Location based sales forecast for superstores

Development of Sales Forecasting Application
for Retail Hypermarkets
using Simulation & Genetic Algorithm
Chachrist Srisuwanrat, Ph.D.
The Founder of ThaiQuants.com

Main Characters of Retail Hypermarkets for this Project
• Sales Area: 6,000 to 12,000 sq.m.
• Land Size: 16 to 24 rais
• #Stories: 1 to 2 floors
• Provide food & non-food products
• Including rent areas

Objectives of this Project
Forecasting Sales for a new potential Hypermarkets (not exist yet)
• Method
• Application
• Workflow
• Database
• Documents
Note: the project was conducted in late 2011 with Confidential & Non-Compete Agreement.
Information in this presentation is only a broad idea about its methods and development.

Software
• Camtasia for documenting and consulting
• MS Excel for existing and a new proof-of-concept calculation
• Visual Studio Dot Net for prototyping and developing application
• MS Access for Database
• GIS Application for retrieving demographic information

Requirements from Executives (key decision makers)
• Result in 90% accuracy
• Eliminate subjective judgement
• Keep information secrete
• No “Linear Regression”
Requirements from Strategies (end users)
• Use/modify the current method
• Expedite the process of forecasting
• Remain some flexibility

Parameters of Retail Hypermarkets
1. Size
2. Location
3. Demographic factors
4. Visibility
5. Traffic
6. Competition
7. Design
8. Access
9. Activity
10. Parking
11. Signing
12. …

Sales ForecastingMethods
1. Regression Method (Linear & Non-linear)
2. Gravity Model (Demographic/Catchment Calculation)
3. Analogue Method (Stores’ Parameter Comparison)
Sales (Million Baht)
Sales Area (sq.m.)
8,0006,000 10,000
1,000
1,200
800
600
1,400
12,000
1. Regression Method 2. Gravity Model 3. Analogue Method

1. Regression Method
Sales = a × X1 + b × X2 + c × X3 + … + K
• Xs are primary parameters:
• Store Size
• Catchment Area & Competition
• Demographic Variables
• …
• Xi could be a ratio.
• K is a constant such as 500 million baht
Sales (Million Baht)
Sales Area (sq.m.)
8,0006,000 10,000
1,000
1,200
800
600
1,400
12,000
No. Actual Sales X1 X2 X3 …
1
2
3
4
5
6
7
Derive a, b, c, …, and K

2. Gravity Model
Sales = Adjusted Population × Expenditure/head
• Primary Data
I. Store’s physical data
II. Geographic data
III. Demographic data
IV. …
Consider I, II, III, IV, …
Transportation
Mountain

3. Analogue Method (Traditional)
Sales = Average (Actual Sales × Parameters)
• Based on actual sales of exiting stores
• Compare parameters between Analog & Target Stores
• The parameters including both:
• Primary data: Demographic/Geographicdata
• Secondary data: Size, Location, …
Analog Stores Target
Store A B X
Actual Sales 1,000 800 ?
Size ?% ?%
Location ?% ?%
Sum
???
Store A B X
Size +15% -5%
Location +5% +15%
Sum +20% +10%
1,200 880 1,040
Assign %parameters

Problems with Current Practice,
Analogue Method
• Assigning subjective values
• Missing parameters (cannibalization, …)
• Weighting parameters equally
• Omitting an impact from other parameters
• Selecting ambiguous analog stores (A, B, C, …)
• Taking time
Store A B C X
Actual Sales 1,000 800 1,000 ?
Size +15% -5% -
Location +5% +15% +5%
Visibility - - +5%
Traffic ? - -15%
Competition +%5% - +5%
Sum …% …%
… … ???
Store A B X
Size +15% -5%
Location +5% +15%
Sum +20% +10%
1,200 880 1,040

During Proof-Of-Concept Development
• Researching/learning about the topic & data
• Limiting effort from the team
• Requesting data for 10 existing stores
• Asking for only available data from the team
• Finding/Testing new data
• Discussing/Proposing ideas
• Focusing on primary data: size, population, household income
ACCURACY: 75%
Store A B C X
Actual Sales # # # ?
P1 % % %-
P2 % % %
P3 % % %
… % % %
P6 % % %
Size # # # #
Population # # # #
HH Income # # # #
Sum …% …% …%
… … … ???

