Three examples of value
from @Risk in decision
making at the LEGO Group
Hans Læssøe
Senior Director
Strategic Risk Management
The LEGO Group
31 March 2014
©2014 The LEGO Group
l
31 March 2014 | SRM
About the LEGO Group
• Family owned single-brand company
• Founded in 1932
• Developed through organic growth only
• Turmoil turned into success …
• 21% sales growth 2007-2013
• 34% profit growth 2007-2013
• Strategic Risk Management 2007
• Palisade @Risk since 2008
Page 2
©2014 The LEGO Group
Three examples …
3. Performance analytics,
volatility assessment
and -addressing
2. Consolidation across a
project portfolio for
management reporting
1. Consolidation of a risk
portfolio and alignment
with risk tolerance
Page 3
©2014 The LEGO Group
Too often we see invalid risk consolidation
Wrong Approach
• Multiply impact with likelihood … and add up
• Average loss over a million years
• Risk Management is not about averages
• Approach is invalid … and potentially
dangerous to your company/organization
Not an uncommon approach
Impact
Probability
VH
H
Risks
M
(> 500)
(250)
(100)
• Multiply the scores to generate “severity”
• Extremely dangerous as 1x5 = 5x1
• No consistency in assessments
Alas … seen even more often
Simulation is “the way” to consolidate
Page 4
©2014 The LEGO Group
VL
(< 25)
VH (90%)
H (30%)
1
M (10%)
1
L (3%)
2
VL (1%)
2
3
1
2
2
1
1
1
Even Worse Approach
• Use a (not defined) 1 through 5 scale
L
(50)
Risks
Impact
Probability
5
4
3
2
1
5
4
1
3
1
2
2
1
2
3
1
2
1
2
1
1
We use a standard approach to simulation …
• Impact is “blurred” using a triangular distribution between half and double impact
• Likelihood and impact has been defined based on VL … VH scaling – translated into values
• Name “calculated” (column I) for ease of reference and use of tornado diagrams
• Simulation figures set to “input” for the use of tornado diagrams
=IFERROR(RiskMakeInput(B6*(RiskTriang(C6/2;C6;C6*2)*RiskBinomial(1;D6));0)
Page 5
©2014 The LEGO Group
… which provides us with consistent information
• Easy to read and understand graphics of potential outcomes
… there are graphic parameters I’d like to be able to set myself
• Tornado diagrams of most important risks and opportunities
Page 6
©2014 The LEGO Group
We use this approach three ways
• Enterprise Risk Management
• Depicting overall exposure
• Comparing net exposure with define risk tolerance
• Discuss level of risk taking
• Business Projects (Active Risk & Opportunity Planning)
• Showing overall exposure levels
• Informed discussion with steering committees on actions
• Credit Risk Management
• Based on customer data
• Portfolio view and discussion with
credit risk insurance partners
• Regional as well as global monitoring
Page 7
©2014 The LEGO Group
3. Performance analytics,
volatility assessment
and -addressing
2. Consolidation across a
project portfolio for
management reporting
1. @Risk is used to create a
valid image of exposure
… and gauge this vs. the
defined risk tolerance
Page 8
©2014 The LEGO Group
Monitoring a portfolio of projects
• Each project deploy the standard approach … in each their separate
spreadsheet database
• Cross project consolidation needs workaround …
• @Risk becomes extremely slow if you wish to consolidate across
e.g. 25 or 50 open files at the same time
• Interrelated risks and opportunities cannot be addressed validly
So … we workaround …
• Copy simulation sheet data to common file
• Adjust risk/opportunity name to include project annotation
• Address most obvious links using correlation (generally +1 or -1)
• Leading to a consolidated monitoring
Page 9
©2014 The LEGO Group
We get a strong management reporting
• Strong overview of project portfolio risks
and opportunities
• Show exposure on individual projects
