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Supply Chain Analytics with Simulation

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Understand what value can be gained by using simulation-based predictive analytics for supply chain, distribution center, logistics and warehouse design, operations, and improvement

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Supply Chain Analytics with Simulation

  1. 1. 1 Supply Chain Analytics Michael J Rice Director | Manufacturing & Logistics Eastern US & Canada mrice@promodel.com O: 207.406.4993 M: 845.781.3514 Andy Schild Director | Manufacturing & Logistics Central US & Canada aschild@promodel.com O: 585.398.8178 M: 585.507.8499 Mike Townsend Director | Manufacturing & Logistics Western US & Canada mtownsend@promodel.com 405.850.7610
  2. 2. 2  ProModel Story ◦ Founded in 1988 by Dr. Charles Harrell. ◦ Developed and distributed easy to use simulation products for use of standard PC’s with focus on manufacturing/logistics ◦ Expanded Simulation to the Planning Domain in the late 90’s ◦ Expanded Simulation to Custom Decision Support Applications in early 2000’s  3 Major US Offices ◦ Allentown, PA – Admin, Marketing & Sales ◦ Orem, UT - Development, PM, Sales & Support ◦ Ann Arbor, MI – Development, PM & Sales  90 Direct employees  24+ VARs with offices world wide.  Over 7000 users world wide, 50%+ Fortune 500 Main Offices Remote Employees 24 VARS
  3. 3. 3 ProModel Provides Simulation-Based Decision Support Solutions that allow organizations to understand system performance in a low-risk environment ProModel combines a Powerful Suite of Simulation Technology with 28 years of delivering training and consulting solutions enabling our clients to: • Maximize Throughput • Control Operational Costs • Increase labor productivity • Understand effects of budget system constraints (bottlenecks) Understand the Impact of Critical Business Decisions… Prior to Implementation
  4. 4. 4 Definition of analytics “Data analytics is the science of examining raw data to help draw conclusions about information. It is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify (or disprove) existing models or theories.” Definition of supply chain ” the sequence of processes involved in the production and distribution of a commodity.”
  5. 5. 5 • Seasonal Fluctuations • Oscillations in demand / “Bullwhip” effect • Ineffective forecasting methods/data • Cost of holding inventory vs. cost of stock outs • Logistics Systems • Limited visibility to suppliers • Customer satisfaction • Responsiveness to market changes • Synchronizing ordering to inventory levels
  6. 6. 6 Forecast Volumes Design Specs Shift Setups Productivity Rates Volume Splits Conversion Factors Process Throughput % Utilization Productivity Impact Kanban Requirements Headcount Planning Equipment Requirements Yard Capacity “In other words, we convert information we do have into the answers we want to have…”
  7. 7. 7  Dynamic platform that predicts/explains current and/or future behavior  Accounts for  Variability  Real World Complexities and Constraints  Reports on Key Metrics  Throughput, inventory, lead times and resource utilization  Reduce Risks  Test changes prior to implantation  Quantify costs and changes to flow  Quantify the impact of change
  8. 8. 8 Variation: “Averages” are Dangerous How much Risk can you afford to take?
  9. 9. 9 Trying to optimize a complex supply chain/logistic systems is like trying to solve the Rubik's cube. Simulation can help understand the trade-offs!!  Maximize Resilience  Reduce Fulfillment Times  Right Size Inventory  Minimize CapEx Interdependencies: The Domino Effect
  10. 10. 10  Supplier Disruptions  Equipment Failures  Resource breaks / days off  Staffing  Transportation Delays
  11. 11. 11 Facility Design Slotting Analysis Material Handling Analysis Labor Analysis Process Improvement Support Throughput / Capacity CapEx Validation Pick & Put-away Strategies Effects of Seasonality
  12. 12. 12 System Logistics Transportation Network Design Supply Chain “Optimization” Optimize Location of Facilities Process Improvement Support Material Replenishment Strategies Determine Quantity of Facilities Service Level
