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Project #3: Production Cost Savings at Washington Post

Project #3: Production Cost Savings at Washington Post. SYST 798/OR 680 Final Presentation 6 May 2011 Team Power Wash Post Thomas Kuklinski Timothy Smith Ling Wu Vladimir Zivkovic. Clients and Introduction Background, Objective, and Scope Technical Approach Model Architecture

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Project #3: Production Cost Savings at Washington Post

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  1. Project #3: Production Cost Savingsat Washington Post SYST 798/OR 680 Final Presentation 6 May 2011 Team Power Wash Post Thomas Kuklinski Timothy Smith Ling Wu Vladimir Zivkovic

  2. Clients and Introduction Background, Objective, and Scope Technical Approach Model Architecture Results and Analysis Recommendations Future Work Acknowledgements Questions Overview

  3. Clients and Introduction • Kent Renk, Materials Handling Foreman, renkk@washpost.com • Kim Hammett, Assistant Superintendent for Materials Handling, hammettk@washpost.com

  4. In the Fall of 2010, GMU students did initial static analysis of the Washington Post Shipping and Receiving Department in Springfield, VA They conducted a process evaluation, an analysis of labor, and an analysis of routes which resulted in three groups of recommendations: Labor reductions (implemented) Route simplifications Improved data tracking Background

  5. Objective Provide a baseline simulation model that helps the Washington Post maximize the efficiency of the materials handling processes Scope Build upon the static analysis of the previous group by modeling the helper component of the Shipping and Receiving Department Deliver a flexible simulation model that can be used by the Post to make workforce planning decisions Objective and Scope

  6. Conduct a System Process Analysis Build upon last group’s work done on process evaluation Develop a complete and detailed qualitative understanding of how the Shipping and Receiving Department operates Collect and Analyze Data Collect data from both the materials tracking system (i.e. MTC) and subject matter experts Obtain descriptive statistics on each component of the system Select and Construct Model Evaluate model alternatives based on quantitative/qualitative data and client needs Construct and test model (e.g. turn system processes into Arena modules) Perform Output Analysis Evaluate output in terms of helper utilization, moves, and total production Iterate Technical Approach

  7. Model Architecture • Process AnalysisOverview • General Arena Module • Helpers zones and arrivals • General Process Modules • Daily Insert/Sunday Packaging raw ads from rack to machine processing • Completed Daily Insert pallets from SLS 1-6 to CSLD/NDSL • Completed Sunday Packaging from Collators 1-4 to rack/NDSL • Completed Sunday Packaging from rack to CSLD • Schedule Module • Output Analysis Module

  8. Process Analysis (actual) Trucks 1. Daily Insert Ads & Sunday Packaging Materials Handling 2a. Daily Insert Ads Racks 3a. Completed Jackets Daily Insert 2c. Jackets Run of Press Sunday Packaging 2a. Sunday Packaging 3b. Collated Sunday Packaging

  9. Process Analysis (scope) Materials Handling Racks Output: Completed Jackets Input: Daily Insert Ads Daily Insert Output: Collated Sunday Packaging Sunday Packaging Input: Sunday Packaging Output: Head Sheets Output: Collated Sunday Packaging Run of Press

  10. General Module Monday, Shift 1 Raw Materials EXP(5) EXP(5) TRIA(6,8,9) C1HS C1PZ Rack Dock S1HS C1PZ Production Targets TRIA(2,5,6)

  11. General Process Modules • Utilizes “Transporter” modules and non-stationary Poisson Process schedule • Internal business logic dictated by historical statistics (e.g. CSLD vs. NDSL) and internal business logic (e.g. Z-Loader selection)

  12. Schedule Module • Utilizes “Halt” and “Activate” modules

  13. Output Analysis Modules • Utilizes “Record” and “ReadWrite” modules • Records current markup • Records utilization time by helper by shift/day • Records helper moves by helper by shift/day • Records total pallets moved through model by week

  14. Raw materials start at rack (95% go to rack first) Exponential/Poisson arrival processes Triangular route time and Z-Loader distributions Machine and rack processes as terminate and arrive modules Internal business logic NDSL vs. CSLD; NDSL vs. Rack Least busy Z-Loaders machines are used Unlimited trucks available for delivery of completed materials 1 week reflects all weeks Model Architecture (Assumptions)