• Standard “high-low-close” chart
• Using 10th, 50th and 90th percentiles
• Manual updating of chart
• Highlight top 10 key risks and
opportunities … driving executive
attention
• Applied in Operations … to be rolled our
cross company
• Supported by Program Officer commenting
• Updated and discussed twice per year
Page 10
©2014 The LEGO Group
3. Performance analytics,
volatility assessment and
addressing
2. Consolidating across a
portfolio of projects
enables executive to
focus based on facts
1. @Risk is used to create a
valid image of exposure
… and gauge this vs. the
defined risk tolerance
Page 11
©2014 The LEGO Group
We use “distribution fitting” as analytical tool …
• The toy industry is highly volatile
• Toys is a fashion to children
• 2/3 is new every year
• 20% sold over the three weeks
preceding Christmas
• Making forecasting extremely
difficult
• Analyze the data to get insight
• Define Actual/Plan factor
• Ask @Risk to find a distribution
• Select “best fit”
• Address parameters and what
the fit tells you
Page 12
©2014 The LEGO Group
Theme (cur)
Adventurers
Atlantis
Belville
Bricks & More LEGO
Cars TM
Castle
City Airport
City Construction
City Farm
City Fire
City Harbour
City Police
City Space Port
City Town
City Traffic
City Trains
Creator Expert
DUPLO Cars TM
DUPLO Creative Play
DUPLO Learning Play
DUPLO LEGO Ville
DUPLO Toy Story TM
DUPLO Winnie the Pooh TM
Harry Potter TM
Hero Factory
LE Preschool
LEGO Architecture
LEGO Creator
Actual
Plan
26.937.424
24.225.586
5.042.120
89.470.505
89.616.703
33.119.639
37.360.084
1.929.945
2.784.716
36.848.879
51.791.039
140.750.897
44.110.043
23.696.672
1.829.598
43.530.364
42.067.468
39.617.273
43.883.807
8.065.761
118.626.217
1.833.884
10.585.164
84.145.019
89.011.255
14.181.461
13.460.600
109.382.080
28.807.913
25.299.577
4.584.994
95.310.836
99.937.109
37.501.308
41.750.854
4.457.826
2.252.095
39.827.051
51.051.870
121.549.401
45.264.285
22.150.912
1.167.444
34.751.742
32.108.027
38.644.738
48.135.448
15.118.903
136.951.667
1.671.942
11.898.666
69.642.765
103.598.116
16.656.648
14.156.434
101.157.106
A/P
Factor
0,94
0,96
1,10
0,94
0,90
0,88
0,89
0,43
1,24
0,93
1,01
1,16
0,97
1,07
1,57
1,25
1,31
1,03
0,91
0,53
0,87
1,10
0,89
1,21
0,86
0,85
0,95
1,08
Large
Themes
0,94
0,96
0,94
0,90
0,88
0,89
0,93
1,01
1,16
0,97
1,07
1,25
1,31
1,03
0,91
0,53
0,87
0,89
1,21
0,86
0,85
0,95
1,08
… to provide insights we can act on
What do we learn … at key theme level …
• Forecast uncertainty is ± 25%
• Be mindful of the graphic skewing … Both
0,5 and 2,0 are a factor of two “off”
• Ensure
planning and
manufacturing
setup is ready
for this … it’s
real life
• Embrace the
volatility … it
is not easier
for your
competitors
i.e. handling
this can be a
strategic
advantage
Page 13
©2014 The LEGO Group
On total/overall
sales volume it is
slightly better …
only ± 16%
We also did budget simulations
Budget Assessment
• Documented uncertainties on sales
Monte Carlo simulation model
• Assessed/analyzed assessment on
cost element uncertainty
Area P/L
• Some a ratios of sales
• Some as fixed costs
• Correlating relevant cost elements…
• Marketing Spending and Sales
• Sales between regions
• Currency flows and volatility included
• A few key risks and opportunities
(which were not already catered for
in the budget) added as risks
Simulation provided ranges on
earnings, ROS, as well as key
influencers (Tornado diagrams)
BUT
The high sales volatility lead to
us abandoning budgeting … and
do resource allocation with a
more dynamic approach
©2014 The LEGO Group
Share of
Budget
Variable Costs
% of
Most
90%
90%
Gross
Likely Negative Positive
Cost
DKKm
Fixed Costs
Most
90%
90%
Likely Negative Positive
Simulated
budget
Market Group X
MGx Gross Sales
MGx Free of charge, discounts etc.
