Transcript Process Improvement for the H.E.B. Retail Support Center

“Process Improvement for the
H.E.B. Retail Support Center”
by Daniel Martinez and John Stovall
Industrial Engineering
Ingram School of Engineering
Capstone Design Project
Supervisor: Dr. Jesus Jimenez
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Agenda
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Background/Terminology
Objectives
Simulation Overview/Deliverables
Experimental Design and Analysis
Suggestions for Improvement
Future Recommendations
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Background
 A Retail Support Center is a warehouse that is stocked with
inventory to be redistributed to wholesalers, retailers, or
directly to consumers.
 These types of distribution centers are the foundation for
supply networks, involving a wide industry of operations
such as material handling and logistics.
 According to the U.S. Department of Commerce and
Bureau of Labor Statistics, material handling and logistics
equipment and systems in America exceeds $156 billion per
year, and producers employ in excess of 700,000 workers.
Source: Material Handling Institute of America (http://www.mhi.org)
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Company Background
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Formally known as H.E. Butt Grocery Company.
Started in Kerrville, Texas with one family-owned store in 1905.
Had a total revenue surpassing $20 billion USD in 2013.
Supplies families all over Texas and Mexico in 155 communities,
with more than 340 stores and 76,000 employees.
 Named Retailer of the Year in 2010 by Progressive Grocer
Magazine.
 Ranked 15th in America’s Largest Private Companies by Forbes.
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H-E-B Logistics
 5 million square feet of
distribution/warehouse space.
 4,000 workers employed at
several sites.
 Approximately 40 store deliveries
per week.
 H-E-B forecasts its replenishment
rates to minimize inventory.
Source: Hendricks D. (May 15, 2012) “H-E-B shelved route as
logistics costs rose” San Antonio Express News [Online]
MySanAntonio.com
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San Marcos Retail Support Center
(SM-RSC)
Purpose: Slow-Moving Merchandise
Size: Approx. 400K sq. ft.
System Configuration
Existing Facilities Layout:
PTP
 ~200 store orders per
day
 60-80 shipments per
day
 33 docks
 Three Order
PTB
Fulfillment Methods:
PTL
Loading Docks
PTP
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 Pick to Pallet (PTP)
 Pick to Belt (PTB)
 Pick To Light (PTL)
Terminology
 Component of the Pick-toPallet order fulfillment.
 A “Selector” is the human
resource who:
1.
2.
3.
Retrieves a work assignment
(pallets) from the computer
system
Pulls the pallets from the
racks, and
Transport the pallets to the
loading docks supported by
power equipment.
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Terminology
 The Pick-to-Belt order
fulfillment:
 Automates the transport of
order requirements from
storage to shipping
locations.
 Uses conveyors and
sortation systems.
 Works well for full case
picking orders that will be
palletized for shipment.
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Terminology
 The Pick-to-Light order
fulfillment:
 Begins with reading a
barcode on the carton or
tote.
 Shows the amount of
product to pick from each
location by using a light
system.
 Stands out as one of the
quickest and most efficient
order picking methods.
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Terminology
 A “Loader” is the human
resource who:
1.
Wraps the order-filled
pallets, and
2. Loads all work
assignments into the
truck supported by
machinery equipment.
 Component of docking
operations.
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Problem Statement
 Pallets are currently experiencing long waiting times
at the loading docks.
 The delay is even longer in some cases.
 Deterring Loader Utilization
 Poses a safety hazard for workers
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Objectives
 PLAN: Understand shipping operations and collect data.
 DO: Simulate the current baseline configuration for the
shipping operations using WITNESS simulation software.
 CHECK: Analyze the simulation output to identify any
bottlenecks or human resource allocation problems in the
baseline configuration.
 ACT: Provide recommendations through statistical analysis.
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Why Simulation?
 A simulation: imitation of the operation of a realworld process or system over time:
 Involves generation of an artificial history of a system.
 Observes that history and draws inferences about
system characteristics.
 Explore new policies without disrupting ongoing
operations
 Performs analysis
 Provides animation
Simulation Methodology
Formulate The Problem
Collect Data and Construct
Conceptual Model
Is the Conceptual Model
Valid?
No
Yes
Source: Averill M. Law, Proceedings of the 2003 Winter
Simulation Conference - “How to Conduct A Successful
Simulation Study”
Program the model
Is the Programmed Model
Valid?
No
Yes
Design, Conduct, and Analyze
Experiments
Document and Present the
Simulation Results
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Simulation Flowchart
1
2
3
• Pre-Determined Schedule enters the system
• Selector downloads Assignment on RF scanner
• Assignments are picked in a FIFO rule of order
• Selector travels to appropriate picking location
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• All items in the Store order are picked and
palletized
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• Completed assignment is transported to
corresponding loading dock
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• Loader completes the operation by loading pallets
into truck
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Scope of Simulation Model
 Simulation Constructs:
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7 Selection Areas
2198 Work Assignments (integrated with Microsoft Excel)
33 Loading Docks
113 Selectors
11 Loaders
80 Truck Shipments
 Simulation Responses:
 Labor Utilization Rate
 ∑ Average Cycle Time of Assignments
 ∑ Weighted-Average Queue Time of the Loading Docks
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WITNESS Simulation Model:
Demo
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Model 1: Baseline Model
 The baseline model represents the current system
configuration.