  13. 13. 13 • How Robust is the Supply Chain • What are the dynamics of the supply chain? • What strategies will best mitigate against undesirable effects? • How will a supply chain react to fluctuations in demand? • How will customer behavior affect supply? • Financial Performance • How can we improve system performance and cut costs? • Supply Chain Design • Placement factories, distribution centers. wholesalers, & retailers • What inventory rules should be engaged at various places in the network? • How Do we Minimize Risk • Can Risks be mitigated? • Can changes to the system be made without affecting supply?
  14. 14. 14 “Profits for your company can rocket upward if you achieve sufficient savings in supply chain costs. It's not uncommon for a concerted effort to yield annual savings of between US $2 million and $10 million, depending on the size of the company.” (Supply Chain Quaterly)
  15. 15. 15 Optimize Scenarios & KPI Comparisons Analyze the Results Visualize the Process • Demand & Mix • Process times • Qty Personnel • Qty Equipment • Yields • Batch Sizes • Utilizations • Inventory Levels • Throughput • Lead Times • Costs • Manufacturing Facility • Value Stream Map • Warehouse • Supply Chain • Business Process
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  17. 17. 17  Large Big Box Retailer Distribution Center  Physical PILOT was underway testing a Flat Flow concept favored by management  Additional VIRTUAL Simulation PILOT was run to validate results Flat Flow Layout Original Layout
  18. 18. 18 Physical Pilot Results  Physical Pilot being tested in an actual facility under intense MANAGEMENT scrutiny yielded 17% productivity Improvement  Validated a spend of $1,000,000 x 6 facilities to recreate in all of their facilities Simulation Pilot Results  ProModel modeled current (to validate model) and proposed designs  Surprisingly, model showed the OPPOSITE of the observed improvement  Model showed A LOSS in productivity (-2.4%) by implementing the proposed flat flow  After further study, it was realized that the initial gain in productivity was due to the “Hawthorne Effect”, i.e the intense scrutiny drove higher than normal levels of productivity among the worker.  Client was able to cancel the proposed $6M investment and led to the future mandate that all projects above specific $ values would be modeled and to validate the spend
  19. 19. 19  Multi-phased project to support new production facility  The focus of the study was to look at Line-Side & Market Place Inventory Levels, Reorder Points and Reorder Quantities while taking into account variable lead times from their supplier to minimize the WIP held onsite  The material handling types and quantities were analyzed to right-size the labor and equipment for the specified frequency of deliveries
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  21. 21. 21  The Company was able to assess multiple scenarios around material replenishment strategies and assess their viability with regards to meeting production demands
  22. 22. 22  Company was able to evaluate the scenarios and understand how the decisions they made regarding replenishment frequencies, and amounts affected the subassembly availability Line-side, over time
  23. 23. 23  Simulation Analysis was able to show the utilization levels of the various material handling equipment (Tuggers & Fork trucks  Model showed that the replenishment strategy that was most cost effective and manageable from a supplier standpoint would require an additional resource
  24. 24. 24  Takeaways: ◦ Established minimum SAFE level of inventory the client could keep Lineside and in their Marketplace to support assembly  50% reduction in inventory holding costs for the client based on the proposed design ◦ Space for the subassemblies lineside was reduced making room for future growth (and was used for future expansion) ◦ Planned MHE level could NOT support the level of deliveries  Client avoided potential bottlenecks by identifying in the model the specific number of MHE needed to support the replenishment strategies
  25. 25. 25 • Supply Chain Modeling and Simulation can provide organizations with ability to design, test and deploy robust networks that create value. “Organizations can gain competitive advantage by running supply chain network scenarios, evaluating and proactively implementing changes in response to dynamic business scenarios like new product introduction, changes in demand pattern, addition of new supply sources, and changes in tax laws.” (Industry Week, 2013; L.N. Balaji)

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