The Home Depot Paint Department Analysis
GEORGIA TECH SENIOR DESIGN FINAL REPORT: FALL 2013
Team Members
Bobby Faulk
Bryce Ferguson
Michael Gilkenson
Jing Mei Ho
Drew Keller
Lauren Kley
Melanie Ostis
Silvana Vivanco
Rfaulk6@gatech.edu
Bferguson31@gatech.edu
Mgilkenson3@gatech.edu
Jho35@gatech.edu
Dkeller7@gatech.edu
Lkley3@gatech.edu
Mostis3@gatech.edu
Svivanco6@gatech.edu
Advisor
Dr. Steve Hackman Shackman@isye.gatech.edu
Client Contact
Will Welch William_S_Welch@homedepot.com
Cory German Cory_German@homedepot.com
Jennifer Smith Jennifer_L_Smith@homedepot.com
This document has been created in the framework of a student design project.
The Georgia Institute of Technology does not officially sanction its content.
Executive Summary
The Home Depot’s (THD) paint department (D24) is a major driver of store revenue, and its
success relies on superior customer service. The Home Depot’s Store Support Center (SSC)
believes that increased profits can be realized through an improvement in the customer
experience. During data collection at three Atlanta-area locations, the senior design team
observed an average of 3% of customers reneging and 18% of customers waiting more than 2.5
minutes, supporting the SSC’s belief.
To supplement current staffing tools, the senior design team has developed a Paint Department
Analysis Tool (PDAT). This tool allows the SSC to analyze different staffing scenarios and their
effects on profit and customer service metrics.
Through analysis of PDAT outputs, the senior design team has developed a number of service
protocol recommendations which are outlined below:
 Associate Roles – The senior design team suggests that associates be assigned to either
the desk or the aisle. Through several protocols, associates’ role assignments will change
to reflect customer traffic and needs.
 Desk Queue Threshold – When the desk queue reaches a specific threshold, aisle
associates will come to the desk to assist with mixing paint.
 Additional Accessory Sales – Accessories are the main drivers of profit in D24. By
staffing associates in the aisle, they have the opportunity to suggest additional accessory
purchases to customers.
By applying these recommendations in a simulation model, the average percent of customers
reneging decreased from 3.3% to 3.1%, while the average percent of customers waiting more
than 2.5 minutes for service decreased from 17% to 14%. Additionally, the number of additional
accessories sold to customers increased, leading to an average incremental daily profit due to
accessory sales of $13.53. This incremental daily profit translates to roughly 5 additional
accessories sold per day per store. Staffing recommendations suggested by the tool led to an
average reduction of approximately 1.65 labor hours per day.
By following these recommendations and using the PDAT, THD will realize an annual
incremental profit of $10.4 million (pre-tax) from recovered lost sales and increased revenue on
existing transactions. This conservative estimate does not account for labor changes. A more
optimistic scenario will provide incremental profit of $29.1 million (pre-tax), but this can only be
achieved if shifts are perfectly in sync with labor modification suggestions. More realistically,
the value of this project lies somewhere within that range depending on how closely THD can
match their staffing schedules to PDAT suggestions.
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Table of Contents
1. Business Overview ............................................................................................................. 1
1.1. Paint Department Overview................................................................................................. 1
1.2. Current Staffing Process ....................................................................................................... 1
2. Project Scope ..................................................................................................................... 2
2.1. Problem Overview ................................................................................................................ 2
2.2. Solution Overview ................................................................................................................ 2
3. Design Approach................................................................................................................ 4
3.1. Data Analysis and Modeling ................................................................................................. 4
3.2. Customer Arrival Model ....................................................................................................... 5
3.3. Customer Renege Model ...................................................................................................... 6
3.4. Customer Profile Model ....................................................................................................... 7
3.5. Associate Role Model ........................................................................................................... 7
3.6. Desk Queue Length Threshold Model .................................................................................. 8
3.7. Accessory Sales Model ....................................................................................................... 10
3.8. Staffing Adjustments Model............................................................................................... 10
4. Deliverables..................................................................................................................... 12
4.1. Paint Department Analysis Tool ......................................................................................... 12
4.2. Simulation........................................................................................................................... 13
5. Recommendations and Valuation .................................................................................... 15
5.1. Recommendations ............................................................................................................. 15
5.2. Valuation ............................................................................................................................ 15
6. Appendices ...................................................................................................................... 18
6.1. Appendix A: Customer and Mixer Data Collection Procedure ........................................... 18
6.2. Appendix B: Mixer Utilization Data Analysis ...................................................................... 19
6.3. Appendix C: Customer Arrival Regression.......................................................................... 21
6.4. Appendix D: Customer Renege Model ............................................................................... 22
6.5. Appendix E: Recommendation and Valuation Data ........................................................... 23
6.6. Appendix F: PDAT User Guide ............................................................................................ 24
6.7. Appendix G: Associate Task Statistical Distributions ......................................................... 27
6.8. Appendix H: Simulation Flow Detail ................................................................................... 28
6.9. Appendix I: Simulation Validation Hypothesis Testing ...................................................... 30
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1. Business Overview
1.1. Paint Department Overview
The Home Depot’s paint department sells paint and accessories that help in the painting process.
Associates serve customers by processing paint orders at the desk, answering project questions,
and selling accessories in the aisle. D24 contributes to 20% of all THD transactions and $5.9
billion in annual revenue. Accessory sales, with an average profit margin of 55%, have the most
impact on department profit, while the margin for paint is only 30%. In addition to enhancing
department revenue, it is imperative to provide an excellent customer experience, as it is one of
THD’s core values.
1.2. Current Staffing Process
The Home Depot uses the Forecasting and Scheduling Tool (FaST) to allocate labor two weeks
in advance. FaST compares the previous four weeks of transaction data against the previous
year’s data to forecast the upcoming number of customer transactions. Each THD location is
unique due to the types of customers and service needs specific to that store. However, FaST
cannot reflect the intricacies of individual store activity. FaST assigns the same number of labor
hours to D24 for each transaction. A customer that only buys a paint brush and a customer that
orders a five gallon bucket of paint will be allocated the same amount of labor, even though these
customers clearly have different service needs.
