Solidbricks vs Odoo

1. Introduction and context

An ERP reconsideration usually does not stem from "dissatisfaction", but from a changed reality: growth in order volume, additional locations or entities, new product lines, stricter compliance requirements or an integration landscape that has become too complex. In such situations the question arises whether the existing ERP (here: Solidbricks) is still the best starting point, or whether an alternative such as Odoo better fits the next 3-5 years.

This comparison is relevant for three groups of decision-makers, each asking different questions:

Management/MT: does the system fit the strategic course, what are the risks (vendor lock-in, continuity), and how predictable is the ROI?
Operations: does the system support the desired processes (lead time, inventory reliability, service levels), and how much change does that demand of teams?
IT/data: what about architecture, integrations, security, compliance and manageability (incl. upgrades)?

The scope of this blog is deliberately decision-supportive and limited to: (1) positioning and starting points, (2) functional and strategic differences, (3) AI and data aspects including integrations, and (4) costs and impact of a switch. Where choices strongly depend on configuration, customisation and implementation quality, trade-offs are made explicit.

As a decision framework it helps to formulate and weigh criteria upfront. Commonly used criteria include: process fit (40%), total cost of ownership (20%), integration/architecture (15%), compliance & data governance (10%), scalability (10%) and vendor/roadmap risk (5%). A practical outcome of such a weighting is usually one of three directions: stay (current system suffices), optimise (improve process/reporting/integrations without an ERP switch) or migrate (functional/architectural mismatch is structural).

2. Type of ERP and starting point of existing ERP system versus Odoo

Solidbricks is the existing ERP landscape in this comparison: the system in which processes are already configured, data has been historically built up and users are used to working. The strongest indication of "fit" is usually not a feature list, but the extent to which the system today supports core processes stably, with acceptable lead times, correct inventory and financial figures and manageable integrations. The positioning and customer base of Solidbricks often determine the implementation model in practice: more standardised configuration with limited variation, or rather a lot of customer-specific configuration/customisation with associated dependence on the supplier.

Odoo is a modular ERP platform (suite) that is widely deployed in SMB-midmarket. The platform combines an ERP core (finance, sales, purchasing, inventory, manufacturing) with adjacent domains (CRM, projects, service, website/e-commerce, marketing automation, HR), allowing organisations to consolidate: fewer separate tools, more end-to-end processes and a single data model. Odoo has a large partner ecosystem for implementation and extensions (apps/modules).

In type of ERP and architecture there are typically a few dimensions that colour the rest of the comparison:

Monolithic vs. modular: Odoo is built modularly, allowing phased adoption. Existing ERP systems are often less modular in "rollout" (but can have broad functional coverage). The practical question is: can you replace or extend components without rebuilding the entire landscape?
Cloud/on-prem options: Odoo can run in different forms (incl. cloud and on-premise). For existing ERP this depends on the delivery model. For decision-making, it is mainly relevant: where is data physically located, who manages patches, and which audits/controls are available?
Extensibility: Odoo has a marketplace and a large community, accelerating innovation and variation. Trade-off: more choice also means more quality variation; governance on modules and version compatibility becomes more important.

The management philosophy also often differs. Odoo works with frequent releases; this is favourable if you want to leverage innovation, but requires discipline in testing, integration management and change management. Existing ERP solutions are sometimes more predictable in release impact, but can therefore innovate more slowly or rely more heavily on customisation that makes upgrades complex.

A workable "fit" hypothesis as a starting point: Solidbricks is probably preferable if the current system is demonstrably strong in the organisation's specific core processes and the organisation mainly needs stability. Odoo is more likely to be preferable if there is a need for suite consolidation, growth to multi-company, faster process standardisation and a modern integration and data model—provided the organisation is willing to harmonise processes.

3. Where Solidbricks is stronger

The biggest strength of an existing ERP often lies in what already works: processes are configured, exceptions are known, and teams have developed routines. This delivers operational predictability that should not be underestimated in a migration consideration.

