The document outlines a methodology to measure the impact of MET services on sales at Home Depot stores. It involves: 1. Calculating sales coefficients with and without MET services to determine the percentage increase in sales. However, initial results showed high variability and a low confidence level. 2. Refinements were made by normalizing the sales data to filter out effects from factors like pricing and seasonality. Aggregating sales across multiple SKUs also increased the data points and confidence level. 3. Recommendations include improving data quality, applying the methodology more broadly across stores, and obtaining more data points to better measure the true impact of MET services on sales.

1. MET SERVICE
IMPACT
ANALYSIS
I S Y E 6 3 3 7 G L O B A L S U P P LY C H A I N
D E S I G N
Team:
Rimadina Nawangwulan, Pedro Josefsson, Xin Song,
Tushar Sinha, Akanksha Goel, Peng Chen
Advisor: Prof. Vande Vate

2. www.linkedin.com/in/
xin-song-400bbb103
www.linkedin.com/in
/peng0630
Our team
www.linkedin.com/in/ri
madinanawangwulan
www.linkedin.com/in/tu
shar-sinha-16988957
www.linkedin.com/in/pedr
o-josefsson-549499113/en
www.linkedin.com/in/g
oelakanksha
TEAM MEMBER
2

3. AGENDA
• Problem Statement
• Methodology
• Recommendation
3

4. Problem Statement:
After few years of MET service implementation,
Home Depot wants to know how to allocate this
resource in a more effective location.
Objective:
Find approach to calculate MET service impact and thus
where allocating MET resources provides most benefit.
4

5. Approach
4
3
Confidence Level
Calculation
2
Calculation
1
Measurement
Refinement
5

6. 添加标题
锐普，中国专业PPT设计领跑者，用卓越PPT为
您创造价值。
?
How to
measure MET
Impact
MEASUREMENT
- What to Measure?
• $ Revenue Instead of Units Sold
• Bay Instead of SKU
- Duration?
$Sales
$Sales
Day-of-Week $ Sales variation
Mon Tue Wed Thu Fri Sat Sun
1
6

7. Get clean cut of period with service and without service
Service Impact: Week 2, 6
No-Service Impact: Week 1, 3, 4, 5, 7
CALCULATION2 Week 1 Week 3
Week 2 Week 4
Week 5
Week 6
MET Service MET Service
Week 7
7

8. CALCULATION2 Week 1 Week 3
Week 2 Week 4
Week 5
Week 6
MET Service MET Service
Week 7
The Coefficient of Service Group = Average( Sales ,
Week 2
Week 1 Week 5
Week 6
8

9. 3CONFIDENCE LEVEL
Service Group
Mean of Coefficient: 1.37
Standard Deviation: 1.43
Service Group
• Data used is sample from 1 bay
• Average coefficient of 1.37 means that
on average, change in sales from week
to week when MET service is applied in
sample data is +37%
• High standard deviation shows large
variability in data. This means that
other factors have substantial influence
on sales, not only MET service.
9

10. CALCULATION2 Week 1 Week 3
Week 2 Week 4
Week 5
Week 6
MET Service MET Service
Week 7
The Coefficient
of Control Group (No Service) = Average( Sales , )
Week 3 Week 7
Week 4
Week 8
Week 8
10

11. 3CONFIDENCE LEVEL
Control Group
• Sample data is taken to measure the
organic change in sales without service
being done in the bay.
• Average coefficient of 126 means that
on average, change in sales from week
to week although MET service is not
applied in sample data is +26%
11
Control Group
Mean of Coefficient: 1.26
Standard Deviation: 1.16

12. Service Group
Mean of Coefficient: 1.37
Standard Deviation: 1.43
Sales increases by 11% when Service is applied.
How accurate is it?
3CONFIDENCE LEVEL
Control Group
Mean of Coefficient: 1.26
Standard Deviation: 1.16
12

13. Service Group
Mean of Coefficient: 1.37
Standard Deviation: 1.43
Confidence Level：15%
3CONFIDENCE LEVEL
Control Group
Mean of Coefficient: 1.26
Standard Deviation: 1.16
Pictures taken from:
http://www.stat.yale.edu/Courses/1997-98/101/meancomp.htm
http://develve.net/t-test.html
Control
Group
Service
Group
Two-sample t-Statistic test
• s – Sample Standard Deviation
• X – Sample Mean
• n – # Data Points in Sample
• t – Critical value for the t
distribution that allows retrieval of
confidence level
13
Both results have large variance shown by standard deviation. Statistic Test shows how confident we are that the
increase in sales is not just random, but that the service group coefficients are actually higher than the ones of the
Control group. Higher confidence level is better, at least more than 50% to distinguish both results.

