In an increasingly competitive retail landscape, heavy investment in artificial intelligence and omnichannel tools only pays off if the digital core—that is, the ERP, CRM, PIM, customer and inventory databases—can keep pace. Legacy architectures, data silos, and technical debt stifle competitiveness and render innovation promises illusory.

To make AI and omnichannel projects truly viable, it is imperative to first renovate the IT foundation: unify data, migrate to the cloud, and simplify monolithic platforms. This article explains why this modernization is a strategic priority for retailers and how to carry it out successfully.

Unify data for a 360° view

Without a unified data foundation, marketing and operational activities remain fragmented and ineffective. Consolidating customer and inventory streams is the cornerstone of the 360° view necessary to drive data-driven commerce.

Consolidation of customer data

Customer information is often scattered across CRM systems, brick-and-mortar stores, e-commerce platforms, and loyalty programs, each generating separate reports. This dispersion prevents a clear understanding of the purchase journey and the ability to anticipate needs in a personalized way. A unified pipeline can ingest, normalize, and correlate these data streams in real time, ensuring every channel relies on the same single source of truth. Open-source data orchestration solutions facilitate the setup of synchronous or asynchronous flows according to business constraints. Ultimately, this consolidation provides a solid foundation for predictive analytics and targeted campaigns.

Implementing standardized connectors combined with enrichment and cleansing scripts ensures data reliability. Validation routines can detect duplicates, invalid email addresses, or inconsistencies in order histories. Continuous monitoring of these pipelines issues alerts on flow interruptions or quality degradation, preventing business errors from propagating through the digital ecosystem. Adopting common formats (JSON, Parquet) also enhances interoperability with analytics modules or data science tools. By structuring data at ingestion, teams gain both agility and confidence.

By optimizing these streams, marketing and operations teams can tailor re-engagement, promotion, or replenishment scenarios based on a single source of truth. Costs associated with multiple data exports and manual corrections are reduced, while the time-to-market for new offers is shortened. Management via consolidated dashboards then becomes a true differentiator, helping to better steer advertising spend and inventory levels.

Map data flows

Mapping all exchanges between ERP, CRM, PIM, and other systems is often underestimated but essential. Without this map, IT leaders cannot pinpoint the exact origin of data or quickly identify potential breakpoints. Documenting data schemas and dependencies is the first step toward designing robust, scalable pipelines. Open-source data lineage tools automatically animate these maps, making them accessible to all stakeholders.

Once the topology is documented, you can rationalize flows by eliminating duplication points and redundant transformations. At that stage, formats can be standardized, file system changes minimized, and network performance optimized. This approach cuts infrastructure costs and simplifies maintenance. Moreover, by clearly identifying functional data owners, you establish shared responsibility between the IT department and business units.

When flows are controlled, upgrading software or moving to a new version of a critical component no longer disrupts daily operations. End-to-end automated tests replicate the entire cycle, ensuring reliability with every update. If needed, restores or rollbacks can be completed in hours instead of days, reducing the risk of service interruptions.

Governance and data quality

Data governance goes far beyond setting up a catalog. It involves defining clear ownership, lifecycle, and priority rules for each data type. Cross-functional data councils—comprising IT, marketing, and operations—approve new enrichment or removal policies. This transverse governance ensures aligned priorities and faster responsiveness to regulatory changes (GDPR, e-privacy).

A Swiss fashion retailer with around fifty stores consolidated its customer and inventory databases into a centralized data lake orchestrated by open-source pipelines. This example shows how rigorous governance can cut pricing errors by 30% and optimize real-time product availability. Marketing teams then launched hyper-personalized campaigns based on reliable data, achieving a conversion rate over 25% higher.

Establishing quality metrics and SLAs for data streams allows you to measure compliance with business objectives. Dedicated dashboards tracking quality incidents (duplication, missing data, time lag) alert managers in real time. As a result, data becomes a controlled asset capable of supporting advanced use cases without breaching operational or regulatory requirements.

Migrate to a secure, scalable hybrid cloud

The cloud offers an ideal playground to deploy extensible, secure, and resilient capabilities. A phased migration to a hybrid model ensures flexibility and cost control while strengthening compliance and security.

Choosing a hybrid cloud architecture

Opting for a hybrid architecture lets you keep critical workloads on-premises while leveraging on-demand public cloud resources. This dual approach enables shifting certain functions (analytics, machine learning, backups) to ultra-scalable environments without disrupting day-to-day operations. Containers and Kubernetes orchestration facilitate microservice portability between on-premises and cloud, ensuring a seamless operational continuity.

Setting up a secure virtual private network (VPN) or dedicated direct connections (Azure ExpressRoute, AWS Direct Connect) guarantees performance and confidentiality for sensitive data exchanges. You can then operate multiple clouds in parallel, spreading risks associated with outages or pricing fluctuations. This multi-cloud strategy avoids vendor lock-in and aligns your IT approach with the open-source, modular philosophy championed by Edana.

Finally, centralized monitoring of the hybrid model via open monitoring solutions provides a consolidated view of performance, costs, and security. Alerts can automatically trigger scaling actions or load shifts to maintain availability. IT teams thus gain agility and can quickly adapt the architecture to traffic variations, especially during commercial peaks.

Cloud security and compliance

Moving customer and transactional data to the cloud requires strengthened access controls and encryption both in transit and at rest. Centralized Identity and Access Management (IAM) solutions, combined with zero-trust policies, ensure every request is authenticated and authorized. Audit logs and compliance reports are generated automatically to meet regulatory requirements and certification standards.

A Swiss retail chain migrated its e-commerce services to a public cloud while achieving ISO 27001 certification and adhering to PCI-DSS standards. This case illustrates how robust security frameworks in the cloud bolster customer trust and pave the way for new partnerships, all while simplifying internal and external audits.

Locally hosted cloud services in Switzerland also guarantee data sovereignty, valued by both authorities and end customers. This legal and geographic proximity eases compliance with data protection regulations and mitigates risks associated with international data transfers. Retailers can thus reconcile performance with risk management.

Scalability and resilience

Cloud adoption allows real-time resource adjustment based on traffic and marketing campaigns, avoiding overcapacity for most of the year. Serverless architectures and on-demand functions help reduce operational costs and improve development team responsiveness. Pre-production and testing environments can be provisioned and decommissioned automatically, accelerating delivery cycles.

In the event of a major incident, multiple availability zones and automatic failover mechanisms ensure a minimal Recovery Time Objective (RTO). Continuously replicated data across regions enables instantaneous business resumption in case of a failure. This resilience is a key competitive advantage for retailers who cannot tolerate revenue losses or service interruptions during traffic peaks.

Incremental backup mechanisms coupled with automated restoration workflows significantly reduce the risk of irreversible data corruption or loss. IT teams can regularly test their disaster recovery procedures and validate the robustness of business continuity plans. The guarantee of continuous availability thus becomes a decisive selling point.

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Simplify and reduce technical debt

Monolithic architectures concentrate risk and slow down the rapid rollout of enhancements. Reducing technical debt through a phased decomposition and targeted refactoring plan is essential to gain agility and performance.

Breaking down into microservices

Decomposing a monolith into microservices involves gradually isolating key functions (authentication, product catalog, promotions, payments) into independent services. Each service becomes autonomous, testable, and deployable on its own, minimizing side-effects during updates. This modularity also allows scaling each component based on usage and observed performance.

Implementing standardized RESTful APIs or gRPC ensures reliable communication between services while maintaining functional consistency. Each microservice can choose the most suitable technology—be it Node.js for asynchronous processing or Java for transactional robustness. This technical freedom prevents vendor lock-in and aligns the architecture with an open-source, evolving approach.

Automated CI/CD pipelines trigger unit, integration, and end-to-end tests on every change, ensuring consistent code quality. The ability to deploy a single microservice without affecting others accelerates the time-to-market for new features. Teams can experiment, fix, and iterate faster without compromising overall stability.

Refactoring and incremental modernization

Refactoring should follow a pragmatic roadmap that identifies critical areas and quick wins. Start by extracting the most volatile or heavily used components while keeping the monolith operational. This incremental approach limits risks and costs compared to a full rewrite, which is often lengthy and expensive.

Static code analysis and test coverage tools help pinpoint outdated or poorly tested functions. A robust automated test suite ensures that each refactoring maintains functional integrity. With every iteration, technical debt decreases, code readability improves, and new hires can ramp up more quickly on a modernized foundation.

This gradual process also provides the opportunity to introduce new coding standards and best practices, such as dependency injection, clear separation of business layers, and proven design patterns. Ultimately, the codebase becomes more robust, secure, and easier to evolve in support of omnichannel commerce needs.

Proactive management of technical debt

A Swiss online wine retailer launched a technical debt reduction plan after deployment times doubled in less than a year. This example shows that a focused audit followed by targeted refactorings can cut the time needed to introduce new seasonal offers by 40%. The initiative freed teams to focus on product innovation rather than inherited bug fixes.

Regular code reviews, combined with metrics on cyclomatic complexity and test coverage, allow continuous monitoring of debt levels. Technical committees now include a dedicated debt indicator in their dashboard, ensuring each new feature integrates without increasing the backlog. Technical debt thus becomes a natural part of IT governance.

Finally, integrating short, frequent clean-up sprints into the backlog ensures debt doesn’t accumulate. Each sprint allocates capacity to technical tasks, enforcing proactive maintenance and avoiding a snowball effect. This agile debt governance keeps the digital foundation always ready to embrace new innovations.

Build a robust digital foundation for AI and omnichannel

A modernized digital core is the necessary prerequisite for deploying high-performing omnichannel experiences and AI models. It is this foundation that guarantees the reliability of product passports, real-time analytics, and large-scale personalization.