Core processes that are already "locked down" are an advantage when Solidbricks has proven in practice that it supports critical flows, such as order-to-cash, inventory administration and financial closing. Even when an alternative seems functionally comparable, the difference is often that the existing system has already survived years of real-life exceptions: returns, backorders, contract agreements, customer-specific pricing logic or internal quality checks. The trade-off is that this strength sometimes partly stems from customer-specific configuration or customisation; this is valuable, but can also lead to technical debt.

Sector-specific functionality can be a decisive factor if Solidbricks offers sector templates, reports, compliance support or specific process steps that can only be achieved in Odoo through customisation or additional modules. Think of industries with strict traceability requirements, audit trail needs, specific certifications or deviating invoicing models. The uncertainty here is the extent to which that sector functionality is "product" versus "project": if it is mainly built on a project basis, it can be difficult to upgrade or replicate to new entities.

User experience and adoption are not just "users are used to it", but mainly: how is the work organised? If the current way of working is effective, staying with Solidbricks minimises the change curve. An ERP switch almost always leads to redistribution of tasks (who enters what when), new controls and different screen logic. This has direct impact on productivity, especially during order peak periods.

Stability and predictability is a real advantage when releases are minimally disruptive and the system causes few unexpected regressions. This reduces the burden on key users and IT. The turning point is when stability equals stagnation: if integrations remain difficult, reports require a lot of manual work, or expansion to new business models doesn't fit well.

Supplier relationship and support model weighs heavily in B2B environments: support availability, knowledge of your configuration and predictable response times. With an existing system, this relationship is often well established. With Odoo, the support structure is usually partner-driven; this can work excellently, but requires selection and contracting on the right SLAs and governance.

4. Where Odoo is stronger

Odoo's distinction lies mainly in the combination of broad suite coverage, modularity and a modern ecosystem. This is not automatically "better", but can deliver structural advantages in organisations with fragmented tooling or growth ambitions.

Broad functional coverage as a suite makes it possible to bring multiple domains into one platform: finance, CRM, sales, purchasing, inventory, manufacturing, projects, service and (if relevant) website/e-commerce or HR. The strategic advantage is reduction of system and data couplings, and more uniformity in master data (customers, items, prices, employees). Trade-off: working suite-wide often requires process standardisation; if business units differ strongly, harmonisation can be difficult.

Modularity and scalability means you can start with the core (e.g. finance + inventory + purchasing) and expand later. This supports phased implementation per entity, location or product line. Multi-company functionality is relevant for holdings with multiple legal entities, intercompany transactions and consolidation needs. Note: multi-company requires solid data and process governance (e.g. item structures, charts of accounts, pricing policy) to prevent uncontrolled growth.

Integrations and extensibility are often a reason to look at Odoo: APIs, available connectors and a marketplace of modules. This is favourable in a landscape with e-commerce, EDI, WMS, PLM or specialised planning tools. The trade-off is that extensions require maintenance during upgrades; selection criteria for modules (quality, maintenance, community activity) become essential.

Process standardisation and end-to-end flows are an important effect of one data model. Lead-to-cash and procure-to-pay can work with fewer manual handovers, which can improve control and lead time. In practice, this strongly depends on the configuration: if you mainly use Odoo as a "recording system" and solve exceptions outside the system, the advantage remains limited.

Innovation pace and ecosystem is a double-edged sword. Frequent releases and a large community accelerate innovation. At the same time, this requires mature change management: assessing release notes, regression tests, and discipline in limiting customisation. For organisations with limited IT capacity, this can be a reason to remain in standard or consider managed services.

5. Comparison

A useful comparison combines positioning, functional coverage, process fit, IT architecture and strategic risks. Below is a neutral side-by-side approach, including a high-level scorecard. Scores are indicative and must be validated in a fit-gap based on your processes and data.

Customer base & positioning (side-by-side)

Solidbricks: starts from an existing, proven configuration within the organisation. Positioning and target group depend on how the product has been developed and how strongly it aligns with sector processes. Implementation complexity is often already "paid" in the form of configured flows, integrations and training.
Odoo: broad suite for SMB-midmarket with strong modularity and partner ecosystem. Implementation complexity mainly depends on the degree of standardisation you accept, and on the number of integrations/exceptions.

