RFQ Attachment Two - Go No-Go Factor 1 Criteria Worksheet.xlsx
XLSX spreadsheet 19 KB Posted
- Attached to
- Supply Chain Design, Modeling, Simulation & Optimization Federal contract opportunity
- Solicitation number
- M95494-20-Q-0026
- Issued by
- United States Marine Corps
View the file
Other files for this federal contract opportunity
| File | Type | Posted |
|---|---|---|
| M95494-20-Q-0026.docx | DOCX document | |
| M95494-20-Q-0026.docx | DOCX document | |
| RFQ Attachment Four - Form DD 254.pdf | ||
| RFQ Attachment Five - Applying the CPFR Model to the Marine Corps Supply Chain (Excerpt).pdf | ||
| Questions and Answers Document No. 1.pdf | ||
| RFQ Attachment Four - Form DD 254.pdf | ||
| RFQ Attachment Three - IDIQ Contract Task Order Pricing Sheet.xlsx | XLSX spreadsheet | |
| M95494-20-Q-0026.docx | DOCX document |
On GovTribe
Work with this file on GovTribe
- Download the original file
- Contacts named in this file
- Similar government files
- Ask GovTribe AI about this file
Text version
Criteria
| Instructions: Complete all yellow cells. Indicate whether your quoted technology solution meet each criterion by entering a "Yes" or "No" in column C. Provide evidence that your firm meets each criterion by attaching a corresponding screen shot(s) to this document for each criterion and insert narratives in column D that explain how each screenshot(s) meets each corresponding criterion. Number each screen shot attached to this document and enter the corresponding number in column E. | ||||
| Criterion Number | Criteria Description | Meet Criteria 'Yes' or 'No' | Narrative 'Yes' or 'No' | Screen Shot Number |
| 1 | Configure multiple levels of democratized personas that interact and are collaborative with the model, based on user and role configurations. | |||
| 2 | Provide a means to integrate all existing data in one end-to-end data reference model from current organizational operating systems. | |||
| 3 | Provides a means to efficiently gather, clean, and blend data in a centrally stored model, to include data cleanup, normalization, and automation. | |||
| 4 | Repeatable and automated data extraction, transformation, and loading from numerous enterprise and local sources to refresh the model's data through schedule data refreshes. | |||
| 5 | Provides a means to solve supply chain descriptive, diagnostic, predictive, and prescriptive analytics, to include all of the following types of analysis: network, product flow, transportation or distribution, safety stock inventory, multi-echelon inventory optimization of raw materials and finished goods, simulation, production, risk, demand, cost-to-serve, Greenfield analysis, labor, warehouse, capacity. | |||
| 6 | Capable of creating digital supply chains from both a geographic and logical mapping perspective. | |||
| 7 | Demonstrates the ability to perform complex supply chain optimization solves, to include multi-echelon inventory optimization, to include safety stock and cycle stock, dynamic stocking algorithms; capable of optimization based on preference, to include: cost, service level, speed, and priority. | |||
| 8 | Capable of multiple product mode and vehicle routing analysis and optimization (air, truck, ocean, rail, intermodal), to include inbound, outbound, interleaved, backhaul multi-stop route optimization, hub optimization. | |||
| 9 | Captures and/or incorporates information from across an organization's enterprise, to include internal, transactional, and policy from current operating systems in an end-to-end view. | |||
| 10 | Ability to simulate multiple, concurrent scenarios and compare hundreds of scenarios side by side; capable of simulating sourcing, network, inventory, and transportation from same data model; capable of using simulation to test supply chain designs, optimization, and evaluate downstream effects. | |||
| 11 | Ability to understand and incorporate NIIN demand profiling in overall product flow, network design, multi-echelon inventory policies, and transportation routing, and consolidation | |||
| 12 | Ability to build and maintain demand profiles for NIINs and NIIN categories for upstream planning of network, inventory, transportation, and replenishment; ability to seemlessly integrate demand models with complex supply chain optimization solves. | |||
| 13 | Ability to improve Class 9 parts forecast accuracy by at least 10% from current state for each weapon system across at least 85% of the NIINs associated with each weapon system's Bill of Materials; ability to incorporate available Condition Based Maintenance Data to inform all demand signals across condition-based (predictive), usage-based (probabilistic or preventive), and time-directed (planned) demand. | |||
| 14 | Technology must be able to achieve all of the functions listed in 1 through 13 in a suite of technology that enables multi-user collaberation on one model, technology communicates with each other, and is operated and managed by one user experience (UX). | |||
| 15 | Technology and/or contractor must be willing and compatible to comply with Federal, Department of Defense (DoD), and Department Of Navy (DON), and Marine Corps (USMC) cybersecurity requirements. |
&"-,Bold"&K000000Attachment 2 - Go/No-Go Factor 1 Criteria Worksheet
File details come from the government source that posted it. Updated .