Integration of product passports

Product passports demand granular tracking of each item from manufacture to sale. A centralized PIM (Product Information Management) system brings together technical specifications, traceability data, certifications, and marketing content. A modernized digital core directly incorporates this information into inventory flows and sales channels, ensuring total consistency between product promises and on-the-ground reality.

Orchestrating these passports involves automated update and validation workflows linked to ERP systems and e-commerce platforms. Secure RESTful APIs distribute enriched data to mobile apps, in-store kiosks, and websites, providing accurate real-time information to both customers and staff. This traceability becomes a differentiator, especially amid growing demands for sustainability and transparency.

With a unified repository and rigorous governance, retailers can also offer resale or second-life journeys based on product quality and history. This approach generates new revenue streams, strengthens customer engagement, and aligns with more responsible business strategies.

Real-time analytics

To make analytics truly actionable, you must move beyond batch processing and shift to continuous data streams. Streaming technologies like Apache Kafka or Pulsar deliver the responsiveness needed to instantly detect sales trends, stock outages, or emerging purchase behaviors. A modernized architecture leverages these streams to trigger business alerts and AI scenarios in near real time.

Deploying dynamic dashboards connected directly to data pipelines gives decision-makers an operational and strategic view without delay. Teams can then target promotions on high-value items or adjust supplier orders before stockouts impact revenue. This ability to anticipate and act in real time is a major competitive lever.

Streaming cluster scalability ensures controlled load increases during commercial peaks. Open-source real-time analytics tools can run in cloud-native or on-premises modes, depending on sovereignty and latency requirements, while maintaining usage-based billing. Retailers thus gain a modern decision cockpit ready for next-generation AI use cases.

Omnichannel and personalization

Merging digital and physical channels relies on a centralized platform capable of broadcasting the same customer and inventory data in real time to all touchpoints. Product recommendations, personalized offers, and upsell scenarios then operate on a unified customer profile, regardless of entry channel. This consistent experience strengthens loyalty and increases average order value.

An unnamed Swiss retailer with multiple formats implemented a unified digital core combining CRM, PIM, and e-commerce. It reported a 20% increase in omnichannel revenue and a 15% reduction in product returns. This case demonstrates that dynamic personalization powered by synchronized data transforms customer engagement into a true competitive advantage.

Retailers can tailor promotions based on purchase history, local stock, and seasonality, while respecting customer preferences and consent. This advanced orchestration is the indispensable foundation for generative AI strategies, intelligent chatbots, and truly relevant virtual assistants.

Modernize your digital core for sustainable competitiveness

Digital core modernization rests on three pillars: data unification, cloud migration, and progressive technical debt reduction. Together, they establish an agile, secure platform ready to support AI and omnichannel initiatives. This strategic transformation cuts costs, enhances the customer experience, and positions retailers to anticipate market changes with confidence.

Our Edana experts are available to guide you through renovating your IT architecture, from audit to execution, prioritizing open-source and modular solutions. With our contextual and agile approach, you will gain a sustainable, scalable, high-performance digital foundation.

Discuss your challenges with an Edana expert

In retail, digital modernization doesn’t stall for lack of vision but because of persistent organizational and technological silos. Whether in teams, systems, or data, these divisions fragment customer insights, generate costly redundancies, and create security vulnerabilities.

To speed up a sustainable digital transformation, it’s not enough to simply “break” these silos—you must coherently link them through a cross-functional framework combining strategy, data, IT, and corporate culture. Five key levers help build this integrated network: formulating a shared vision, mapping data flows, clarifying architecture and governance, defining a fluid ecosystem, and measuring change readiness.

Formulate a Shared Vision and Common Goals

A shared digital ambition unites teams around a single business purpose. Clear, measurable goals build trust and guide priorities.

Align the Strategic Vision

To create a coherent roadmap, executive leadership and IT must jointly define priority challenges: optimizing sales channels, personalizing experiences, or improving operational efficiency. This shared vision translates into common metrics that steer every initiative. Without it, each department may develop isolated solutions, reinforcing the very silos you aim to dismantle.

For instance, a mid-sized retailer launched multiple digital projects without aligning marketing and IT. Each pursued its own KPIs, resulting in the simultaneous release of two incompatible mobile apps. This failure highlighted the necessity of shared governance before any development begins.

Integrated oversight involves continuous communication through steering committees that include both business and IT representatives. Every quarter, objectives are revisited, priorities adjusted, and stakeholder buy-in secured. Agility emerges from this structured dialogue.

Ultimately, a single strategic governance model ensures every digital project serves the same business ambition, preventing duplication and optimizing resources.

Establish Cross-Functional Governance

Beyond vision, forming an interdepartmental digital committee centralizes decisions and responsibilities. Comprising operations, marketing, finance, and IT representatives, this committee validates roadmaps, adjudicates budgetary trade-offs, and prioritizes initiatives.

Each member has a voice, ensuring decisions balance business needs with technical constraints. Deliverables are approved collectively, reducing frustration and late-stage resource reallocations.

In this context, agile rituals (monthly reviews, prototype demos) reinforce transparency and commitment. Rapid feedback allows course corrections before costly developments veer off track.

This cross-functional governance creates a single source of truth, aligning everyone to global priorities and avoiding contradictory decisions.

Define Customer-Centric, Measurable Objectives

Every initiative is tied to customer-focused key performance indicators (KPIs): conversion rate, average basket value, Net Promoter Score, or order processing time.

A unified dashboard aggregates these indicators in real time, accessible to both IT and business decision-makers. This shared visibility informs tactical and strategic adjustments.

When goals are explicit and quantified, each team understands its role in achieving the common aim. Priorities shift based on measured results, ensuring continuous adaptation to market needs.

Experience shows that a clear, shared target reduces digital project delivery delays by 40%, as everyone knows the outcome they’re striving for.

Map Data Flows to Reveal Redundancies

Understanding the origin and path of every data element is essential to optimizing its use. Mapping highlights duplicates, bottlenecks, and value losses.

Identify All Data Sources

Before integration, catalog every system that produces or consumes customer, product, or transactional data: ERP, CRM, e-commerce platform, logistics solutions, etc. This exhaustive list is the starting point.

Then document the nature, frequency, and format of each flow. The goal is to pinpoint manual exchanges or makeshift interfaces that create silos.

This fieldwork reveals each data element’s properties: uniqueness, volume, confidentiality requirements. This information guides the next project phases.

The initial mapping provides the global overview needed to prioritize improvements and plan integration efforts.

Detect and Eliminate Redundancies

Once flows are mapped, identify where the same data is collected or stored in multiple places without synchronization. These duplicates cause inconsistencies and high maintenance costs.

Assign a criticality score to each redundancy based on its business impact: order errors, delivery delays, billing discrepancies. The most critical duplicates rise to the top of the priority list.

Custom analysis scripts can automate the detection of divergent versions of the same customer or product record. These tools simplify consolidation.

Visualize the Omnichannel Customer Journey

Mapping also tracks the customer journey at every touchpoint: website, mobile app, physical store, call center. This breakdown shows when and how data flows.

By diagramming this journey, you uncover where information is lost: unsynchronized forms, manual entries, or non-interoperable interfaces.

Visualization highlights critical handoffs where a data break leads to poor customer experience (loyalty errors, inconsistent pricing, outdated inventory).

This comprehensive diagnosis then forms the basis for prioritizing the connectors and APIs to develop, reducing end-to-end data breaks.

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Clarify Data Architecture and Governance

A unified data model and rigorous governance rules ensure information integrity and reliability. Clear processes assign responsibilities and quality levels to each data element.

Centralized Data Model Design

Create a centralized data model that unites key entities: customer, product, transaction, inventory, etc. Describe every attribute: type, format, validation rules.

This single repository becomes the system of record for all connected systems, avoiding discrepancies and easing exchanges.

Establishing a data lake or data warehouse can support this model by aggregating and historizing flows.

For a mid-sized industrial manufacturer, this approach demonstrated that consolidating to a single product repository cut configuration errors by 50%.

Data Governance Policies and Quality Standards

Data governance encompasses defined processes for creating, modifying, and deleting records. Every action is traced to ensure full auditability.

Implement quality rules: minimum completeness, uniqueness, standardized formats, and automated checks. Anomalies trigger correction workflows.

Data quality dashboards alert business and IT teams whenever reliability indicators drop below critical thresholds.

This organizational rigor prevents regressions and bolsters trust in the figures that drive retail operations.

Define Roles and Responsibilities

Establish key data governance roles: data owners (business), data stewards (quality), and data engineers (technical). Each person knows their decision-making scope.

A capability-building plan supports this distribution of responsibilities, with targeted training for data stewards and clear process documentation.

The governance charter, ratified by the cross-functional committee, formalizes these roles and schedules periodic reviews to adjust the organization.

This shared accountability embeds data quality in the corporate culture, a sine qua non for reliable exploitation.

Develop an Ecosystem Strategy and Measure Change Readiness

A hybrid digital ecosystem connects internal and external partners to provide a 360° customer/product view. Regular organizational maturity assessments anticipate resistance and ensure adoption.

Build an Open, Modular Ecosystem

Choose scalable software components—open source or interoperable—rather than monolithic solutions. These modules connect through standardized APIs.

This modular approach enables gradual integration of new services (analytics, personalization, payments) without rebuilding the existing platform.

Such an ecosystem also invites external innovation: startups or partners can propose extensions without disrupting the core.

This balance between flexibility and centralized oversight ensures optimized time-to-market for each new retail offering.

Ensure Technical Interoperability

The ecosystem strategy relies on a catalog of documented, versioned APIs to manage exchanges between systems and partners.

Deploy a data bus or an integration platform (iPaaS) to orchestrate real-time or batch flows as business needs dictate.

Automated interoperability tests validate each API update, reducing breakage risk when deploying new features.

One financial institution saw its API error rate drop by 70% after implementing an iPaaS, demonstrating the direct impact on operational reliability.

