mUreka - The Case
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Transcript of mUreka - The Case
MuMart – Mu Sigma Business Case
Mu Sigma is a leading provider of decision sciences and analytics solutions, helping companies
institutionalize data-driven decision making. We work with market-leading companies across
multiple verticals, solving high impact business problems in the areas of Marketing, Supply Chain and
Risk analytics. For these clients we have built an integrated decision support ecosystem of people,
processes, methodologies & proprietary IP and technology assets that serve as a platform for cross-
pollination and innovation. Mu Sigma has driven disruptive innovation in the analytics industry by
integrating the disciplines of business, math, and technology in a sustainable model. With over 75
Fortune 500 clients and over 2000 decision science professionals we are one of the largest pure-play
decision sciences and analytics companies.
MuMart Case Study
Mu Sigma Proprietary 1
Background:
MuMart was founded by Mr. Bill Billiardson in 1983 in Great Bend, IN. From a small corner store
selling local produce, over the last 30 years, MuMart has grown rapidly to become the convenience
retailer of choice in the United States. MuMart now has over 3,000 stores spread in 47 states (it
doesn’t have a presence in Hawaii, Alaska and North Dakota) across the country.
Historically, they have expanded through new store openings in rural, suburban and exurban
centers. Their strategic focus area for the next three years is to expand into urban centers and large
cities. While their strategy for suburban centers has typically been to carry a large assortment, due
to limited real-estate availability and the costs associated with it, they need to relook at their
assortment strategy.
The Executive Leadership team at MuMart recently approved new store launches in three cities
across the US. The three cities are Phoenix-AZ, Des Moines-IA and Atlantic City-NZ. The Real Estate
Strategy team in conjunction with the Store Operations teams has determined that the new stores
will have a total Mod space of 85,000 units. All shelves will be of uniform length of 100 units.
The Customer Insights team has worked with a third party provider to map out every transaction in
their database to a customer household using some scientific as well as approximate methods.
MuMart would like to determine the right assortment strategy that can be carried out in these new
stores to maximize both Reach as well as Revenue.
Deliverables Expected:
1.
An executive presentation (PPT) with your recommendations and supporting evidence
2. Executable source code (as applicable) used to manipulate data and arrive at your
recommendations (SAS/SPSS/R/Excel/Java/C/C++ are the only technologies allowed for this
exercise)
3. Thorough documentation of the source code
Data Available:
1. Transaction History for the two years (2010 and 2011). A Representative subset (1M) of the
total data is provided. For all practical purposes, you can assume that this is a complete
database of transactions
2. Customer Database: A database of 120,000 households and their demographic traits
3. (Stock Keeping Unit) SKU Database: A database of 18000 SKU that are potential SKU for
these markets
Assumptions:
1.
The new stores are scheduled to open on January 1, 2013
MuMart Case Study
Mu Sigma Proprietary 2
2. MuMart’s assortment decision is made independent of the competition’s assortment
strategy
3. There is a propensity for customers to walkaway completely from a transaction when a
particular product is missing
4. The product prices will not change significantly in 2013, compared to 2010-2012
5. While seasonality is a factor, assortment decisions/changes for MuMart are made only on a
quarterly basis (assume due to logistical challenges)
Disclaimer:
1. The client name, founder’s name, business problem and supporting data provided are all
fictitious and have been created by Mu Sigma purely for the purpose of this contest
2.
The dataset is programmatically created. While due care has been taken to make the data as
realistic as possible, owing to the manner in which it is generated (using probability
distributions), some of it might seem illogical. Please take the data at face value and
accurate as is
Note:
Send your deliverable to [email protected] as an attachment
Do mention your username (that was created at the time of registration) in the mail
Last date to send your submissions – Jan 4th
, 2013
Reach out to [email protected] in case of any queries regarding the case study