During Prototype Development
• Changing assigned value to boolean-type data
• P2.1: Is it visible in 150 meters?
• Creating levels for parameters
• P2.2: Is it visible in 300 meters?
• Requesting more data and 3-point estimates
• Incorporating Monte Carlo Simulation
• Grouping secondary parameters into 3-4 groups
• Testing Genetic Algorithm Optimization
ACCURACY: 86%
Store A B C X
Actual Sales # # # ?
P1 % % %-
P2.1
P2.2
… … … … …
P15
Size # # # #
Pop @ 5 Km # # # #
Pop @ 15 Km # # # #
Pop @ 30 Km # # # #
HHI @ 5 Km # # # #
HHI @ 15 Km # # # #
HHI @ 30 Km # # # #
Sum …% …% …%
… … … ???

Example of Using Boolean Parameters
Store A B C X
Actual Sales 1,000 800 1,000 ?
P1: +10% % % %-
P2.1: +4%
P2.2: +6%
… … … … …
Sum …% …% …%
… … … ???
Store A B C X
Actual Sales 1,000 800 1,000 ?
P1: +10% 0 +10% +10%
P2.1: +4% -4% -4% 0
P2.2: +6% +6% 0 +6%
… … … … …
Sum +2% +6% +16%
1,020 848 1,160 1,009
𝑆𝑎𝑙𝑒𝑠 𝑋:𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 = ൘෍
𝑠=1
𝑆=3
𝑆𝑎𝑙𝑒𝑠 𝐴,𝐵,𝐶:𝐴𝑐𝑡𝑢𝑎𝑙 × ෍
𝑝=1
𝑃
∆𝑃 𝑆
%𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 100 −
𝑆𝑎𝑙𝑒𝑠 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 −𝑆𝑎𝑙𝑒𝑠 𝐴𝑐𝑡𝑢𝑎𝑙
𝑆𝑎𝑙𝑒𝑠 𝐴𝑐𝑡𝑢𝑎𝑙
× 100

Three-point Estimates & Simulation
Store A B C X
Actual Sales 1,000 800 1,000 ?
P1: % % %-
P2.1:
P2.2:
… … … … …
Sum …% …% …%
… … … ???
10% 12%4%
4% 6%2%
6% 10%4%
P1
P2
P3
Simulate 1,000 runs to get
1,000 values of Salesforecast
0.25
0.50
0.75
1.00
1,000 1,200 1,400800600
P1 = 10%
P2.1 = 4%
P2.2 = 6%
Probability
Salesforecast

60 + 10 Parameters
• About 60 Boolean parameters
• …Visibility in 150 m
• …
• …
• About 10 Numerical parameters
• Store size
• Population in 5, 15, 30 Km
• Household Income in 5, 15, 30 Km
•Should these parameters beweighted equally for SalesForecast?

Improving Accuracy with Optimization
• Categorizing Boolean Parameters into 6 groups
• Introducing Weights for each group
• Therefore, there will be Weight1, Weight2, …, Weight6
𝑠=1
𝑆=3
𝑆𝑎𝑙𝑒𝑠 𝐴,𝐵,𝐶:𝐴𝑐𝑡𝑢𝑎𝑙 × ෍
𝑝=1
𝑃
∆𝑃 𝑆
𝑠=1
𝑆=3
𝑆𝑎𝑙𝑒𝑠 𝐴,𝐵,𝐶:𝐴𝑐𝑡𝑢𝑎𝑙 ×
σ 𝑤=1
𝑊=6
(𝑊𝑒𝑖𝑔ℎ𝑡 𝑤 × σ 𝑝=1
𝑃
∆𝑃𝑤)
σ 𝑤=1
𝑊=6
𝑊𝑒𝑖𝑔ℎ𝑡 𝑤
𝑆

To find Optimum Weights:
•How to derive the optimum Weights???
Remember. We don’t have SalesX:Actual for the calculation of %Accuracy !!!
𝑂𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛 = 𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒(%𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦)
× 100
𝑠=1
𝑆=3
σ 𝑤=1
𝑊=6
(𝑊𝑒𝑖𝑔ℎ𝑡 𝑤× σ 𝑝=1
𝑃
∆𝑃𝑤)
σ 𝑤=1
𝑊=6
𝑆

Can we derive the Optimum Weights for StoreX?
• No. We can’t. We don’t have Target Store’s Actual Sales, SalesX:Actual .
• So. How can we maximize %Accuracy to solve this problem?
•Well. Let’s make assumptions.
× 100