Measure Maturity and Support Change

Strategy success depends on team buy-in. Assess change readiness with indicators: tool adoption rates, training participation, number of ideas submitted.

An initial organizational audit identifies strengths and barriers: technical skills, agile culture, innovation appetite.

Based on this, roll out a support plan combining workshops, mentoring, and coaching, informed by concrete user feedback.

Continuous measurement of maturity—via internal surveys and performance indicators—allows trajectory adjustments to maintain momentum.

Turn Silos into a Client Advantage

Intelligently connecting organizational and technological silos creates a seamless network where data, processes, and teams collaborate harmoniously. By establishing a shared vision, mapping data flows, clarifying architecture and governance, and deploying a modular, agile ecosystem, you gain a unified customer view that drives responsiveness and personalization.

Measuring change readiness and supporting teams ensures this transformation endures, delivering sustainable competitive advantage. The levers presented here optimize resources, eliminate redundancies, and anticipate tomorrow’s challenges.

Our experts are ready to co-build a tailored digital roadmap centered on customer experience. Together, let’s transform your silos into an interconnected network that powers your performance.

Discuss your challenges with an Edana expert

The constant rise in e-commerce returns now weighs heavily on margins, overloads warehouses, and increases retailers’ carbon footprint. With an average cost per return of around CHF 20 and packaging waste up to five times greater than for an in-store purchase, the issue quickly becomes a strategic challenge.

For IT leaders, transformation managers, and executive teams, it’s no longer a matter of passively absorbing a cost center but of transforming this reverse cycle into a lever for economic performance and sustainability. A systemic, coordinated, data-driven approach is essential to reimagine returns as an optimized process—from product recommendation to reverse logistics.

Logistical and Environmental Challenges

Return volumes have exploded, straining logistics capacity and customer service. Direct and hidden costs are overwhelming budgets and eroding margins.

Surge in Return Volumes and Logistical Burden

With the democratization of online shopping and free returns, the number of packages to process in reverse has doubled for some retailers in under two years. Warehouses—often sized for initial distribution—struggle to absorb this growing flow, creating bottlenecks and delaying restocking for subsequent orders.

Each returned item requires a quality-control cycle, restocking, or refurbishment. These tasks tie up staff, storage space, and processes that weren’t part of the original logistics design. The lack of advanced automation, compounded by poor coordination between warehouses and e-commerce platforms, only worsens the situation.

Beyond simple sorting, returns trigger complex operations such as cleaning, repackaging, relabeling, and sometimes destruction. These steps add weight both to reverse transportation costs and to financial reporting, as every movement hides costs in ill-suited ERP systems.

Direct and Indirect Costs of a Siloed Process

The average cost of a return often exceeds CHF 20, including shipping, handling, and administrative fees. But that figure doesn’t account for stockouts, depreciation during storage, or potential discounts offered to clear unsold inventory.

In a siloed scenario, marketing doesn’t see the impact of free-returns campaigns, finance struggles to manage the associated budget, and IT faces surging return-management requests without the ability to reprioritize. Management becomes opaque, and no one holds a comprehensive view of the reverse cycle.

This fragmentation hits cash flow and conversion rates directly: customers who experience poorly managed returns place fewer future orders, eroding loyalty and increasing churn, which is costly to recapture.

Environmental Impact and CSR Pressure

Beyond budgetary concerns, multiplying returns produces vast amounts of packaging waste and additional transport legs that can quintuple the carbon footprint of an in-store purchase. Retailers are now judged on their CSR performance, with investors and consumers scrutinizing these metrics.

To address this, some brands have conducted internal environmental audits to compare outgoing versus incoming recycled cardboard tonnage. They discovered most returned packaging wasn’t reusable due to inadequate refurbishment processes and ended up shredded—undermining their emissions-reduction goals.

For example, a fashion retailer found nearly 60% of its returns were deemed non-recyclable for lack of proper sorting. This highlighted the urgent need for a systemic collection, sorting, and refurbishment process to meet CSR commitments and minimize site pollution.

Limitations of Point Solutions

Standard return-management platforms offer basic features but remain siloed and disconnected from cross-functional concerns. Without ecosystem integration, gains are limited to superficial KPIs.

Limited Features and an IT Silo

White-label return tools let customers generate return labels but often don’t integrate stock management, packaging traceability, or predictive analysis of return reasons. They usually follow a linear workflow, ignoring multiple variations (extended warranties, refurbishment, donations).

In many cases, the solution is deployed as SaaS without real connectors to local ERPs or warehouse management systems (WMS). The returns service remains an isolated application, offering no real-time feedback to business teams that continue running marketing campaigns without precise data on success rates or regional disparities.

The absence of an extensible API for real-time reverse-logistics data fragments the information chain. Each department keeps its own dashboards and metrics, stifling any initiative for global optimization and preventing the unified view needed to allocate budget and resources effectively.

Lack of Contextual Personalization

Standard solutions offer basic configurations (return windows, statutory conditions, shipping rates) but don’t account for assortment complexity, dynamic pricing policies, or third-party locations (drop-off points, stores) unique to each retailer.

A bulky, fragile, or seasonal product may require specific handling that no point solution natively anticipates. Exceptions get handled manually, generating support tickets and ad-hoc operations that weigh down responsiveness and final consumer satisfaction.

Without a contextual rules engine, return conditions tighten or loosen arbitrarily, causing confusion and frustration. Retailers lose sight of their sustainability and profit goals, replaced by low-level operational logic disconnected from business priorities.

No Cross-Functional Cycle Visibility

A point tool doesn’t connect the purchase journey, reverse logistics, finance, and CSR. It cannot correlate customer feedback with logistics costs or anticipate future returns through behavioral analysis and product recommendation.

The goal isn’t merely to make the return widget more elegant; it’s to reengineer the reverse cycle as a full-fledged component of the value chain. Without a shared orchestrator, each department reinvents its processes, wasting resources and time.

For example, one retailer deployed a standard tool for bulk returns, but the lack of integration between the platform and financial system prevented automatic recalculation of per-unit costs. As a result, over 10% of returns were billed at a loss in one quarter, proving a point solution falls short for a cross-functional need.

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An Integrated Ecosystem Approach

Return management must become a collaborative process co-managed by logistics, marketing, IT, finance, and CSR. Each department contributes essential data for global optimization. Only an integrated ecosystem ensures effective governance.

Aligned Objectives and Cross-Functional Governance

A dedicated governance body—bringing together CIOs, supply chain leads, marketing, finance, and CSR—defines shared KPIs: cost per return, packaging reuse rate, post-return satisfaction, and carbon footprint. This body steers prioritization and ensures consistency in return policies.

IT becomes an enabler by implementing a centralized data bus where each event (return request, quality approval, restocking) is logged and shared. Business teams access a unified dashboard to monitor workflows, identify savings opportunities, and adjust automation rules in real time.

Unified Data Streams and a Modular Platform

Orchestrating the reverse cycle requires an agile, open-source, modular technical foundation interoperable with existing ERP and WMS. A micro-services architecture simplifies adding features: return-reason scoring, refurbishment-potential prediction, or automated refusal handling.

Each micro-service communicates via standardized APIs, ensuring full traceability of operations and smooth collaboration between teams. This approach avoids vendor lock-in, supports A/B testing of new processes, and scales without major overhauls.

Concrete example: an electronics platform deployed a micro-services orchestrator for returns. Within weeks, it added an automatic sorting module and an open-source rules engine for routing to workshops or recycling centers—demonstrating how modularity enables continuous system evolution.

Key Transformation Phases

To shift from cost center to strategic lever, three key phases are needed: a global assessment, return-journey redesign, and ecosystem platform implementation. Each phase builds on the last for sustainable results.

Cross-Functional Assessment and Global Audit

The first phase is a comprehensive audit of existing processes—from reverse logistics flows to information systems and packaging traceability. Teams conduct stakeholder interviews, analyze financial reports, and examine operational data to pinpoint bottlenecks.

This cross-functional assessment reveals friction points: untracked returns, restocking delays, lack of real-cost monitoring, and missing CSR indicators. It also uncovers duplicate operations across platforms and warehouses previously thought independent.

An example in French-speaking Switzerland involved a food retailer discovering that 30% of returns were handled manually across three separate warehouses without a single portal. This finding validated the urgency of a central orchestrator and shared KPIs for all sites.

Redesigning the Return Journey

With the assessment complete, map each existing return flow—from customer request to final disposition (restock, refurbishment, donation, destruction). The goal is to simplify, automate repetitive tasks, and introduce contextual rules by product type.

This phase engages UX leads, supply chain, quality, and CSR to co-create seamless user scenarios, minimizing unnecessary handling and maximizing reuse rates. Workflows are modeled and simulated before deployment to ensure no disruptions and controlled costs.

Building an Integrated System

The final phase implements the ecosystem platform: micro-services, data bus, open-source rules engine, and unified dashboard. IT teams work in agile, test each component under real conditions, and refine routing logic based on pilot feedback.

Modularity guarantees the future addition of use cases (cross-border returns, packaging consignment, refurbishment partnerships). Each feature relies on a secure, scalable architecture free from vendor lock-in and adherent to DevSecOps best practices.

An FMCG example showed this model’s robustness: a home-appliance manufacturer integrated return data continuously into its platform. In six months, it optimized reverse processes, cut external sorting center throughput by 40%, and tripled the reuse rate of spare parts.

Achieving Sustainable, Profitable E-commerce Returns

Redefining the return cycle as a high-value process significantly reduces logistics costs, enhances customer experience, and meets CSR objectives. With a global assessment, journey redesign, and a modular ecosystem in place, companies turn an expense into a competitive advantage.

Whether you’re planning a return-process overhaul or ready to adopt an ecosystem-driven approach, our experts can support you at every stage. From strategy to technical deployments, we help you combine performance, ROI, and sustainability—without vendor lock-in.