MGx CTT & cash discounts
Net Sales
MGx COGS
MGx Distribution
GC2
3.012,3
100%
-5,37%
-5,22%
-6,00%
-4,80%
-201,6
100%
-6,85%
-6,69%
-7,50%
-6,25%
-931,2
100%
-30,9% -30,91%
-32,16%
-29,66%
-245,9
100%
-8,0%
-9,16%
-6,66%
0%
0,0%
0,00%
0,00%
-152,2
0%
0,0%
0,00%
0,00%
-233,8
3.012
2.711
3.313
0
0,0
0,0
0,0
-931
-240
-154,4
-169,4
-134,4
-223,2
-263,2
-208,2
-152
-12
-234
2.644
-8,16%
1.476,2
MGx Overhead
-223,2
3.012
-162
-206
0
0,0
0,0
2.653,3
-154,4
Market Contribution
3.012,3
-157,3
MGx Consumer & General Marketing
MGx Other marketing
1.473
-12,2
1.086,5
1.075
Supply Chain
Total Gross Sales
6.369
Outsourced Costs
-309,8
100%
-4,86%
-4,86%
-5,01%
-4,72%
SC distribution costs 1
-182,2
100%
-2,86%
-2,86%
-2,95%
-2,77%
6.454
SC costs 2
-509,6
100%
-8,00%
-8,00%
-8,24%
-7,76%
SC MRO
-127,8
100%
-2,01%
-2,01%
-2,08%
-1,93%
SC Energy
SC Freight & duty
PPV
-111,4
100%
-1,75%
-1,75%
-1,81%
-1,68%
-211,4
100%
-3,32%
-3,32%
-3,44%
-3,19%
-13,5
100%
-0,21%
-0,21%
-0,22%
-0,20%
Variable costs
-313,9
-184,6
-516,4
-129,5
-112,8
-214,2
-13,7
-1.465,6
-1.485
SC Costs 3
SC T&E, Consulting etc.
SC Other costs
-370,9
Semi fixed costs
-615,6
SC Rent
SC Equipment
SC Depreciation
-125,2
Capacity costs
-471,7
-472
-2.553,0
-2.573
SC Gross Costs (excl. restructuring)
-370,9
-370,9
-361,6
-80,6
-80,6
-80,6
-82,6
-78,6
-164,2
-164,2
-168,3
-160,1
-125,2
-125,2
-126,4
-123,9
-370,9
-80,6
-164,2
-616
-88,3
-88,3
-88,3
-89,1
-87,4
-258,3
-258,3
-258,3
-260,8
-255,7
784,7
100%
12,79%
12,32%
12,20%
13,55%
0,0
Coverage FMC costs
1.644,7
100%
26,81%
25,82%
25,56%
28,41%
0,0
Contribution
-380,1
-164,2
Coverage Distribution
-125,2
-88,3
-258,3
825,7
1.730,5
-123,5
-16
Corporate Areas
Total Sales
6.369
Shared Services
Company Business Support
Corporate Finance & Management
-393,9
0%
-383,3
-393,9
-418,9
-343,9
-117,0
0%
-117,0
-117,0
-122,9
-111,2
6.454
-122,9
0%
-127,6
-122,9
-140,0
-116,8
Currency net exposure
Contribution
Calculated below
-633,8
Currencies
Flow is measured in local currencies
Negative means net flow "out"
Positive flow means net flow "in"
Page 14
Budget
CZK/HUF flow as share of global gross sales
USD
JPY
CZK
HUF
-383,3
-117,0
-127,6
21,1
-607
Std Rate Sim Flow M Likely 90% Neg 90% Pos
5,7500
9,9%
9,5%
8,6%
11,4%
0,0472
4.170
4.000
3.600
4.800
0,3157 -24,9% -26,0% -28,6% -20,8%
0,4545 -14,3% -14,9% -16,4% -11,9%
Sim ER
M Likely 90% low90% high
4,87
6,33
0,0500
0,0425
0,0575
0,2900
0,2610
0,3190
0,4150
0,3362
0,4939
Result
-51,3
11,7
41,3
19,4
Currency Impact Total
21,1
5,6000
5,60
0,05000
0,29000
0,4150
A defined approach for Credit Risk overview
• Top 700 customers covering 80% of sales … the remainder seen as “group” customers
• Actual customer sales and market growth
Assess future sales level
• Seasonality and Days of Sales Outstanding
Risk exposure (peak debt)
• External data
Likelihood of default within 12 months
• Linked/Group Ownership
Link international chains (e.g. Toys “R” Us)
• Credit insurance data
External vs. LEGO residual risk exposure
We simulate – and assess the overall risk exposure
• Scenarios with changes of payment terms, risk profile, seasonality, etc.