International deployability is for both mainly a question of: multi-company, multi-currency, local taxation, and the availability of partners/support in relevant countries. For Odoo, the ecosystem is often an advantage; for an existing system the advantage may lie in proven local configuration.

Functional comparison per domain (indicative scorecard)

Legend: 1 = limited/strongly dependent on customisation, 3 = average/dependent on configuration, 5 = strong/lots of standard coverage.

Finance: Solidbricks 4 | Odoo 4 (difference often lies in reporting, consolidation and process discipline)
Purchasing: Solidbricks 4 | Odoo 4-5 (Odoo strong in end-to-end procure-to-pay if well configured)
Sales/CRM: Solidbricks 3-4 | Odoo 4-5 (Odoo often has broader CRM and pipeline functionality)
Inventory/WMS: Solidbricks 4 | Odoo 4 (depending on complexity: multi-warehouse, barcode, locations, traceability)
Manufacturing/MRP: Solidbricks 4 | Odoo 3-5 (strongly depending on manufacturing complexity, routing, planning and shopfloor needs)
Projects: Solidbricks 3 | Odoo 4 (Odoo can bring project + timesheets + invoicing into one flow)
Service: Solidbricks 3 | Odoo 4 (field service/after-sales can often be end-to-end in Odoo, but configuration determines value)
HR: Solidbricks 2-3 | Odoo 3-4 (HR is often "nice to have" and rarely decisive in ERP choice)

Important: these domain scores say little without process context. In a distribution company with simple assembly, "manufacturing" is less critical; in a manufacturing company with complex capacity planning, that is precisely the core.

Process fit and customisation needs

Process fit not only determines success, but also maintenance burden. Where a standard flow fits, the total lifecycle cost is usually lower. Where customisation is needed, costs and risks rise (upgrade impact, test load, dependence on specific knowledge).

Typical fit-gap patterns:

Standard suffices when processes are largely "best practice": standard purchasing, regular sales orders, simple inventory movements, standard invoicing.
Customisation or extensions are needed for customer-specific pricing models, complex bundles, advanced planning/APS, industry-specific compliance, or exceptional logistics flows.

An underestimated trade-off: customisation in the current ERP often feels "free" because it is already there, but it can be precisely the reason that changes are slow or expensive. In Odoo, the temptation to quickly realise customisation is also great; governance (what is really needed, what can be done through process adjustment) makes the difference here.

IT architecture, security and compliance

For IT, the core question is: how manageable is the system over time, including integrations, updates and security?

Hosting options: Odoo can run in cloud or on-premise; this allows you to make choices around data sovereignty and operational control. With Solidbricks, this depends on the current delivery model.
Roles & permissions: both can usually offer authorisation models, but the practical quality lies in granularity, audit trails and the ease with which you can enforce segregation of duties (SoD).
SSO and identity: connectability with Azure AD/Entra ID or other IdPs is relevant for security governance. The degree of standard support versus customisation is a decision point here.
Auditability: logging, change history, approvals and exportability are important for internal control and external audits.
Data location & data sovereignty: if EU hosting is required, you want to explicitly record where data is stored, which subprocessors are involved, and what rights you have to export, retention and deletion. With on-prem or private cloud, the organisation takes more operational responsibility, but often increases control.

The uncertainty mainly lies in implementation details: a system can be "safe" on paper, but unsafe in configuration (too broad roles, shared accounts, insufficient logging, no periodic review).

Strategic fit and roadmap risk

Strategic fit revolves around the question: what happens when the organisation changes? Important dimensions:

Vendor lock-in: with an existing system, lock-in can arise through customisation and limited availability of external knowledge. With Odoo, lock-in can arise through chosen partner, custom modules and dependence on specific apps.
Exit options: how easily can you export data (master data, transactions, documents), and is the data model well mappable to alternatives?
Upgrade path: if upgrades in the current ERP are minimally disruptive, that is valuable. With Odoo, you must set up governance to manage release impact, especially with customisation and integrations.
Knowledge availability: Odoo's ecosystem usually offers more choice; with a niche system, expertise can be scarcer.