Discuss your challenges with an Edana expert

Swiss manufacturers are facing shrinking margins and intensified competition from global players offering low-cost hardware solutions. At the same time, customer expectations are evolving: they now demand digital services that are as seamless and intuitive as those in the business-to-consumer sector.

To stay relevant, manufacturers must shift from a one-time transaction model to a continuous value model based on connected applications and subscription plans. This transition goes beyond merely adding a software layer: it requires redefining the offering, a clear monetization strategy, and a scalable, secure technology architecture.

Competitive Pressure and the New Model

One-off sales are no longer sufficient to cover operating costs and fund innovation. Manufacturers must offer value-added services to retain customers and diversify revenue streams.

Margins Under Pressure

In a context where the global market offers increasingly competitive machine prices, the only advantage left for local manufacturers is the quality of their service. However, pay-per-incident maintenance and traditional support contracts struggle to generate sufficiently recurring revenue to stabilize cash flows.

The gradual erosion of margins due to falling hardware prices forces management to rethink their business model. An integrated digital offering creates an additional revenue source while boosting customer satisfaction through increased service availability.

By adopting a subscription service, even with a moderate average ticket price, a manufacturer can smooth revenue over the long term. This gives investors and executive management better financial visibility and enables more agile growth management.

Changing Customer Expectations

Professionals using industrial machinery now expect a user experience comparable to that of their personal devices: clear interfaces, mobile access, and real-time notifications. This demand is becoming ubiquitous across industries.

Workshops are seeking predictive dashboards and automated reporting to manage production and anticipate breakdowns. Digital applications play a central role in turning machine data into efficiency metrics.

Service personalization—configurable alerts and tailored maintenance recommendations—becomes a differentiating factor. Manufacturers that meet these new expectations can leverage user engagement to build long-term partnerships.

Example of Proactive Adaptation

An automated cutting equipment manufacturer integrated a digital performance analysis platform into its production lines. Through this subscription-based service, the company offers monthly performance reports and predictive alerts.

This setup drove a 20% increase in operational machine utilization and enabled the manufacturer to capture 15% of its revenue as recurring income.

This example demonstrates that a well-integrated, user-centric software module can transform customer relations and stabilize a manufacturer’s financial outlook.

Digital Apps as a Strategic Lever

Connected applications optimize equipment operation and strengthen customer relationships. They also provide a reliable stream of recurring revenue to fund innovation.

Optimizing Machine Operations

By continuously collecting operational data, a digital application can detect anomalies before they cause production downtime. This predictive maintenance reduces breakdown costs and minimizes downtime.

By analyzing performance histories, manufacturers can recommend optimal configurations and maintenance cycles tailored to each use case. This builds customer trust and extends equipment lifespan.

Operational support teams benefit from automated alerts and remote diagnostic modules. They can respond more quickly and precisely, improving service quality and user satisfaction.

Strengthening Customer Relationships

A dedicated mobile or web app serves as a permanent point of contact between the manufacturer and the user. It centralizes contracts, service reports, stock of consumables, and training modules.

Push notifications and integrated chatbots enable asynchronous dialogue and reduce response times. This digital proximity turns technical support into a proactive, value-adding experience for the customer.

By gathering usage data, the manufacturer can offer personalized deals or relevant upgrades based on performance history and each customer’s specific needs.

Generating Recurring Revenue

Switching to a subscription model creates a predictable revenue stream, making it easier to plan R&D investments and develop robust financial models. Suppliers can then offer multiple service tiers.

‘Basic’, ‘Premium’, or ‘Enterprise’ plans cater to different budget constraints and requirements. They cover standard support, predictive maintenance, and even data science consultancy to optimize production.

This hybrid model—machine sale plus subscription to digital services—helps distribute risk more evenly and increases the customer’s lifetime value (LTV). It creates a virtuous cycle of loyalty and continuous innovation.

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Common Pitfalls and Strategic Alignment

Many initiatives fail due to a lack of holistic vision that includes architecture, user experience, and the sales model. A lack of coordination among these vital dimensions hinders adoption and monetization.

Poorly Thought-Out Architecture

Opting for a closed or non-scalable platform exposes you to vendor lock-in and makes future upgrades costly. Some players bet on proprietary solutions without evaluating migration costs and compatibility with existing systems.

A poorly architected application often results in fragile integrations and high latencies, harming the user experience. Changes become complex and risky, stifling innovation.

Conversely, a modular, open-source architecture makes it easier to add new services and scale. It also ensures a better long-term cost-efficiency ratio while preserving technological freedom.

Unprepared Sales Teams

Salespeople trained in transactional approaches often struggle to promote a recurring model. Their compensation and messaging remain focused on delivering the machine rather than continuous digital support.

Without dedicated training and appropriate management tools, sales teams fail to demonstrate the added value of digital services. This undermines customer buy-in and slows momentum.

Lack of a Monetization Strategy

Without a clear and appropriate pricing model, digital services are perceived as a free bonus and fail to generate the expected revenue. Some players launch apps without considering service tiers and add-on options.

Poor pricing can lead to low adoption rates or, conversely, insignificant margins. It’s crucial to calibrate the offering based on usage, production impact, and the customer’s willingness to pay.

A successful monetization strategy relies on granular segmentation, limited free trials, and automatic upgrade triggers when usage metrics exceed predefined thresholds.

Winning Models and Sustainable Co-Innovation

Freemium models, pay-per-use, and integrated bundles offer flexibility and appeal for testing and adopting new features. Co-innovation with partners enhances relevance and accelerates time to market.

Freemium and Pay-Per-Use to Test and Win

The freemium model provides free access to basic features, encouraging customers to try the application with no initial financial commitment. This approach facilitates value demonstration and user engagement.

Partnerships and Co-Innovation

Collaborating with AI or IoT startups accelerates the design of advanced digital services. Manufacturers can leverage external expertise without large-scale internal hiring.

Data Monetization and Predictive Services

Monetizing machine data opens up opportunities for complementary services: benchmarking analyses, energy optimization, and predictive software updates. These services can be offered as separate subscriptions.

Discuss your challenges with an Edana expert

Facing energy infrastructures where nearly 40% are over forty years old, Europe’s carbon-neutrality targets require a thorough overhaul of the system. The challenge isn’t limited to producing “green” electricity but orchestrating a smart grid capable of managing diverse and variable flows. Digitalization emerges as the keystone of this transition, providing the visibility, automation and interconnection needed to ensure stability, resilience and efficiency.

Strained Energy Sector in Europe

Europe’s power grid is under unprecedented load, with often outdated equipment ill-suited to new demands. In this context, digitalization appears to be the only way to effectively integrate renewable energy sources and decentralized flexibility.

Infrastructure Status and Stability Challenges

High-voltage lines, substations and distribution networks were designed when production was centralized and predictable. Today, injection of electricity from thousands of rooftop solar installations and wind farms causes frequency and voltage fluctuations that this equipment wasn’t built to handle.

Without real-time monitoring systems, automatic restarts and instant rebalancing remain slow and manual. This lack of responsiveness can jeopardize service continuity and limit the absorption of new renewable capacity.

Planned load-shedding and safety outages become emergency measures, often poorly received by industrial stakeholders and municipalities, even though predictive management could have prevented these interruptions.

Proliferation of Producer-Consumers

The shift to decentralized energy has given rise to countless prosumers: industrial enterprises, agricultural cooperatives and local authorities now produce electricity. While beneficial to the transition, this trend radically complicates grid management.

Each autonomous producer introduces an additional control point, with its own consumption and production profiles. Traditional siloed management approaches are no longer sufficient to coordinate these actors and ensure a coherent collective response to demand peaks or production drops.

Without a unified platform and standardized exchange protocols, aggregation of these decentralized flows remains ad hoc and fragile, potentially undermining system resilience in the event of a failure or peak demand.

Example: Regional Power Utility

A regional power utility implemented a digital dashboard centralizing measurements from over 2,000 smart meters. Thanks to this granular visibility, it anticipates consumption peaks and adjusts inputs from the local hydroelectric plant in real time.

This project demonstrates that digitalization provides forecasting capabilities that reduce overload risks and the costs associated with emergency interventions on the national grid. The utility increased its ability to integrate new renewable installations by 15% without massive investment in traditional infrastructure.

Why Digitize the Energy System

The digital transformation of the energy sector rests on shifting from manual supervision to automated orchestration of flows. IoT sensors, combined with artificial intelligence, turn every data point into optimized action.

From Periodic Readings to Continuous Measurement

Previously, meter readings were taken on site or via time-stamped batches, with delays of several days in consumption analysis. Today, IoT sensors transmit the status of every energy resource—solar production, industrial consumption or electric vehicle fleet charging—every minute.

This continuous measurement offers unprecedented granularity, enabling instant detection of anomalies, abnormal usage or network losses. Fault detection algorithms intervene upstream, drastically reducing intervention times and associated costs.

Automating data processing frees teams from repetitive tasks, allowing them to focus on performance and service-quality improvements.

AI and Demand Forecasting

AI models leverage consumption histories, weather forecasts and market data to anticipate supply or demand peaks. They enable real-time adjustments of heat pumps, virtual power plants and energy storage.

This forecasting capability improves grid stability by reducing gaps between supply and demand. It also facilitates financial optimization by directing automations toward periods when energy is most available or cost-effective.

Beyond forecasting, some platforms integrate feedback loops that continuously learn from each adjustment, enhancing system accuracy and responsiveness.

Example: Industrial Park

An industrial group deployed IoT sensors in its workshops to monitor production-line consumption and machine performance. A machine-learning algorithm detects periods of high demand and dynamically manages power supply from an on-site micro hydroelectric plant.

