�control�fraud,�How�to�crossͲsell other products
• Walmart:�supplyͲchain• Harrah’s:�customer loyalty and�service• Oakland�A’s:�human resources• Netflix &�Amazon:�customer preferences• Marriott:�optimal�room�pricing
Davenport�&�Harris�(2007)
Importance�of�analytical orientation
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 11
Where to�competing on�analytics?
Internalanalytics
Externalanalytics
• Human resources• Financial�management• Research &�Development• Manufacturing &�logistics
• Customers• Suppliers
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 12
Financial�analytics:�revenue�forecasting
• Prediction of�future�sales�based on�sales�in�previous (most recent)�periods
• System�yielded optimistic sales• 2001:�Sales�down�by�32%
• Predictive models anticipatedslowdown in�sales
• Preemptive actions�on�prices and�products:�cut costs,�slash�prices
• 2001:�Sales�down�only 2.3%• Boost in�market share after
downturn
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 14
Davenport�&�Harris�(2007)
Manufacturing analytics:�quality control
• Early warning�system• Honda�dealers�record�any warranty service�request in�central�
database with categorized quality problem and�free�text• Additional textual data�is recorded from calls�of�mechanics
to�HQ�experts�and�customer call�centers• Text mining algorithms analyze data�to�identify potential quality issues• Potentially serious problems are�flagged for�human investigation• Reports�are�sent�to�HQ
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 15
Davenport�&�Harris�(2007)
Manufacturing analytics:�quality control
• Human quality control�(postͲproduction)�binary• If�not�perfect,�glass�is destroyed
• New�analytics system�(classificationͲbased)• Digital�camera�over�end�of�production�line• Each product is captured by�camera• Image�is analysed automatically by�AI�software• Potential defects are�analyzed and�categorized• According to�severity of�imperfection,�glass�is dispatched to�alternative�uses
• Results:• Considerable drop�in�wasted glass
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 16
Competing on�analytics with external processes
• Identify most profitable�customers and�most atͲrisk of�switchingto�competition using predictive modeling
• Improve customer understanding by�integrating data�inͲhouse�with external data
• Optimize supply chains
• Establish prices in�real�time�to�get highest yield per�transaction
• Optimize advertizing and�marketing�strategies
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 18
Customer�analytics:�realͲtime�marketing
• Customers use�loyalty card• Capture�data�on�their behaviour
• Data�is used in�realͲtime�by�marketing�&�operations• Optimize yield• Set�prices for�slots�and�hotel rooms• Design�optimal�traffic flow�within casino
• If�customer loses too much money�too fast• Problem identified in�real�time• Message�is sent�(electronically or�through local�service�representative)• « Looks�like you are�having a�tough�day at�the�slots.�It�might be a�good�time�to�visit the�
buffet.�Here’s a�$20�coupon�you can use�in�the�next hour. »
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 19
Customer�analytics:�personalized marketing
• Clubcard (loyalty card)• Every online�or�offline�purchase is recorded in�client
history
• Data�is used to�profile�customers and�target promotions
• Results• 7�million�variations�of�product coupons�issued per�year• Redemption rate�highest in�the�retail industry worldwide:�20Ͳ50%�
• Average in�EU/US�retail industry:�2%�redemption rate• CrossͲselling on�Tesco�online• Tesco�now largest retailer in�the�UK
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 20
Customer�analytics:�revenue�management
• Revenue�Management:�« Predict highest price that would stilllead�to�full�occupancy »
• System�entirely automates�the�pricing optimization process• Rooms,�restaurants,�meeting�spaces,�etc.Î « Total�hotel optimization »
• Results• 2%�increase in�revenue• 17%�increase in�operating�income• Extra�$86�millions�in�profit
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 21
Customer�analytics:�price optimization
• Price�Management�&�Price�Optimization (PMPO)�solutions• Retail industry:
• 5Ͳ10%�increase in�gross margin from PMPO
• Downside:�Amazon’s example• Loyal�customers are�ready to�pay higher price than fickle customersÎ Smaller price elasticity
• Amazon�priced DVDs higher to�people�identified as�more�loyal• Public�outcry forced Amazon�to�back�off
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 22
Connecting suppliers &�customers
• What do�you do�with 583Tb�of�customer,�sales�and�inventory data?