14. STEP
01
STEP
02
STEP
03
Filter out other
factors
Aggregate numbers
REFINEMENTS
Low confidence level
1. High standard deviation
2. Limited data points
4
14

15. Promotion
Seasonality
MET Service
Others
 How does price changes change
revenue/demand?
 Sales variance due to seasonal
effects
 Impact of MET service
 Weather
 Events
 Traffic
FILTRATION OF VARIANCE
15

16. Normalization Formulation (Seasonal Effect)
• For Bay with only one SKU:
• For Bay with multiple SKUs:
Normalized Bay Sales = 𝑖∈𝐵𝑎𝑦(
𝑆𝑎𝑙𝑒𝑠 𝑆𝐾𝑈−𝑖∗𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑆 𝑖
𝑆(𝑖)
) , ∀ Days in FY 2015
Normalized Bay Sales =
𝑆𝐾𝑈 𝑆𝑎𝑙𝑒𝑠 ∗𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑆𝑢𝑏𝑐𝑙𝑎𝑠𝑠 𝑠𝑎𝑙𝑒𝑠
𝑆𝑢𝑏𝑐𝑙𝑎𝑠𝑠 𝑆𝑎𝑙𝑒𝑠
, ∀ Days in FY 2015
𝑆 𝐴 = 𝑆𝑢𝑏𝑐𝑙𝑎𝑠𝑠 𝑠𝑎𝑙𝑒𝑠 𝑓𝑜𝑟 𝑆𝐾𝑈 "𝑖"
16

17. REFINEMENT – Filtering Out Noise
Mean
Standard
Deviation
Coeff.
Control
Coeff.
Service
Confidence
Level
Bay Sales 163.03 302.87 1.26 1.37 15%
Normalized Bay
Sales 124.07 111.15 1.37 1.19 -
Assumes SKU sales follow same seasonality as subclass which
then can be used to normalize sales
17

18. Assumptions
‘Stores without Service data’ taken as Observation
set for calculation of sales lift due to promotions
Maximum Unit Price assumed as Benchmark Price
Unusual high Unit prices represent bad data rows
Steps
Remove Outliers – records with unusually high unit
price
Eliminate negative Sales records
Calculate Sales Lift % with base as Original Price
Normalize sales data by eliminating Sales lift effect
due to promotions
18
Normalization Approach (Pricing Effect)

19. Normalization Formulation (Price
Promotions Effect)
• For SKU-i in bay
• Normalized bay-level sales (with multiple SKUs)
𝑵𝒐𝒓𝒎𝒂𝒍𝒊𝒛𝒆𝒅𝑺𝒂𝒍𝒆𝒔 𝑆𝐾𝑈−𝑖,𝐷𝑎𝑦−𝑘,𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙𝑃𝑟𝑖𝑐𝑒 𝑆𝐾𝑈−𝑖
=
𝑺𝒂𝒍𝒆𝒔 𝑆𝐾𝑈−𝑖,𝐷𝑎𝑦−𝑘,𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡𝑒𝑑𝑃𝑟𝑖𝑐𝑒 𝑆𝐾𝑈−𝑖,𝐷𝑎𝑦−𝑘
1 + ∆𝑷𝒓𝒊𝒄𝒆% 𝑆𝐾𝑈−𝑖,𝐷𝑎𝑦−𝑘(
𝑺𝒂𝒍𝒆𝒔𝑳𝒊𝒇𝒕% 𝑆𝐾𝑈−𝑖
100
)
𝑵𝒐𝒓𝒎𝒂𝒍𝒊𝒛𝒆𝒅𝑺𝒂𝒍𝒆𝒔 𝐵𝑎𝑦,𝐷𝑎𝑦−𝑘,𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙𝑃𝑟𝑖𝑐𝑒 =
𝑖=1
𝑛
𝑵𝒐𝒓𝒎𝒂𝒍𝒊𝒛𝒆𝒅𝑺𝒂𝒍𝒆𝒔 𝑆𝐾𝑈−𝑖,𝐷𝑎𝑦−𝑘,𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙𝑃𝑟𝑖𝑐𝑒 𝑆𝐾𝑈−𝑖
19

20. REFINEMENT – Filtering Out Noise
(Pricing)
2/3/15 3/3/15 4/3/15 5/3/15 6/3/15 7/3/15 8/3/15 9/3/15 10/3/15 11/3/15 12/3/15 1/3/16
Sales($)
Sales Normalized Sales
Coefficient of Sales Lift (without promotions) for
Serviced and Un-Serviced sales
Mean
Standard
Deviation
Coeff.
Control
Coeff.
Service
Confidence
Level
Bay Sales 163.03 302.87 1.26 1.37 15%
Normalized Bay
Sales 126.84 207.72 1.17 1.39 33%
20

21. AGGREGATE NUMBERS
Time
In-Store
Other Store
Others
 How does price promotions change
effect revenue/demand?
 Other Bays in same Sub-
Class/Class
 Same Bay/SKU
In-Store
21

22. REFINEMENT – Aggregate of Multiple
SKUs at Same Subclass Level
Coeff.
Control
Coeff.
Service
Confidence
Level
Bay Sales 1.06 1.10 56%
Aggregate Sales 1.07 1.13 66%
• Adding more SKUs at the same subclass to get more data points
• Assuming distribution of coefficient is similar for all SKUs in the same subclass
22

23. Recommendation
• Improve data quality
• Increase data points
• Apply the methodology to other bay, subclass, class,
and department.
23

24. Flow Chart of Process
Our target:
Low Standard Deviation
High Confidence Level
Calculate sales ratio
for Control & Service
Group
Calculate standard
deviation for each
group
High
Standard
Deviation?
Calculate the
Confident Level
High
Confident
Level?
Filter out noise by
Normalization
Get more data pointAggregate numbers
Yes
NO
NO Yes Done
24

25. 25

26. MONTHLY SALES FOR WHOLE
STORESWeekly$SalesinMarch
W1 W2 W3 W4 W5
26