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RAW vs Odoo for wholesale and manufacturing

Many Benelux wholesalers and manufacturers are running up against the limits of their ERP. This blog compares Real Applied Wholesale (RAW) with Odoo: logistics depth and complex pricing versus platform breadth, ecosystem and international scale. Focuses on WMS, finance, integrations, data governance, AI, risks, TCO and concrete next steps for fit-gap and PoC.

By Rutger Hofste Updated

1. Introduction and context

Many mid-market wholesalers and manufacturers have been running for years on an ERP that is functionally "good enough", but whose limits are becoming visible due to growth, new sales channels or increasing integration needs. In the Benelux, we regularly see two directions: further optimise the existing, sector-specific ERP (such as Real Applied Wholesale, hereinafter: RAW) or migrate to a broader, modular platform like Odoo.

This blog is intended as decision support for the question: stay and optimise or migrate? The scope of the comparison is broad enough to discuss the most important trade-offs: core ERP (sales, purchasing, inventory, manufacturing), logistics/WMS, finance, reporting/BI and integrations.

This is relevant for three groups of stakeholders:

  • Management and finance: ROI, risks, vendor/partner dependence, compliance and predictability of costs.
  • Operations (supply chain, warehouse, production): process fit, WMS depth, performance and exception handling.
  • IT: architecture, integrations, data governance, security, hosting choices and extensibility.

A reconsideration is mainly logical in situations such as: growth to multi-site/multi-company, expansion with e-commerce or 3PL-like services, a need to add new applications faster (CRM, website, service), or plans for international rollout.

Important: this comparison is based on publicly available information about RAW (including modules, integration and reporting pages) and on generic, well-known capabilities of Odoo as a modular ERP platform. The exact fit depends heavily on configuration, implementation choices and the maturity level of processes. In practice, process workshops and fit-gap analyses are necessary before a final decision can be made.

Reading guide: per topic we name where RAW is typically strong, where Odoo is usually stronger, and where the trade-offs lie. Then we go deeper into AI, integration and data governance (including data sovereignty), and we conclude with costs/impact, a decision framework and practical next steps.

2. Type of ERP and starting point of Real Applied Wholesale versus Odoo

RAW positions itself as a solution for wholesale/distribution and manufacturing companies, with emphasis on the combination of ERP and WMS. Public signals point to a clear sector focus (including the packaging sector, FMCG wholesale and technical wholesale). Typical size is around 20–70 administrative users, with peaks to larger environments (references up to about 350 users) and scenarios with multi-site and multi-company. The geographical focus is visibly Benelux and surrounding countries; local compliance is explicitly mentioned for accounting in BE/NL/LU/FR.

Odoo is mainly known in the market as a broadly deployable, modular ERP platform for SMEs and mid-market. The starting point is "horizontal": many standard modules (finance, sales, inventory, manufacturing) and extensions outside the core ERP (CRM, website/e-commerce, projects, HR). Growth is often achieved by adding modules, with implementation by partners or internal teams, and in many cases with integrations with modern SaaS applications.

The difference in solution type is therefore fundamental:

  • RAW: a more specialised suite in which ERP and WMS are positioned as an integrated core. A named concept is "Single-Logistics – Multi-Finance": logistics central, with separate financial administrations per entity.
  • Odoo: a platform approach with many standard building blocks. Sector fit arises through configuration, process choices and any add-ons/customisation.

There is also an important starting-point difference in extensibility. RAW mentions a set of web services (and various known integrations), but there is no public "marketplace" model like Odoo explicitly has. In practice, this often means that extension at RAW mainly runs through vendor/partner projects and integrations. Odoo has a large module ecosystem; this can speed up extension, but the downside is that governance (quality, version compatibility, security) rests more heavily on the customer.

Finally, there is the international starting point. RAW shows a regional focus with explicit statutory accounting for several countries. Odoo is more international by design (multi-company, multi-currency, multi-language), but practical feasibility depends on localisations, partner knowledge and the extent to which you want to harmonise processes per country.

3. Where Real Applied Wholesale is stronger

RAW's strengths lie mainly in depth for wholesale and logistics, and in the fact that logistics and administration are designed in one solution.

ERP + WMS as integrated core

For organisations where warehouse processes and inventory reliability form the core of operations, the degree of integration between WMS and administration is often decisive. RAW positions ERP and WMS as "seamlessly combined". In practice, this can mean: fewer separate connections between a separate WMS and the ERP, fewer synchronisation problems (e.g. inventory statuses), and a simpler operational data model for logistics KPIs.

