{
  "docId": "019dd923-5ca1-7489-b634-07323ef76570",
  "docSlug": "8141e074e67aa064",
  "documentTitle": "Distress Alert July 2024",
  "authorId": "AlvarezMarsal",
  "authorName": "Alvarez & Marsal",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "portrait",
  "aspectRatio": 0.707,
  "pageNumber": 9,
  "pageCount": 25,
  "prevPage": 8,
  "nextPage": 10,
  "slideType": "industry_trends",
  "function": "diagnose",
  "density": "overcrowded",
  "nDataPoints": 18,
  "notes": "The slide uses a custom table layout to present sector-specific distress metrics over time (FY20-FY23).",
  "elementsJson": [
    "headline_text",
    "bullet_list",
    "data_table"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5ca1-7489-b634-07323ef76570/9",
  "deckHref": "/decks/019dd923-5ca1-7489-b634-07323ef76570",
  "deckJsonHref": "/decks/019dd923-5ca1-7489-b634-07323ef76570.json",
  "deckAnchorHref": "/decks/019dd923-5ca1-7489-b634-07323ef76570#slide-9",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Nearly 15% of Fashion Retail companies in our dataset are in distress, compared to 9.6% a year earlier.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-a675-704b-b9ab-3d5095e3a390",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Distress in %: 15%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-a675-704b-b9ab-43ee73f3e2c7",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.42,
        "x": 0.52,
        "y": 0.16
      },
      "kind": "paragraph",
      "text": "Additionally, the sector struggles with a relatively high-cost base (e.g., personnel, marketing, rents) and the need to make significant investments to operate more efficiently (e.g., renewal of IT systems) in a fiercely competitive environment.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "77df0a6b-eacf-4434-9608-205017946397",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.06,
        "w": 0.42,
        "x": 0.52,
        "y": 0.25
      },
      "kind": "paragraph",
      "text": "In the U.K., several retailers have buckled under these market pressures, with insolvencies surging among both brick-and-mortar and e-commerce players in the past two years.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "8b8f467f-5b29-48d7-b007-4881b8696bbb",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.42,
        "x": 0.06,
        "y": 0.16
      },
      "kind": "paragraph",
      "text": "Nearly 15% of Fashion Retail companies in our dataset are in distress, compared to 9.6% a year earlier. This is by far the biggest year-on-year rise across all sectors measured by the ADA.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a107f03a-5033-474d-9a73-edea71f10c0f",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.06,
        "w": 0.42,
        "x": 0.06,
        "y": 0.25
      },
      "kind": "paragraph",
      "text": "Fashion businesses have experienced a significant deterioration in both performance and balance sheets, with nearly 20% of them lacking performance and 31.3% lacking robustness.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "ade5fad9-9ec9-43fb-b6a8-3c829ddcb1d0",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.42,
        "x": 0.06,
        "y": 0.32
      },
      "kind": "paragraph",
      "text": "Firms' top-line growth has been under pressure due to restrained consumer spending and changing buying behaviour, including the persistent shift to online shopping and the preference for more affordable and/or sustainable products.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f60a4a64-156d-476c-86f3-9b1f6366a9d9",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.42,
        "w": 0.88,
        "x": 0.06,
        "y": 0.47
      },
      "kind": "table",
      "text": "Table showing sector distress metrics (Worst sectors, Worst trend sectors, Distress %, Lacking performance %, Lacking robustness %)",
      "attrs": null,
      "subkind": "data",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "d573de32-0568-43b8-b76a-88efbe3d8259",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.25,
        "x": 0.06,
        "y": 0.08
      },
      "kind": "title",
      "text": "FASHION RETAIL",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "86ec0032-738a-460d-aaa4-892c9ba4e4ac",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.12,
        "x": 0.06,
        "y": 0.43
      },
      "kind": "title",
      "text": "Key findings",
      "attrs": null,
      "subkind": "subtitle",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "dd5e8850-9dae-4337-b594-8dda035b858b",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [
    {
      "metricRaw": "Distress in %",
      "numberRaw": "15%",
      "numberKind": "percent",
      "actionTitle": null,
      "calloutText": "Nearly 15% of Fashion Retail companies in our dataset are in distress, compared to 9.6% a year earlier.",
      "numberScale": null,
      "numberValue": 15,
      "metricFamily": "rate_catchall",
      "numberCurrency": null
    }
  ],
  "tools": [
    {
      "name": "Chunking",
      "slug": "chunking",
      "agent": "Architect",
      "layer": "loop",
      "matchId": "019dd95a-1055-74e0-a95f-c75d3e85f381",
      "evidence": "Findings chunked into 4 metric rows × sub-segments",
      "confidence": 80
    },
    {
      "name": "Annotation",
      "slug": "annotation",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-1055-74e0-a95f-c9862f017847",
      "evidence": "Pill-style labels annotate each metric value",
      "confidence": 70
    },
    {
      "name": "Chart Selection Guide",
      "slug": "chart-selection-guide",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-1055-74e0-a95f-bd8407951d59",
      "evidence": "Tabular 'Key findings' with worst sectors / trend / time-series rows",
      "confidence": 80
    },
    {
      "name": "Concrete Language",
      "slug": "concrete-language",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-1055-74e0-a95f-c3f4f943ebeb",
      "evidence": "'19.3% Apparel Retail', '31.3% lacking robustness' — specific KPIs",
      "confidence": 85
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 21,
      "from": 8,
      "beatId": "019dd95a-0682-776c-8e36-3872265fa326",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Evidence & Proof",
      "beatSlug": "consultants-gambit-solution-approach",
      "evidence": "Sector + country deep dives with KPI tables and trend data",
      "position": 3,
      "confidence": 85,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    },
    {
      "to": 21,
      "from": 3,
      "beatId": "019dd95a-0682-776c-8e36-47c15ccb08d4",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Distress data across geographies and sectors",
      "position": 1,
      "confidence": 60,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    }
  ],
  "loops": [
    {
      "to": 12,
      "from": 8,
      "name": "Segmentation Split",
      "slug": "34-segmentation-split",
      "bestFor": "Customer analysis, market research, resource allocation",
      "matchId": "019dd95a-07fe-70ce-8d3e-fbbca78a5042",
      "evidence": "Aggregate '10 of 16 industries' splits into Fashion Retail, Media, Chemical, Construction deep dives with comparable metric tables.",
      "position": 2,
      "objective": "Segment distress by industry to surface worst-affected sectors",
      "structure": "The Aggregate View -> Segment A Behavior -> Segment B Behavior -> The Insight in the Difference",
      "confidence": 85,
      "description": "Divide a whole into meaningful segments to reveal hidden patterns"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}