1
2. Project Scope
2.1. Problem Overview
The Home Depot has an opportunity to improve customer service in D24. They have had
difficulty finding objective ways to quantify their level of customer service, so the senior design
team looked into two THD-defined key metrics:


The percentage of customers who wait for service at the paint desk longer than 2.5
minutes
The percentage of customers who renege, or leave without service
The Home Depot would like for these values to be as close to zero as possible. The senior design
team collected customer service data at three Atlanta THD stores to evaluate these metrics (see
Appendix A). As shown in Table 1, D24 is failing to realize its customer service goals.
Table 1: This table shows metrics shown by customer data collected by the senior design team at the Austell,
Buckhead, and Midtown stores over 8 days.
Number of Observed Customers
% Customers Waiting
% Customers
% Customers that
(Purchasing Paint)
> 2.5 Minutes
Waiting > 5 Minutes
Renege
706
18%
7%
3%
The biggest opportunity to improve service is to increase THD’s visibility into D24. They have
little data about the specific customers that shop in the department or the customer service they
provide to those customers. For example, in a recent attempt to improve D24 customer service,
the SSC added one full-time employee to a quarter of all stores, costing $12.5 million. The
assignment of additional labor was not based on data or specific scenarios, and the project was
unsuccessful.
The Home Depot’s insufficient staffing policies are perpetuated by this lack of department
visibility. As described in section 1.2, THD does not differentiate between customers when
assigning labor, which prevents their staffing levels from appropriately meeting customer needs.
While investigating long wait times and customer reneges, the senior design team addressed both
staffing policies and potential bottlenecks caused by paint mixing machines. Analysis suggests
that the current utilization of paint mixers in the stores does not cause a bottleneck (Appendix B),
suggesting that these metrics result from staffing policies.
2.2. Solution Overview
To improve customer service in D24, the senior design team explored various approaches to
allow THD to staff the department to closely match customer needs. The team defined a method
for modeling a store’s customers to understand when they are shopping, why they might leave
2
before receiving service, and what their service needs are. The team then defined associate
protocols, focusing on how defining associate roles, responding to the number of customers
waiting for desk service, and selling of additional accessories in the aisle can impact the
customer experience.
These solutions address not only customer service, but also revenue enhancement opportunities.
When a customer reneges or an associate misses the chance to sell additional accessories,
potential revenue is lost. By improving customer service, THD can drive customers sales to
increase store revenue.
The senior design team defined recommendations for improving customer service and
department profit and also designed a Paint Department Analysis Tool (PDAT) that will give
THD’s Labor Engineering and Allocation Team visibility into the effects of daily staffing
decisions in D24. This tool will help the corporate team to understand how changes in staffing
levels or service protocols will impact the customer experience prior to implementing those
changes in stores.
3
3. Design Approach
3.1.Data Analysis and Modeling
Using in-store data collection results, transaction and associate schedule information, and
associate time study data, the senior design team developed a number of models to support the
project solution, all of which are incorporated in an Arena simulation of the D24 customer
service processes. The simulation includes an understanding of how much time each customer
service task requires, who a store’s customers are, and how adjusting associate protocols can best
meet customer needs.
The senior design team used 300 hours of associate time study data provided by THD to define
107 unique customer service tasks in the paint department. The team then fit these tasks to
statistical distributions to model the amount of time required by each task. The simulation model
is based on these customer service tasks.
The next element to modeling D24 is understanding who is shopping in the department. The
senior design team modeled customer arrival times, customer renege decisions, and customer
profiles (Table 2).
Table 2: This table describes the methodology used to develop customer models.
Model
Data Used
Customer Arrival Model
 In-Store Data Collection

 Transaction Timestamps
Customer Renege Model
 In-Store Data Collection

Customer Profile Model
 Itemized Transaction Data

Methodology Used
Regression
Logistic Regression
Analysis of Transaction
Data
Based on this understanding of the customer, the model then evaluates the effects of associate
protocols on the customer experience, including changes in defining associate roles, responding
to the number of customers waiting in the desk queue, and selling accessories in the aisles (Table
3).
Table 3: This table describes the methodology used to develop associate protocol models.
Model
Data Used
Methodology Used
Associate Role Model
 Number of Staffed Associates
 Simulation
Desk Queue Length Threshold Model  Simulated Customer Service
 Simulation
Accessory Sales Model
 Kurt Salmon Retail Study
 Simulation
Finally, the model allows the user to evaluate the impact of increased or decreased labor on the
customer experience by making changes to the number of scheduled associates throughout a
given day.
4
3.2.Customer Arrival Model
In order to staff D24 properly, THD needs to understand when customers are arriving to the paint
desk. Currently, the only data they have is point-of-sale transaction timestamps, but this data
does not directly indicate when customers arrive to the desk.
Using transaction timestamps provided by THD and customer arrival times collected in Atlantaarea stores, the senior design team developed a regression equation to model arrival times from
transaction data. Figure 1 shows the cumulative D24 transactions and arrivals on October 10,
2013 at the Midtown location. Arrivals precede transactions by approximately 30 minutes.
Figure 1: This graph shows an example of arrivals that have been regressed from transaction data.
Using linear regression as explained in Appendix C, the number of arrivals in a fifteen-minute
interval t can be modeled from the number of transactions occurring in intervals t+1 and t+2.
To estimate the arrival time for one customer based on his transaction timestamp, the regression
coefficients were scaled to determine the likelihood that a given customer arrives in either one or
two fifteen-minute intervals prior to their transaction time. The arrivals are assumed to be
uniform in each time interval (Table 4).
Table 4: This table shows how long customers tend to stay in the store between their arrival to D24 and their
transaction timestamp.