Decision matrix (summary)

A practical decision matrix works with criteria, weighting and "situation profiles". Examples of criteria:

Process fit core processes (high weight): does the system support the 5-10 most critical processes without structural workarounds?
Suite consolidation: how many systems (CRM, projects, service, e-commerce) do you want to integrate or replace?
Multi-company & growth: number of entities, intercompany, international expansion.
Integration complexity: number of couplings, real-time need, EDI, external warehouses.
Data & governance: data location, audit trails, reporting need, master data quality.
TCO/ROI: licences, implementation, management, change impact.

The "best choice" is therefore rarely universal. It is an outcome of your profile: stability vs change capacity, standardisation willingness vs local autonomy, and IT capacity vs outsourcing.

6. AI and Integration

AI and integration are not separate themes: AI applications stand or fall with data quality, process discipline and access to data. That is why it makes sense to immediately link AI use cases to the data foundation and the integration landscape.

AI: use cases for management/operations

Forecasting and demand planning: better demand forecasting based on historical orders, seasonal patterns and customer behaviour. Practical gain: lower inventory and fewer stockouts. Uncertainty: requires consistent item hierarchies and clean sales data.
Cash flow forecasts: combining outstanding items, expected purchase commitments and order book to identify liquidity risks earlier. Trade-off: accuracy depends on payment terms, invoicing and collection discipline.
Anomaly detection: signalling deviating transactions (e.g. unexpected price deviations, duplicate supplier invoices, exceptional scrap). This is especially useful in high-volume environments. Condition: good baseline data and clear exception definitions.
Assistive workflows: suggested actions, such as "which orders are at risk of late delivery" or "which purchase orders need to be expedited". Value only arises when teams incorporate these signals into work agreements.

AI: use cases for IT/data teams

Document processing: automatic recognition and posting suggestions for incoming invoices, packing slips and contract documents. Trade-off: model training and exception management; also legal requirements around retention obligation and audit trail.
Classification and tagging: automatic categorisation of items, suppliers or tickets to improve reporting and routing.
Master data cleansing: deduplication of customers/suppliers, normalisation of addresses, enrichment of item data. This often delivers immediate returns in fewer errors and better reports.
Copilot/search functionality: semantic search in orders, invoices, product information and support cases ("show all deliveries with quality deviation X for customer Y"). Condition: correct authorisations and indexing; attention to data leak risk.

Which AI possibilities you can leverage in Solidbricks or Odoo depends on (a) availability of APIs and data extracts, (b) data quality, and (c) the willingness to standardise processes so that AI signals are consistently interpretable.

Data foundation and reporting/BI

For reporting, the most important choice is: do you continue reporting "in the ERP", or do you build a data platform (DWH/lakehouse) where ERP is just one source? In growing organisations, the second model is more common, because you can then also combine e-commerce, marketing, manufacturing data and support data.

Practical points of attention:

Data model and definitions: one source of truth for KPIs (revenue, margin, OTIF, inventory value). This requires unambiguous definitions, not just tooling.
Dashboards: operational (daily) vs management (weekly/monthly). The bar is higher if you want to steer near real-time.
Data quality: many BI problems are process problems (incomplete fields, inconsistent use of statuses). An ERP switch does not automatically solve this.
Integration with BI tooling: e.g. Power BI, Tableau or Looker. The decisive factor is often the quality of extract capabilities and a stable data model.

Integration landscape (current vs desired)

An ERP rarely stands alone. Typical integrations are: CRM, e-commerce, WMS, PLM, payroll, EDI, banking links and shipping platforms. The most important design question is whether you mainly need real-time integrations (inventory availability, order status) or whether batch suffices (financial reporting, HR).

