From Forecasting to Drink – and how we could
be more sociable with business
Peter Gormley, Business Development Manager,
Gordon MacMillan, Promotional Analysis Manager,
Scottish Courage Ltd.
Scottish Courage Brands Ltd.
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Part of Scottish & Newcastle plc
26% domestic share, 30 core brands + own label
250 SKUs, 130 new each year
200 staff, £800m turnover, over £60m profit
Market - Interbrew, Coors, Carlsberg, A-Busch, Guinness
11.3 million barrels, underlying growth 4% per annum
70% of volume from 3 brewers
53,000 outlets, but 4 store groups (1700 stores) = 30%
500 brands, but top 13 brands > half of volume
Take Home 31% of UK beer market: USA - 70%, Germany 65%, France - 61%, Ireland - 10%
Criticality of Forecasts
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Sales & Operations Planning - total beer business - 2 yr.
All aspects of planning - sales, marketing, finance, supply..
Pricing and promotional activity - 60% sold on promotion
Impacts on service, stock, waste, efficiency, profit
On-trade stable, off-trade highly volatile
Polarisation - grocers, wholesale, specialists, convenience..
Price and promotional offers, BOGOFs,….
In-store display and feature, events, weather, competitors..
Promiscuous, elastic market
Highly seasonal
Beck’s Bier Supply to Major Customer
L eg end
£11.49 £11.49
BE C KS
1 50 00
£12.49
12pk BOGOF
£12.99
£12.49 £11.99
1 00 00
£12.49
5 00 0
£12.99
0
1 99 5
1 99 6
1 99 7
1 99 8
1 99 9
2 00 0
2 00 1
2 00 2
2 00 3
2 00 4
Forecast Process Evolution
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Output - forecast by customer by SKU by period - 2 years
Statistical forecast based on supply data
Sales & Marketing edit forecast at various horizons
Assumptions captured in database
Valuation of forecast
Forecast review meetings and submission to group S&OP
Move to top down forecast managed by one function
Information passed from Sales & Marketing
Price and promotion models used
Demand Factors
Lancaster Regression Models
• Different levels of forecast
• Considered
– price, price differential, media spend, promotion, multibuy, display,
feature, temperature, sunshine, seasonality, distribution, etc.
• Regression outperformed exponential smoothing model
– 10% MAPE vs. 15% for total beer
– 17% MAPE vs. 27% for major brands
• Different brands reflected different driver weights
• Significant factors:
– Promotion, Price and price differential, Seasonality, Weather,
Distribution
• Effort relative to exponential smoothing
Model Results for Total Lager Sales
x 10
4
Long term (32 wks.) out-of-sample forecast originating at sample 99 : Tot.lagr
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data
model fit (within sample)
forecast (out of sample)
forecasting origin
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12
10
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6
4
19-Dec-1998
01-Aug-1998
14-Mar-1998
25-Oct-1997
07-Jun-1997
18-Jan-1997
2
Interrelationship Formed
• SCB & Lancaster University
• Methodologies analysed
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Wlodek Tych Transfer Function Models
ACNielsen Promotional Evaluator
SPSS implementation using Lagged Effects
Procast
• SCB recognition of benefits of new techniques
• Permanent resource employed
Price Focus
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Price - the single most important driver of sales volume
Major cause of forecast error and stock shortages/surpluses
Requirement of tactical and strategic price planning
Series of requirements - advice & forecasting
Comparing price to share (removing seasonality aspects)
By total grocery market and individual customers, where EPOS
data available
• SKU & Brand versus product sector
• SKU & Brand versus competitor brand
• Cannibalisation effects
Price Focus
• How elastic is the Beer Market
• What is the impact on competitors
Price vs. Volume
Price Ratio (100 = Parity)
– Steal
– Cannibalisation
– Volume
Brand X Vs Vs Brand Y
140.00
120.00
100.00
80.00
60.00
-0.1672
y = 221.13x
R2 = 0.8122
40.00
20.00
0.00
0.00
50.00
100.00
150.00
Volume Ratio (100 = Parity)
Source: ACNielsen Scantrack
200.00
250.00
300.00
Price Focus
•Identify most profitable Price Level
•Price (RPB) x Volume = Profit
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Example: Brand X in Account when Brand Y @ £15.99
X
Profit
15
10
Profit
The Golden Egg
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0
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15.5
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16.5
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17.5
Price
Maximising Profit Contribution
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Price Elasticity Models
• Use output from exponential smoothing model as
base
• Recognise confidence interval and implications
• Document assumptions made
• Used for temporary price reductions
• Caution in use as guide for strategic price movement
• Need to maintain models reflecting changes in
market dynamics
• Used with supervision from forecasting team
currently
Cross Elasticity
Start Date
End Date
Premium Lager 12PK
WE 29.08.98
WE 17.06.00
Instructions:
5% Confidence Intervals
CARLING,12PK
TENNENTS,12PK
FOSTERS,12PK
MILLER PILS,12PK
CARLSB LAGER,12PK
-6.41
-0.03
1.40
-1.95
CARLING,12PK
-5.80
-5.18
0.83
1.69
2.39
3.37
2.47
6.90
0.23
-6.53
0.74
-1.70
The columns highlighted in yellow offer the cross and own-price elasticity's.