Distribution of Time Between D24 Arrival and
Likelihood of this Distribution
Transaction Timestamp (Minutes)
49%
Uniform(15,30)
51%
Uniform(30,45)
5
3.3.Customer Renege Model
In addition to knowing when customers arrive to D24, THD must also understand under what
conditions a customer might leave prior to receiving service. During data collection, the senior
design team observed that a higher percentage of arriving customers reneged from the paint desk
when there were more customers ahead of them in the system. A logistic regression was used to
determine the likelihood that a customer reneges. Figure 2 shows the observed likelihood that a
customer reneged during data collection (in gray) and the expected likelihood that a customer
reneges (in orange) according to regression results.
Figure 2: When there are more customers ahead of an arriving customer, the arriving customer is more likely to
renege. The gray points represent the observed probability that an arriving customer reneges, while the orange line
shows the expected renege probabilities.
When collecting data, the senior design team ensured that no customer who was counted as
having reneged returned on the same day. Because there is no way to accurately estimate how
many of these customers returned on a later date to make the purchase, the renege model
assumes that no reneging customers return. Appendix D shows further calculations regarding this
model.
6
3.4.Customer Profile Model
Because different types of customers have unique service needs, the senior design team defined
customer profile types that are derived from transaction data. Table 5 shows the four types of
D24 customers that have been identified with approval from THD.
Table 5: This table describes the breakdown of customer profiles and the characteristics of each group.
Customer Profile
Description
Definition
Service Requirements
Professional
 Tend to buy large
 Purchase at least 3
 Require desk service
quantities of paint
paint items or at
 Do not require aisle
least 5 gallons of
 Have significant product
service
paint
knowledge
 Purchase a
“contractor pack”
accessory
Do-It-Yourself (DIY)  Have little knowledge of
 Purchase both paint  Require desk and aisle
With Accessories
paint products and what
and accessories
service
kinds of accessories their
 Do not meet the
 May agree to increase
projects may require
professional
purchase size when
 Need help with paint
requirements
associates suggest
selection
additional accessories
 Most important factor to
them is customer service1
Do-It-Yourself (DIY)  Similar to “DIY With
 Only purchase
 Only require desk
Without Accessories
Accessories” customers
paint
service
 Do not intend to purchase  Do not meet the
 May be willing to
acccessories (possibly due
professional
purchase accessories in
to lack of product
requirements
addition to paint
knowledge)
Aisle
 Only interested in
 Only purchase
 Only require aisle
purchasing accessories
accessories
service
 Do not affect the paint
 Do not meet the
desk queue
professional
requirements
3.5.Associate Role Model
Currently, THD does not staff specific associate roles within D24. This sometimes results in
associates waiting for customers to approach them rather than looking for meaningful customer
interaction. The senior design team recommends that THD defines two roles: desk and aisle
associates. These roles will serve as customer service home bases for associates. Desk associates
will mix paint and fulfill customer orders. Aisle associates will answer customers’ questions and
1
According to a study done by J.D. Power and Associates, 80% of home improvement customers request help
when looking for a product. Additionally, 27% of customers rate store staff as the most influential factor to their
satisfaction, more than any other factor.
7
sell paint accessories. Defining and implementing these two roles will help associates to provide
service that reflects changes in customer traffic throughout the day.
Every fifteen minutes, the simulation assigns these roles by allocating the appropriate number of
associates to work at the desk and then assigning all remaining staffed associates to the aisle. The
number of associates who will be staffed at the desk is equal to the maximum number of desk
associates who were helping customers at one time during the previous fifteen-minute period.
Also, there must always be at least one associate at the desk and the number of desk associates
can never exceed a store-defined value. Throughout the fifteen-minute interval, associates move
from the aisle to the desk if the number of customers in the desk queue reaches a certain
threshold.
Realistically, THD stores will not redefine associate roles every fifteen minutes. By
understanding the recommended desk queue threshold and how to react to changes in customer
traffic, store associates will be able to mirror the logic that is modeled in the simulation.
3.6.Desk Queue Length Threshold Model
The desk queue threshold is the maximum number of customers waiting in the desk queue before
an aisle associate will move to the desk to assist with customer service. The senior design team
simulated customer service in 12 stores over multiple months (Appendix E), comparing the
baseline state (with no associate role definition or desk queue threshold) against scenarios with
desk queue thresholds of 1, 2, 3, and 4. As shown in Figures 3 and 4, the percentage of customers
waiting more than 2.5 minutes for service or deciding to renege was lowest when the desk queue
threshold was equal to 1. This shows that THD is best meeting their customer service goals when
implementing a desk queue threshold of 1.
Figure 3: This figure compares the percentages of customers waiting > 2.5 min for each desk queue threshold
scenario.
8
Figure 4: This table compares the percentages of customers reneging for each desk queue threshold scenario.
However, there is a tradeoff between customer service and profit. Figure 5 shows the daily,
average incremental profit above the baseline state. This profit is realized from reduced reneges
and increased accessory sales. When the desk queue threshold is higher, more associates are able
to work in the aisle rather than moving to the desk. Therefore, they are able to sell more
accessories, increasing profit.
Figure 5: This table shows the average daily change in profit for each desk queue threshold scenario as compared
to the baseline, current state.
Due to THD’s commitment to providing high customer service, the senior design team
recommends that they define associate roles and implement a desk queue threshold of 1 despite
the lower incremental profit.
9
3.7.Accessory Sales Model
By staffing associates in the aisle, THD can significantly increase the number of accessory sales,
which have the highest impact on department profit. According to a Kurt Salmon retail study,
75% of customers will increase their purchase size (upsell) by 25% when an associate
encourages them to buy additional items. The accessory sales model calculates the number of
customers that will agree to upsell service from an aisle associate.
