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Understanding and Improving the Supermarket Price Reduction Process Duncan Apthorp Supply Chain Development
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Transcript of Understanding and Improving the Supermarket … · Understanding and Improving the Supermarket...
Understanding and Improving the
Supermarket Price Reduction Process
Duncan Apthorp
Supply Chain Development
Tesco: Multinational retailer
12 countries
> 530,000 people
£72.4bn sales
Tesco UK
3,000+ stores
30,000+ products in big stores
23 depots
60,000,000 cases delivered a week
Supply Chain Development Projects Improving promotions
Replacing our sales forecasting
Reduced
Waste
Stock
Predicting weather effects on sales
Less
summer
food waste
Better
on-shelf
availability
Optimising store operations
Less
Waste
Where does Matlab fit in Tesco?
• Runs the business
• Hard Real Time
• 24/7/365 uptime
• Monthly updates
•12 month lead time
IBM Mainframe Teradata Data Warehouse
• 5 years of data
•100 Tbytes, 150 cores
• Soft real time
• SQL Only for now
• Batch jobs / user queries
• Agile Development
• Analysis
• Model Development
• Simulations
Matlab
Desktop and Servers
Optimising Reductions
Why reduce products going out of date?
• It’s good for our customers
• It’s good for the environment
• It’s good for business
• It’s a legal requirement
What is the Process ?
Up to 2010
• Products going out of date are scanned each evening
• Reduced up to 3 times
• Expiring product taken off sale before midnight
• Reduction percentages based on colleague’s experience
What is the Process ?
2010 – Automated Reduction Percentages
• Reduction percentages automated
• Reductions calculated automatically:
• Quantity going out of date
• Sales forecast
• Product type
The automated process brought major benefits
0%
100%
200%
300%
400%
500%
0% 25% 50% 75% 100%
Sale
s U
plift
Reduction
Price Elasticity
Fruit
& Veg
Meat
Increased
Sales
Less
Waste
2014 – New Reduction Model
• Second Tesco Mathworks joint development
• Tesco • Business / systems knowledge
• Big data expertise
• Mathworks • Increase in capacity
• Data Analytics – new ways of analysing and visualising data
• Statistical Modelling – new approaches
• Production model development
• MATLAB as the common language
2014 - Detailed Reduction Optimisation Model
Programme was based on learnings from previous project:
1. Define programme aims and KPIs
2. Understand the data
3. Build a measurement framework
4. Build your first models, get a quick win
5. Then build the final models
KPIs - what do we want to achieve?
• Make it simple and clear for our customers
• Minimise our impact on the environment (waste tonnes)
• Minimise the cost (waste in £)
• Minimise the effort involved for our colleagues in store
Historic data shows a good spread of reductions
Relationship between reduction and rate of sales
14 Apr 00:00 15 Apr 00:00 16 Apr 00:00 17 Apr 00:00 18 Apr 00:000
5
10x 10
5
£ W
aste
14 Apr 00:00 15 Apr 00:00 16 Apr 00:00 17 Apr 00:00 18 Apr 00:000
5
10x 10
5
Sto
ck
Date Scan
Overstock
Reduction
Sales
Stock, Waste
START
Evaluation Framework
Model
• Tesco retail and data knowledge
• Mathworks statistics and data analytics
• Models effect of reduction on sales rate
• Predicts KPIs
• Creates optimum reductions
Model Simplified Schematic
Merge
Scans
Sales
Stock
Test
Train Models for
Products
Select
Reduce %
Calculate
Uplift
Calculate
Quantity
Phased roll out to learn and measure benefits
Fully
Introduced
(2000+ stores)
Store director
group
(30 to 40 stores)
Nationally representative trial
(200 stores)
Customer Service Level Availability
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.3% 98.1% -0.2%
Control Stores: 98.4% 98.0% -0.4%
Dot Com Availability
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 96.7% 96.3% -0.4%
Control Stores: 96.7% 96.0% -0.8%
AM Gap Scan Availability Unweighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.8% 98.4% -0.4%
Control Stores: 98.8% 98.5% -0.3%
AM Gap Scan Availability Weighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.5% 98.2% -0.3%
Control Stores: 98.5% 98.2% -0.4%
PM Gap Scan Availability Unweighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.6% 98.4% -0.2%
Control Stores: 98.6% 98.4% -0.2%
PM Gap Scan Availability Weighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 97.3% 97.2% -0.1%
Control Stores: 97.4% 97.1% -0.3%
Stock Holding as Days Cover
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 9.7 9.9 0.2
Control Stores: 9.1 9.6 0.4
Back room Stock Holding as Days Cover
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 3.4 3.4 0.0
Control Stores: 3.2 3.4 0.2
Sales Forecast error
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 58.6% 54.0% -4.6%
Control Stores: 55.7% 56.3% 0.6%
Sales Forecast Bias (Adj sales vs 0hr Forecast)
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: -14.5% -6.2% 8.3%
Control Stores: -12.7% -13.5% -0.8%
DC to store service level
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 97.4% 95.8% -1.6%
Control Stores: 97.4% 97.1% -0.4%
Trial vs Control
Performance
-1.2%
Trial vs Control
Performance
-5.2%
Trial vs Control
Performance
9.0%
Trial vs Control
Performance
0.14%
Trial vs Control
Performance
-0.17
-0.25
Trial vs Control
Performance
BSF Grocery National Roll-Out
(All)
Trial vs Control
Performance
-0.01%
Trial vs Control
Performance
0.25%
Trial vs Control
Performance
Trial vs Control
Performance
0.09%
0.31%
0.04%
Trial vs Control
Performance
96.5%
97.0%
97.5%
98.0%
98.5%
99.0%
99.5%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
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04 M
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07 M
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10 M
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13 M