• Used for discussion on overall risk taking vs. risk tolerance as well a challenging our partner
2013 Credit Risk Analysis
Exposure (timing) Peak/Average
Global Chains Linked/Separate
Credit Insurance % effect
LEGO Self insurance limit (mEUR)
… and has now led us to
abandon external credit
risk insurance
Exposure Level - Stress factor
Days Taken - Stress factor
Risk of Default - Stress factor
1
1
100%
0,0
Peak
Link ed
Fully Insured
mEUR
1,00
1,00
1,00
0,00%
Definition of coverage
Page 15
LEGO
Customer
Number
151252
39348
151239
12445
117847
117359
108162
108913
84118
40913
149172
124705
35047
140834
138321
118822
35312
136322
10909
49082
110011
OK
1
1
1
1
1
1
1
1
1
1
2012
Actual
Sales EUR
307.140
549.572
258.421
217.996
605.423
2.318.224
677.528
5.469.382
489.489
593.305
291.319
1.301.966
295.501
1.606.762
230.778
961.457
5.544.917
211.620
433.549
1.168.357
207.592
©2014 The LEGO11 Group
2012/
2013
ratio
1,199
1,199
1,199
1,199
1,199
1,691
1,691
1,691
1,293
1,293
1,199
1,199
1,071
1,199
1,071
1,071
1,199
1,170
1,199
1,019
1,212
2013
Projected
Sales EUR
368.295
658.998
309.875
261.402
725.969
3.918.970
1.145.365
9.246.021
632.799
767.011
349.324
1.561.200
316.371
1.926.684
247.077
1.029.361
6.648.965
247.616
519.872
1.189.987
251.519
Days
Taken
(DSO)
58,6
81,6
58,3
33,0
52,3
15,0
39,1
45,0
87,5
52,6
57,0
22,8
88,0
128,3
77,8
125,9
26,5
49,0
39,7
152,2
36,3
Gross
Risk Exposure
(EUR)
81.322
205.209
68.423
31.819
145.913
159.013
162.117
1.491.921
208.247
154.162
77.133
126.692
104.114
868.602
72.567
464.064
674.456
44.862
73.584
631.387
35.600
Atradius
Limit
47.044
126.089
38.306
23.221
89.888
261.800
201.615
1.815.000
416.381
920.000
88.911
Atradius
Coverage
0%
90%
90%
90%
0%
0%
90%
90%
0%
0%
0%
0%
90%
85%
0%
0%
90%
90%
90%
90%
90%
EKF
EKF
Limit Coverage
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Bank
Guarant
67.204
33.602
470.430
1.000.000
-
Atradius
Cover
42.339
113.480
34.476
20.899
80.900
235.620
171.373
1.633.500
40.375
374.743
828.000
80.020
EKF cover
further
-
Weighted
Cover Probability
Max of Default Customer
2,19% "ETG" SIA
42.339
1,84% "SOLIDEX" DOO
113.480
1,84% "WINDPOL" Sp z o.o.
34.476
0,69% #Extra Leker AS
1,58% #Notabene Center AS
67.204
1,84% (Ju) Toyconcert
54.501
1,84% (ju) Woridle Toy
551.330
1,84% (Ju)Toyfocus
1,96% *** KING JOUET SAS SOJOUDIS
0,69% ***SCA LOISIRS ET ARTS MENAGERS
3,04% 220 LEGO.HU KFT
1,34% 2T-Toys
235.620
1,01% A & P Kindergartenbedarfsgroßhandel
171.373
1,41% A. DESYLLAS LTD.
1,20% A. Haberkorn & Co GmbH
1.000.000
1,84% A. Hausmann GmbH
1.633.500
1,84% A.S. Watson B.V.
40.375
1,76% A/C CLOSED: BRICKS TO THE WORLD
374.743
1,49% AB Turax
828.000
1,84% ABACUS, S.C.C.L
80.020
1,84% ABE GANGU
Happens
0/1
Direct C
TOTAL
LOSS EUR Nu
-
We see a lot of tangible value from @Risk
3. Knowing the drivers and
level of uncertainty
enables executives to
improve performance
2. Consolidating across a
portfolio of projects
enables executive to
focus based on facts
1. @Risk is used to create a
valid image of exposure
… and gauge this vs. the
defined risk tolerance
Page 16
©2014 The LEGO Group
Now what …
• Focus now is roll-out to more projects and issues
• Ensure management understanding and buy-in
• Strengthen discussions on levels of risk taking
i.e.
• We are NOT looking for further sophistication
THANK YOU
Hans Læssøe
Senior Director, Strategic Risk Management
LEGO System A/S
 [email protected]
 +45 2030 8699
Page 17
©2014 The LEGO Group
The agenda is … leveraging
the insights and capabilities