Designing Digital Architecture for Cement Groups: 7-Step Framework from Process to AI

A cement group embarking on digital transformation first faces boundary conflicts: headquarters wants unified management analysis, factories prioritize production problems, finance demands consistent data definitions, production emphasizes site differences, equipment teams focus on failures and maintenance, while IT must handle integration, permissions, and long-term maintenance. Without an overall architecture, departments may procure separate systems creating new data silos; an architecture that remains only a complex blueprint may never land.

Core Approach

Start from business goals, understand factory differences, clarify system responsibilities, build shared data capabilities, then implement through concrete scenarios in phases.

1. What Must the Overall Architecture Answer?

The architecture must enable shared judgment on: which businesses to improve, which capabilities to build centrally, which to keep at factories, how to reuse existing systems, who produces/confirms/manages data, how new applications connect, construction sequence, and who operates after launch. It covers systems, technology, data rules, organizational responsibilities, and implementation paths. It should guide concrete choices: reuse existing data services or re-collect for new energy analysis? Which systems update when a factory replaces equipment? Which factory businesses must run when the group platform is down?

2. Step 1: Clarify the Group's Business Map

Cement groups vary widely: some focus on clinker and cement, others also operate aggregates, ready-mix concrete, logistics, environmental services. Some own full production bases; others manage clinker bases, independent grinding stations, and sales companies. These differences directly affect system layout. For example, clinker bases need coverage of mining, raw meal, fuel, burning, cooling; grinding stations focus on clinker procurement, batching, grinding, inventory, delivery; ready-mix concrete involves order scheduling, vehicle dispatch, site delivery. The map must include 3-5 year expansion expectations (new builds, acquisitions) to influence capacity design and standardization reserves. First, produce a business map showing businesses, operating entities, production bases, and their transactions/collaborations. Do not assume all factories need the same system.

3. Step 2: Identify Priority Problems to Improve

Vague slogans like "build smart factories" or "achieve data-driven" are insufficient. Break direction into specific problems: Why do group management reports need repeated manual reconciliation? Can energy consumption across factories be compared under unified conditions? Can raw fuel quality link to clinker cost? Can equipment failure records support cross-factory experience reuse? Can sales, shipping, inventory, and payment connect end-to-end? What reliable data services do AI applications need? Group these into targets: management collaboration, production efficiency, quality stability, equipment reliability, energy management, customer delivery. Each target needs an owner, current state, and evaluation method. Every subsequent build must answer: which problem does it serve, what is impacted without it?

4. Step 3: Understand Current State Beyond System Counts

Current state research must go beyond listing systems (ERP, production, energy). Need to understand actual work performed. At least five dimensions: Business status (process flow, manual steps, pain points); Application status (systems in use, scope, duplicate functions); Data status (key data sources, consistent definitions, traceability); Technical status (factory networks, servers, interfaces, accounts, O&M conditions); Organizational status (who owns business, systems, data; vendor dependency). Watch for "single person knows" critical systems — knowledge silos more dangerous than technical debt. Pay special attention to spreadsheets and manual ledgers doing critical work invisible in systems; a repeatedly used Excel often exposes the biggest gap in current architecture.

5. Overall Architecture Layering

For cement groups, adopt this logical structure (top-down):

Business & Usage Scenarios – management analysis, production collaboration, quality traceability, equipment maintenance, energy optimization, customer service.

Business Applications & AI Applications – ERP, production management, quality management, equipment management, logistics applications, analytics & intelligent assistants.

Shared Data & Service Capabilities – object models, master data, indicator services, data catalog, interfaces, analytics & model services.

Factory Data Access & Local Processing – real-time collection, historical storage, caching, validation, edge computing, system connectivity.

Field Devices & Control Systems – instruments, sensors, PLC, DCS, related field systems.

Security, permissions, O&M, governance run through all layers. This layering aligns with the data foundation layers from article 56; difference is placing "Business & Usage Scenarios" as top layer — architecture serves business goals first, not technology stack. Subsequent sections expand business, application, data, technical architecture, interfaces, and AI design. This is a responsibility division; not every layer requires a new platform purchase. Reuse existing capabilities first.

