AppSheet: the quiet game-changer that organizes processes for small businesses

In many small and mid-sized companies, the instinctive reaction to operational chaos is “buy an ERP.” But the uncomfortable truth is that not every process requires a large, expensive, hard-to-adopt system. For a big portion of day-to-day flows: warehouse receiving, field work orders, cycle counts, delivery notes and pre-invoices... a simple, mobile, and traceable operational layer is enough.

That’s where AppSheet (Google) changes the game. You build internal apps on top of your current data (Sheets, AppSheet DB, Drive, or BigQuery), with no code, in days, at very low cost. You can capture photos, signatures, and GPS, define business rules, and automate notifications, PDFs, and approvals. The result is fewer errors, more visibility, and faster decisions, without overhauling your entire stack.

Before you invest months and millions in an ERP, try this: an AppSheet app that organizes receiving, delivery notes, and work orders within the next two weeks. If it works, scale it. If not, pivot without sinking the budget.

At Datamapping we have already done this for small businesses. We mapped real processes, turned them into AppSheet apps, and measured their operational impact. Below are two industry prototypes you can clone and adapt.

Case 1: Distributor and Importer

Goal: organize warehouse receiving, in-transit stock, and digital delivery notes with AppSheet.

Common pains:

• Purchase orders in Excel via email

• Receivings over WhatsApp with no evidence

• Manual delivery notes and stock out of sync with sales

Minimum architecture:

• Tables: Suppliers, POs, PO Lines, Receivings, Locations, Shipments, Customers

• Data source: AppSheet DB or Google Sheets

• Role-based security: warehouse, sales, management

Key views:

• Receiving: barcode scanner, quantity, lot or expiry, photo, GPS

• Shipments: picking assignment, digital delivery note, customer signature

• Management: open POs, reserved vs available stock, ETA and alerts

Automations (Bots):

• Receiving Bot: on save, adjust stock and email a PDF receiving record

• Discrepancy Bot: if received is different from ordered, notify purchasing

• Shipment Bot: on delivery note creation, generate PDF and send to customer and accounting

Expected impact in 30 days:

• Fewer reworks on delivery notes

• Fewer customer status inquiries

• Greater visibility of saleable stock

  
  

Case 2: Field Services and Maintenance

Goal: execute work orders with checklists, evidence, and frictionless pre-invoicing.

Common pains:

• Work orders on paper or WhatsApp

• Scattered or missing evidence

• Delayed invoicing due to missing information

Minimum architecture:

• Tables: Customers, Sites, Work Orders, Tasks, Materials, Evidence (photos), Labor Hours, Approvals

• Native integrations: Calendar, Drive, Gmail

• Map: geolocate work orders and estimate distance

Key views:

• Technician: today’s work orders, guided checklist, parts via scanner, photos, customer signature

• Supervisor: status board (pending, en route, in progress, closed) and SLA by technician or zone

• Administration: approved work orders and prefilled data for invoicing

Automations (Bots):

• Assignment Bot: on work order creation, notify technician with address and schedule

• Close-out Bot: on work order closure, consolidate photos and send a PDF certificate to the client

• Pre-invoice Bot: on approval, create a pre-invoice record and notify accounting

• Quality rules: block submission if a required photo or signature is missing

Expected impact:

• Same-day invoices after the visit

• Full traceability without chasing technicians

• Fewer disputes thanks to standardized evidence

Final note

At Datamapping we implement these prototypes with AppSheet, automate with bots and webhooks, and publish reporting in Power BI. If you want to clone one for your company and adapt it to your tables and rules, we can spin it up in days and scale it in stages.