Trade-off: integrated does not automatically mean "more flexible". The question is whether the standard processes match your variants (e.g. cross-dock, value-added services, batch/lot handling, returns). With sector-specific suites, the fit is often high, but deviations sooner lead to customisation or process compromises.

Logistics depth (warehouse)

RAW mentions an "advanced (mobile) WMS" and extensibility with voice picking. That points to maturity in scanning-driven processes, pick/pack/ship flows and exception handling on the floor. For wholesalers with high order volumes, complex picking strategies or multi-warehouse operations, WMS depth is usually a primary success factor.

Uncertainty: without detailed demos, it remains essential to test your own critical scenarios, e.g.: multi-order picking, wave planning, repackaging activities, handling units, and how the system handles inventory differences (cycle counting, quarantines, blocks).

Complex pricing and promotion structures

RAW explicitly emphasises complex B2B pricing and promotions. In many wholesalers, pricing is not a "rate per item", but a network of customer-specific agreements: tiers, contract prices, temporary promotional prices, exceptions per channel, and combinations of discounts and surcharges (transport, energy, packaging).

The value of this lies not only in functionality, but also in controllability: why did a customer get exactly this price, which rules apply, and how do you audit exceptions? In a migration project, pricing is often an underestimated risk; a few percent error margin in pricing logic can directly affect margins and customer relationships.

Multi-company/multi-site concept (Single-Logistics – Multi-Finance)

The "Single-Logistics – Multi-Finance" concept is relevant for holdings or groups with multiple legal entities, but with shared logistics (e.g. one central warehouse that supplies multiple BVs). This design can have advantages for:

  • warehouse execution: one operational logistics reality;
  • finance: separate ledgers, VAT reporting and statutory requirements per entity;
  • intercompany: clear transactions and settlements.

Trade-off: such concepts work well if you set up governance (master data, item coding, customer accounts, intercompany pricing) tightly. Without discipline, "central logistics" can lead to discussions about ownership of inventory and margins.

Reporting/BI in suite context

RAW mentions reporting and BI options such as Microsoft Reporting Services (ad-hoc), QlikView (trend/BI), Crystal Reports, embedded dashboards and "real-time statistics". Concepts such as "management by exception" and activity-based costing are also mentioned. This points to an environment in which reporting is traditionally set up via BI tools, with possibilities to monitor operational KPIs and exceptions.

Practically, this often means: you extract data from the ERP via standard exports or ODBC and build a BI layer around it. The advantage is freedom in tooling; the disadvantage is that definitions (e.g. margin, service level, inventory availability) can start "living" outside the application if governance is lacking.

Integration catalogue (predefined connections)

RAW mentions a set of web services (a "basket of 26 web services") and integrations with, among others, Dynamics CRM (2013/2015), Alfresco (DMS), QlikView, Reporting Services/Crystal Reports, transport services (DPD/TNT) and Office connections. For companies in this ecosystem, this can shorten the lead time of integrations and limit the risky "custom interface" layer.

Trade-off: if you add modern SaaS apps (new e-commerce platform, CPQ, product information management), you may still need customisation or middleware. Then the question is how transparent APIs are, how versioning works, and who has ownership over integration monitoring.

4. Where Odoo is stronger

Odoo's strength usually does not lie in one specific domain, but in platform breadth, speed of extension and bringing multiple teams together on one application layer.

Broader functional platform outside core ERP

In addition to finance, inventory and manufacturing, Odoo offers a broad package of modules as standard: CRM, marketing automation, website and e-commerce, helpdesk/service, project management and HR-like functions. For organisations with a fragmented landscape (separate CRM, separate ticketing, separate webshop back office), one platform can help to harmonise end-to-end processes, such as:

  • lead → quote → order → delivery → invoice;
  • e-commerce orders → fulfilment → customer service → return → credit note;
  • service contracts → planning → parts → invoicing.

Trade-off: the platform is broad, but not every domain has the same "depth" as a niche solution. The question is where your differentiation lies: in warehouse execution, or in commercial chains (CRM/e-com/marketing) and process breadth.

Ecosystem and extension speed

Odoo benefits from a large ecosystem of modules and implementation partners. This can shorten time-to-value for new use cases (e.g. specific shipping connectors, EDI, portals), because you can more often choose "buy" rather than "build". At the same time, this requires selection criteria: which modules are maintainable, compatible with upgrades, and meet security and privacy requirements?