▲ From field devices to business scenarios: five layers of responsibility, security and governance throughout

6. Business Architecture: Connect Cross-Department Processes

Identify key end-to-end processes:

Procure-to-Pay: purchase plan, supplier, arrival, weighing, acceptance, warehousing, settlement.

Produce-to-Stock: production plan, material consumption, process records, quality inspection, finished goods warehousing.

Order-to-Cash: customer order, pricing, shipping, sign-off, invoicing, receivables, collection.

Exception-to-Closure: detect exception, confirm cause, arrange handling, verify result, capture experience.

These four cover the group's value flow mainlines: how to spend, produce, collect, continuously improve. Each process must define: responsible party per step, system used, information handed over, who confirms final result, exception handling. Without clear business handoff rules, system interfaces only pass data, not true collaboration. Sort out business handoffs first, then talk system collaboration.

7. Application Architecture: Define Each System's Responsibilities

Focus on clear responsibilities and collaboration boundaries:

Control Systems: field control and related process recording.

Production Management Systems: production organization, execution, statistics.

Quality Management Systems: samples, testing, quality records.

Equipment Management Systems: assets, inspection, maintenance, work orders.

ERP & Financial Systems: corresponding transactions, resources, accounting.

Group Data & Analytics Platform: cross-system association, unified indicators, analytics services.

Boundaries adjustable per current state, but avoid same business record maintained independently in multiple systems. Example: purchase order queryable by many systems, but one system owns creation, modification, confirmation. Data can be shared, business responsibility must be centralized.

8. Data Architecture: Unify Objects First, Then Analytics

Three foundations:

1. Unify Business Objects: group & factory, production lines/processes/equipment, functional locations & physical assets (functional location = equipment's "position" in process; physical asset = actual equipment installed — position can host different equipment over time), measuring points, materials & products, customers & suppliers, samples/batches/business documents. Each object needs stable ID and mapping to source system codes.

2. Unify Data Meaning: define units, time, status, statistical boundaries, applicable conditions. Example: "kWh per ton cement" — does it include packing and conveying? Must be explicit.

3. Unify Indicator Management: for key indicators define: definition, formula, source, owner, effective date, revision rules.

Unification does not ignore differences. For incomparable factories/products, retain difference notes, use different groupings or adjustment methods.

9. Group vs Factory Division of Labor

Divide by "who needs it, who knows it best, continuity requirements".

Group-centralized capabilities: core object & indicator standards, cross-factory data catalog, common interface requirements, permission & audit principles, common analysis methods, model management & evaluation standards, common components & O&M requirements.

Factory-retained capabilities: field control & protection, local production operation guarantee, raw data collection & caching, field object & measuring point confirmation, operating condition & exception interpretation, local application operation & maintenance.

Group provides common rules, factories maintain field facts — consistent with article 56 principle: "group unifies rules, factories confirm facts, both jointly maintain mappings." Need clear change mechanism: when factory modifies equipment, adjusts metering scope, or changes business process, how to sync updates to group model. Division is not splitting tasks, but giving every task a clear owner.

10. Technical Architecture: Choose Processing by Data Usage

Different data needs different processing paths:

Real-time trends & alerts: low latency.

Daily management reports & cost analysis: update per business settlement/confirmation rhythm.

High-frequency equipment data: may need full segments stored locally, upload by event or demand.

Historical quality & maintenance records: focus on time association, versioning, traceability.

Technical architecture must specify: collection frequency, transmission method, local vs group storage scope, update timeliness, retention period, offline caching & retransmission, duplicate handling, historical revision mechanism. Technology selection serves these requirements. Comparing platform feature lists alone rarely judges fit for actual production.

11. Interface Architecture: Connect Reliably Long-Term

Interface is more than an address and fields. Reliable interface must specify: data structure & units, timestamp meaning, object codes, update/delete rules, error states, retry & deduplication methods, permission requirements, version compatibility, responsible person & maintenance windows. Examples: A shipping order later cancelled — how should group platform update? Same weigh record uploaded repeatedly — will it double-count sales? Engineering calls "same record arriving multiple times, result counted once" idempotency — mandatory for all weighing, metering, settlement interfaces. Quality result revised after recheck — which downstream analytics recalculate? These exception paths must be designed upfront. Everyone writes happy paths; exception paths reveal architecture depth.

