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{
"event": {
"name": "hack27",
"slug": "hack27",
"repo": "hack27",
"topics": [
"solution-centre",
"hack27",
"microsoft-teams",
"microsoft-forms",
"youtube",
"hackathon",
"innovation",
"solution-design",
"pitching",
"judging",
"challenge1",
"data-visualisation",
"dashboards",
"survey-data",
"ndas",
"workplace-harassment",
"policy-reform",
"data-evidence",
"advocacy",
"next-js"
],
"technologies": [
"Microsoft Teams",
"Microsoft Forms",
"YouTube",
"data-visualisation",
"dashboards",
"survey-data",
"Next.js",
"TypeScript",
"Supabase",
"LLM",
"Tailwind CSS",
"Power BI",
"Microsoft Excel",
"PowerPoint",
"Python",
"Jupyter",
"pandas",
"scikit-learn",
"XGBoost",
"SHAP",
"NumPy",
"SciPy",
"matplotlib",
"Streamlit",
"Plotly",
"Ollama",
"AI",
"large-language-models",
"Microsoft 365",
"local-ai-models"
]
},
"visibility": {
"event_repo": "public",
"team_repos": "public"
},
"repos": [
{
"type": "team",
"challenge_name": "Rolls\u2011Royce \u2013 Forecast Fade: Restoring Confidence in Supply Chain Spend",
"challenge_description": "This challenge, presented by Rolls\u2011Royce, focuses on addressing \u2018forecast fade\u2019 in supply chain spend commitments. Programme Directors must commit forecasts to the CFO that directly influence funding and investment decisions, yet these forecasts often deteriorate over time and lack credibility. Teams are asked to design a product or analytical approach that not only improves forecast accuracy, but explains why forecasts fail, identifies the suppliers, contracts or behaviours driving error, and provides prescriptive, executive\u2011grade insights that leaders can confidently act on.",
"challenge_slug": "challenge3",
"team_name": "Forecast Confidence Lens",
"team_slug": "a",
"name": "hack27-forecast-confidence-lens",
"child_repo_path": "submissions/hack27-challenge3-a",
"url": "https://github.com/Projecting-Success-Solutions-Portal/hack27-forecast-confidence-lens.git",
"description": "Forecast Confidence Lens focused on turning unreliable supply chain spend forecasts into a confident, decision\u2011ready view for Programme Directors. Using the provided Rolls\u2011Royce datasets, the team verified key drivers of forecast fade and reframed them into a clear confidence and action narrative that leaders can stand behind when committing numbers to the CFO.",
"key_outcomes": "Improves executive confidence in supply chain spend commitments by making forecast reliability explicit, highlighting early warning signals, and reducing the risk of over\u2011 or under\u2011committing capital due to forecast fade.",
"important_files": [
{
"path": "Supporting Documentation/1. Painpoint.docx",
"purpose": "Evidence\u2011backed validation of forecast fade drivers, confidence issues and supplier behaviours using the provided datasets."
},
{
"path": "Supporting Documentation/Challenge_3_Discovery_Report.docx",
"purpose": "Discovery report defining success criteria, key business questions and a decision\u2011support framing for the challenge."
},
{
"path": "Supporting Documentation/miro link.txt",
"purpose": "Link to the Miro board used to map insights, drivers and the Programme Director decision journey."
}
],
"required_fields": {
"summary": "A confidence\u2011led analytical approach that explains why supply chain spend forecasts fade and what Programme Directors should do about it.",
"problem": "Programme Directors lack defensible forecasts and clear explanations of risk drivers when committing spend to the CFO.",
"approach": "Analyse forecast revisions, confidence bands, change activity and supplier attributes to surface systemic drivers of forecast failure.",
"deliverables": "Verified pain point analysis, discovery report and decision\u2011support framing for an executive\u2011grade forecasting product."
},
"thumbnail_path": "",
"topics": [
"solution-centre",
"hack27",
"challenge3",
"data-analytics",
"excel",
"data-modelling",
"visualisation",
"supply-chain",
"forecasting",
"confidence-metrics",
"decision-support",
"risk-management"
],
"technologies": [
"data-analytics",
"Excel",
"data-modelling",
"visualisation"
],
"members": []
}
]
}