Modernisation and uniform UX/workplace

A practical advantage of a platform approach is a more uniform user experience across departments. This is especially relevant if not only warehouse and finance, but also sales, customer service and management work intensively in the system. Less context switching can speed up adoption, but only if processes are consistently designed and authorisations are clearly arranged.

Integration possibilities (breadth)

Odoo is often deployed in landscapes with multiple SaaS apps. Through the broad community and connectors, connection with modern tools (e.g. e-commerce, payment providers, BI platforms, ticketing) can in many cases be realised faster. The downside is that "easy to connect" is not the same as "well managed": monitoring, error handling, data quality and ownership must be explicitly set up.

International scalability (scenarios)

For organisations rolling out to new countries, multi-company, multi-currency and multi-language features are important. Odoo is in principle set up for this, but in practice localisations, fiscal requirements (VAT, e-invoicing, reporting) and bank connections are decisive. Here too, scenarios must be validated per country, preferably with local stakeholders and a partner who knows that market.

Data/AI innovation potential (platform effect)

In a platform with many modules, there is often more room for data-driven applications more quickly, because more processes come together in one dataset. The chance is also greater that partners or third parties offer AI-driven add-ons. That does not mean AI "by default" solves everything, but it does mean that you can start experiments faster, provided data governance and security are in order.

5. Comparison

The choice between RAW and Odoo is rarely a purely functional checklist. It is mainly a trade-off between logistics depth and platform breadth, between sector fit and ecosystem, and between predictability and freedom of choice.

Customer base and positioning (fit thinking)

RAW typically fits well with wholesale/distribution and manufacturing companies where WMS and pricing complexity are central, and where regional compliance (Benelux/surrounding countries) is dominant. Odoo more often fits with organisations that want to harmonise processes broadly and consolidate a wider application portfolio on one platform, including commercial processes and digital channels.

A practical fit question is: is your differentiation mainly warehouse execution and pricing, or mainly end-to-end customer processes and digital growth? That determines where you accept the most risk: in WMS depth (with Odoo) or in platform breadth/innovation speed (with RAW).

Functional comparison per domain (scorecard as a starting point)

The comparison below is intended as a starting point for a fit-gap scorecard. The exact scores depend on your process variants and on which Odoo modules/add-ons you deploy.

  • Sales, pricing, promotions: RAW has explicit focus on complex B2B pricing rules and promotions. Odoo can configure pricing, but edge cases (combination rules, exceptions, contract logic) may require customisation or extra modules.
  • Purchasing: both offer standard purchasing processes. Differences often lie in exceptions (drop-ship, intercompany supply, EDI) and in integration with supplier portals.
  • Inventory/WMS: RAW seems stronger in warehouse depth, mobile flows and voice picking. Odoo can cover warehouse processes, but the required level (RF scanning, wave picking, value-added services) must be explicitly tested.
  • Manufacturing: RAW focuses on light to integrated manufacturing (make-to-order/make-to-stock). Odoo also supports manufacturing, but complexity (shop floor control, quality, traceability) determines whether standard is sufficient.
  • Service: Odoo generally has a broader suite for service/helpdesk/field service. RAW has service mentioned as a module, but the depth and integrations must be validated.
  • Finance: RAW mentions statutory accounting for BE/NL/LU/FR. Odoo can do multi-company accounting, but local compliance and reporting must be verified per country.
  • Reporting: RAW mentions classic BI tooling (Reporting Services/QlikView/Crystal) and data extract (Excel/ODBC). Odoo has built-in reports, but external BI is often added for enterprise reporting.
  • E-commerce: Odoo is generally stronger due to standard website/e-commerce modules. With RAW, this is more often via integrations with external e-business solutions.
  • CRM: Odoo is often stronger due to native CRM and marketing modules; RAW mentions integrations with CRM (including Dynamics CRM), which can work but depends on integration quality and data consistency.

Process complexity vs standardisation

RAW is designed for variable logistics processes and pricing agreements typical of B2B wholesale. That can mean you have to "bend" less and need less customisation to model exceptions. Odoo is strong when you want to standardise processes across teams and when your organisation is willing to harmonise practices around the standard. If you want to continue to support many exceptions, the configuration and customisation burden in Odoo can rise.

A useful workshop question is: which exceptions are truly competitively distinctive, and which are historically grown variants we can rationalise?

