Manufacturing Software Development for Connected Operations

Most plant software fails on the floor, not in the boardroom: the system assumed a network the plant does not have, a workflow the shift does not follow, or a data model the ERP disagrees with. Discovery starts on the line — which machines can be read, which cannot, where the data already exists but is never joined, what the operator actually does at handover — and settles the sequence: what to connect first, what to modernize, what to leave alone. The output is a roadmap costed against downtime risk, not a transformation deck, and a first release the shift will use.

Production planning and scheduling, work-order tracking, digital Andon, shift handover and quality gates built in the plant’s own language rather than a vendor’s module list. For a multi-location manufacturer we built a production and workforce platform that put output, attendance and wage rules on one record, making wage calculation 55% faster and cutting manual payroll effort by half. Systems are designed for the conditions of the floor — gloves, glare, intermittent connectivity, three shifts — and integrated with the MES, ERP and SCADA layers already there, following ISA-95 so the boundaries between control, operations and business systems stay clear.

Machines speak OPC UA, Modbus, proprietary protocols or nothing at all, and the plant network was never designed to carry their data to the cloud. We build the connectivity layer that gets it there safely: edge gateways and OPC UA or MQTT with Sparkplug B for the machines that can talk, retrofit sensors for the ones that cannot, buffering and store-and-forward for the network that drops, and an IT/OT boundary designed under ISA/IEC 62443 so connecting a controller does not expose it. The predictive-maintenance platform we built for a tractor OEM connected legacy and modern fleets this way.

Condition-based and predictive maintenance models that forecast asset health from sensor, telematics and service data, and turn it into schedules the service network can act on. For a leading tractor OEM we built an AI-powered predictive maintenance platform that forecasts machine health, optimizes service schedules and connects legacy and modern fleets into one system; component issues were detected 45% faster and unplanned-downtime risk fell 35%. Every model ships with documented features, thresholds and review cadence, an explanation the maintenance planner can check, and drift monitoring, because a false alarm on the floor costs trust faster than a missed one.

Surface defects, assembly errors, label and packaging faults and dimensional drift caught at line speed by cameras rather than sampled by hand at the end of the shift. We design the inspection pipeline from the optics and lighting to the model and the reject signal, decide with you what to build and what to buy, and integrate the result with the quality system so a defect becomes a non-conformance record with the image attached. Models are validated against the plant’s own defect library and retrained under change control.

OEE, MTTR, MTBF, yield and energy calculated once from a governed production data layer, with lineage back to the machine and the batch, instead of three spreadsheets that disagree. For a global natural-resources operator we built a manufacturing intelligence platform that turned scattered plant data into automated predictions and production control decisions, making insight discovery 45% faster and cutting manual analysis effort by 35%. Pipelines ingest historians, MES, LIMS and ERP; dashboards are designed per role; and forecasting engines for demand, maintenance and energy run on the same trusted data.

Attendance, skills, shift allocation, piece-rate and incentive wages, and the applications operators and supervisors touch every hour, built for the floor rather than the office. The production-to-payroll platform we built for a multi-location manufacturer joined real-time production visibility, workforce tracking and automated wage calculation on one record, so wage calculation became 55% faster and manual payroll effort fell by half, with a calculation the worker and the auditor can both follow. Interfaces are designed for gloves, glare and shift changes, and statutory and contract rules live as configuration under version control.

Materials tracked by barcode and RFID from receipt to consumption, batch and lot traceability that survives a recall, stock-level alerts before the line stops, and supplier coordination that does not depend on a phone call. We build the data pipelines and applications that make inventory a fact rather than an estimate: event-driven movements from the warehouse and the line, reconciliation against the ERP, and traceability queries that answer in seconds when a customer or a regulator asks which lots went where.

Plenty of plant software runs on code written a decade ago, patched to keep going, on servers under a desk in the maintenance office. We do not tear it out unless the line requires it. An API layer first, so new applications and analytics ship against the old system; then modules re-platformed one at a time — reporting, scheduling, quality — each run in parallel against production and reconciled before the old path is retired, with cutovers planned around shutdowns and shift patterns. The plant sees continuity; the team gets software it can change between shifts instead of between shutdowns.

Specifications, test reports, regulatory filings, SOPs and decades of R&D notes hold the plant’s real knowledge, and finding the right document can take a day. For a global consumer-goods company we built a production-grade retrieval platform and internal AI data assistant that turned millions of scattered documents into a searchable, source-backed intelligence layer for R&D and compliance teams, making information retrieval 70% faster and cutting manual knowledge discovery by 60%. Answers come with the citation attached, access respects who may see which document, and the system is grounded in approved information.