Modeling & Simulation
As supply chains, logistics networks, and business processes grow and interconnect, continuous performance optimization is critical to meet customer demands and restrain costs. Increasing complexity means that disruptions due to internal or external events will grow in magnitude, posing great risks to firms that can’t afford delays, resets, or lost revenue. Bättra’s expertise in discrete event simulation enables organizations to accurately replicate complex, real-world processes, conduct predictive performance analysis on potential changes, and optimize across any performance measure with no disruption to current operations. Discrete event simulation offers many analytical benefits over static models and spreadsheet tools, especially in resource constrained environments.
For higher end supply chain network optimization projects we have consultants trained in using LLamasoft’s Supply Chain Guru® software, a supply chain design application that enables the modeling, analysis, and optimization of end-to-end supply chains using a unified user-interface for accessing all networks and product flow paths.
The following is a description of our recent 13 simulation projects:
1. AM2 Matting – Simulated the supply chain and detailed manufacturing process steps for AM2 matting (portable runway system for fixed wing aircraft) and measured the impact of buffer materials and capacity enhancements.
2. C&T Industrial Base Stabilization Model – Simulated the DLA C&T supply chain’s demand and supply management system and measured the impact of an industrial base stabilization strategy – increasing guaranteed minimum order quantities in exchange for reductions in production lead time.
3. UH-60 Blackhawk critical bearing supply chain – Simulated the complex manufacturing environment for a heavily backordered critical bearing for the UH-60 Blackhawk that included re-creating a 6-month backorder queue, order variability, variable product lot sizes for over 80 bearings, cycle times, changeover/ set-up times, and process routings across the same shared equipment/ resources multiple times. Used the simulation to measure the impact of adding buffer materials to reduce overall lead time and lean strategies aimed at reducing changeover and batch size.
4. ECWCS Layer III – Simulated the supply chain for Layer III (fleece jacket) of the Extreme Cold Weather Clothing System (ECWCS) program and measured the impact of buffer materials at various supply chain tiers.
5. MRE/ UGR Entrée Manufacturing & Assembly – Simulated Meals-Ready-to-Eat (MRE)/ Unitized Group Ration (UGR) Heat & Serve (H&S) ration production at one of the three MRE manufacturers and evaluated the impact of adding thermal processing equipment (retorts). Simulation synchronized fill line scheduling with projected retort availability and included mandatory daily FDA pathogenic bacterium reduction requirements.
6. Rotary Aircraft Critical Safety Items (CSI) Manufacturer – Simulated machine shop’s complex manufacturing environment that included product lot sizes, cycle times, changeover/ set-up times, expanding shift schedules for surge, and process routings across the same shared equipment/ resources multiple times that have caused excess queue time. Used the simulation to measure the impact of adding additional production equipment to reduce queue time, buffer materials to reduce overall lead time, and lean strategies aimed at reducing changeover time and batch size.
7. Hard Body Armor Supply Chain – Simulated the supply chain and detailed manufacturing process steps of one key manufacturer of the Enhanced Small Arms Protective Inserts (ESAPI) and measured the impact of buffer materials on surge output and lead time reduction.
8. Operational Ration (OPRAT) Supply Chain – Simulated daily MRE & UGR H&S orders moving from production locations in the U.S. to prime contractor assembly locations, and then to final destinations where the output is measured over time. This simulation utilized integration with MS Excel to drive the simulation and manage the model’s variable parameters. Capacity limitations among primes and subtier suppliers are vetted in the Excel spreadsheet, rather than within the simulation logic in order to make it easier for novice users to run different scenarios.
9. Lithium Battery Supply Chain – Simulated DLA’s demand and supply management system for key lithium batteries in order to compare various supply management alternatives, such as a Vendor Managed Inventory (VMI) system that used a combination push/pull system for production, to the current supply chain support strategy.
10. Advanced Combat Helmet (ACH) Supply Chain – Simulated the supply chain and detailed manufacturing process steps for the ACH at one key manufacturer. Measured the impact of buffer materials at various supply chain tiers and a VMI system.
11. F-15E Heads-Up-Display (HUD) combiner assembly – Simulated the manufacturing environment for the combiner assembly and measured the impact of adding buffer materials to better support Air Force customers through lead time reduction and lower overall working capital.
12. Joint Industrial Capability Analysis Process (JICAP) Stock-Out Risk Model – Simulated DLA’s demand and supply management system for several hundred thousand DLA items coded as critical, and measured stock out risk under normal demand variability and surge conditions by factoring in stock on-hand and time phased due-ins.
13. HHS Supply Service Center (SSC) – Simulated the performance of HHS’s new inventory policy against a demand generator that creates orders based on historical demand patterns for each item. The simulation tracks the number of orders created, fill rates by inventory policy type, and average inventory to demonstrate that the policy is effective.