ay
16 M
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19 M
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22 M
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25 M
ay
11 J
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20 J
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23 J
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26 J
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29 J
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02 J
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08 J
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11 J
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14 J
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20 J
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23 J
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26 J
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01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
90%
91%
92%
93%
94%
95%
96%
97%
98%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
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04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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08 J
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11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
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04 A
ug
07 A
ug
10 A
ug
13 A
ug
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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05 J
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08 J
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11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
97.6%
97.8%
98.0%
98.2%
98.4%
98.6%
98.8%
99.0%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
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17 J
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21 J
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25 J
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29 J
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03 J
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07 J
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11 J
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15 J
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19 J
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23 J
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27 J
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31 J
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04 A
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08 A
ug
12 A
ug
96.0%
96.5%
97.0%
97.5%
98.0%
98.5%
99.0%
99.5%
100.0%
100.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
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21 J
un
25 J
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29 J
un
03 J
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07 J
ul
11 J
ul
15 J
ul
19 J
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23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
95.5%96.0%96.5%97.0%97.5%98.0%98.5%99.0%99.5%
100.0%100.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
un
21 J
un
25 J
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29 J
un
03 J
ul
07 J
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11 J
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15 J
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19 J
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23 J
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27 J
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31 J
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04 A
ug
08 A
ug
12 A
ug
95.0%
95.5%
96.0%
96.5%
97.0%
97.5%
98.0%
98.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
un
21 J
un
25 J
un
29 J
un
03 J
ul
07 J
ul
11 J
ul
15 J
ul
19 J
ul
23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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05 J
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08 J
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11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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05 J
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08 J
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11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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05 J
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08 J
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11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
70%
75%
80%
85%
90%
95%
100%
105%
110%
115%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
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26 J
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29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
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17 J
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20 J
ul
23 J
ul
26 J
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29 J
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01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
Customer Service Level Availability
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.3% 98.1% -0.2%
Control Stores: 98.4% 98.0% -0.4%
Dot Com Availability
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 96.7% 96.3% -0.4%
Control Stores: 96.7% 96.0% -0.8%
AM Gap Scan Availability Unweighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.8% 98.4% -0.4%
Control Stores: 98.8% 98.5% -0.3%
AM Gap Scan Availability Weighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.5% 98.2% -0.3%
Control Stores: 98.5% 98.2% -0.4%
PM Gap Scan Availability Unweighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.6% 98.4% -0.2%
Control Stores: 98.6% 98.4% -0.2%
