The project analyzes the impact of Merchandise Execution Team (MET) services on store sales for a major US retail store company. A new methodology was developed to measure revenue lift in the 7 days after MET service compared to the 7 days before, rather than comparing to periods without service. Initial analysis of a sample set found a positive revenue impact of MET service but only with 15% confidence due to high data variance and a small sample size. Methods were developed to filter out underlying variability factors and aggregate more data, increasing the confidence level to 66% for a subclass of SKUs. Recommendations include ensuring clean data, filtering out promotions and seasonality, using control groups in different areas, and analyzing

1. ExecutiveSummary
Merchandise Execution Team Service Impact Analysis for a Major Retail
Store Company in the US
Project Period: April – May 2016
Project Performed by: Rimadina Nawangwulan, Pedro Josefsson, Xin Song, Tushar Sinha, Akanksha Goel,
Peng Chen
Project Advisor: Dr. John Vande Vate (Instructor ISyE 6337 – Supply Chain Engineering III)
Problem Statement
Merchandise Execution Team (MET) associates provide service to improve in-store display of Stock Keeping
Units (SKUs). After a period of service implementation, the client requires to assess the impact of the service
on store sales to be able to differentiate service frequency across SKU classes for optimal service
impact. The project deliverable is to develop a method for calculating the magnitude of impact and
demonstrate how to refine data to attain desired confidence in measured results.
Methodology
A new method is explored which
consists of measuring revenue lift during
7 days after service contrasted with 7
days before, as opposed to the same lift
when there is no service. The period of 7
days is chosen as a means of reducing
intra-week seasonality effects while still
comparing two moderately similar
periods of demand.
After measuring a sample set of data
points (one bay and its SKUs), it is found that
MET service does have a positive revenue
impact, however only with a 15% confidence
level (based on calculation using a statistical t
test). The low confidence level is attributed to
high data variance as well as a low number of
data points used.
Underlying factors for data variability is
analyzed and as a means for increasing result
accuracy, methods for filtering out such
factors as well as aggregating data is developed. Through a continuous refinement process, sales data is
refined and accumulated, increasing confidence level of positive MET service impact on an example of a
small subclass of SKUs to 66%.
Recommendation
Recommendations for client are to ensure data cleanliness and thereafter filter out above all promotion
(pricing) as well as SKU type seasonality accordingly as proposed. In addition, it is better to issue control
group by time study where same SKUs located in different bay area. The experiments can advantageously
be made to ensure a clear cut data of with or without MET service. It is suggested to analyze longer period
sample to improve data quality. The proposal to the client is to apply the methodology into assessment by
same bay, same class and same department in the same store.