The numbers in italics which straddle the elasticity estimates are the
lower and upper bound confidence intervals respectively.
The tables offer confidence intervals at both 5% and 10%, 5% being the most cautious.
TENNENTS,12PK
0.70
1.17
-5.88
-5.23
1.42
1.65
2.10
5.00
1.26
1.69
-4.94
2.12
3.40
FOSTERS,12PK
1.74
2.23
2.36
3.03
-4.17
-3.40
2.82
3.53
6.86
10.32
0.41
1.38
0.75
-4.29
MILLER PILS,12PK
0.84
1.26
1.97
2.56
1.43
2.11
-3.67
-3.06
-0.29
0.14
-0.49
CARLSB LAGER,12PK
0.11
0.51
0.69
1.25
0.14
0.78
1.34
1.80
-4.82
2.24
3.97
FOSTERS,12PK
1.74
2.15
2.36
2.92
-4.17
-3.53
2.82
3.41
6.86
9.75
0.48
1.48
0.86
-4.19
MILLER PILS,12PK
0.84
1.19
1.97
2.46
1.43
2.00
-3.67
-3.16
-0.23
0.23
-0.39
CARLSB LAGER,12PK
0.11
0.44
0.69
1.16
0.14
0.68
10% Confidence Intervals
CARLING,12PK
TENNENTS,12PK
FOSTERS,12PK
MILLER PILS,12PK
CARLSB LAGER,12PK
-6.31
0.11
1.57
-1.22
CARLING,12PK
-5.80
-5.28
0.83
1.55
2.39
3.21
2.47
6.17
0.31
-6.42
0.85
-1.16
TENNENTS,12PK
0.70
1.09
-5.88
-5.33
1.42
1.65
1.99
4.45
Regression Application
• Price not only factor, need to understand all
factors that drive beer sales
– dynamic/changing market
– increase in importance of 24Pk
– seasonality/Xmas effect
• Factors considered
– price, competitor pricing, media spend, promotion,
multibuy, display, feature, temperature,
seasonality lagged effects, FABs and wine effects
Methodology
• Link with J.Canduela (PhD Napier University)
• Multiple Regression Techniques
• Three Autoregressive algorithms using SPSS
– Cochrane-Orcutt
– Exact maximum-likelihood
– Prais-Winsten
• Autobox
• Trying to optimise Forecasts whilst keeping
things easy for the user
Current & Future
• Methodology running in Multiple Grocer
accounts
– Price & Promotions
– Strategic Planning
• Infiltrate other segments – Wholesale,
Convenience etc.
• Understand & Test different mechanics to
evaluate optimum performance
• Continue to optimise profitability
What Affects Sales ?
Sales =
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Own Promotions + Own Trade Activity
Competitor Promotions + Competitor Trade Activity
Own Regular Price
Own Regular Price vs Competitors Regular Price
Own TV Advertising
Competitor TV Advertising
Distribution + Store Effects
Seasonality
Random Term
Econometric Modelling
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Identifying the relationship between volume sales and
marketing activity from store-level data
In-Store
Activity
156+ weeks
250+
stores
Modeling enables us to understand the impact on sales of
price, promotions and advertising.
Being More Sociable
• Unfortunately – no samples
• Why are we here – I want to learn from others – why wait?
• Benchmarking – my experience
– Compare performance
– Discussion leads to new ideas, new approaches, new solutions
– Reduce the number of pitfalls on the way to success
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Networking – more informal
Would like to identify other interested parties in supply chain
Agree goals
Actively involve others
“Meet” on regular basis – may be electronically