In comparison, it makes sense to draw the desired landscape from processes:

Lead-to-cash: website/CRM → ERP → invoicing → payment status.
Procure-to-pay: purchasing → receipt → invoice processing → payment.
Plan-to-produce: demand → MRP → shopfloor → quality registration → inventory.

The more systems you have in the chain, the more important event handling, error handling and data consistency become.

Integration approach and management

A manageable integration strategy deliberately chooses between point-to-point couplings and a middleware/iPaaS layer. With growth and many couplings, an iPaaS is often more robust, because you centralise monitoring, retries, version management and mapping.

APIs: check rate limits, authentication, and stability during upgrades.
Version management: record contracts (API schemas), test automatically where possible.
Monitoring: make error messages visible to operations (not just IT) and define an owner per coupling.
Upgrade impact: upgrades are not just ERP; connectors and middleware must also come along. Plan this as a periodic lifecycle activity, not as an incident.

10. Costs and impact of a switch

The costs of an ERP choice rarely lie only in licences. For decision-making, it is useful to work with a TCO model over 3-5 years, in which one-off and recurring costs, but also organisational impact are made explicit.

Cost components (TCO) Solidbricks vs Odoo

One-off costs (usually dominant in year 0-1):

Implementation/configuration (processes, parameterisation, roles)
Integrations (build, testing, monitoring)
Customisation or extensions (incl. code review and documentation)
Data migration (extract, mapping, cleansing, reconciliation)
Training and change (key users, work instructions)

Recurring costs (year 1-5):

Licences/subscription
Hosting and environment costs (prod/test/dev)
Support/managed services
Further development (new wishes, process improvements)
Upgrade and test load

With staying on Solidbricks, one-off costs are usually lower, but recurring costs can rise if the integration landscape becomes more complex or customisation maintenance is intensive. With migrating to Odoo, the initial investment is higher, with the potential payoff: fewer systems, lower integration costs, better data consistency and faster process adjustments. However, this is only achievable if implementation is done sufficiently in standard and governance on customisation is tight.

Migration complexity and data quality

Data migration is often the critical path activity. Important decisions:

Master data: customers, suppliers, items, BOMs, price lists, locations. This must almost always go over completely and clean.
Transactions: open orders, open purchasing, inventory levels, open items. This is necessary for operational continuity.
History: how many years of transaction history do you migrate? Alternative is archiving in a read-only environment or DWH. Trade-off: less migration complexity versus less detail in the ERP.
Attachments: drawings, certificates, contracts. This can be extensive and requires governance (who has access, retention periods).

Data quality is usually the biggest risk: inconsistent item management, duplicate relations, unclear statuses. An ERP switch is a moment to improve, but it costs time and business attention. Without dedicated data owners, migration often becomes an IT problem, while it is primarily a business problem.

Operational impact and change management

The impact mainly lies in work agreements. Even if functionality is "the same", screens, statuses, responsibilities and controls change. Typical impact areas:

Process changes: harmonisation between teams/locations, new approvals, different inventory operations.
Training: role-oriented (purchasing, warehouse, finance) with emphasis on exceptions, not just standard cases.
Key users: freeing up capacity is essential; otherwise knowledge shifts to consultants and adoption remains superficial.
Temporary productivity dip: count on a period in which speed and error-free are lower. Mitigation: extra support ("hypercare") and clear escalation paths.

The ROI strongly depends on how quickly the organisation is back at level after go-live, and whether process improvements are actually anchored.

Risks and mitigations

Scope creep: wish list grows during implementation. Mitigation: tight change control board, priorities per business goal.
Customisation explosion: too much deviation from standard. Mitigation: "standard-first" principles, make TCO impact explicit per customisation wish.
Integration breaks: couplings fail at go-live or upgrades. Mitigation: end-to-end test scenarios, monitoring, fallback procedures.
Cutover issues: inventory or open items not correct. Mitigation: trial conversions, reconciliation, dual-run where necessary.
Security & authorisation: too broad rights at start. Mitigation: least privilege, role review, logging and periodic checks.