 The main objective is to set baseline metrics from the
current configuration, which enables further system
improvements.
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Baseline Model Results
600
500
300
200
Work Assignment Analysis
by Category
100
# Pallets
Total Out
0
Avg. Wait Time
In the Baseline: 11 Loaders
113 Selectors
Labor Statistics
Worker
700
1450
600
1400
500
1350
400
1300
300
1250
200
1200
100
1150
0
% Busy
Loaders
11.63
Selectors
71.43
PTP
PTP – EM
PTP – GM
Avg. WIP
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PTP – LVD
PTP – Building 5
PTB
Avg. Cycle Time
PTL
1100
Time (Min)
400
Time (Min)
100
90
80
70
60
50
40
30
20
10
0
Door 206
Door 207
Door 208
Door 211
Door 212
Door 213
Door 214
Door 215
Door 216
Door 217
Door 218
Door 223
Door 224
Door 225
Door 226
Door 227
Door 228
Door 234
Door 235
Door 236
Door 237
Door 238
Door 239
Door 240
Door 242
Door 243
Door 244
Door 245
Door 246
Door 247
Door 248
Door 249
Door 250
# Pallets
Dock Analysis
Model 2: Selector’s Model
 The Selector’s model was created after analyzing
results from the Baseline Model.
 The main objective is to dedicate an optimal number
of selectors into each selection area
 WITNESS Experimenter was used to minimize the
average time that each pallet spends at the loading
docks.
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WITNESS Experimenter
 Decision Variables:
X1 = Number of Selectors in PTP
X2 = Number of Selectors in PTB
X3 = Number of Selectors in PTL
X4 = Number of Selectors in Bdg5
X5 = Number of Selectors in EM
X6 = Number of Selectors in GM
X7 = Number of Selectors in LVD
 Objective Function:
Minimize total average time in buffer for each pallet
 Constraints:
0 ≤ Total Number of Selectors ≤ 130
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WITNESS Experimenter
Sample
Run 1:
Sample
Run 2:
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Selector’s Model Results
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Selector’s Model Results
Labor Statistics
Worker
% Busy
Loaders
56.13
Selector PTP
21.94
Selector PTP -GM
63.94
Selector PTP -EM
28.77
Selector PTP –LVD
64.68
Selector PTP –Bdg5
34.66
Selector PTB
69.06
Selector PTL
57.07
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Model 3: Loader’s Model
 The Loader’s model was created after analyzing
results in Experimenter from the Selector’s Model.
 The main objective is to optimize number of loaders
in the docking operations.
 WITNESS Experimenter was used to minimize
minimize the summation of the weighted-average
times in the loading dock buffer zone.
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Loader’s Model Results
600
80
500
400
60
300
40
200
20
Time (Min)
100
100
Door 250
Door 248
Door 246
Door 242
Door 244
Door 239
Door 237
Door 235
Work Assignment Analysis
by Category
Avg Time (min)
# Pallets
In the Baseline: 15 Loaders
113 Selectors
Labor Statistics
Worker
% Busy
Loaders
38.20
Selectors
63.98
700
600
500
400
300
200
100
0
1450
1400
1350
1300
1250
1200
1150
1100
PTP PTP – PTP – PTP – PTP – PTB
EM GM LVD Bdg 5
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Avg. WIP
PTL
Avg. Cycle Time
Time (Min)
Total Out
Door 228
Door 226
Door 224
Door 218
Door 216
Door 212
Door 214
0
Door 208
0
Door 206
# Pallets
Dock Analysis
Summary of Results
Baseline Model
Total Average Time in
Buffer
7039.11
Total Average Cycle
Time
9493.91
Selector Utilization
71.43%
Loader Utilization
11.63%
Loader’s Model
Total Average Time in
Buffer
7023.60
Total Average Cycle
Time
9353.2
Selector Utilization
63.98%
Selector’s Model
Total Average Time in
Buffer
3679.17
Total Average Cycle
Time
8371.72
Selector Utilization
56.13%
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Loader Utilization
38.20%
Recommendations
 Generalize Selectors
 Increase Loaders
 Re-validate the model
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Future
 Acquire additional data from high volume days i.e. holiday
seasons
 Model the selection process in detail
 Expand the model to replicate weekly operations
 Explore new areas of improvement through Scheduling
Theory
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Lessons Learned
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Working in a professional team environment
Value of integrity
Time management
Practice of humility
How to conduct a successful simulation study
Exploring methods of improvement through designs
of experiments
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Q&A & Acknowledgements
Q&A Session
Special thanks to:
*Sponsor of problem and data
language
*Sponsor of WITNESS simulation
Would like to become a sponsor of a IE capstone design project?
If Yes, please contact Dr. Jesus Jimenez ([email protected]) or Dr. Stan
McClellan ([email protected]).
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