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.

• 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• 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

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Legend

BECKS

Beck’s Bier Supply to Major Customer

12pk BOGOF

£11.49 £11.49

£12.99

£12.49

£12.49

£12.99

£11.99

£12.49

Forecast Process Evolution• 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

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Model Results for Total Lager Sales

Interrelationship Formed

• SCB & Lancaster University• Methodologies analysed

– 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

• 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

Source: ACNielsen Scantrack

Price Focus

• How elastic is the Beer Market• What is the impact on competitors

– Steal– Cannibalisation– Volume

Price vs. Volume

Brand X Vs Vs Brand Y

y = 221.13x-0.1672

R2 = 0.8122

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Price Focus

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Pro

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Profit

•Identify most profitable Price Level•Price (RPB) x Volume = Profit

Example: Brand X in Account when Brand Y @ £15.99

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The Golden EggThe Golden Egg

Maximising Profit ContributionMaximising Profit Contribution

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 WE 29.08.98 Premium Lager 12PK End Date WE 17.06.00

Instructions: 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.

5% Confidence Intervals The tables offer confidence intervals at both 5% and 10%, 5% being the most cautious.

CARLING,12PK TENNENTS,12PK FOSTERS,12PK MILLER PILS,12PK CARLSB LAGER,12PK

CARLING,12PK -6.41 -5.80 -5.18 0.23 0.70 1.17 1.26 1.74 2.23 0.41 0.84 1.26 -0.29 0.11 0.51

TENNENTS,12PK -0.03 0.83 1.69 -6.53 -5.88 -5.23 1.69 2.36 3.03 1.38 1.97 2.56 0.14 0.69 1.25

FOSTERS,12PK 1.40 2.39 3.37 -4.94 -4.17 -3.40 0.75 1.43 2.11 -0.49 0.14 0.78

MILLER PILS,12PK 0.74 1.42 2.10 2.12 2.82 3.53 -4.29 -3.67 -3.06

CARLSB LAGER,12PK -1.95 2.47 6.90 -1.70 1.65 5.00 3.40 6.86 10.32

10% Confidence Intervals

CARLING,12PK TENNENTS,12PK FOSTERS,12PK MILLER PILS,12PK CARLSB LAGER,12PK

CARLING,12PK -6.31 -5.80 -5.28 0.31 0.70 1.09 1.34 1.74 2.15 0.48 0.84 1.19 -0.23 0.11 0.44

TENNENTS,12PK 0.11 0.83 1.55 -6.42 -5.88 -5.33 1.80 2.36 2.92 1.48 1.97 2.46 0.23 0.69 1.16

FOSTERS,12PK 1.57 2.39 3.21 -4.82 -4.17 -3.53 0.86 1.43 2.00 -0.39 0.14 0.68

MILLER PILS,12PK 0.85 1.42 1.99 2.24 2.82 3.41 -4.19 -3.67 -3.16

CARLSB LAGER,12PK -1.22 2.47 6.17 -1.16 1.65 4.45 3.97 6.86 9.75

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 =

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• Identifying the relationship between volume sales

and marketing activity from store-level data

156+ weeks

250+store

s

In-StoreActivity

33% Free

Multi-buy plus Display

& Shelf Talker

Multi-buy plus Display

Multi-buy plus Display

Modeling enables us to understand the impact on sales ofprice, 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

• 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