--- title: "Provision of External Support to Project Data Analytics" ocid: "ocds-h6vhtk-03345f" canonical_url: "https://d3tenders.com/contract/?ocid=ocds-h6vhtk-03345f" markdown_url: "https://d3tenders.com/contract/ocds-h6vhtk-03345f.md" json_url: "https://d3tenders.com/contract/ocds-h6vhtk-03345f.json" source: "Find A Tender Service" current_stage: "Award" buyer: "MINISTRY OF DEFENCE" published: "2022-05-05" --- # Provision of External Support to Project Data Analytics Buyer: MINISTRY OF DEFENCE Current stage: Award OCID: ocds-h6vhtk-03345f [View canonical contract page](https://d3tenders.com/contract/?ocid=ocds-h6vhtk-03345f) [Download OCDS JSON](https://d3tenders.com/contract/ocds-h6vhtk-03345f.json) ## Summary The procurement process titled "Provision of External Support to Project Data Analytics" is led by the Ministry of Defence, which is based in Glasgow, UK. This procurement falls under the services category, specifically focusing on data analysis services as defined by CPV classification 72316000. The procurement method employed is a limited procedure, which involved an award without prior publication of a call for competition. The process was signed on 5th May 2022 with a contract value of £200,000. Current activities related to this contract are ongoing. This tender presents a significant opportunity for businesses specialising in data analytics, particularly those with expertise in artificial intelligence and machine learning. Companies that can deliver innovative solutions to enhance project delivery analytics for defence projects would be well-suited to compete in this space. Additionally, small to medium enterprises (SMEs) that possess unique tools or databases for reference class forecasting may find themselves particularly competitive, as the Ministry of Defence seeks to improve cost efficiency and project transparency through advanced analytics. ## Notice * 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). ## Key Details | Field | Value | | --- | --- | | Publication source | Find A Tender Service | | Latest notice | https://www.find-tender.service.gov.uk/Notice/011714-2022 | | 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 | | All stages | Award | ## Dates | Field | Value | | --- | --- | | Publication date | 5 May 2022 | | Submission deadline | Not specified | | Future notice date | Not specified | | Award date | 4 May 2022 | | Contract period | Not specified | | Recurrence | Not specified | ## Values | Field | Value | | --- | --- | | Tender value | Not specified | | Lots value | Not specified | | Awards value | Not specified | | Contracts value | £200,000 | ## Status | Field | Value | | --- | --- | | Tender status | Complete | | Lots status | Not specified | | Awards status | Active | | Contracts status | Active | ## Buyer | Field | Value | | --- | --- | | Main buyer | MINISTRY OF DEFENCE | | Locality | GLASGOW | | Post town | Glasgow | | Postcode | G2 8EX | | Country | Scotland | | ITL 1 | TLM Scotland | | ITL 2 | TLM3 West Central Scotland | | ITL 3 | TLM32 Glasgow City | | Local authority | Glasgow City | | Electoral ward | Anderston/City/Yorkhill | | Westminster constituency | Glasgow North | | Delivery location | Not specified | ## Supplier | Field | Value | | --- | --- | | Number of suppliers | 1 | | Supplier names | FORESIGHT WORKS | ## CPV Codes ### Divisions - 72 - IT services: consulting, software development, Internet and support ### Codes - 72316000 - Data analysis services ## Release History - 5 May 2022 at 13:36 - Award - Award Notice - https://www.find-tender.service.gov.uk/Notice/011714-2022 ## Notice URLs - https://www.gov.uk/government/organisations/ministry-of-defence ## Provenance This Markdown file is an alternate public rendering of the D3 Tenders contract record. The canonical page is https://d3tenders.com/contract/?ocid=ocds-h6vhtk-03345f. The underlying structured data is available as OCDS JSON at https://d3tenders.com/contract/ocds-h6vhtk-03345f.json.