PM Gap Scan Availability Weighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 97.3% 97.2% -0.1%
Control Stores: 97.4% 97.1% -0.3%
Stock Holding as Days Cover
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 9.7 9.9 0.2
Control Stores: 9.1 9.6 0.4
Back room Stock Holding as Days Cover
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 3.4 3.4 0.0
Control Stores: 3.2 3.4 0.2
Sales Forecast error
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 58.6% 54.0% -4.6%
Control Stores: 55.7% 56.3% 0.6%
Sales Forecast Bias (Adj sales vs 0hr Forecast)
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: -14.5% -6.2% 8.3%
Control Stores: -12.7% -13.5% -0.8%
DC to store service level
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 97.4% 95.8% -1.6%
Control Stores: 97.4% 97.1% -0.4%
Trial vs Control
Performance
-1.2%
Trial vs Control
Performance
-5.2%
Trial vs Control
Performance
9.0%
Trial vs Control
Performance
0.14%
Trial vs Control
Performance
-0.17
-0.25
Trial vs Control
Performance
BSF Grocery National Roll-Out
(All)
Trial vs Control
Performance
-0.01%
Trial vs Control
Performance
0.25%
Trial vs Control
Performance
Trial vs Control
Performance
0.09%
0.31%
0.04%
Trial vs Control
Performance
96.5%
97.0%
97.5%
98.0%
98.5%
99.0%
99.5%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
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16 M
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19 M
ay
22 M
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25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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05 J
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08 J
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11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
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04 A
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07 A
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10 A
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13 A
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90%
91%
92%
93%
94%
95%
96%
97%
98%
16 A
pr
19 A
pr
22 A
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25 A
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28 A
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01 M
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04 M
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07 M
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10 M
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13 M
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16 M
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19 M
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22 M
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25 M
ay
11 J
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14 J
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26 J
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29 J
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02 J
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05 J
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08 J
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11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
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04 A
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07 A
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10 A
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13 A
ug
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
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13 M
ay
16 M
ay
19 M
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22 M
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25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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05 J
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08 J
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11 J
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17 J
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20 J
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23 J
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26 J
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29 J
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01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
97.6%
97.8%
98.0%
98.2%
98.4%
98.6%
98.8%
99.0%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
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17 J
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21 J
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25 J
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29 J
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03 J
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07 J
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11 J
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15 J
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19 J
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23 J
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27 J
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31 J
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04 A
ug
08 A
ug
12 A
ug
96.0%
96.5%
97.0%
97.5%
98.0%
98.5%
99.0%
99.5%
100.0%
100.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
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21 J
un
25 J
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29 J
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03 J
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07 J
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11 J
ul
15 J
ul
19 J
ul
23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
95.5%96.0%96.5%97.0%97.5%98.0%98.5%99.0%99.5%
100.0%100.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
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17 J
