D3 Tenders | Provision of External Support to Project Data Analytics

Notice Information

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Notice Title

Provision of External Support to Project Data Analytics

Notice Description

* Advise and peer review the Department's optimism bias policy. Advise and peer review the Department's approach to cost growth and cost escalation. * Introduction of a reference class forecast for Defence projects, to prevent approval of overly optimistic projects. * Conduct Reference Class Forecasting (RCF) on a minimum of 100 projects/programmes to provide benchmarking and forecasting on live and future MOD projects, enriched with RCF data from projects from around the world (e.g., US DOD). * Develop detailed RCF from comparing individual project work breakdown structures. * Identify solutions to blocks in the Department's current data landscape that prevent end-to-end oversight of projects from Head Office to suppliers. * Run two experiments on selected and agreed MOD programmes to better understand cost and schedule gaps by mapping granular project-level data from the various fragmented systems for domains such as cost and time etc and applying pre-built unique Artificial Intelligence / Machine Learning (AI/Ml) algorithms. * Propose solutions that can help the Department move from descriptive analytics to predictive analytics with portfolio data. The above will provide the MOD with better Project Delivery Data Analytics such as: a. Enhanced LFE to support forecasting at project initiation and through life. b. Reduced cost of projects - delivering more for less. c. Reduced duplication of work across common projects. d. Improved delivery performance and confidence. e. Continuous improvement. f. Improved pan-MOD and MOD-Industry collaboration. g. Increased transparency.

Lot Information

Lot 1

* Advise and peer review the Department's optimism bias policy. Advise and peer review the Department's approach to cost growth and cost escalation. * Introduction of a reference class forecast for Defence projects, to prevent approval of overly optimistic projects. * Conduct Reference Class Forecasting (RCF) on a minimum of 100 projects/programmes to provide benchmarking and forecasting on live and future MOD projects, enriched with RCF data from projects from around the world (e.g., US DOD). * Develop detailed RCF from comparing individual project work breakdown structures. * Identify solutions to blocks in the Department's current data landscape that prevent end-to-end oversight of projects from Head Office to suppliers. * Run two experiments on selected and agreed MOD programmes to better understand cost and schedule gaps by mapping granular project-level data from the various fragmented systems for domains such as cost and time etc and applying pre-built unique Artificial Intelligence / Machine Learning (AI/Ml) algorithms. * Propose solutions that can help the Department move from descriptive analytics to predictive analytics with portfolio data. The above will provide the MOD with better Project Delivery Data Analytics such as: a. Enhanced LFE to support forecasting at project initiation and through life. b. Reduced cost of projects - delivering more for less. c. Reduced duplication of work across common projects. d. Improved delivery performance and confidence. e. Continuous improvement. f. Improved pan-MOD and MOD-Industry collaboration. g. Increased transparency.

Procurement Information

Under PCR Regulation 32 b) ii:- Foresight Works has pre-built unique Artificial Intelligence / Machine Learning (AI/Ml) algorithms that address MOD project delivery system objectives such as early warnings or predicting & preventing risk drivers. Foresight Works also have a unique database for reference class forecasting to compare MOD projects against various global benchmarks. Foresight Works tooling is capable of connecting to MOD datasets, but also its multiple dataset providers (SharePoint, MS Power BI, Office365, MS Project, Oracle Primavera, Oracle E-Business).

Notice Details

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Publication & Lifecycle

Open Contracting ID: ocds-h6vhtk-03345f
Publication Source: Find A Tender Service
Latest Notice: https://www.find-tender.service.gov.uk/Notice/011714-2022
Current Stage: Award
All Stages: Award

Procurement Classification

Notice Type: Award Notice
Procurement Type: Standard
Procurement Category: Services
Procurement Method: Limited
Procurement Method Details: Award procedure without prior publication of a call for competition
Tender Suitability: Not specified
Awardee Scale: SME

Common Procurement Vocabulary (CPV)

CPV Divisions: 72 - IT services: consulting, software development, Internet and support
CPV Codes: 72316000 - Data analysis services

Notice Value(s)

Tender Value: Not specified
Lots Value: Not specified
Awards Value: Not specified
Contracts Value: £200,000 £100K-£500K

Notice Dates

Publication Date: 5 May 20224 years ago
Submission Deadline: Not specified
Future Notice Date: Not specified
Award Date: 4 May 20224 years ago
Contract Period: Not specified - Not specified
Recurrence: Not specified

Notice Status

Tender Status: Complete
Lots Status: Not Specified
Awards Status: Active
Contracts Status: Active

Buyer & Supplier

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Contracting Authority (Buyer)

Main Buyer: MINISTRY OF DEFENCE
Contact Name: Available with D3 Tenders Premium →
Contact Email: Available with D3 Tenders Premium →
Contact Phone: Available with D3 Tenders Premium →

Buyer Location

Locality: GLASGOW
Postcode: G2 8EX
Post Town: Glasgow
Country: Scotland
Major Region (ITL 1): TLM Scotland
Basic Region (ITL 2): TLM3 West Central Scotland
Small Region (ITL 3): TLM32 Glasgow City
Delivery Location: Not specified
Local Authority: Glasgow City
Electoral Ward: Anderston/City/Yorkhill
Westminster Constituency: Glasgow North

Supplier Information

Number of Suppliers: 1
Supplier Name: FORESIGHT WORKS

Further Information

Notice URLs

https://www.gov.uk/government/organisations/ministry-of-defence

Open Contracting Data Standard (OCDS)

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The Open Contracting Data Standard (OCDS) is a framework designed to increase transparency and access to public procurement data in the public sector. It is widely used by governments and organisations worldwide to report on procurement processes and contracts.

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