Assumptions used to derive Optimum Weights
• Selected analogue stores should be able to effectively predict their sales.
• (A & B predict C); (A & C predict B); (B & C predict A).
• Then Optimize (%Accuracy) with proper Weights should improve their (analogue stores) forecasts.
• Given that Target Store X and Selected Analog Stores (A, B, and C) have similar characteristics.
Thus,
• Weights from Optimize (A & B predict C) should improve accuracy in (A & B forecast X).
• Weights from Optimize (A & C predict B) should improve accuracy in (A & C forecast X).
• Weights from Optimize (B & C predict A) should improve accuracy in (B & C forecast X).
• Next step: use Genetic Algorithm Optimization to derive Weights.

Why do we need GA Optimization?
All Possibilities
• 3 pairs of Analogue Stores: (A & B), (A & B), and (B & C)
• 10 possible values for each Weight: 0.1, 0.2, 0.3, …, and 1.0
• 6 groups of parameters: W1, W2, W3, …, and W6
All Possibilities = 3 x 106 = 3,000,000
• Simulation: 200 runs per a set of weights (2 minutes per 200 runs)
Total Duration = 3 x 106 x 2 = 6,000,000 minutes or 4,166 days or 11.5 years
Therefore, we need Genetic Algorithm Optimization.
Note: Sets of Weights from each pair of analogue stores are unique to that pair.

Genetic Algorithm Optimization
“The Greater %Accuracy, the Greater Survival Rate to the new generation”
• 3 Main GA Operations
1. Reproduction
2. Crossover (mating)
3. Mutation
• GA parameters
• totalGeneration = 50, and 1 initial generation
• totalPopulation = 50 for each generation
• probCrossover = 0.20, and nCrossover = 1
• probMutation = 0.03, and nMutation = 2

Generation i
Pop ID W1 W2 W3 W4 W5 W6 %Accuracy Probability
1
2
3
…
50
Total
Generation i
Pop ID W1 W2 … W6 %Accuracy Probability
1
2
3
4
5
Total
Actual Optimization Data Structure Simplified Example

Simplified Example of GA: (A & B) predict C, then X
InitialGeneration: Random all the Weights
1 0.4 0.6 0.2 0.7
2 0.5 0.7 1.0 0.9
3 0.8 0.1 0.4 0.2
4 0.6 1.0 0.8 0.8
5 0.5 0.4 0.3 0.9
Total
× 100
𝑆𝑎𝑙𝑒𝑠 𝐶:𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 = ൘෍
𝑠=1
𝑆=2
𝑆𝑎𝑙𝑒𝑠 𝐴,𝐵:𝐴𝑐𝑡𝑢𝑎𝑙 ×
σ 𝑤=1
𝑊=4
(𝑊𝑒𝑖𝑔ℎ𝑡 𝑤× (σ 𝑝=1
𝑃
∆𝑃𝑤)
σ 𝑤=1
𝑊=4
2
InitialGeneration
1
2
3
4
5
Total

200 Simulation Runs
• Each %Accuracy is derived from 200 simulation runs, 200 values of SalesC:forecast
InitialGeneration
1 0.4 0.6 0.2 0.7
2 0.5 0.7 1.0 0.9
3 0.8 0.1 0.4 0.2
4 0.6 1.0 0.8 0.8
5 0.5 0.4 0.3 0.9
Total
InitialGeneration: 200 Simulation Runs
1 0.4 0.6 0.2 0.7 70
2 0.5 0.7 1.0 0.9 95
3 0.8 0.1 0.4 0.2 65
4 0.6 1.0 0.8 0.8 20
5 0.5 0.4 0.3 0.9 80
Total
200 runs
200 runs
200 runs
200 runs
200 runs

1. GA Reproduction
InitialGeneration (Gen00): Create BiasedRouletteWheel
1 0.4 0.6 0.2 0.7 70 0.21
2 0.5 0.7 1.0 0.9 95 0.29
3 0.8 0.1 0.4 0.2 65 0.20
4 0.6 1.0 0.8 0.8 20 0.06
5 0.5 0.4 0.3 0.9 80 0.24
Total 330 1.00
Generation 1 (Gen01): Reproduction
1 0.5 0.4 0.3 0.9
2 0.4 0.6 0.2 0.7
3 0.5 0.7 1.0 0.9
4 0.5 0.4 0.3 0.9
5 0.5 0.7 1.0 0.9
Total
0.21
0.50
0.70
0.76
For the new Generation 1:
1. Random = 0.80 then Gen00Pop05 is selected for Pop01.
2. Random = 0.15 then Gen00Pop01 is selected for Pop02.
3. Random = 0.44 then Gen00Pop02 is selectedfor Pop03.
1.00