All DIY customers may be encouraged to purchase additional items, but the senior design team
believes that “DIY without accessories” customers will be the most likely to purchase additional
items. Desk associates should encourage customers to visit the aisles while their paint orders are
being fulfilled. Then, aisle associates will approach customers to offer service and suggest
additional items. In the simulation model, when a “DIY without accessories” customer receives
customer service from an aisle associate, there is a 75% chance that the customer will increase
their purchase size by 25% of the average “DIY without accessories” purchase price on the same
day in that store.
3.8.Staffing Adjustments Model
To provide customers with a more consistent customer experience, the senior design group
analyzed the current staffing levels throughout the day in D24. After running the simulation for
the baseline scenario, the customer service metrics were analyzed to strategically determine
where the number of staffed associates should be increased or decreased to increase upselling,
reduce reneging, and reduce unnecessary labor. Figure 6 gives an example of over- and understaffed times.
Figure 6: This table shows the average understaffed and overstaffed times over 4 fiscal months for the 20 THD
stores that were run to establish the best practice recommendations.
10
When running the comparison state in the PDAT, the model suggests labor changes from the
baseline state, according to the following logic:
Add an associate if:
 No one is staffed to work in the 15 minute interval.
 There are more than 5 “DIY without accessories” customers in the 15 minute interval, no
one is assigned to work in the aisle, and no customers have agreed to upsell.
 There is a renege in the 15 minute interval, no one is working in the aisle, and there are
currently fewer associates working than the specified maximum associates at desk
protocol.
Remove an associate if:
 The number of associates in the aisle is greater than the number of customers who agreed
to upsell.
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4. Deliverables
4.1.Paint Department Analysis Tool
The Paint Department Analysis Tool provides the SSC Labor Allocation Team with visibility
into daily paint department operations, giving them the ability to fundamentally change the way
they staff associates and interact with customers.
For a baseline scenario analysis, the user inputs past transaction data and the corresponding
associate schedule. The simulation model then outputs the following expected customer service
metrics and associate information in fifteen-minute intervals:





The number of customers that wait more than 2.5 minutes for service
The number of customers that renege
Associate role assignments
Associate idle time
Machine utilization rates
The user also has the option to adjust service protocols and the number of staffed associates for a
given day. After making these changes, the user runs the alternative scenario that compares
results from the updated service and labor selections against the results given in the baseline
scenario analysis. This comparison analysis uses the previously defined service protocols:



Associate role assignments (Section 3.5)
Desk queue length threshold (Section 3.6)
Additional accessory sales (Section 3.7)
The PDAT then shows the user how the selections made in the alternative scenario affected
customer reneges, wait times, the number of customers who agree to purchase additional
accessories, and the incremental change in D24 profit. Table 6 shows an example of PDAT
results on July 22, 2013 at the Plano, TX location.
Table 6: This table outlines the outputs given by the PDAT for both the baseline and comparison states.
Metric
Baseline State
Comparison State
5
4
# Customers Waiting > 2.5
Minutes
2
1
# Customers Reneging
0
7
# Customers Agreeing to Upsell
$57.66
Incremental Profit
–
12
By defining associate roles, setting the maximum desk queue threshold to 1, adding upsell logic,
and removing one associate from 8:30am to 11am, this comparison state created $57.66 in
incremental profit.
See Appendix F for more in depth instructions on using the PDAT.
4.2.Simulation
The PDAT’s output comes from an Arena simulation that models D24 customer service
processes. The model contains 107 distinct customer service tasks, each of which uses a
statistical distribution to determine service time allotted to that task. The senior design team fit
these distributions from over 300 hours of associate time study data provided by THD. Appendix
G shows a selection of these distributions. Figure 7 depicts the general flow of the simulation,
where each block represents a submodel in the customer service process.
Figure 7: This flowchart shows an overview of the simulation model. Customer orders are processed through the
model based on information provided by the PDAT.
The simulation model reads customer arrivals, associate schedules, and resource capacities from
the PDAT. Throughout the day, the number of staffed associates is updated to reflect the number
of staffed associates. For a more detailed explanation of the customer service process within the
simulation, see Appendix H.
This model focuses solely on customer service tasks in D24. Other tasks are also relevant, like
packdown (restocking) and cleaning, but THD prioritizes customer service. To ensure that
associates are staffed with appropriate time to complete these less time-sensitive tasks, the
simulation model also tracks and reports the amount of idle time that associates have throughout
the day. The Home Depot sets standards for how much time should be spent doing non-customer
service tasks. Therefore, tracking associate idle time will help THD to determine whether or not
the number of staffed associates allows for non-customer service tasks to be completed
effectively.
To validate the simulation model, the senior design team compared simulation output to the data
that the team collected throughout the course of the project. The FaST baseline simulation model
was used for validation because it reflects current state protocols (i.e. it does not include
associate role definition or require that the desk is never left unattended). Existing transaction
13
and associate schedule data were used to run the PDAT for each day to ensure that renege rates
and wait times were representative of what was observed during data collection.
For each day that the senior design team collected data, the simulation was run 10 times to get an
average representation of simulation output. The average results are shown in Table 7.
Table 7: This table shows the validation results from comparing observed customer service metrics against
simulation service metrics for 8 days.
Metric
Observed
Simulated
Difference
2%
3%
1%
% Customers that Renege
18%
24%
6%
% Customers Waiting > 2.5 Minutes
7%
8%
1%
% Customers Waiting > 5 Minutes
The senior design team used hypothesis testing to determine whether or not the mean of the
observed and simulated metrics were equal. The results show that the difference between the two
means is insignificant with a 95% confidence interval, validating the model. See Appendix I for
an explanation of these calculations.
14
5. Recommendations and Valuation
5.1.Recommendations
The senior design team ran the PDAT for 20 stores for multiple months in 2013 (Appendix E).
Each store and day was run for the following scenarios:

FaST Baseline Scenario: In this scenario, no associate roles were defined. This scenario
is represents the current state.