This led to a 10% reduction in its energy bill and a 20% decrease in indirect emissions, while improving predictive maintenance of critical equipment. The project illustrates how intelligent automation turns a factory into an active participant in the energy transition.

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Energy Paradigm Based on Micro-Decisions

The grid of the future won’t rely on a few centralized decisions but on the coordination of millions of production and consumption points. Each micro-decision contributes to the global balance of the system.

Microgrids and Energy Communities

Energy communities bring together localized consumers and producers who share energy close to the point of need. These microgrids can temporarily island a zone in case of main-grid failure and continue operating in island mode.

Decentralized governance of these communities requires data-exchange protocols and collaborative platforms so each participant can view their contributions and usage in real time.

Microgrids strengthen local resilience while relieving long-distance transmission lines, but their integration demands advanced digitalization.

Real-Time Coordination

To synchronize highly diverse uses—office buildings, data centers, charging stations—it’s essential to have a unified repository of statuses and forecasts. Energy management systems (EMS) aggregate this information and control flexibilities via standardized APIs.

Implementing cloud-native services allows infinite data-processing scalability and ensures 24/7 availability. Automatic failover mechanisms preserve continuity even if part of the service goes down.

The resilience of each micro-decision therefore depends on the interoperability of digital components and the robustness of the software layer.

Role of Aggregators

Aggregators act as intermediaries between small producers/consumers and energy markets. They pool flexibilities and negotiate offers on marketplaces while ensuring local balance.

To fulfill this role, they rely on digital platforms capable of collecting, validating and aggregating heterogeneous data in near-real time. Service quality depends as much on software architecture as on flexibility-valuation algorithms.

This third layer of coordination makes the aggregator a key player, requiring a robust and scalable digital infrastructure.

Foundations of the Energy Transformation

Digitalization relies on technological building blocks: smart meters, data governance platforms, predictive-planning tools and IT/OT convergence. These modules form a modular and scalable ecosystem.

Smart Meters and Remote Reading

Deploying smart meters is the first step. Each metering point now continuously transmits its consumption and technical status without manual intervention.

These meters connect to Low Power Wide Area (LPWA) networks or fiber-optic infrastructure, depending on local context. The open architecture of these networks allows selecting the most suitable technology without being tied to a single provider.

Standardized reading protocols ensure interoperability between manufacturers and simplify integration of data into existing information systems.

Data Governance and Cybersecurity

The multiplication of data flows requires a clear data-governance strategy: cataloging, traceability, access controls and encryption. Each metric must be classified by criticality to apply appropriate security measures.

Critical infrastructures demand zero-trust approaches and segmented environments where strong authentication is the norm. IT/OT gateways are protected by dedicated firewalls and anomaly-detection mechanisms tailored to industrial protocols.

Trust in the digital system is essential to guarantee the reliability of automated decisions and regulatory compliance.

Predictive Planning and Digital Twins

Digital twins replicate in real time the state of the grid, production infrastructures and consumption points. They become the basis for predictive planning, enabling simulation of evolutions, failures or the addition of new equipment.

Mathematical models leverage historical data, weather scenarios and growth projections to propose optimized investment plans. Network expansion budgets are thus better targeted, avoiding overcapacity or future bottlenecks.

The agility provided by these tools reduces planning costs and secures the trajectory toward carbon neutrality.

Example: Municipal Cooperative

A cooperative of several municipalities integrated its industrial SCADA with its ERP and an open-source BI solution. Heat-network pressure data and public-building consumption metrics flow in real time to a single portal.

This setup identified leaks and excessive consumption, reducing heat-network losses by 12% and overall energy costs by 8%. The example illustrates the concrete impact of an intelligent, open platform combining existing modules and custom developments.

Transforming Your Energy System into a Sustainable, Data-Driven Pillar

Digitizing the energy system is the foundation of a resilient grid capable of accommodating massive renewable influx and synchronizing millions of micro-decisions. By combining smart meters, AI, IoT, IT/OT integration and digital twins, you build an architecture that is scalable, secure and interoperable.

Whatever your role—CIO, transformation manager, CEO, IT project leader or business manager—our experts support you in defining and implementing your digital roadmap. Together, we’ll design a contextualized ecosystem based on open source and governance best practices to achieve your Net Zero goals.

Discuss your challenges with an Edana expert

The AI revolution is transforming ways of working, but it won’t succeed without a structured human-centered approach. Swiss mid-market companies risk seeing their AI initiatives fail if they focus solely on technological aspects.

Beyond tool selection, the real challenge lies in AI change management: defining a clear “Why,” framing use cases, securing governance, providing hands-on training, and measuring business impact. Without these steps, concerns over cognitive load, resistance to change, and a lack of executive sponsors will hinder AI adoption. This article outlines a pragmatic approach to turn your AI efforts into a sustainable competitive advantage.

Clarify the “Why” and Frame Use Cases

A shared vision of AI drives engagement and prevents unnecessary deployments. This first step allows you to establish an internal AI policy aligned with business objectives.

Define a Business Vision and Objectives

Human-oriented digital transformation begins by formalizing a clear vision of what AI can deliver. This vision must link each use case to a specific operational challenge—such as improving time-to-market or service quality—and fit within your agent-based AI innovation strategy.

A steering committee brings together IT, business, and compliance stakeholders to validate priorities. It’s important to appoint an executive sponsor to legitimize the initiative and secure the resources needed.

This strategic framework serves as a compass for the rest of the AI change management process. It also ensures consistent communication about expectations and anticipated benefits, thereby reducing internal resistance.

Establish a Responsible AI Governance Charter

Responsible AI governance sets the rules of use and ethical principles to follow. It draws on open-source standards and frameworks tailored to the Swiss regulatory context.

This charter defines roles—data owner, AI architect, security officer—and the approval processes for new models. It includes audit milestones to monitor algorithmic compliance and fairness.

Framing these use cases also ensures safe, transparent handling of sensitive data. It helps anticipate risks and prevent misuse that could harm the company’s reputation.

Example of a Romandy-Based SME

A Swiss SME in the Romandy region’s financial services sector clarified its “Why” by targeting a 30 % reduction in customer response times. It codified its use cases into an internal AI policy and formed a governance committee to validate each initiative.

This groundwork showed that initial formalization accelerates business teams’ buy-in. The company avoided tool overload and focused on three priority cases, reducing failures and securing ROI.

This approach demonstrates that a shared vision and clear rules are the pillars of successful AI adoption. It creates an adaptable framework for integrating new technological opportunities.

Secure Contracts and Strengthen Governance

Robust governance and clear contracts ensure the long-term viability of AI projects and effective risk management. They protect against vendor lock-in and ensure compliance with regulatory requirements.

Structure Governance and Appoint Sponsors

AI governance involves a cross-functional committee of IT, business units, legal, and cybersecurity. This body oversees the AI adoption strategy and arbitrates project priorities, relying on a zero-trust IAM model.

An executive sponsor, typically at the C-level, ensures visibility and funding. They help remove organizational barriers and ensure alignment with the digital roadmap.

This cross-functional leadership minimizes silos and promotes a unified approach—essential for successful human-centered digital transformation. It also provides a framework for agile transformation.

Draft Agile, Secure Contracts

Contracts with AI vendors should cover model portability, data ownership, and complete algorithm documentation. These clauses prevent excessive dependency and enhance solution scalability.

Include SLAs for performance and availability, along with strict data confidentiality rules. Penalties for non-compliance ensure commitments are met.

Particular attention should be given to audit rights and maintenance of the AI processing pipeline. This contractual detail reduces legal teams’ cognitive load and secures responsible AI governance.

Example of a Cantonal Hospital

A cantonal hospital implemented a contractual framework requiring model portability and auditability for diagnostic algorithms. This approach maintained control over the algorithms and met patient data confidentiality standards.

The example shows that rigorous contracting prevents vendor lock-in and safeguards the investment. The institution could evolve its models without renegotiating heavy contracts, while adhering to Swiss security standards.

This case highlights the importance of agile clauses to accommodate rapid AI technology changes and ensure secure management.

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Train with Use Cases and Establish Experimentation Rituals

Hands-on training through practical cases and the establishment of experimentation rituals accelerate adoption and generate quick wins. These rituals build a library of prompts and turn your teams into AI champions.

Use Case–Focused Training Programs

AI upskilling is based on practical workshops where each participant solves a real business problem. This approach drives engagement and makes AI’s value tangible, inspired by personalized learning.

Sessions combine theory with prototyping workshops, allowing teams to explore open-source tools and modular frameworks firsthand. They foster confidence and reduce fear of new technologies.

Internal benchmarking of initial results encourages sharing best practices and replicating successes. This contextualized training becomes a key element of your AI adoption strategy.

Create a Network of AI Champions

Identifying motivated, curious employees forms the basis of an AI champions program. These ambassadors support pilot projects, share feedback, and nourish the internal community.

Each champion leads internal workshops, promotes the prompt library, and encourages autonomous experimentation. They bridge IT, business, and leadership to facilitate knowledge flow.

This network fosters ongoing experimentation and knowledge sharing. It enhances AI productivity in the organization and significantly reduces adoption resistance.

Example of a Basel Logistics Firm

A Basel-based logistics company launched an AI pilot to optimize delivery routes. It trained six champions from operations and IT who co-developed a prompt library to fine-tune optimization models.

The pilot’s success—achieving a 12 % reduction in fuel costs—proved the effectiveness of the pragmatic approach. The example shows that use case–based learning, combined with experimentation rituals, eases adoption.

It also illustrates the value of a modular ecosystem built on open-source components, allowing rapid scaling of successes to other regions.

Measure Business Impact and Structure Change Management

Tracking key metrics enables you to refine the AI approach and embed change sustainably. A structured change management framework—incorporating communication, KPIs, and sponsors—turns AI into a competitive advantage.