2. GA Crossover
Generation 1: Crossover
1 0.5 0.6 0.3 0.9
2 0.4 0.4 0.2 0.7
3 0.5 0.7 0.3 0.9
4 0.5 0.4 1.0 0.9
5 0.5 0.7 1.0 0.9
Total
Generation 1: Pairing
1 0.5 0.4 0.3 0.9
2 0.4 0.6 0.2 0.7
3 0.5 0.7 1.0 0.9
4 0.5 0.4 0.3 0.9
5 0.5 0.7 1.0 0.9
Total
If rand(0,1) < probCrossover then
For nCrossover
randInteger[1, 6]
crossover()
End For
End if
//probCrossover = 0.20
//nCrossover = 1

3. GA Mutation
Generation 1: Mutate
1 0.5 0.6 0.7 0.9
2 0.4 0.4 0.2 0.7
3 0.5 0.7 0.3 0.9
4 0.5 0.3 1.0 0.8
5 0.1 0.7 1.0 0.9
Total
Generation 1: Randomly select
1 0.5 0.4 0.3 0.9
2 0.4 0.6 0.2 0.7
3 0.5 0.7 1.0 0.9
4 0.5 0.4 0.3 0.9
5 0.5 0.7 1.0 0.9
Total
For nMutation
If rand(0,1) < probMutation then
randInteger[1,6]
mutate()
End If
End For
//nMutation = 2
//probMutation = 0.03

200 Simulation Runs
Generation 1: Create Biased RouletteWheel
1 0.5 0.6 0.7 0.9 60 0.15
2 0.4 0.4 0.2 0.7 75 0.19
3 0.5 0.7 0.3 0.9 90 0.23
4 0.5 0.3 1.0 0.8 70 0.18
5 0.1 0.7 1.0 0.9 95 0.24
Total 390 1.00
Generation 1: 200 Simulation Runs
1 0.5 0.6 0.7 0.9
2 0.4 0.4 0.2 0.7
3 0.5 0.7 0.3 0.9
4 0.5 0.3 1.0 0.8
5 0.1 0.7 1.0 0.9
Total
200 runs
200 runs
200 runs
200 runs
200 runs

Create the next Generation
Repeat the processes
until Generation 50
Generation 2
1
2
3
4
5
Total
Generation 1
1 0.5 0.6 0.7 0.9 60 0.15
2 0.4 0.4 0.2 0.7 75 0.19
3 0.5 0.7 0.3 0.9 90 0.23
4 0.5 0.3 1.0 0.8 70 0.18
5 0.1 0.7 1.0 0.9 95 0.24
Total 390 1.00
1. Reproduction
2. Crossover
3. Mutation

Rank the Sets of Optimum Weights by %Accuracy
No Gen ID Pop ID W1 W2 … W6 ±Error %Accuracy
1 00 01 … … … … … …
2 00 02 … … … … … …
… … … … … … … … …
50 00 50 … … … … … …
51 01 01 … … … … … …
52 01 02 … … … … … …
… … … … … … … …
100 01 50 … … … … … …
101 02 01 … … … … … …
… … … … … … … …
… … … … … … … …
2501 50 01 … … … … … …
2502 50 02 … … … … … …
… … … … … … … …
2550 50 50 … … … … … …
Rank
&
Select
Top 100 Sets of Optimum Weights (A & B predict C),
Ranked by %Accuracy
Use these Sets of Optimum Weights & ±Error
for (A & B forecast X)
RANK No Gen ID Pop ID W1 W2 … W6 ±Error %Accuracy
1 … … … … … 98
2 … … … … … 96
3 … … … … … 96
… … … … … … …
100 … … … … … 88

Optimum Weights from (A & B predict C) to Forecast X
200 runs
200 runs
200 runs
200 runs
200 runs
(A & B predict C)
RANK W1 W2 … W6 ±Error Sales Forecast
1 … … … … …
2 … … … … …
3 … … … … …
… … … … … …
100 … … … … …
(A & B predict C) X
1 … … … … … … 200 values
2 … … … … … … 200 values
3 … … … … … … 200 values
… … … … … … … 200 values
100 … … … … … … 200 values

