Key Takeaways

• Treat your plant systems as a cohesive stack: sensors and PLCs at the edge, SCADA for supervision, MES for execution, ERP for business management, and analytics as the essential data layer.
• Design integrations around critical flows—orders and recipes moving down, and events, quality, and genealogy moving upward—ensuring all teams, from executives to IT, communicate effectively about priorities.
• Start small but aim for connectivity: early standardization of tags and master data, utilizing open protocols, and establishing an analytics-ready hub to support scalable AI use cases beyond initial pilots.

The Integration Challenge

When technologies within the food and beverage industry fail to communicate, it creates a significant barrier to digital transformation. A clear understanding of the technology stack—defining each layer’s purpose, data generation, and how they interconnect—allows leaders in executive management, operations, and innovation to map out realistic roadmaps. This strategic understanding mitigates duplicate expenditures and accelerates improvements in digital quality, traceability, and AI integration.

According to Deloitte’s 2025 Smart Manufacturing Survey, 29% of manufacturers currently utilize AI/machine learning at the facility level, while 24% have implemented generative AI, with many more in the pilot phase. However, successful integration hinges on a solid foundation of data management. McKinsey’s 2025 workplace AI research highlights a disparity—though nearly all companies invest in AI, only 1% consider themselves to be at maturity, a gap often attributed to inadequate integration.

Understanding the Technology Stack

Sensors

Sensors act as the eyes and ears of the production line, providing raw signals that reflect the physical state of the process—temperature probes, flow meters, and vision systems are integral to this layer.

PLCs (Programmable Logic Controllers)

These resilient real-time controllers interpret sensor signals and orchestrate equipment operations, such as motors and valves, functioning as the plant’s reflexes.

SCADA (Supervisory Control and Data Acquisition)

Serving as the operator interface, SCADA systems visualize processes, manage alarms, and execute commands, often maintaining a historian for time-series data.

MES (Manufacturing Execution Systems)

This system is pivotal for orchestrating work orders, recipes, batch records, and quality checks, thereby transforming plans into executable actions.

ERP (Enterprise Resource Planning)

ERP systems underpin the business framework, managing finance, procurement, and inventory, answering critical operational questions regarding production schedules and costs.

Analytics and AI Platforms

This cross-cutting layer integrates time-series, transactional, and quality data to develop KPIs and predictive models, typically through an industrial data platform feeding dashboards and data science tools.

Data Flow in a Modern Food Plant

Downward Flow: Plans and Recipes

Production orders are initiated in the ERP, which MES subsequently dispatches with the appropriate recipes to SCADA/PLCs.

Upward Flow: Events, Quality, and Genealogy

As operations occur, MES logs all relevant data—start/stop times, deviations, and quality status—while SCADA captures high-frequency conditions. Analytics then compile these data points for overall equipment effectiveness (OEE) and predictive insights.

Closing the Loop: Finance and Supply Chain

ERP systems capture confirmations and consumption data from MES to update inventory and performance metrics.

Integration Points That Matter

• Link your ERP system with your MES to automate the flow of orders and inventory confirmations, reducing manual processes.
• Tighten connections between MES and SCADA/PLCs to enhance job control and ensure precise parameter handoffs.
• Integrate MES with a LIMS system for real-time checks, maintaining full lot genealogy.
• Feed edge historian data into an analytics platform to create a consolidated time-series source that supports continuous improvement initiatives.
• Connect your CMMS or EAM with MES/SCADA to facilitate condition-based maintenance and shared downtime tracking.
• Incorporate labeling and serialization linkage to ensure accurate tracking of GTINs and allergen information.

A Scalable Architecture

Early adoption of ISA‑95 modeling can streamline equipment, materials, and personnel mapping, enhancing communication between ERP↔MES and MES↔SCADA. Implementing open industrial protocols like OPC UA for OT data and MQTT for data distribution will set a solid foundation.

Establish a “hub” data layer using an industrial data platform to publish clean, time-stamped events while standardizing tags and master data to ensure efficient communication throughout the system. Network segmentation, observability instrumentation, and solid cybersecurity measures should not be overlooked.

Conclusion: The Good Food Tech Stack

An effective food tech stack may seem straightforward, as systems operate coherently to automate job dispatch, ensure accurate setpoints, and trigger quality checks seamlessly. Accurate data flow results in faster financial closes and immediate traceability of lot genealogy. When teams share a common vocabulary and efficient interfaces, technology becomes an enduring advantage in quality, throughput, and trust.

FAQ for Food Manufacturing Leaders

What’s the practical difference between SCADA and MES?

SCADA supervises equipment in real-time, while MES plans and records work operations.

Do we need an ERP if our MES is robust?

Yes, ERP manages commercial aspects like demand, purchasing, and financial closes.

Can MES communicate directly with PLCs?

Yes, but SCADA is preferred for effective visualization and alarm management.

Where does a historian fit in the architecture?

It operates alongside SCADA, retaining high-resolution time-series data for analytics.

How can we prioritize cybersecurity without hindering projects?

Network segmentation, client identity verification, and controlled logic changes are essential.

What if my legacy machines lack modern PLCs?

Consider retrofitting with edge sensors or adding a micro-PLC for data publication.