Define and Track Productivity and Quality KPIs

AI productivity KPIs include time savings in processes, error reduction, and improved employee satisfaction. They should be measurable from early pilots to demonstrate impact and fit within an enterprise-wide agile transformation.

Automated dashboards facilitate real-time monitoring and keep sponsors informed. Pilot data serves as a benchmark for setting future objectives.

This measurement rigor guides the AI adoption strategy and supports decision-making. Metrics become communication levers for executive management.

Establish Communication and Steering Rituals

Regular check-ins (weekly or biweekly) bring together sponsors, AI champions, and business owners. They’re used to share successes, identify obstacles, and plan adjustments.

Internal newsletters and live demos create positive AI storytelling. These rituals boost buy-in and nurture an experimentation culture.

Transparent communication about wins and failures limits resistance and promotes collective learning. It steadily builds an ongoing innovation mindset.

Example of a Swiss Manufacturer

An industrial components manufacturer set up weekly detailed AI reporting on production defect reduction. Technical and business sponsors meet weekly to approve adjustments.

This structured governance enabled scaling from a pilot to a full rollout in six months, with a 20 % drop in defect rates. The example demonstrates that KPI tracking and disciplined communication are essential for lasting change.

The case also underlines the need for a flexible framework that can incorporate new measures and pivot based on field feedback.

Make AI a Competitive Advantage

Successful AI projects hinge not just on model quality but on managing human-centric change. Clarifying the why, framing use cases, securing governance, hands-on training, and impact measurement are the pillars of sustainable AI adoption.

A structured change management program—backed by sponsors and driven by AI champions—turns AI into a lever for performance and continuous innovation. Experimentation rituals and KPIs help refine the trajectory and reduce resistance.

Whether your organization is in the exploratory phase or running its first pilot, our experts are here to help you define a pragmatic, modular, and secure AI adoption strategy.

Discuss your challenges with an Edana expert

When faced with the choice between an open source or proprietary ERP, the decision goes beyond the software license to include the selection of the implementation partner who will guide the project. Technical expertise, code governance, contractual terms, and reversibility are all critical parameters to secure the deployment and control the total cost of ownership.

For Swiss SMEs, where every Swiss franc invested must generate lasting value, an architecture-first integrator favoring tailored open source solutions can offer flexibility and control while minimizing vendor lock-in risks. This article details the essential criteria, compares open source and proprietary approaches, and covers the contractual clauses necessary to protect your business.

Revisiting the True Decision Criteria

The ERP decision must be based on an analysis of your specific processes and in-house capabilities. Integration capabilities and the need for reversibility drive the choice of integrator more than the license itself.

Highly Specialized Business Processes

Before selecting a provider, you should map out your business processes in detail. Operations with high specificity—batch management, regulatory traceability, or complex workflows—require a deep understanding of the context. A standard integration may prove insufficient, leading to costly, hard-to-maintain overlays.

For example, a capital‐goods manufacturer in French-speaking Switzerland commissioned an integrator to map its customer and supplier processes. The study revealed that 60 % of workflows criticized the rigidity of the standard inventory module. This analysis demonstrated the need for a modular, adaptive solution, avoiding unnecessary custom developments.

The outcome was a clear roadmap prioritizing an extensible open source ERP core with targeted developments for differentiating needs. The selected integrator proposed agile governance, ensuring a gradual transfer of skills to the internal teams.

Integrations Within the IT Ecosystem

Interconnection with CRM, PIM, BI, or specific tools such as e-invoicing (QR invoice, ISO 20022, eBill) is a major concern. API-first architectures and service-oriented approaches facilitate these exchanges without compromising system stability.

In a financial services SME, the integrator implemented an API data bus to synchronize eBill invoices with the accounting module. This approach demonstrated that choosing an open source ERP with a robust integration framework significantly reduces development time and interoperability testing.

A provider without API-first experience would have produced poorly documented ad-hoc scripts, leading to incidents and billing delays. This example highlights the importance of deep technical expertise to integrate multiple software components.

Reversibility Requirements and In-House Skills

Reversibility must be anticipated from the provider selection phase. Data export clauses, access to customized code, and a migration plan in case of termination ensure the freedom to switch.

A semi-public Swiss organization included a software escrow clause in its contract, ensuring delivery of customized source code and documentation. This provision demonstrates that reversibility isn’t a mere formality but a negotiation lever to secure ongoing support and guarantees on evolutionary maintenance.

Internal skills are also often overlooked. Without a minimum level of architectural know-how, the company becomes fully dependent on the provider. Targeted training and a gradual transition plan ensure shared commitment and limit the ramp-up costs.

Open Source Integrator

An open source integrator combines modularity and adaptability with an API-first approach. Skills transfer and community contributions strengthen the project’s longevity.

Modular Design and API-First

A modular architecture breaks the ERP into microservices or functional modules, each independently evolvable and replaceable. API-first design ensures native integrations with other systems, reducing the risk of lock-in.

In the canton of Vaud, an agrifood SME adopted an open source solution structured into separate modules: procurement management, production tracking, invoicing. The provider developed REST APIs to interface with its PIM and BI solution, cutting new feature deployment times by 40 %.

This approach showed that functional decoupling preserves system stability while enabling incremental evolution without redeploying the entire platform.

Contributions and Community

Open source components benefit from an active community that regularly publishes security patches, performance updates, and new features. An experienced integrator knows how to leverage these contributions and can even contribute back to address specific needs.

An industrial software publisher collaborated with its integrator to release an open source plugin addressing a local regulatory requirement. This contribution was validated by the community, providing additional recognition and demonstrating the project’s transparency.

Shared development reduces costs and accelerates innovation. It also limits dependence on a single vendor, as the code remains accessible to any third party if a switch becomes necessary.

Security, QA, and Operational Maintenance

An open source ERP does not mean compromising on quality. The integrator must implement CI/CD pipelines, automated tests, and code coverage reporting. Security audits and compliance with data protection regulations (LPD/GDPR) are systematic.

An IT services company relied on an open source integrator to deploy an ERP compliant with the latest data protection directives. The integrator set up weekly vulnerability scans and a release management process to ensure reliable operational maintenance.

Operational maintenance is based on a detailed runbook, up-to-date documentation, and clear SLA-based support. Interventions are logged, with performance indicators (MTTR, availability rate) measured and shared.

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Proprietary Vendor/Integrator

The proprietary solution offers a pre-configured toolset and standardized vendor support. However, customization and reliance on the vendor’s roadmap can generate unexpected costs and hinder evolution.

Toolset Framework and Support

Proprietary solutions benefit from a pre-built, documented functional scope. Vendor support ensures regular core updates, security patches, and a formal assistance channel.

A logistics SME chose a proprietary ERP to leverage a robust foundation. The provider managed the core modules, reducing the initial deployment time to six months.

However, this toolset can become rigid once requirements deviate from the standard, necessitating additional paid options or modules often billed based on the vendor’s development hours.

Customization Limits and Roadmap Dependence

Customizing a proprietary ERP remains costly, as any feature outside the standard scope is treated as a separate project with its own estimate and schedule.

A Swiss distribution company had to wait for the next major ERP release to obtain essential returns-management features. The delay resulted in a 15 % increase in support tickets and a 20 % rise in maintenance costs over a year.

The vendor’s roadmap dictates the pace of evolution. If priorities diverge, the company is at the mercy of the vendor’s schedule, unable to accelerate or redeploy internal resources.

License and Data Security Risks

Annual license fees can rise unpredictably, often indexed to company size or user count. Price revision clauses are sometimes opaque and hard to negotiate.

A semi-public organization saw its costs increase by 30 % over three years, without significant functional gains. The contract restricted code access and left data managed exclusively by the vendor, posing a risk in case of dispute or vendor insolvency.

On the security side, lack of access to third-party audits prevents assurance against backdoors or unpatched vulnerabilities. Total trust then rests on the vendor’s integrity and contractual commitments.

Contracts & Risks

A well-negotiated contract is the bulwark against vendor lock-in and budget overruns. Data export clauses, escrow deposits, and SLAs guarantee reversibility and operational continuity.

Fee-Free Export Clauses and Escrow Deposit

To ensure independence, it is imperative to include a data and custom code export clause at no extra cost. An escrow deposit guarantees access to source code if the provider fails to meet commitments or the roadmap.

In a recent case, an integrator repeatedly missed contractual deadlines. The company then invoked the escrow clause to retrieve the customized code and hand it to another provider, avoiding system paralysis.

The escrow repository must be automatically updated with each major release, along with associated documentation and database schemas.

SLAs, MTTR, and Reversibility Plan

Service Level Agreements (SLAs) specify response and resolution times (MTTR), non-compliance penalties, and availability commitments. They serve as a powerful lever to ensure provider responsiveness.

An educational consortium included a strict 99.5 % availability SLA and a maximum four-hour MTTR. In case of breach, tiered financial penalties applied, prompting the vendor to maintain a dedicated 24/7 support team.

The reversibility plan defines data export formats (dumps, schemas, dictionaries), timelines, and responsibilities. It is essential for planning a provider change without losing history or data integrity.

Penalties and Arbitration

Beyond SLAs, penalties for delivery delays or specification breaches help secure the schedule and budget. They must be sufficiently deterrent to ensure the provider honors commitments.

A Swiss municipal authority isolated a penalty schedule proportional to project cost in its contract, up to 15 % for missed critical milestones. This clause unlocked additional provider resources to accelerate development.

An independent arbitration mechanism can be included to resolve disputes swiftly, avoiding lengthy litigation and ensuring project continuity.

Choosing the Right Integrator to Secure Your ERP

The choice between open source and proprietary should not hinge solely on licensing but on the integrator’s ability to offer open governance, guaranteed reversibility, and controlled TCO. API-first architecture skills, modularity, code export clauses, and SLAs are the pillars of a sustainable, flexible, and scalable ERP project.