Optimum Weights from the 3 pairs to Forecast X
(A & B predict C) X
1 … … … … … … 200 values
… … … … … … … 200 values
100 … … … … … … 200 values
(A & C predict B) X
1 … … … … … … 200 values
… … … … … … … 200 values
100 … … … … … … 200 values
(B & C predict C) X
1 … … … … … … 200 values
… … … … … … … 200 values
100 … … … … … … 200 values
3 x 100 x 200 values
of SalesX:forecast
0.25
0.50
0.75
1.00
1,000 1,200 1,400800600
Probability
SalesX:forecast

Selection of Analog Stores
1. Size & Design
2. Region: North, South, …
3. Regional Characteristics: CBD, around Bangkok, international trading area, …
4. Population: 5, 15, and 30 Km
5. Household Income: 5, 15, and 30 Km
6. Competition Level: rival stores, local stores, cannibalization, …
To calculate “Analogue Store Selection Score”

Example Table of Analogue Store Selection Score
No
Score Store ID Size Design Region Regional
Characteristics
Population Household
Income
Competition
Level
1 98 56
2 96 42
3 95 31
4 92 7
5 87 19
… … …
10 71 28

Optimize the Processes of GA & Simulation
1. Consider tradeoff between Time & Accuracy
2. GA Optimization
• Adjust GA parameters to suit the problem
• Start the initial generation (Gen00) better/smarter
3. Simulation
• Reduce the number of simulation runs
• Skip already-tested parameter sets, and just copy their results: %Accuracy and ±Error
From 11 years (all possibilities) to 2 months (GA), to 2 days (Optimized GA),
and 2 hours (Optimized GA & Simulation).

Final Formula for Sales Forecast
𝑠=1
𝑆=3
σ 𝑤=1
𝑊=6
(𝑊𝑒𝑖𝑔ℎ𝑡 𝑤× σ 𝑝=1
𝑃
∆𝑃𝑤)
σ 𝑤=1
𝑊=6
× 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑁𝑢𝑚𝑒𝑟𝑖𝑐𝑎𝑙−𝑃𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠 𝑆
𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑁𝑢𝑚𝑒𝑟𝑖𝑐𝑎𝑙−𝑃𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠 = 𝑘 𝑆𝑖𝑧𝑒 𝑓𝑢𝑛𝑐 𝑆𝑖𝑧𝑒 × 𝑘 𝑃𝑜𝑝 𝑓𝑢𝑛𝑐 𝑃𝑜𝑝× 𝑘 𝐻𝐻𝐼 𝑓𝑢𝑛𝑐 𝐻𝐻𝐼× 𝑘 𝐶𝑜𝑚𝑝 𝑓𝑢𝑛𝑐 𝐶𝑜𝑚𝑝
Simplified Example: 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑆𝑖𝑧𝑒 = ൘
𝑆𝑖𝑧𝑒 𝑋
𝑆𝑖𝑧𝑒 𝐴𝑛𝑎𝑙𝑜𝑔𝑢𝑒
with Maximum and Minimum Value Range
(Store Size, Population, Household Income, and Competition)

Requirements from Executives (key decision makers)
• Result in 90% accuracy  Final accuracy is about 90%++
• Eliminate subjective judgement  Use 60 boolean parameters with 3-point estimates
• Keep information secrete  Data is locked in only one laptop.
• No “Linear Regression”  Employ Simulation and Genetic Algorithm Optimization
Requirements from Strategists (end users)
• Use/modify the current method  Use Analogue Method with modification
• Expedite the process of forecasting  Optimize the processes of GA & Simulation
• Remain some flexibility  Analogue store selection, and k values for numerical parameters

Keys to success for this project
• Fully supported by executives
• Professionally participated by the team
• Great contribution from the head of the team (expert view)
• Willing to test new idea & data
• Effectively/flexibly manage time
• Reasonable expectation for the project

Future Improvement
• Consider a dedicated weight for each boolean parameters
• Adjust 3-point Estimates
• Improve numerical-parameter functions
• Enhance Analogue Store Selection Scoring System
• Test different types of Optimization methods and other Machine Learning methods
• Use better Hardware and GPU
• Incorporate other Sales Forecasting Methods
• Develop automated and repeatable improvement processes in the application

THANK YOU
Chachrist Srisuwanrat, Ph.D.
The Founder of ThaiQuants.com

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