Project #3: Production Cost Savings at Washington Post

1. Project #3: Production Cost Savingsat Washington Post SYST 798/OR 680 Progress Report #2 31 March 2011 Thomas Kuklinski Timothy Smith Ling Wu Vladimir Zivkovic

2. Clients Problem Statement Earned Value Management Task Breakdown Structure and Status Methodology/Technical Approach Issues/Concerns Way Forward Questions Overview

3. Clients • Kent Renk, Materials Handling Foreman, Washington Post, renkk@washpost.com, 703-916-2471 (office), 703-916-2471(cell) • Kim Hammett, Assistant Superintendent for Materials Handling, hammettk@washpost.com, 703-916-2447 (office), 703-869-2463 (cell)

4. Background Fall of 2010 GMU students did initial static analysis of the Washington Post’s 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 cuts (implemented) Route simplifications Improved data tracking Problem Statement Our objective will be to maximize the efficiency of the Washington Post’s materials handling system at their four work centers (i.e. Shipping and Receiving Department) in an effort to generate cost savings to the overall operation We would like to bring to life and build upon the static analysis done by previous group in a dynamic simulation model of the helper component of the work centers Stakeholders Materials Handling Foreman (Kent Renk) Problem Statement

5. Task Breakdown Structure and Status

6. Earned Value Management

7. Process Analysis (completed) 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 Data Collection and Analysis (in progress) Collect data from both the materials tracking system (i.e. MTC) and subject matter experts Obtain descriptive statistics on each component of the system Model Selection and Construction (in progress) Evaluate model alternatives based on quantitative/qualitative data and client needs Construct and test model (e.g. turn system processes into Arena modules) Output Analysis (not started) Evaluate output in terms of cost and throughput Iterate Methodology/Technical Approach

8. Process Analysis

9. Process Analysis (actual) Trucks 1. Daily Insert Ads & Sunday Packaging Materials Handling 2a. Daily Insert Ads Racks 3. Completed Jackets Daily Insert 2c. Jackets Run of Press Sunday Packaging 2a. Sunday Packaging 4. Collated Sunday Packaging

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

11. Data Collection and Analysis • Labor data • 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 so we know which moves they would be responsible for • This required splitting helpers assigned to specific machines into subsets (e.g. Collator 4)

12. Data Collection and Analysis • Route data • Obtained Summary Report for February 7-12 created by MTC • Data obtained for a “normal” week of work • Contains pallet moves for all materials throughout the system • Mapped all of the routes to specific areas within our system so that we know how everything interacts • Client assisted with determining which routes were not relevant • This all informed our Process Analysis understanding • Obtained descriptive statistics on route times for model input, specifically route times

13. Route Time Statistics • Data options on route duration time • Use estimated data from sponsor (max, min, mean) for a triangular distribution • Sponsor recommended because of “bad” MTC data • Use Arena Input Analyzer to find the best fit distribution based on MTC sample of “good” MTC data • Very small sample size • Looked at both to obtain conclusive results

14. Arena’s Input Analyzer

15. Arena Input Analyzer Results • Production Data • Missing production data • Invalid production data • Inconclusive results for route times • *Estimated client SME data • Client recommended this approach for route times

16. Discussion with client and additional system understanding has allowed us to settle with an Arena simulation model It is very important for the client to be able to use the model to forecast the impact of different labor markups on production targets for both daily inserts and Sunday packaging Phase 1 - Develop an Arena model that allows the client to play with “what-if” labor markups to hit production targets (required) Phase 2 – Add on an optimization engine that determines labor markups at minimal cost to the client to hit production targets (desired) Based on Process and Data Analysis, an Arena model has been started Architecture, labor markups by day/shift, and route time & assignments finished Arrival processes by day/shift, internal business logic, and production targets still being analyzed Model Selection and Construction

17. Model Selection and Construction (actual) Monday, Shift 1 Raw Materials Normal(2, 4) U(6,8) EXP(10) C1 Machine Dock Dock S1 Machine U(2,5) Production Targets U(4,5) Run of Press Jackets EXP(5)

18. Model Selection and Construction (scope) Monday, Shift 1 Raw Materials EXP(5) EXP(5) U(6,8) C1HS C1PZ Rack Dock S1HS C1PZ U(2,5) Production Targets EXP(5) ROP Jackets

19. Model Selection and Construction (Arena)

20. Issues/Concerns • Working with sponsor to obtain accurate business rules to apply to model components • Currently there is no clearly defined business rules on the production processes at the work center • Depends on sponsor’s ability to articulate, analyze, and abstract general business rules of the production processes • Packaging the Arena simulation model • Graphical User Interface • Model for each day vs. Large dynamic model

21. Way Forward • Data Collection and Analysis • Arrival Process Distributions (Input Analyzer) • Determine raw material arrival processes from the rack • Determine machine output arrival process (e.g. C1, S1, etc.) • We are waiting for all of this data to be provided • Modeling • Determine internal business rules • Rack to C1 vs. C2 vs. C3 vs. C4 • S1 to Z1 vs. Z2 vs. Z3 • Etc. • Look into production targets • Begin identifying output analysis components to investigate

22. Questions? Washington Post Project - Web site