Multi-site/multi-company and governance

RAW explicitly mentions a concept for central logistics with separate finance. That can be an advantage in groups with shared warehouse and multiple entities. Odoo supports multi-company, but success depends heavily on design choices: master data governance, intercompany flows, rights structure, and how you consolidate reporting.

In both cases: multi-company only works well if you decide in advance what is "shared" (items, customers, suppliers, price lists) and what differs per entity. Without those choices, data drift arises and consolidation becomes expensive.

Implementation approach and dependencies

With RAW, the implementation approach seems to revolve more often around a suite with a fixed set of building blocks and integrations. That can give predictability, but also dependence on the vendor/implementation partner for extension outside the core area. With Odoo, the approach is composable: you choose modules, apps, and build integrations. This gives flexibility, but makes scope management and architectural governance more critical. The quality of the implementation partner and internal product ownership are usually decisive for success with Odoo.

Risks and mitigations per choice

  • Keep/optimise RAW: risks often lie in innovation and roadmap speed, ecosystem lock-in, and international growth outside the core region. Mitigation: roadmap discussions, clear integration strategy, and testing whether extensions (e-com, CRM) remain manageable via integrations.
  • Migrate to Odoo: risks lie in safeguarding WMS depth, pricing edge cases, data migration and organisational change. Mitigation: proof-of-concept on critical flows (warehouse + pricing), phased implementation where possible, and a robust test and reconciliation approach for inventory and finance.

6. AI and Integration

AI maturity (current signals)

Based on publicly available product information, RAW positions itself mainly with BI, dashboards and real-time statistics; explicit AI/ML functionality is not described. That does not mean AI is impossible, but it does mean it likely takes place outside the core application (e.g. in BI or a data warehouse).

With Odoo, AI is mainly relevant as a platform layer and through integrations with external AI services or add-ons. The practical difference is that Odoo can often collect more data in one model (sales, e-com, service) due to broad process coverage, which can speed up AI use cases. At the same time, AI remains dependent on data quality, rights, and the willingness to adjust processes based on recommendations.

Data architecture and access

RAW mentions Excel export, ODBC and BI/data warehouse context. This is a classic route: extract data to an analytical environment, where you build KPIs and possibly predictive models. The advantage is that you have tooling freedom; the disadvantage is that near-real-time use cases (e.g. dynamic pick priorities) are more difficult if the chain is mainly set up in batch mode.

Odoo often has a "central dataset" across multiple teams. Built-in reports may be sufficient for operational management, but for enterprise BI or data science you usually still end up with a data warehouse/lakehouse. The decision point is then: do you want analytics primarily in the ERP, or in a separate data layer with its own governance?

Integration strategy (make vs buy)

RAW offers a defined web service set and specific integrations (CRM, DMS, transport, reporting). This fits a strategy where you choose known, supported connections and deviate to a limited extent. Odoo often offers more "buy" options through connectors and apps, supplemented with API customisation. That gives freedom of choice, but requires clear standards: data models, event handling, retry mechanisms and monitoring.

In both scenarios, it is wise to classify integrations:

  • mission-critical (transport labels, EDI, payments): strict SLA, monitoring, fallback;
  • analytical (BI): batch may be acceptable, definitions need governance;
  • experience (portals, CRM): data consistency and latency are decisive for user experience.

Real-time vs batch, and operational monitoring

RAW mentions "management by exception" and real-time statistics. For warehouse KPIs (order backlog, pick performance, inventory differences), this can be valuable, provided KPI definitions are unambiguous and operational users have a clear "exception flow": who does what in case of deviation?

Odoo can facilitate dashboards across multiple teams (sales, service, operations), but success depends on setup: which events trigger alerts, how do you avoid "dashboard overload", and how are actions logged (audit trail)?

Data governance and security (decision criteria)

For decision-making, it is useful to make data governance explicit, regardless of functionality. Think of:

  • ownership: who is the owner of master data (items, customers, price lists)?
  • auditability: can pricing rules, inventory movements and user actions be explained afterwards?
  • access and roles: segregation of duties (warehouse vs finance), least privilege, logging.

In addition, hosting and data location are essential for data sovereignty. With RAW, it is not publicly clear whether and how EU hosting, on-premises or specific data location options are offered. That means you have to ask this explicitly in the pre-trajectory: where is the database physically located, which sub-processors are involved, how are backups arranged, and what export options exist on exit.

With Odoo, data sovereignty depends on the chosen edition and hosting model (e.g. vendor-hosted versus own hosting/partner-hosting) and on where you run additional services (BI, AI). In an EU context, this concretely means: record whether data stays within the EU, how you manage encryption, and whether you can contractually enforce that training data for AI is not used out of scope.