12. Where Does AI Architecture Fit?

AI can be upper-layer application or shared service for multiple businesses. Examples: management Q&A calls unified indicator service; equipment warning reads relevant time-series & work order data; quality prediction links production process & test results; process knowledge assistant retrieves governed documents. AI integration must clarify: data permissions, model applicability scope, result basis, human confirmation, effect feedback. Query, prediction, suggestion, auto-execution designed separately. Generating analysis conclusions ≠ directly adjusting field parameters. Not all problems need large models; rules, statistical methods, professional models, and large models can collaborate per scenario — continuing article 50 conclusion: "first judge what the problem is, then decide which AI to use." AI architecture key is not which model, but letting every model run on controlled, auditable tracks.

13. Security & Continuity in Overall Design

Security and reliability considered when systems connect. Key items: boundary between field control network and other networks, data exchange direction & access methods, user/application/vendor permissions, remote maintenance mechanisms, critical operation logs, backup & recovery, factory operation mode during group platform failure. Avoid making basic field operation depend on unnecessary external link. Example: group analytics down → local collection caches, reports delay, but field control continues per its design. Overall architecture must define recovery objectives and degradation modes per capability, not uniform "all real-time, all never down". Engineering uses Recovery Time Objective (RTO) : each business type's max downtime — field control, production reports, management analysis obviously different numbers. Security & continuity are not add-ons; they are the first boundaries drawn in architecture design.

14. Construction Sequence in Four Phases

Phase 1: Unify Key Definitions — confirm business goals, system responsibilities, core objects, key indicators. Select pilot factory, validate with real data.

Phase 2: Connect Key Scenarios — around management reports, energy analysis, quality traceability, equipment management, build necessary interfaces and data services. Accept with actual business.

Phase 3: Consolidate Group Common Capabilities — turn stable, effective object templates, indicators, interfaces, application components from pilot into reusable capabilities. Roll out factory-by-factory confirming differences.

Phase 4: Extend Analytics & AI — within reliable data/process scope, gradually add prediction, diagnosis, decision support. Phases may overlap, but never skip foundational definitions to demand group-wide intelligent optimization immediately.

▲ Unify definitions → Connect scenarios → Consolidate commonalities → Extend AI: step up gradually, never skip foundation

15. How to Select Pilot Factory

Pilot not necessarily largest or most systems. Key criteria: clear business problems, willing continuous participation, data basically available, representative operating conditions & equipment, results easily verifiable, reusable for later rollout. If pilot relies on many special conditions, success may not replicate. Ideal pilot delivers current value and helps group build reusable method.

16. What Should Overall Architecture Deliver?

Not just a single master diagram. At least six artifact types:

Business Goals & Scenario List: what problems to solve, priorities.

Current Systems & Gap List: existing capabilities, duplicate builds, key gaps.

Target Architecture & Responsibility Boundaries: group, factory, each system responsibilities.

Data & Interface Specifications: objects, indicators, sources, exchange rules.

Phased Construction Roadmap: project dependencies, implementation scope, acceptance methods.

Operations & Governance Mechanisms: who maintains data, approves changes, handles failures, evaluates effectiveness.

Artifacts must stay consistent and update with implementation feedback.

17. How to Judge Architecture Effectiveness

Test with practical questions:

Adding a new factory — clear onboarding method?

Building a new app — can it reuse existing data services?

Querying a group indicator — traceable to factory source?

Replacing equipment — know which relationships to update?

Network/platform failure — clear operating mode?

AI model drifts — someone detects, handles, revalidates?

Check answer "quality": qualified answers point to specific documents, services, or owners, not "in principle possible." If these questions get concrete answers, the overall architecture is already working.

Final Summary

Designing digital overall architecture for a cement group follows seven steps:

1. Clarify business map, set improvement targets.

2. Understand field & system current state.

3. Divide business processes & application responsibilities.

4. Unify objects, semantics, indicators.

5. Define group-factory division.

6. Design reliable data connections & operation mechanisms.

7. Validate via pilot, then roll out in phases.

Architecture value: every build knows its goal, dependent capabilities, collaboration with other systems, future maintainer. Good architecture organizes group common requirements with factory real differences, giving current projects a path and future builds reusable assets. From factory process, data panorama, to scenario design methods and overall architecture, these form a cement digital reference framework — once architecture stands, remaining work is turning it into value in each concrete scenario.