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“Process Improvement for the H.E.B. Retail Support Center” by Daniel Martinez and John Stovall Industrial Engineering Ingram School of Engineering Capstone.

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Presentation on theme: "“Process Improvement for the H.E.B. Retail Support Center” by Daniel Martinez and John Stovall Industrial Engineering Ingram School of Engineering Capstone."— Presentation transcript:

1 “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 1

2  Background/Terminology  Objectives  Simulation Overview/Deliverables  Experimental Design and Analysis  Suggestions for Improvement  Future Recommendations Agenda 2

3  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) 3 Background

4  Formally known as H.E. Butt Grocery Company.  Started in Kerrville, Texas with one family-owned store in  Had a total revenue surpassing $20 billion USD in  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 15 th in America’s Largest Private Companies by Forbes. Company Background 4

5  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 H-E-B Logistics San Marcos Retail Support Center (SM-RSC) Purpose: Slow-Moving Merchandise Size: Approx. 400K sq. ft. 5

6 System Configuration 6  ~200 store orders per day  shipments per day  33 docks  Three Order Fulfillment Methods:  Pick to Pallet (PTP)  Pick to Belt (PTB)  Pick To Light (PTL) Existing Facilities Layout: Loading Docks PTL PTB PTP

7 Terminology  Component of the Pick-to- Pallet order fulfillment.  A “Selector” is the human resource who: 1.Retrieves a work assignment (pallets) from the computer system 2.Pulls the pallets from the racks, and 3.Transport the pallets to the loading docks supported by power equipment. 7

8 Terminology 8  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.

9 Terminology 9  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.

10 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. 10

11  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 Problem Statement 11

12  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. Objectives 12

13  A simulation: imitation of the operation of a real- world 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 Why Simulation?

14 Simulation Methodology 14 Formulate The Problem Collect Data and Construct Conceptual Model Is the Conceptual Model Valid? Program the model Is the Programmed Model Valid? Design, Conduct, and Analyze Experiments Document and Present the Simulation Results Source: Averill M. Law, Proceedings of the 2003 Winter Simulation Conference - “How to Conduct A Successful Simulation Study” Yes No

15 Simulation Flowchart 15 1 Pre-Determined Schedule enters the system 2 Selector downloads Assignment on RF scanner Assignments are picked in a FIFO rule of order 3 Selector travels to appropriate picking location 4 All items in the Store order are picked and palletized 5 Completed assignment is transported to corresponding loading dock 6 Loader completes the operation by loading pallets into truck

16  Simulation Constructs:  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 16 Scope of Simulation Model

17 WITNESS Simulation Model: Demo 17

18  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. 18 Model 1: Baseline Model

19 19 Baseline Model Results Labor Statistics Worker% Busy Loaders11.63 Selectors71.43 In the Baseline: 11 Loaders 113 Selectors

20  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. 20 Model 2: Selector’s Model

21  Decision Variables: X 1 = Number of Selectors in PTP X 2 = Number of Selectors in PTB X 3 = Number of Selectors in PTL X 4 = Number of Selectors in Bdg5 X 5 = Number of Selectors in EM X 6 = Number of Selectors in GM X 7 = Number of Selectors in LVD  Objective Function: Minimize total average time in buffer for each pallet  Constraints: 0 ≤ Total Number of Selectors ≤ WITNESS Experimenter

22 22 Sample Run 1: Sample Run 2:

23 23 Selector’s Model Results

24 24 Selector’s Model Results Labor Statistics Worker% Busy Loaders56.13 Selector PTP21.94 Selector PTP -GM63.94 Selector PTP -EM28.77 Selector PTP –LVD64.68 Selector PTP –Bdg Selector PTB69.06 Selector PTL57.07

25  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. 25 Model 3: Loader’s Model

26 26 Loader’s Model Results Labor Statistics Worker% Busy Loaders38.20 Selectors63.98 In the Baseline: 15 Loaders 113 Selectors

27 27 Summary of Results Baseline Model Total Average Time in Buffer Total Average Cycle Time Selector UtilizationLoader Utilization %11.63% Loader’s Model Total Average Time in Buffer Total Average Cycle Time Selector UtilizationLoader Utilization %38.20% Selector’s Model Total Average Time in Buffer Total Average Cycle Time Selector Utilization %

28  Generalize Selectors  Increase Loaders  Re-validate the model 28 Recommendations

29  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 29 Future

30  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 30 Lessons Learned

31 Q&A Session Special thanks to: *Sponsor of problem and data *Sponsor of WITNESS simulation language Would like to become a sponsor of a IE capstone design project? If Yes, please contact Dr. Jesus Jimenez or Dr. Stan McClellan 31 Q&A & Acknowledgements


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