• 17,400�suppliers from 80�countries�use�Retail Link�system• Track their products at�Walmart’s• Get info�on�sales,�shipments,�purchase orders,�invoices,�claims,�returns,�forecasts,�etc.
• Walmart managers�use�the�system�to�optimize product assortment• Ensure customers have�the�products they want• E.g.�Kellog’s strawberry pop�tarts just before a�hurricane�hits�a�region
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 23
Supplier�analytics
• Business�very sensitive�to�(volatile)�cocoa prices• Prediction of�price fluctuations�a�necessity to�hedge risks
• Mars�invested in�own earth observation�satellite�network• Satellites�monitor�weather conditions�in�main�cocoa production�regions• Data�mining algorithms use�current and�historical data�to�predict production�
levels per�region and�future�prices• Mars�optimizes its purchasing based on�price expectations
• Results:• Key�competitive advantage from excellence�in�procurement and�risk
management
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 24
Logistics analytics
• Fedex�and�UPS�are�heavy analytical competitors• Route�optimization
• « It’s vital�that we manage�our networks�around the�world�the�best�way we can.�Whenthings don’t go�exactly the�way we expected because volume�changes�or�weather gets in�the�way,�we have�to�think of�the�best�ways to�recover and�still keep our service�levels. »�Mike�Eskew,�CEO�UPS
• CEMEX�optimizes routes�of�its delivery trucks�in�realͲtime• GPS�chips�+�traffic monitoring• Cut�delivery time�from 3�hours to�20�minutes�in�Mexico
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 25
Foundations for�analytical competition
1Ͳ2ͲALL
• From the�examples seen in�class,�what are,�according to�you,�the�main�elements needed for�an�organisation�to�take advantage of�analytics?• Think of�different layers if�it helps
• Think alone,�then in�groups�of�2�or�3
• Write�down�your ideas on�a�piece of�paper with your names
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 27
Foundations for�analytical competition
• Strategy and�focus�to�determine most relevant�use�of�analytics
• Transactional systems to�generate accurate/relevant/granular data
• Processes to�get insights�and�act upon them
• People who embrace analytics• IT�experts,�business�analysts &�data�scientists,�decisionͲmakers,�employees
• Governance to�share the�data�and�foster factͲbased decision
• Software�technology to�extract and�analyze the�data
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 28
People:�the�beer and�diaper legend
• Analysts found interesting pattern�in�retail:• Men�coming to�buy beer for�the�weekend�also tend�to�remember that their
wives had asked them to�buy diapers• So�they put�both products in�their carts
• Retail store�managers�decide to�place�diapers next to�beers• Sales�explode
Davenport�&�Harris�(2007)
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 29
People:�the�beer and�diaper legend
• Data�mining can identify patterns
• Humans are�still needed to�interpret themÎ Analysts
• Other humans are�still needed to�act upon themÎ Executives
• AnalyticsͲbased decisions can only be as�good�as�the�quality of�the�data�they rely on
Competing on�analytics:�key�take aways
• Analytical competition can really change�the�rules of�the�game• No�industry is safe
• Competing on�analytics requires• To�support�a�strategic,�distinctive�capability• An�entepriseͲwide�approach• SeniorͲmanagement�commitment• LargeͲscale ambition�(not�local�optimization)
• Can�serve�both internal and�external processes• Analytics infrastructure�is made�of
• Data�(transactional,�granular,�high�quality)• Technology (never selfͲsufficient)• Processes• People�(to�analyze and�to�decide)• Governance
Your assignments
Assignments on�Analytics
• Read�the�technical�note�on�typical�applications�of�analytics• Read�the�two�reference�articles
• Davenport:�Competing�on�Analytics• McAfee�&�Brynjolfsson:�Big�Data:�the�management�revolution
GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 33