Whether your priority is bespoke customization or adopting a proven foundation, our experts are here to define your strategy, negotiate contracts, and manage an implementation that aligns with your business and regulatory objectives.

Discuss your challenges with an Edana expert

Successfully implementing an ERP project goes beyond merely choosing the right software solution. A requirements document that fails to map out processes, plan data governance, or address change management quickly encounters roadblocks. Budget overruns, vendor lock-in, and loss of agility are often the result of unclear foundations rather than poor technology. This article identifies four broad categories of errors, outlining ten pitfalls to avoid when designing a modular, interoperable, and reversible ERP requirements document.

Aligning Processes and ERP Governance

An ERP requirements document must start with a mapping of actual processes. It should then establish clear data governance and anticipate change management.

Map Processes Before Defining Requirements

Without a precise understanding of business workflows, an organization risks imposing unsuitable or redundant features on the ERP. A BPMN map or process-mining audit helps visualize interactions between departments and friction points. Functional requirements can only be prioritized once these processes are validated.

One example illustrates this challenge: a Swiss industrial company had defined its inventory management needs solely based on a standard process. After a BPMN workshop, it uncovered several undocumented, specialized workflows between maintenance and production. This correction prevented the integration of an unsuitable module and reduced implementation time by 25%.

Involving business stakeholders from this stage ensures the future ERP is built on a solid foundation, reducing costly adjustments at the end of the project. For more details on drafting the requirements document, see our comprehensive guide.

Establish Robust Data Governance

Data quality and ownership must be defined even before selecting the software. Determining the owner of each repository avoids conflicts during updates. You also need to incorporate privacy by design and compliance with the Swiss Federal Act on Data Protection (FADP) and the General Data Protection Regulation (GDPR).

Clear governance includes tracking changes and defining automated data-cleaning rules. Without this, increasing volumes quickly lead to duplicates, inconsistencies, and regulatory breaches. Data reversibility must be planned by default.

By addressing these points early, the requirements document becomes a living document that ensures data quality, reduces the risk of penalties, and simplifies audits.

Plan for Change Management

Deploying an ERP transforms workflows and operational responsibilities. A phased communication and training strategy must be defined in the requirements document. Without this component, internal resistance slows adoption and leads to manual workarounds.

Scheduling workshops, e-learning sessions, and post–go-live support is essential to ensure users embrace the new features. This program should be coordinated with business project managers to deliver a smooth transition.

Incorporating change management from the drafting phase prevents a divide between IT and business teams and minimizes delays caused by misunderstandings of new processes.

Ensuring Interoperability, Security, and Control

An ERP is never isolated: it needs to integrate with CRM, PIM, e-commerce solutions, and BI. Security and scalability are integral to success, as is performance measurement through relevant KPIs.

Don’t Overlook ERP Interoperability with the Ecosystem

Data exchange with other applications determines the system’s overall effectiveness. The requirements document must list critical integrations and specify standards (REST, GraphQL, JSON, CSV, Parquet). In Switzerland, don’t forget accounting connectors, VAT, and e-invoicing (QR/ISO 20022).

In one case, a Ticino-based SME’s CRM and ERP communicated only via manual imports. After specifying REST APIs, synchronization times dropped from 24 to 2 hours, reducing billing errors by 30%.

This documented interoperability not only streamlines operations but also makes it easy to plan future enhancements or replace software modules.

Security and Scalability in a Swiss Context

The requirements document must mandate encryption of data at rest and in transit, fine-grained role and permission management, and audit logging. Local hosting in Switzerland ensures compliance with data sovereignty regulations.

Load tests anticipate usage peaks and validate the system’s ability to scale. Without these clauses, a surge in users can lead to unacceptable response times or even complete outages.

A secure and scalable ERP minimizes operational risks and builds confidence among internal and external stakeholders.

Define KPIs to Monitor Performance

Implementing indicators such as error rate, order cycle time, Days Sales Outstanding (DSO), and automation rate should be included in the requirements document.

They also serve as a basis for discussions to adjust priorities during iterations and ensure the ERP delivers tangible ROI beyond mere functional delivery.

By documenting these indicators, the company avoids post–go-live surprises and can manage the system with agility.

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Draft an Agile Requirements Document That Controls Cost and Reversibility

An overly rigid requirements document hinders innovation. It must favor an evolving MVP, incorporate total cost of ownership (TCO), and include reversibility clauses to avoid hidden costs.

Avoid a Fixed Requirements Document and Favor the MVP Approach

An overly detailed, waterfall-based document constrains adjustments and makes adding new features costly. The MVP + iterations approach enables rapid deployment and concrete feedback.

Each functional batch is defined by user stories validated by business teams, allowing course corrections starting in the pilot phase. This flexibility ensures continuous alignment between actual needs and development.

Therefore, the requirements document should explicitly outline iteration principles and sprint frequency without fixing the exhaustive list of requirements at launch.

Consider Total Cost of Ownership Rather Than Initial Cost

Total cost of ownership includes licenses, integration, support, scalability, and system exit. Focusing solely on purchase price exposes you to unexpected operational expenses.

The requirements document should include a 3–5 year TCO evaluation grid for each evolution or scaling scenario. This enables an objective comparison of bids and incorporates maintenance and training costs.

A TCO perspective limits decisions based solely on initial budget and ensures a sustainable, modular solution.

Plan for Reversibility to Avoid Vendor Lock-In

A clause for full data export at no cost, escrow deposit, and comprehensive API documentation must be required. Without these guarantees, any future change becomes a standalone project, sometimes more expensive than the initial system.

The requirements document should specify the format for data handover and the migration procedures to an alternative system. Associated costs and timelines must be quantified and included in bid evaluations.

By planning for reversibility, the organization protects its independence and retains control over its information system.

Choose the Right ERP Partner

ERP success depends as much on the provider as on the solution. Its open source culture, agile methodology, and proximity are key factors.

Assess Open Source and API-First Culture

An integrator that prioritizes open source components and an API-first architecture reduces vendor lock-in risk and ensures solution modularity. Their ability to develop custom connectors is essential to adapt to business-specific requirements.

The requirements document should include selection criteria for these aspects, requesting concrete demonstrations of past projects. References help validate technical expertise without limiting comparison.

This choice ensures a flexible, scalable technology foundation capable of accommodating future needs without starting from scratch.

Verify the Methodology and Project Governance

Beyond technical skills, the ability to co-manage progress through a shared governance structure is a determining factor. Roles, steering committees, and rituals (backlog reviews, checkpoint meetings) must be detailed.

An agile methodology, coupled with asynchronous tracking tools, ensures transparency and responsiveness to change. The requirements document should request an action plan for each sprint and a shared roadmap.

This organizational clarity limits scope creep and maintains constant alignment between IT, business teams, and the provider.

Promote Proximity and Continuous Co-Management

A provider with a local or regional presence facilitates communication and responsiveness. They are better positioned to understand Swiss regulatory and cultural contexts. Forming a hybrid project team, combining internal resources and consultants, eases knowledge transfer.

The requirements document should schedule frequent meetings, on-site workshops, and practical training sessions. These interactions strengthen user adoption and mutual trust.

This co-management model ensures internal skill development and effective long-term collaboration.

Ensure an Agile and Independent ERP Implementation

A successful ERP requirements document is based on precise process mapping, robust data governance, and a change management strategy. It must incorporate interoperability, security, scalability, and KPI definition to monitor performance. The MVP approach, TCO assessment, and reversibility clauses guarantee agility and cost control.

Choosing a partner aligned with an open source culture, an agile methodology, and local presence is the final asset to preserve your independence and support your growth.

Our experts are at your disposal to co-create a tailor-made, scalable, and secure ERP requirements document that aligns with your business challenges and Swiss context.

Discuss your challenges with an Edana expert

The pharmaceutical industry faces ever-tightening regulations, fragile supply chains and unprecedented quality pressures. In this context, the rise of artificial intelligence ushers in a new era of smart manufacturing that harmonizes compliance, agility and performance.

Technologies such as predictive maintenance, digital twins and computer vision enable teams to anticipate incidents, optimize processes and ensure full traceability. This article examines the real challenges of Pharma 4.0, highlights concrete Swiss use cases and outlines a roadmap for moving from proof of concept to an AI-ready facility—all while meeting GxP, GMP, FDA and EMA standards.

A critical industry under strain

Pharmaceutical plants are under increasing pressure to boost production and uphold quality. Regulatory constraints, rigid production lines and talent shortages exacerbate these tensions.

Regulatory constraints and uncompromising quality

The pharmaceutical industry operates within an extremely strict regulatory framework where every manufacturing step must adhere to good practice guidelines. Authorities such as the FDA and EMA enforce rigorous traceability standards and conduct regular audits that tolerate no deviations.

Compliance with GMP and GxP standards requires continuous quality controls and fully documented processes. A single lapse can trigger a product recall and pose a serious risk to both reputation and patient safety.

The pressure to minimize quality deviations while maintaining high throughput creates a significant challenge for production teams, often resulting in line stoppages and substantial additional costs.

Production line inflexibility

Pharmaceutical production lines are designed for repeatability and compliance, but this rigidity makes any modification slow and costly. Every reconfiguration demands complete validation and extensive testing.

When a formula changes or a new product is introduced, downtime can last days or even weeks, heavily impacting launch timelines and budgets.

Equipment inflexibility limits the ability to respond quickly to demand fluctuations or stock shortages. Processes often remain manual and siloed, hindering overall responsiveness.

Talent shortages and supply chain vulnerability

The pharmaceutical sector faces a scarcity of specialized skills—particularly automation engineers, data scientists and regulatory validation experts. Their recruitment and retention represent a major strategic challenge.

International supply chains are vulnerable to geopolitical crises, raw material fluctuations and logistical disruptions. Manufacturers often have to switch to alternative suppliers without guaranteed equivalent quality.

