Field Operations Management Software — FSM Platform

Paper work orders, WhatsApp groups, Excel sheets — most field-operations businesses in Turkey still run on this trio. The courier company fills out a delivery slip by hand, the technical service writes the maintenance form on paper, the field sales rep forwards the order to the office over WhatsApp. The downstream effect: someone at the office spends most of the day on manual data entry; error rates are high, reporting arrives days late, the customer cannot get an answer to "where is my order?", and senior management knows what really happened on the field only through the worker's verbal report. When we talk about digital transformation, the first thing we touch on the field side is exactly this: moving from paper work orders to a real-time, location-aware, evidence-backed (photo plus signature) field operations platform that flows automatically into the ERP.

Our Approach

On a field operations project we'd spend the first week listening to the office, the field, and management separately. Because all three define "field operations software" differently: for the office it means "data enters once and flows automatically to the ERP", for the field worker "easy to use, works without internet, does not micromanage me", for management "real-time visibility and KPI tracking". Building software that satisfies all three at once is the line between "acceptable" and "actually makes the user's life easier". So on every field project we'd start by collecting your current paper work order forms — not to produce a one-to-one mobile equivalent, but to challenge each field with "is this really needed or is it written down out of habit?" and design a simplified mobile equivalent.

The second critical decision is the offline-first architecture. Field conditions in Turkey (basements, parking garages, rural areas, tunnels) make it impossible to build on the assumption of reliable connectivity. In the React Native app we'd keep a local SQLite store (WatermelonDB or Realm); work orders download in a batch when the app opens, the field worker operates on them all day, captures photos and signatures — everything written locally. When the network returns, background sync runs and conflicts are resolved per business rule. The cost gap between building this from day one versus retrofitting it later is 5 to 10x — so we never recommend skipping it under "maybe later".

The third layer is location plus evidence plus dispatcher. On PostgreSQL plus PostGIS we'd build a dispatcher engine: every field worker's GPS pings (30 to 60 seconds from the device) recorded; when a new work order arrives, the best candidate is proposed based on distance, skill, and current load; all active workers and open orders showing up live on a map (Mapbox or Google Maps). Route optimisation (OR-Tools or OSRM) sequences a worker's assigned orders in the same district by shortest total distance. For completed work, photo plus customer signature plus GPS stamp plus time stamp are stored together — when a customer objection comes in later, you have solid evidence in hand.

The final layer is ERP integration. Master data (customer, work order, stock, price) comes from the ERP (Logo Tiger/Wings, SAP, Mikro Fly, Netsis); the field completion result (actual time, parts used, upsell, customer confirmation) is written back to the ERP. For ERPs with standard APIs we'd build two-way REST/OData; for older ERPs an RPA bridge. The field worker should not need to know the ERP exists — in their view there is just a clean, fast mobile app.

Our Preferred Technology Stack

We typically reach for the following — adapted per project to your existing ERP and field volume.