Desk Queue Threshold Scenario of {1, 2, 3, 4}: In these four scenarios, associate roles
were defined and tested with desk queue thresholds of 1, 2, 3, and 4.

Desk Queue Threshold Scenario of {1, 2, 3, 4} and Additional or Reduced Labor: In
these four scenarios, associate roles were defined and tested with desk queue thresholds
of 1, 2, 3, and 4, and labor levels were adjusted based on the algorithms in the Staffing
Adjustments Model (Section 3.8).
The senior design team recommends that THD defines associate roles, implements a maximum
desk queue threshold of 1, encourages the purchase of additional accessories, and continues
investigating opportunities for reallocating labor.
5.2.Valuation
Due to the intricacies of staffing individual shifts, the senior design team defined two different
project values: one without labor modifications and one with labor modifications. The project
value without labor modifications is the more conservative estimate of value, while the project
value with labor modifications is the more optimistic scenario and can only be achieved if shifts
could be perfectly in sync with the labor modification suggestions. The Home Depot will be able
to use the average suggested labor changes to understand where they are currently overstaffed
and understaffed. This will allow them to staff associates to create a more consistent customer
experience, improving customer service and profitability.
15
The project recommendations without labor modifications provide THD with an annual
incremental profit (pre-tax) of $10.4 million (Table 8). Additionally, the improved customer
services metrics are outlined in Table 9.
Table 8: This table shows the itemized buckets by sales volume and the annual incremental profit gained by
implementing project recommendations without labor modifications.
Store Sales
# of Stores
Avg Daily ∆
Avg Daily
Avg Daily ∆
Annual
Volume
Accessory Sales
∆ Renege
Profit
Incremental
Profit
606
$6.72
$2.02
$8.75
$1.9 M
A ($0-25M)
924
$12.29
$0.03
$12.32
$4.1 M
B ($25-40M)
417
$25.14
$1.44
$26.58
$4.0 M
C ($40-70M)
36
$25.56
$2.03
$27.59
$0.4 M
D ($70-90M)
1983
$13.53
$0.97
$14.50
$10.4 M
Total
Table 9: This table shows improved customer service metrics as a result of implementing project recommendations.
Store Sales Volume
Reduction in Percentage of
Reduction in Percentage of
Customers that Renege
Customers Waiting > 2.5 Minutes
0.3%
2.2%
A
0.04%
2.2%
B
0.1%
2.1%
C
0.2%
3.2%
D
The project recommendations with labor modifications provide THD with an annual incremental
profit (pre-tax) of $29.1 million (Table 10). Furthermore, the customer service metrics still
improve despite the overall reduction in staffed hours (Table 11).
Table 10: This table shows the itemized buckets of profit and the annual incremental profit gained by implementing
project recommendations with labor modifications.
Store
# of Stores
Avg Daily ∆
Avg
Avg Daily
Avg Daily ∆
Annual
Volume
Accessory Sales
Daily ∆
∆ Staffing
Profit
Incremental
Renege
Profit
606
$5.68
$12.56
$8.88
$27.12
$6.0 M
A
924
$10.47
$9.44
$19.06
$38.96
$13.1 M
B
417
$21.18
$12.76
$24.94
$58.88
$8.9 M
C
36
$20.34
$12.40
$48.41
$81.16
$1.1 M
D
1983
$11.43
$11.14
$17.72
$40.29
$29.1 M
Total
16
Table 11: This table shows improved customer service metrics as a result of implementing project
recommendations.
Store Volume
Reduction in Percentage of
Reduction in Percentage of
Customers that Renege
Customers Waiting > 2.5 Minutes
2.1%
2.1%
A
1.3%
1.0%
B
0.1%
0.9%
C
0.8%
1.7%
D
17
6. Appendices
6.1.Appendix A: Customer and Mixer Data Collection Procedure
The senior design team collected data regarding customer arrivals, traffic, and service times as
well as mixer utilization times at three Atlanta-area THD stores. Table A1 details the locations
and dates of data collection.
Table A1: The table depicts the location and dates of data collection in the Atlanta area.
Store Location
Austell
Buckhead
Midtown
Customer Collection
Dates
8/24/2013
9/3/2013, 9/28/2013,
10/10/2013
9/13/2013, 9/14/2013,
10/1/2013, 10/10/2013
Mixer Collection Dates
Store Volume Bucket
9/28/2013
B
C
9/13/2013, 9/14/2013,
10/1/2013
C
On these dates, the team collected timestamp data to record:
Customer Data
● Customer arrivals
● Customer wait times
● Customer service times
● Customer reneges
○ A brief physical description of each customer was also recorded to ensure that
customers who reneged did not return
Mixer Utilization Data
● Number of mixers in use
18
6.2.Appendix B: Mixer Utilization Data Analysis
The following tables depict the utilization of paint mixers in fifteen-minute intervals (Tables B1
and B2).
Table B1: Mixer utilization for the Midtown THD location on September 14, 2013.