  15. Lack of route time data Plenty of arrival data Changing work center business logic makes it hard to keep up Lack of fully developed business logic for the internal model process (e.g. 50% NDSL vs. CSLD) Recipes for advertisements and rack waiting times make it difficult to model a seamless transition of materials through the system Model Architecture (Limitations)

  16. Results and Analysis • Input Analysis • Labor Analysis • RouteAnalysis • Arrival Process Analysis • Output Analysis • Base vs. Suggested Schedule • Helper utilization and moves • Total production

  17. Labor Input Analysis • Obtained labor markup data for this period • Broken down by work center, day, shift, and role • Assigned helpers within the work centers to specific routes they were responsible for • This required splitting helpers assigned to specific routes into subsets (e.g. Collator – Deep Reach) • Trimmed off helpers in the markup that would be working on tasks not reflected in the model (i.e. Loaders/Unloaders working at the receiving dock)

  18. Route Input Analysis • Mapped all of the routes to specific areas within our system • Routes obtained from MTC Pallet Move Summary Report for February 7-12, 2011 • Obtained route time and distance for all relevant routes from client • Client provided estimated max, min, and mean routes times for a triangular distribution • Client recommended this approach because of “bad” MTC data • Used route times, distances, and helper assignments to determine velocities of each helper for model input

  19. Arrival Processes Input Analysis • Obtained arrival timestamp summaries for components of the system from MTC • Rack Sunday Packaging and Daily Insert raw ads • SLS 1-6 & Collators 1-4 finished pallets • Empty Run of Press jackets • Rack finished Sunday Packaging • Used Arena’s Input Analyzer to determine distributions on the arrival processes for the components based on timestamps • Distributions broken down by shift/day and machine • Exponential inter-arrival times • Poisson arrival process

  20. Output Analysis • Output analysis was conducted as a demonstration of how the model could be used to inform decisions • Primary deliverable was usable decision tool going forward • Technical session and model instructions ensured client understanding of the model functionality • Compared base scheduleand suggested schedule • Utilization: Percentage of workers busy at any given time • Helper Moves: Moves per helper-hour • Total Production: Total weekly moves • Sensitivity analysis on arrival processes and helper velocities confirmed results

  21. Base Schedule • 165 total shifts over a 5-day week • 6,500 total helper moves • 20% utilization • 6 moves per helper-hour

  22. Suggested Schedule • 75total shifts over a 5-day week • 90 less than base • 6,500 total helper moves • Same as base • 40% utilization • Double base • 12 moves per helper-hour • Double base

  23. Future Work • Build an Excel-based automation of the input data • Develop animation capabilities to help visualizethe processes internal to the model • Enhance the output analysis capabilities to capture more specific details about the statistics of the model • Continue to flesh out big assumptions within the model (e.g. machine processing recipes) • Build a simulation-based optimization engine around the model • Production target goals • Schedule variables

  24. Acknowledgements • Washington Post • Mr. Kent Renk • Mr. Bill Thompson • GMU Team I’m PRO WasP • Justine Blaho • Catalina Gomolka • Ryan Graziano • Laura Rodriguez Lopez

  25. - Remove DVM and schedule chart - Reduced slides on Input Analyzer - Remove a portion of "future works" - On slide 5 ( object and need), remove "life dynamic simulation", instead mention it's a baseline simulation model that allows the customer to play with. - in Technical Approach section slide 8, rewords the four phrases and make them active ( a verb + a noun) - on slide 16, modify the sentence "z-loaders go to least busy" - on slide 17, modify "develop" to "developed" - mention somewhere in the tech secion on the slides, that the custmer accept the model after we did a client-side tech review session. - remove evaluation page - add more details on the output analysis sections - add content on optimizing the model, make it clear optimizing the model is NOT in the sense of "LP", call it "improved model" or "modified model" instead, and eleborate why it's an improved model ( save on labor cost, etc.) - Add sensitivity analysis section to talk about assumptions that we are less sure of, run the model with different distributions, verify the assumptions may not be correct. mention that we provided the customer a tool which need continuing effots to improve - Tim and Tom coordinate with each other so that the produciton process will not be repeated twice Questions?

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