AI use cases to test in workshops

To make AI concrete, it is wise to select a limited number of use cases and assess the data need and process impact per use case:

  • Demand forecasting: predicting per item/customer segment. Requires clean historical sales data, promotion calendars and lead times. Process impact: replenishment policy and exception review.
  • Inventory and order recommendations: automatically proposing order quantities with constraints (MOQ, pallet, transport). Requires inventory positions, open orders and supplier performance. Process impact: buyers move from "manually ordering" to "review & approve".
  • Exception detection: signalling deviating margins, faulty pricing rules, unusual return patterns. Requires pricing audit trail and margin components. Process impact: clear escalation paths.
  • Price optimisation: suggests price adjustments based on elasticity/segments. Requires rich data (prices, volumes, competitive indications). Process impact: governance and commercial decision-making; risk of customer impact.
  • Assistants for service/purchasing: automatic summarisation of cases, suggestions for next best action. Requires ticket data, order history and knowledge base. Process impact: training, quality control and privacy-by-design.

10. Costs and impact of a transition

A transition from RAW to Odoo (or vice versa) is rarely just a licence matter. It is a combination of one-off project costs, recurring costs and organisational impact. Below are the most important components you want to include in a TCO/ROI model.

Cost components (TCO)

  • Licences/subscription: user and module costs; also costs for add-ons/connectors (Odoo) or modules (RAW) and any environment costs (test/acceptance).
  • Implementation: process design, configuration, customisation, testing, project management. This is often the largest item.
  • Integrations: building/rebuilding, middleware, monitoring, error handling, EDI.
  • Reporting/BI: rebuilding reports, KPI definitions, data warehouse or BI licences.
  • Infra/hosting: cloud costs, backup, security tooling, environment management; and costs for EU hosting or dedicated hosting if data sovereignty requires it.
  • Management: application management, release/upgrade, incident management, vendor management.
  • Training and adoption: user training, super user network, process documentation.
  • Further development: backlog after go-live, optimisation and new modules.

ROI is usually indirect: less manual work, fewer errors, higher order processing speed, better inventory reliability, faster month-end close and less IT fragmentation. Therefore, convert ROI into measurable KPIs (e.g. pick errors per 1,000 order lines, DSO, inventory turnover, lead time purchase-to-pay).

Migration impact on core processes

The biggest impact is usually on processes where RAW is strong: WMS and pricing. When migrating to Odoo, you must explicitly determine which warehouse functions can be standard, which via add-ons, and which require customisation. Think of scanning flows, location management, wave planning, and possibly voice picking. In addition, pricing/promotions require a detailed reimplementation including exceptions, authorisations and audit trail.

Finance also deserves attention: local compliance, VAT reporting, audit trails and the connection to banks and invoicing processes. For manufacturing, MRP and shop floor processes are often "set up just differently"; the impact is large if planners and shop floor have to change their working methods.

Data migration and data quality

Data migration is rarely "export/import". You are dealing with:

  • master data: items, units, locations, customers/suppliers, price lists and discount structures;
  • open positions: open sales orders, purchase orders, backorders, production orders;
  • inventory positions: quantities per location, batch/lot/serial numbers, valuation;
  • finance: open items, ledger balances, VAT positions, historical documents (what do you migrate and what do you archive?).

Reconciliation requirements are crucial: inventory and finance must demonstrably tally at cutover moment. For this you need data quality rules, trial migrations and a clear "source of truth" (especially if multiple systems are involved).

Integration rebuild or rationalisation

A migration is an opportunity to rationalise integrations: which connections are still needed, which can disappear due to platform functionality, and which must become more robust? If you currently connect with transport services, CRM, DMS and BI, then the question with Odoo is: do you include CRM and e-commerce in Odoo, or do you continue to connect with best-of-breed?

Rebuild costs can be high, especially if integrations have grown historically "point-to-point". Therefore, consider a middleware layer or integration standardisation (event-driven where useful, otherwise API/batch), including central monitoring and logging.

Downtime/continuity and cutover approach

The cutover strategy is a risk-determining choice. A big bang gives one switching moment, but is heavy in terms of preparation and testing. A phased approach (e.g. first finance/CRM, later WMS) can spread risks, but temporarily increases integration complexity and can lead to dual data flows.