Midtown Resource Utilizations - Saturday, 9/14/13
Time
Bucket
10:15 AM
1
Gallon
0.00%
5
Gallon
0.00%
Multi
Vol
0.00%
Queue
Tint
0.00%
0.00%
10:30 AM
28.06%
0.00%
0.00%
0.00%
8.06%
10:45 AM
18.83%
0.00%
0.00%
0.00%
21.89%
11:00 AM
13.96%
10.89%
0.00%
0.00%
9.28%
11:15 AM
7.19%
0.00%
0.00%
6.00%
18.22%
11:30 AM
25.63%
0.00%
0.00%
39.22%
13.39%
11:45 AM
36.43%
0.00%
0.00%
64.00%
31.67%
12:00 PM
11.20%
0.00%
31.67%
54.89%
57.61%
12:15 PM
35.50%
0.00%
23.00%
34.22%
44.33%
12:30 PM
27.72%
24.78%
0.00%
11.22%
63.28%
12:45 PM
10.15%
16.50%
0.00%
0.00%
6.78%
1:00 PM
34.22%
43.61%
0.00%
20.78%
46.17%
1:15 PM
15.43%
10.89%
0.00%
7.22%
17.78%
1:30 PM
22.13%
0.00%
0.00%
21.11%
28.94%
1:45 PM
15.98%
0.00%
0.00%
0.00%
15.22%
2:00 PM
12.41%
0.00%
0.00%
10.56%
11.11%
2:15 PM
11.93%
13.50%
0.00%
49.33%
10.83%
2:30 PM
15.48%
0.00%
0.00%
14.67%
2.39%
2:45 PM
7.04%
0.00%
0.00%
11.56%
1.28%
3:00 PM
0.00%
0.00%
0.00%
0.00%
0.00%
3:15 PM
0.00%
0.00%
0.00%
0.00%
0.00%
3:30 PM
0.00%
0.00%
23.33%
0.00%
0.00%
3:45 PM
29.56%
0.00%
96.11%
0.00%
0.00%
4:00 PM
1.85%
0.00%
77.89%
0.00%
0.00%
4:15 PM
52.43%
0.00%
54.89%
0.00%
0.00%
4:30 PM
53.43%
0.00%
0.00%
0.00%
0.00%
4:45 PM
18.06%
0.00%
0.00%
0.00%
0.00%
5:00 PM
13.20%
0.00%
0.00%
0.00%
0.00%
5:15 PM
17.43%
0.00%
0.00%
0.00%
0.00%
5:30 PM
4.65%
0.00%
0.00%
0.00%
0.00%
Average
18.00%
4.01%
10.23%
11.49%
13.61%
Max
53.43%
43.61%
96.11%
64.00%
63.28%
19
Table B2: Mixer utilization for the Buckhead THD location on September 28, 2013.
Buckhead Resource Utilizations - Saturday, 9/28/13
Time Bucket
1 Gallon
5 Gallon
Multi Vol
Queue
Tinter
8:00 AM
0.00%
0.00%
0.00%
0.00%
69.22%
8:15 AM
0.00%
0.00%
0.00%
0.00%
0.00%
8:30 AM
0.00%
0.00%
0.00%
0.00%
0.00%
8:45 AM
0.59%
0.00%
0.00%
11.67%
5.33%
9:00 AM
9.28%
0.00%
0.00%
4.22%
6.48%
9:15 AM
6.80%
0.00%
25.22%
25.67%
3.26%
9:30 AM
46.54%
16.28%
48.78%
7.33%
50.89%
9:45 AM
13.50%
19.17%
24.11%
71.56%
18.26%
10:00 AM
6.13%
0.00%
23.11%
7.11%
15.11%
10:15 AM
14.15%
0.00%
0.00%
3.33%
7.11%
10:30 AM
16.89%
0.00%
0.00%
41.56%
20.41%
10:45 AM
34.80%
0.00%
0.00%
39.33%
11.59%
11:00 AM
15.30%
0.00%
17.89%
16.00%
2.26%
11:15 AM
0.00%
0.00%
0.00%
10.22%
5.93%
11:30 AM
29.19%
0.00%
0.00%
12.78%
3.85%
11:45 AM
0.00%
0.00%
0.00%
4.78%
5.67%
12:00 PM
0.00%
0.00%
0.00%
16.22%
3.30%
12:15 PM
0.00%
0.00%
0.00%
0.00%
0.00%
12:30 PM
0.00%
0.00%
24.44%
34.22%
20.33%
12:45 PM
0.00%
0.00%
0.00%
35.11%
1.48%
1:00 PM
10.30%
0.00%
0.00%
0.00%
28.37%
1:15 PM
14.81%
0.00%
0.00%
19.11%
1.93%
1:30 PM
43.37%
0.00%
0.00%
27.89%
31.70%
1:45 PM
43.83%
0.00%
0.00%
25.22%
18.26%
2:00 PM
22.35%
0.00%
0.00%
16.44%
13.67%
2:15 PM
6.85%
0.00%
0.00%
0.00%
0.00%
2:30 PM
0.00%
0.00%
0.00%
0.00%
0.00%
2:45 PM
1.41%
0.00%
0.00%
0.00%
0.00%
3:00 PM
27.69%
0.00%
0.00%
0.00%
0.00%
3:15 PM
24.91%
0.00%
0.00%
0.00%
14.59%
3:30 PM
6.87%
0.00%
0.00%
18.67%
33.33%
3:45 PM
16.81%
0.00%
0.00%
0.00%
0.00%
4:00 PM
3.19%
0.00%
0.00%
0.00%
29.56%
4:15 PM
27.98%
0.00%
23.22%
0.00%
0.00%
4:30 PM
24.52%
0.00%
0.00%
0.00%
0.00%
4:45 PM
0.00%
0.00%
0.00%
0.00%
0.00%
5:00 PM
0.00%
0.00%
0.00%
0.00%
28.63%
5:15 PM
0.00%
0.00%
0.00%
0.00%
16.41%
Average
12.32%
0.93%
4.92%
11.80%
12.29%
Max
46.54%
19.17%
48.78%
71.56%
69.22%
20
6.3.Appendix C: Customer Arrival Regression
Customer arrival times are modeled from a regression equation using the senior design team’s
observed D24 arrival times and existing timestamp transaction data provided by THD. This
model divides the day into fifteen-minute intervals and places customer arrivals and transactions
in the appropriate intervals. The regression equation defines the following variables:
𝑦𝑡 = the number of arrivals in a 15-minute time interval t
𝑥𝑡 = the number of transactions that occur in interval t
Because all paint customers arrive at the cashier after arriving at the paint desk, 𝑦𝑡 can be
estimated through linear regression:
𝑦𝑡 = 0.471𝑥𝑡+1 + 0.498𝑥𝑡+2
A study from the Columbia Business School2 states that the sum of the coefficients should be
positive and less than or equal to 1. The sum of the coefficients in the customer arrival regression
equals 0.969. Because of the potential error in data collection as well as the variability in human
behavior, the team scaled the coefficients in order to account for all customer arrivals.