Phasing and scenarios

The implementation strategy influences risk and lead time:

Big bang: everything at once. Advantage: shorter period with double systems. Disadvantage: higher risk and higher peak load on organisation.
Phased: per entity, department or process cluster. Advantage: learning and adjusting. Disadvantage: temporarily more complex integration landscape and possibly double processes.
Pilot + rollout: start with one location or product line. Advantage: proof of operation and better adoption. Disadvantage: risk that pilot is too "unique" and not scalable.
Dual-run: period in which old and new system run in parallel for critical figures. Advantage: extra certainty. Disadvantage: higher workload and risk of inconsistencies if governance is missing.

Business case setup

A business case becomes stronger if you define concrete KPIs and make them measurable before and after. Examples:

Lead time: order processing, purchasing lead time, manufacturing lead time
Inventory accuracy: differences between system and count, dead stock
OTIF (On Time In Full): delivery reliability
Closing days: days to month-end close and number of correction entries
Cost-to-serve: costs per order/customer segment (incl. returns and service)

Also include sensitivity analysis: what happens to ROI if implementation becomes 20% more expensive, or if adoption takes 3 months longer? This helps to make decision-making more realistic.

11. Conclusion and next steps

When Solidbricks remains the logical choice: when core processes demonstrably run well, the organisation has little need for suite consolidation, and the coming years mainly require optimisation (reporting, limited integrations, process fine-tuning) without drastic changes in business model or entity structure. This also applies when sector-specific requirements are deeply anchored in the current solution and difficult to reproduce in standard Odoo without heavy customisation.

When Odoo is more logical: if the organisation wants to reduce fragmented tooling, expects multi-company growth, wants to standardise processes faster and wants an integration and data model that better moves with expansion. This is especially relevant when new processes (e.g. e-commerce, service, projects) become important alongside the ERP core and you want end-to-end control on one dataset.

A workable "short route" decision plan for 30-60 days consists of four steps:

Requirements workshop: focus on the 10-15 critical processes and KPIs, including exceptions.
Fit-gap analysis: per process assess what can be standard, what requires configuration and what would mean customisation (incl. TCO impact).
Integration scan: inventory current couplings, data ownership, latency requirements and monitoring. Define the desired target landscape.
TCO quickscan + risk assessment: scenarios (stay/optimise/migrate) with bandwidths and risks (data, change, security, planning).

As a follow-up step, a Proof-of-Value can help to reduce uncertainties without immediately starting a full-scope project. Choose 1-2 critical processes (e.g. order-to-cash with inventory, or procure-to-pay with invoice processing) and define measurement points (lead time, error percentages, user effort). Record go/no-go criteria upfront, including: acceptable degree of customisation, integration requirements, performance and auditability.

12. How pantalytics can help with a switch

A switch or reconfiguration requires independent analysis, because both "staying" and "migrating" can be valid depending on process reality and risks. Pantalytics can support in a way that focuses on decision quality and risk management.

Independent fit-gap and process analysis: mapping current processes (incl. exceptions), bottlenecks and KPIs. The aim is a substantiated distinction between what you must retain, what you can standardise and where customisation really adds value.

Architecture and integration design: drawing up a target architecture, including integration principles (API/middleware), security and governance starting points, and agreements on data location and access. This is also the moment to explicitly anchor data sovereignty and EU hosting contractually and technically.

Business case and TCO model: scenario comparison with realistic assumptions, bandwidths and sensitivity. Including naming of organisational impact (key user time, productivity dip) and the conditions under which ROI is achievable (e.g. reduction of tools, less manual work, faster closing).

Implementation guidance and risk management: selection of implementation partner, governance on scope and customisation, test strategy and cutover plan. The aim is predictability: no surprises in data, integrations and authorisations.

Data migration approach: strategy for data quality, migration rules, validations and reconciliation. Including audit trail: demonstrably correctly transferring inventory, open items and essential documentation.

Adoption and operational anchoring: training and key user network, process ownership (who manages which process after go-live) and hypercare setup. This largely determines whether the investment translates into sustainable improvement.