mars-dashboard

byPriyank Dave

Project Brief — GE Powerzone Service Operations & Supply Chain Platform What this product is An internal web platform for a gas & electrical appliance service business. It sits as a wrapper and automation layer on top of ServiceM8 (field service management — jobs, technicians, scheduling), which the company already lives in. The office staff will do their day-to-day work inside this new platform; ServiceM8 continues to act as the underlying data store and technician mobile app, and the two stay in sync both ways. The goal is to replace a lot of manual copy-paste, email chasing, and switching between tabs with a single dashboard that tracks jobs, parts, quotes, and suppliers, and automates follow-ups where a human is currently the glue. Users and roles Admin / office staff (Kylie, Linda, Wendy, Elaine): full access. Create jobs, quote, invoice, manage inventory, run analytics. Technicians (field): light access. Log in to report which parts they used from their van and view their assigned jobs. They do NOT re-enter anything they already put in ServiceM8. Owner / reporting view (Kylie, Elaine only): revenue and analytics dashboards (sourced from ServiceM8 invoice/quote totals). These sections must be access-controlled because random visitors walk through the office. Core modules to build 1. Unified Operations Dashboard (the home screen) A single-pane-of-glass quick view that mirrors how ServiceM8 feels (so staff don't feel like they've left it), but with much better information density: Jobs by stage/queue: pending quote, P&A requested, quote sent, follow-up 1, follow-up 2, intervention required, parts on order, ready to schedule, awaiting invoice, etc. The queue structure should mirror ServiceM8 queues (currently ~5–8 queues, will grow as automation adds stages). "How long has this job been sitting here?" indicator per job (like the McDonald's drive-thru timer — visual cue when a job is aging out). Counts: jobs outstanding, jobs awaiting parts, quotes awaiting approval. Jobs comparison by month across years (e.g. Jan 2024 vs Jan 2025 vs Jan 2026). Top clients by month/quarter (spend and job count, sourced from ServiceM8 invoice totals). Better search than ServiceM8's — must treat multi-word queries as a phrase, not split into individual letters. Search across contacts, jobs, and job notes. Include refinements (date range, stage). Alerts panel: low stock, aged jobs, stuck follow-ups, overdue supplier responses. 2. Job Management List, detail, and create jobs (jobs can be created from the platform OR continue to be created in ServiceM8 — both must sync). Job status lifecycle: Created → Technician Assigned → Diagnosed → Quote Required → Quote Sent → Awaiting Approval → Awaiting Parts → Scheduled → Completed → Invoiced. Jobs can re-enter earlier stages (a completed job can revert to "quote needed" if a second visit finds more parts). Link jobs together by customer / appliance / issue so related jobs are visible to each other (prevents duplicate quoting). Every job has a job ID (format like 260503 — year + sequence). 3. Automated Work Order Ingestion Watches an inbox for incoming work orders (email + PDF attachments). Uses an LLM to extract: customer name & contact, site location, appliance make/model/serial, fault description, requested service. Creates the draft job in ServiceM8 via its API. Office confirms/edits before it goes live. 4. Technician Notes Cleanup (the most important automation per client) Technicians write raw, messy notes in the ServiceM8 "quote description" / "invoice description" field (spelling mistakes, stream-of-consciousness, mixed with internal commentary like who they spoke to). Office staff currently manually copy → clean → paste this into a customer-facing format for every quote and invoice. Trigger: when a technician checks out of a site / signs out of a job. Action: LLM takes the raw note, keeps the original raw version saved in the job's Notes section (audit trail), and writes a cleaned, formatted version back into the quote/invoice description. Format: top-level header with MAKE / MODEL / SERIAL / LOCATION in caps, then body. Preserve data loyalty: do NOT change part numbers the technician quoted — those are ordering-critical (if a tech wrote part X and office orders part X and it's wrong, the tech must not be able to say "I never gave you that number"). Keep the original raw note verbatim for this reason. Idempotency: mark formatted sections with an asterisk delimiter (e.g. ***...***) so the cleaner knows what it already touched. On a 2nd or 3rd site visit, append a NEW cleaned block below the old one — do not re-clean and rewrite earlier ones, because the customer may already have received a quote referencing that text. Learns the house style from examples — the client already experimented with this in Claude and it worked. 5. Inventory / Parts Tracking (has to be custom — ServiceM8 doesn't do this properly) Parts catalog: ~16,000 items already, growing daily (new appliance models constantly). Fields per part: part name, internal SKU, supplier SKU(s) (can differ per supplier), optional photo (stored in AWS S3 — separate cost line), quantity, price, minimum-stock threshold. Storage locations: a main Warehouse + one stock location per technician van (Van 1, Van 2, …). Admin can add new storage locations (warehouse 2, van 6, etc.). Transfers: office moves stock Warehouse → Van when scheduling a job. Technicians do not pull stock themselves — the office does it from the admin UI (decided in the call to reduce technician friction). Usage logging: technicians, when they fit parts on a job, log what they used (either via the existing ServiceM8 mobile billing screen that syncs back, or a light mobile view in this new platform — ideally the former to avoid double entry). Low-stock alerts: for warehouse AND per-van (e.g. "Van 2 is down to 2 solenoids, normal load is 4 — alert office to top up"). Parts-to-job mapping: every part consumption maps to a job ID. A single job can have parts from 8+ different suppliers, so one job → many POs is normal. 6. Purchase Orders & Supplier Communication No formal PO system today — orders happen via supplier websites or Linda emailing "spares@hobart" style addresses asking for price & availability. Platform must support a PO record: supplier, parts, job ID reference, expected delivery, price, status. Supplier follow-up automation: once an email is sent to a supplier for P&A (price & availability), if no reply in a set window, auto-send follow-ups. Client wants aggressive cadence ("every 6 hours" was mentioned, realistic default 24h, configurable). After N automated follow-ups (default 3), STOP auto-sending and alert Linda to call. Requires a dedicated Gmail account for the platform so it can send-and-parse supplier replies. Supplier scorecard: average response time, delivery time, pricing consistency. 7. Customer Quote & Estimate Follow-Up Automation Triggered once the quote is manually sent for the first time (office sends the initial one, automation takes over after). Cadence: Day +3: friendly reminder email (+ optional SMS via ServiceM8's built-in SMS feature, using the existing template). Day +7 (or before the 2-week expiry): "your quote is about to expire" reminder. After that: automation stops, creates an "Intervention Required" alert so a human calls the customer. Don't become the "buy my fence" spam bot. Every automated send moves the job into a corresponding ServiceM8 queue (Follow-up 1, Follow-up 2, Intervention Required) so both systems stay visually aligned. Track: sent timestamp, response (approved / rejected / no response), approval history. 8. Cross-Job Parts Reconciliation Engine (prevents duplicate billing) This is a later but critical piece. Real scenario: same customer, same appliance, two open jobs — office quotes the same part twice by accident. Centralized Parts Usage Ledger: ordered / allocated / installed events per part per job. Cross-job matching on (customer, appliance, issue, SKU, part name) using fuzzy/NLP matching (part names aren't standardized). Pre-quote validation: when a quote is being prepared, flag if the part was recently installed on a linked job or is already on another open quote. Show warning, allow manual override with a reason logged for audit. Auto-link related jobs by customer + appliance + issue. 9. Analytics & Reporting Jobs by month, year-over-year comparison. Component usage: most-used parts, high-failure parts, seasonal demand. Repair trends: most common failures by appliance type, repeat-failure patterns, average repair time. Technician efficiency: completion time, time waiting on parts / approvals, quote approval turnaround. Supplier performance (see §6). Top clients.