𝛽1
0.471
=
= 49%
𝛽1 + 𝛽2 0.969
𝛽2
0.498
=
= 51%
𝛽1 + 𝛽2 0.969
Additionally, it can be assumed that customers arrive with a uniform distribution within a given
15-minute interval (Table C1).
Table C1: The table explains the probability of when a customer will arrive at the Point-of-Sale after arrival at the
paint desk.
Distribution of Time Between D24 Arrival and Likelihood of this Distribution
Transaction Timestamp (Minutes)
49%
Uniform(15,30)
51%
Uniform(30,45)
6.4.Appendix D: Customer Renege Model
2
Lu, Yina, Andres Musalem, Marcelo Olivares, and Ariel Schilkrut. Measuring the Effect of Queues on
Customer Purchases. Columbia Business School, 6 Nov. 2012.
21
This model determines the likelihood that a customer arriving to the paint desk decides to renege
based on the number of customers ahead of him in the system. Some customer reneges that
occurred during data collection happened when associates left the paint desk unattended. The
senior design team recommends that in the future, associates never leave the paint desk
unattended, preventing this type of renege from occurring. Therefore, of the customers who
reneged during data collection, only those who reneged when an associate was at the desk are
included in the following calculations. A logistic regression was used to calculate the probability
that a customer will renege given the number of customers ahead of him in the system at that
time.
The expected probability that an arriving customer reneges given the number of customers ahead
of them in the system at that time, n, is calculated using a logistic regression shown in Figure D1.
Figure D1: The logistic regression equation used to calculate the probability that a customer will renege.
Table D1 shows the observed and calculated probabilities that a customer will renege.
Table D1: Observed and calculated renege probabilities
Customers at Desk Ahead
Number of
of Arriving Customer
Observed
Customers
156
0
166
1
152
2
121
3
53
4
58
≥5
706
Total
Observed P(Arriving
Customer Reneges)
Expected P(Arriving
Customer Reneges)
0.0%
1.8%
4.0%
5.0%
1.9%
6.9%
2.8%
0.0%
2.2%
2.8%
3.4%
4.3%
5.3%
The simulation model uses the expected likelihood that an arriving customer reneges to assign a
renege decision to each customer based on the number of customers ahead of them at the paint
desk. Reneging customers do not receive customer service or make a purchase.
22
6.5.Appendix E: Recommendation and Valuation Data
Table E1: These stores/dates were run to define recommendations.
Store
Volume
A
0424
A
1924
A
0816
A
4031
A
0144
A
1119
B
0539
B
2740
B
0366
B
3312
B
6301
C
8444
Months
January, April, July, October
April, October
April, October
January
January, April, July, October
April
April, October
April, October
January, April, July, October
January
January
April, October
Table E2: These stores/dates were run to define project value.
Store
Volume
A
0424
A
1924
A
0816
A
4031
A
0144
A
1948
A
1119
B
0539
B
2740
B
0366
B
3312
B
0550
B
6001
B
6301
B
1803
C
8444
C
0245
C
1234
C
6306
D
2667
Months
January, April, July, October
April, July, October
January, April, July, October
January
January, April, July, October
January, April, July, October
April, July, October
April, July, October
April, July, October
January, April, July, October
January
January, April, July, October
January, April, July, October
January, April, July, October
January, April, July, October
April, July, October
January, April, July, October
January, April, July, October
January, April, July, October
January, April, July, October
23
6.6.Appendix F: PDAT User Guide
Instructions
Select the Instructions tab.
Enter the store number and date to be analyzed. Enter the file location for the current state
simulation, future state simulation, and data file. If the data file does not already exist, the PDAT
will create the required file at the defined location.
In both Arena simulations (current and future), navigate to Advanced Transfer in the project bar,
then click File. Change the file extension to match the location used for the data file.
Input Transactions
Select the INPUT Transactions tab.
24
Clear any prior entries. Insert the correct data into columns A-F. Columns G and F will be
automatically generated when the program is run.
Input Schedule
Select the INPUT Schedule tab.
Clear any prior entries and input schedule data into columns A-K.
25
Staffing Recommendations
Select the Staffing Recommendations tab.
Type appropriate value for both service protocol options and machine options under the “Current
State”, and then click “Run FaST Benchmark.”
Type appropriate value for both service protocol options and machine options under the
“Comparison Scenario”, and then click “Run Comparison Scenario.”
26
6.7.Appendix G: Associate Task Statistical Distributions
The senior design team used time study data provided by THD to identify statistical distributions
for each of the 107 tasks in the customer service process. Table G1 shows examples of these
distributions (in minutes).
Table G1: A sample of associate task distributions broken down by item and task.
Task
Distribution
LOGN(0.285, 0.193)
Gallon: Queue Manager
ERLA(0.367, 2)
Gallon: Retrieve Paint Base for Customer
LOGN(0.178, 0.151)
Sample: Tint Paint
WEIB(0.323, 1.57)
Quart: Tint Paint
2 * BETA(0.957, 4.67)
Gallon: Tint Paint
EXPO(0.777)
5 Gallon: Tint Paint
ERLA(0.125, 2)
Gallon: Remove Lid
WEIB(0.165, 1.34)
Gallon: Place Base in Mixer
EXPO(0.303)
Gallon: Dab/Dry Sample on Can Lid
3.4 * BETA(0.47, 4.89)
Gallon: Give Mixed Paint to Customer
27
6.8.Appendix H: Simulation Flow Detail
Enter D24
Two entities are created to start the simulation. The first entity moves through the Read Number
of Resources block, where the number of resources and user-defined inputs (Queue Managers,
Tinters, One Gallon Mixers, Five Gallon Mixers, Multi-Volume Mixers, Maximum Capacity of
Employees and Minimum Customer Queue Threshold) are read. From there, the entity is
duplicated twice and each enters two separate loops.