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21 J
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25 J
un
29 J
un
03 J
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07 J
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11 J
ul
15 J
ul
19 J
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23 J
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27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
95.0%
95.5%
96.0%
96.5%
97.0%
97.5%
98.0%
98.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
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17 J
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21 J
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25 J
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29 J
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03 J
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07 J
ul
11 J
ul
15 J
ul
19 J
ul
23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
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14 J
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17 J
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20 J
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23 J
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26 J
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29 J
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02 J
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05 J
ul
08 J
ul
11 J
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14 J
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17 J
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20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
70%
75%
80%
85%
90%
95%
100%
105%
110%
115%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
Customer Service Level Availability
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.3% 98.1% -0.2%
Control Stores: 98.4% 98.0% -0.4%
Dot Com Availability
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 96.7% 96.3% -0.4%
Control Stores: 96.7% 96.0% -0.8%
AM Gap Scan Availability Unweighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.8% 98.4% -0.4%
Control Stores: 98.8% 98.5% -0.3%
AM Gap Scan Availability Weighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.5% 98.2% -0.3%
Control Stores: 98.5% 98.2% -0.4%
PM Gap Scan Availability Unweighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 98.6% 98.4% -0.2%
Control Stores: 98.6% 98.4% -0.2%
PM Gap Scan Availability Weighted
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 97.3% 97.2% -0.1%
Control Stores: 97.4% 97.1% -0.3%
Stock Holding as Days Cover
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 9.7 9.9 0.2
Control Stores: 9.1 9.6 0.4
Back room Stock Holding as Days Cover
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 3.4 3.4 0.0
Control Stores: 3.2 3.4 0.2
Sales Forecast error
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 58.6% 54.0% -4.6%
Control Stores: 55.7% 56.3% 0.6%
Sales Forecast Bias (Adj sales vs 0hr Forecast)
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: -14.5% -6.2% 8.3%
Control Stores: -12.7% -13.5% -0.8%
DC to store service level
Pre Trial
Cumulative
Trial
Cumulative
Trial vs Pre
Trial
Trial Stores: 97.4% 95.8% -1.6%
Control Stores: 97.4% 97.1% -0.4%
Trial vs Control
Performance
-1.2%
Trial vs Control
Performance
-5.2%
Trial vs Control
Performance
9.0%
Trial vs Control
Performance
0.14%
Trial vs Control
Performance
-0.17
-0.25
Trial vs Control
Performance
BSF Grocery National Roll-Out
(All)
Trial vs Control
Performance
-0.01%
Trial vs Control
Performance
0.25%
Trial vs Control
Performance
Trial vs Control
Performance
0.09%
0.31%
0.04%
Trial vs Control
Performance
96.5%
97.0%
97.5%
98.0%
98.5%
99.0%
99.5%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
90%
91%
92%
93%
94%
95%
96%
97%
98%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
97.6%
97.8%
98.0%
98.2%
98.4%
98.6%
98.8%
99.0%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
un
21 J
un
25 J
un
29 J
un
03 J
ul
07 J
ul
11 J
ul
15 J
ul
19 J
ul
23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
96.0%
96.5%
97.0%
97.5%
98.0%
98.5%
99.0%
99.5%
100.0%
100.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
un
21 J
un
25 J
un
29 J
un
03 J
ul
07 J
ul
11 J
ul
15 J
ul
19 J
ul
23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
95.5%96.0%96.5%97.0%97.5%98.0%98.5%99.0%99.5%
100.0%100.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
un
21 J
un
25 J
un
29 J
un
03 J
ul
07 J
ul
11 J
ul
15 J
ul
19 J
ul
23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
95.0%
95.5%
96.0%
96.5%
97.0%
97.5%
98.0%
98.5%
16 A
pr
20 A
pr
24 A
pr
28 A
pr
02 M
ay
06 M
ay
10 M
ay
14 M
ay
18 M
ay
22 M
ay
26 M
ay
13 J
un
17 J
un
21 J
un
25 J
un
29 J
un
03 J
ul
07 J
ul
11 J
ul
15 J
ul
19 J
ul
23 J
ul
27 J
ul
31 J
ul
04 A
ug
08 A
ug
12 A
ug
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
70%
75%
80%
85%
90%
95%
100%
105%
110%
115%
16 A
pr
19 A
pr
22 A
pr
25 A
pr
28 A
pr
01 M
ay
04 M
ay
07 M
ay
10 M
ay
13 M
ay
16 M
ay
19 M
ay
22 M
ay
25 M
ay
11 J
un
14 J
un
17 J
un
20 J
un
23 J
un
26 J
un
29 J
un
02 J
ul
05 J
ul
08 J
ul
11 J
ul
14 J
ul
17 J
ul
20 J
ul
23 J
ul
26 J
ul
29 J
ul
01 A
ug
04 A
ug
07 A
ug
10 A
ug
13 A
ug
Automated daily project tracking
Availability
Stock Holding
Back Room Stock
Forecast Accuracy
Coming to
your local
store 2015
Working with Mathworks – some tips
• Agree the goals, and how to measure them
• Make it a joint development
• Have a single contact for day to day operations
• Hold regular high level reviews
• Don’t accept things that feel wrong
The Future – in database analytics
IBM Mainframe Teradata Data Warehouse
• in database analytics for
heavy lifting
Matlab for:
• Control
• Simulation
• Small models
Matlab
Desktop and Servers
New Technologies for Retail