For companies with critical warehouse operations, a parallel run is sometimes necessary for inventory and order processing, especially around peak periods. Plan cutover preferably around inventory and accounting closures, with clear playbooks and fallback scenarios.

Organisational impact and change management

An ERP migration changes roles and routines. In the warehouse, the way of working often changes (scanning, exceptions, picking strategies). In sales, pricing logic and authorisation may change. In finance, closing processes and reporting change. This requires:

  • role clarity: who manages pricing rules, who manages item data standards?
  • training: task-oriented, with practical cases;
  • super users: ownership and first-line support per department;
  • KPI redefinition: what do we measure after go-live and who steers?

11. Conclusion and next steps

Summary decision criteria (decision framework)

A workable decision framework is to put the choice along five axes:

  • Logistics depth (WMS, scanning, exceptions) versus platform breadth (CRM/e-com/service/HR).
  • Innovation and ecosystem: speed of new use cases and availability of modules/partners.
  • International scale: local compliance, rollout model, multi-country governance.
  • Integration complexity: number of connections, quality of APIs, monitoring and ownership.
  • TCO and change capacity: total costs + capacity to harmonise processes and bring people along.

When keeping/optimising RAW is logical

Keeping and optimising RAW is logical when WMS depth and complex pricing are genuinely differentiating, and when your footprint is mainly regional. Also if warehouse performance and accuracy are central and you have little need for a broad suite (website/e-com/marketing) on the same platform, optimising RAW (plus targeted integrations) can be rational.

When considering Odoo is logical

Odoo becomes more interesting when you want an end-to-end platform for multiple departments, including CRM, e-commerce and service, and when you want to add new use cases faster via modules/partners. Even with international plans, a platform with multi-company/multi-currency can offer advantages, provided you demonstrably cover WMS and pricing needs with standard + add-ons + limited customisation.

Next steps (short and concrete)

  • Process workshops per domain (order-to-cash, procure-to-pay, warehouse, manufacturing, finance) with focus on exceptions.
  • Fit-gap analysis with scorecard and "must-have" acceptance criteria (especially WMS and pricing).
  • Reference cases at comparable complexity (warehouse volume, pricing, multi-company).
  • Proof-of-concept on critical flows (scanning/voice scenarios, pricing and promotion logic, intercompany).
  • TCO/ROI model including migration, integrations, management and change.
  • Roadmap decision: stay and modernise versus phased migration, with clear governance.

Decision documents needed

  • Target Operating Model: desired processes, roles, governance and KPIs.
  • Data and integration architecture: data ownership, integration standards, monitoring, security.
  • Migration plan: phasing, cutover, parallel run, test and reconciliation approach.
  • Risk register: pricing, WMS, compliance, integrations, resource availability.
  • Governance & ownership: who decides on scope, releases, data standards and exceptions.

12. How pantalytics can help with a transition

Fit-gap analysis with scorecard

Pantalytics can facilitate fit-gap workshops per end-to-end process (order-to-cash, procure-to-pay, warehouse, make-to-stock/make-to-order), with a scorecard that distinguishes must/should/could. The goal is not "to measure everything", but a priority list that supports decision-making: which gaps are acceptable, which require customisation, and which are showstoppers.

Architecture and integration design

For both RAW optimisation and Odoo migration, a target architecture is needed: which systems remain, which disappear, which integrations are standardised? Pantalytics can develop a target landscape including API/middleware choices, integration standards, monitoring/logging and a role model for security and access.

Data and migration strategy

Pantalytics can draw up a migration strategy per data domain, including a data quality plan, migration order and reconciliation (inventory and finance). This also includes a test approach: trial migrations, cutover playbooks and "go/no-go" criteria on data quality.

Proof-of-concept for critical differentiators

A proof-of-concept is especially valuable on parts where the greatest risks lie. Think of WMS scenarios (scanning, exceptions, possibly voice), pricing and promotion logic, multi-company/intercompany flows and reporting. By recording measurable acceptance criteria in advance (e.g. pick flow lead time, pricing rule audit, inventory reconciliation), you prevent a PoC from becoming a "demo with a good feeling".

Implementation and change approach

Pantalytics can help with phasing (big bang vs phased), cutover planning, training & adoption and KPI implementation after go-live. This also includes the role distribution between business, IT and implementation partner, so that ownership is secured after go-live.

Vendor and partner selection support

Finally, pantalytics can support partner selection with RFP criteria, demo scripts based on own processes, reference checks and contract/SLA attention points. Especially with a platform choice (such as Odoo), partner quality is often as decisive as product functionality.