The first loop reads in the associate data (schedule time and number of associates) and then
updates the schedule. In the current state model, there are no defined associate roles, while in the
future state model, both desk and aisle roles are defined and scheduled separately. The associates
are staffed at the desk based on the maximum number of associates needed during the past 15
minute interval. The remaining associates will be staffed in the aisles. The second loop is used to
check the utilization of all of the resources every minute. This value is used to record the average
utilization every fifteen minutes, which shows overall idle time. All of this data is then output to
the PDAT for analysis.
The second entity, the order entity, moves through the Read Customer Information block, where
all of the transaction data and user-defined values from the PDAT (Arrival Time, Transaction
Number, Customer Type, Number of Samples, Quarts, Gallons, Five Gallons, and Accessories)
are read. The customer entity is held at a delay block until that customer’s arrival time. From
there, a customer number is assigned and the order enters D24.
Need Paint
If there are any paint items, the order is directed to the desk and enters the desk system. If there
are no paint items, then the order is directed to the aisle.
Renege Decision
The customer may decide to renege based on probabilities defined in the Customer Renege
Model. Once the customer enters the desk system, if there are no associates at the desk, the
customer will renege and go to Leave D24. If associates are present, the probability that the
incoming customer will renege increases according to the number of customers in the queue. If a
customer does not renege, he will go to Process Order.
Process Order
The Process Order submodel is the first process at the paint desk. In the current model, the order
is processed based on available associates. If there are no available associates, the customer will
enter the queue. In the future state model, if the number of customers in the desk queue reaches
the desk queue threshold, an associate will move from the aisle to the desk. The order is then
28
input into the system by seizing a queue manager. The distribution used at the Process Order
block is a weighted average based on processing time from all paint items in the order.
Retrieve Decision
This submodel takes into account the maximum number of paint base items that an associate can
carry at one time. An associate can carry 4 samples, 2 quarts, 2 gallons, or 1 five gallon per trip.
Tint Decision
A temporary variable is assigned to the order based on the total number of each volume of paint
item in the order. Every item is processed (tinted) with a distribution specific on its volume. The
temporary variable is reduced by one as each item is processed. The order will wait in the queue
if all of the tinters are being utilized.
Mix Decision
Similar to the Tint Decision submodel, the Mix Decision submodel uses a loop to reduce the total
number of paint items by one until all items are processed. If one of the normal mixers (1 gallon
or 5 gallon) is available, the paint item will be mixed in that resource for 2.5 minutes. If all
normal mixers are busy and the multi-volume mixer is available, the paint item will be mixed in
the multi-volume mixer for 2 minutes. The multi-volume mixer can process 12 samples, 9 quarts,
4 gallons, or 1 five gallon paint item.
Counter Service Logic
This submodel includes all of the task distributions that occur at the desk counter (e.g. testing the
paint color and giving it to the customer). Once the interaction between the associate and the
customer is complete, the associate is released and the total order service time is recorded. All
customers leaving the paint desk enter the Aisle Logic submodel.
Aisle Logic
“DIY with accessories” and “aisle” customers receive desk service from associates in the Aisle
Logic submodel. In the future state model, if an aisle associate is available when a “DIY without
accessories” customer finishes his service at the desk, the aisle associate will encourage the
“DIY without accessories” customer to purchase additional items. Professional customers will go
to Leave D24 because they do not require aisle service. If no associates are available, DIY
customers will also go to Leave D24.
Leave D24
In this submodel, the customer’s statistics are output for PDAT analysis. Then, all services are
complete and the customer leaves the paint department.
29
6.9.Appendix I: Simulation Validation Hypothesis Testing
The senior design team compared customer service metrics that were observed during in-store
data collection against simulated metrics for the same stores and dates. Tested metrics include
the percentage of customers deciding to renege, the percentage of customers waiting more than
2.5 minutes for desk service, and the percentage of customers waiting more than 5 minutes for
desk service.
The average difference between simulated and observed metrics is shown in Table I1. Ideally,
each of these differences would be equal to 0. However, no matter how accurate the simulation
model is, there will always be some discrepancies due to variability in human behavior.
Table I1: The observed and simulated service metric statistics.
Statistic
% Customers who
% Customers who
Renege
Wait > 2.5 Minutes
2%
18%
Observed Mean
3%
24%
Simulated Mean
Difference Between
1%
6%
Simulated and
Observed Means
% Customers who Wait
> 5 Minutes
7%
8%
1%
To validate the model, the senior design team used hypothesis testing to compare observed
customer service metrics against simulated metrics. The initial hypothesis for each metric was
that the observed metric is equal to the simulated metric. Table I2 shows hypothesis testing
calculations for each metric. Each of the metrics failed to reject the initial hypothesis that the
observed and simulated metrics were equal, thus validating the model.
30
Table I2: Two-tailed hypothesis testing results used to validate the simulation.
Statistic
% Customers who
% Customers who
Renege
Wait > 2.5 Minutes
The difference between
The difference between
observed and simulated
observed and simulated
H0
means is not statistically
means is not statistically
significant.
significant.
2%
18%
Observed Mean
Observed Standard
2.6%
8.9%
Deviation
Number of Observed
8
8
Dates
3%
24%
Simulated Mean
Simulated Standard
1.2%
10.9%
Deviation
Number of Simulated
8
8
Dates
0.0203
0.0995
Sp
-0.907
-1.167
T (observed)
T statistic (2-tailed, df
2.145
2.145
= 14, α = 0.05)
Is T (observe) > T
No
No
statistic?
Fail to Reject H0
Fail to Reject H0
Result
95% Confidence
-3.1%, 1.3%
-16.5%, 4.9%
Interval
% Customers who Wait
> 5 Minutes
The difference between
observed and simulated
means is not statistically
significant.
7%
3.5%
8
8%
8.3%
8
0.0635
-0.431
2.145
No
Fail to Reject H0
-8.2%, 5.4%
31