These disruptions directly affect production schedules and force frequent plan adjustments, increasing the risk of manual-handling errors and process complexity.

Example: A Swiss mid-sized pharmaceutical company implemented a real-time AI-based manufacturing parameter analysis system. They reduced quality deviations by 30% and demonstrated that AI can strengthen compliance while streamlining production lines.

Why AI is becoming indispensable

Artificial intelligence turns raw data into actionable insights and automates continuous quality control. These capabilities are essential to meet the demands of modern pharmaceutical manufacturing.

Predictive maintenance and downtime reduction

Predictive maintenance algorithms analyze sensor data to forecast equipment wear and schedule interventions before breakdowns occur. Unexpected stoppages are reduced, improving line availability.

By incorporating incident history and machine performance indicators, AI identifies optimal maintenance windows. Teams can then focus on higher-value tasks, reducing maintenance costs.

Continuous monitoring of critical components prevents chain disruptions and ensures stable production rates. This proactive approach enhances plant resilience against technical issues.

Digital twins to optimize production

A digital twin virtually replicates the entire manufacturing process, from raw materials to packaging. This model allows teams to simulate production scenarios and pinpoint bottlenecks.

Advanced simulations streamline process parameter optimization and reduce cycle times. Decisions can be based on reliable scenarios, avoiding costly, time-consuming full-scale trials.

Teams can virtually test the impact of new formulations or line changes before implementation, accelerating time to market while maintaining quality control.

Computer vision for quality control

Computer vision systems inspect batches in real time to detect visual anomalies such as particulates or labeling defects. Manual inspection gives way to a more reliable, continuous automated check.

High-resolution cameras and deep-learning algorithms ensure early detection of non-conformities. Deviant products are automatically removed before packaging, reducing recall risks.

This automation of quality control improves traceability and cuts variability caused by human judgment. It provides a granular view of each batch and instantly alerts production managers.

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Two inspiring real-world cases

Several Swiss pharmaceutical players have already demonstrated the industrial-scale value of AI. Their experiences offer practical insights for structuring your own initiatives.

AI-driven standardization in a Swiss laboratory

A mid-sized laboratory deployed a spectral analysis recognition algorithm to automatically validate the purity of active ingredients. The system compares each spectrum against a pre-validated reference and flags deviations.

This approach cut manual lab analysis time by 40% and increased daily sample throughput. Measurement repeatability improved significantly.

The project showed that AI can standardize critical tests and free analysts for higher-value R&D tasks.

Packaging flow optimization in a Swiss facility

A packaging unit implemented a digital-twin solution to simulate line scheduling, covering various bottle and blister formats.

Simulations revealed that reorganizing changeover sequences could cut reconfiguration time by 25%. The plant then adjusted its planning based on these recommendations.

This case illustrates the power of virtual modeling to optimize physical flows and boost productivity without additional capital investment.

Key lessons and future outlook

These two examples demonstrate that AI extends beyond prototypes: it can be sustainably integrated into daily operations. Success hinges on close collaboration between data scientists, process engineers and quality specialists.

It is vital to establish a GxP validation plan from the design phase, including model robustness tests and post-deployment monitoring. Data governance and model traceability are non-negotiable pillars.

Beyond initial gains, these initiatives pave the way for advanced scenarios such as real-time automated process parameter recommendations or multi-site connected maintenance.

From proof of concept to industrialization

Scaling from a pilot to an AI-ready plant requires robust governance, a modular architecture and tailored training. These three pillars ensure solution longevity and compliance.

Establishing a GxP governance and validation framework

Governance must define roles, responsibilities and AI model validation processes. A centralized version registry facilitates traceability and regulatory audit management.

The validation protocol should include performance, robustness and bias tests, along with comprehensive documentation of results. Every model update follows a revalidation process before production deployment.

This framework ensures AI solutions comply with GxP and GMP requirements and provides a strong foundation for scaling projects to additional lines or sites.

Modular, open-source architecture to avoid lock-in

A hybrid architecture combines proven open-source components with custom-built modules. This approach promotes scalability, security and technological freedom.

By breaking functionality into microservices—data ingestion, model training, scoring, user interface—each module can evolve independently. Updates deploy without affecting the entire system.

This model minimizes vendor lock-in risk and simplifies the integration of future tools or methods without a full system overhaul.

Training and internal adoption

For teams to embrace AI, a targeted change management program combining theoretical workshops and hands-on exercises is essential. Key users must understand algorithm principles and their process impact.

A change management roadmap supports tool integration through coaching sessions, operational guides and second-line support. Feedback loops enable continuous adjustments.

This approach fosters operator buy-in and ensures sustainable skill development—an indispensable condition for the success of Pharma 4.0 projects.

Accelerate your transition to smart pharmaceutical manufacturing

By leveraging predictive maintenance, digital twins and computer vision, pharmaceutical companies gain agility and reduce quality risks. A robust governance framework and targeted training are crucial to maintain GxP and GMP compliance throughout the model lifecycle. A modular, open-source approach limits lock-in and ensures solution scalability.

Our experts are available to guide you through your Pharma 4.0 strategy implementation and turn your regulatory and operational challenges into sustainable competitive advantage.

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In an environment where retail is being rapidly digitized, cyberattacks have become a strategic concern for retailers. This sector, long regarded as secondary to banks or hospitals, now faces sophisticated attacks aimed at damaging reputation, customer data and supply chains. Executives must understand that cybersecurity is not just an IT issue but a component of the company’s overall strategy.

Faced with regulatory pressure (GDPR) and rising costs associated with data breaches, a proactive, cross-functional security approach is essential to preserve customer trust and financial viability.

Cyber Risk and Customer Trust

Cyberattacks can trigger a major crisis of reputation and trust, directly impacting revenue. They also expose the company to regulatory penalties and high remediation costs.

Impact on Reputation and Customer Trust

A breach of customer data often results in an immediate loss of confidence. Exposed personal information creates uncertainty among consumers, who then hesitate to make further purchases or share their details.

Publicizing a security incident can also damage brand image in the long term. Social networks and specialist forums amplify the crisis, making reputation recovery more arduous.

For a retailer, losing the trust of just a few thousand customers can lead to a drop in in-store or online traffic by several percentage points—translating quickly into millions of Swiss francs in lost revenue.

Remediation Costs and Regulatory Penalties

Beyond crisis communications, the company must fund forensic analysis, notify affected individuals and implement technical remediation measures. These expenses can reach several hundred thousand francs.

In Europe, GDPR compliance requires fines of up to 4% of global annual turnover for serious violations. Supervisory authorities are increasingly vigilant about incidents affecting privacy.

The indirect cost—from lost business opportunities and delayed product launches—is often underestimated by executive teams.

Manufacturing Example: Customer Data Leak

A consumer goods manufacturer discovered that a SQL injection attack had exposed information belonging to several thousand customers. Compromised data included names, email addresses and purchase histories.

This breach led to a 15% drop in online sales for three months and triggered a GDPR investigation with a potential fine exceeding €200,000. The company had to engage an external team to secure its application and completely overhaul its incident management processes.

This case illustrates how a technical flaw can swiftly escalate into a business crisis, affecting reputation, trust and financial health.

Omnichannel Vulnerabilities and Seasonal Staff

Retailers are modernizing customer journeys by multiplying digital touchpoints, which also increases entry points for cybercriminals. Managing seasonal staff and complex supply chains further complicates security.

Omnichannel and Multiple Integrations

To deliver a seamless experience, brands link their e-commerce site, their mobile applications and in-store point-of-sale systems to a single back end. This interconnectedness creates numerous potential attack vectors.

Each third-party API, plugin or microservice added to the customer journey can introduce vulnerabilities if updates are not rigorously managed and tested.

A flaw in a payment module or CRM system can be enough to exfiltrate sensitive data or inject malicious code, with immediate consequences for operations.

Seasonal Staffing and Limited Training

During peak periods (sales events, year-end holidays), retailers hire seasonal staff. Due to time constraints, cybersecurity training is often superficial.

Lack of awareness exposes the company to human errors: opening malicious attachments, using weak passwords or copying sensitive information onto unsecured devices.

Phishing campaigns targeting staff can thus become critical breaches, giving attackers initial access to deploy ransomware or stealthy malware.

E-commerce Example: Misconfigured ERP Integration

An e-commerce platform integrated a Software-as-a-Service (SaaS) ERP system to manage inventory and promotions. Rapid onboarding of the third-party provider occurred without a detailed security audit.

Less than two months after deployment, an attacker exploited an unchanged default configuration to access customer and supplier data. The incident delayed a marketing campaign by six weeks and incurred €120,000 in remediation and communication costs.

This situation demonstrates that rapid integration without governance or thorough validation weakens the value chain.

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ERP Flaws and Access Management

Legacy monolithic ERP architectures, poorly controlled access rights and the pursuit of agility cause retailers to lose grip on their attack surface. Patches are often delayed or incomplete.

Legacy ERP and Obsolete Monoliths

Many brands rely on ERP systems installed over a decade ago, designed before the era of collaborative cybersecurity and cloud computing. Their source code is rarely documented or updated.

Custom modules added over time form a heterogeneous environment where each update is a high-risk operation, requiring planned maintenance windows.

Without network segmentation and flow controls, compromise of one ERP module can spread throughout all back-office and front-end systems.

Access Management and Deficient Identity Governance

Employee and external vendor roles evolve without corresponding adjustments to access rights. Inactive accounts or excessive privileges often remain open for months or even years.

Without a centralized Identity and Access Management (IAM) system and periodic rights reviews, a departed employee can retain access to critical applications.

Multi-Factor Authentication (MFA) solutions are frequently optional rather than mandatory, leaving vulnerabilities exploitable by credential-stuffing attacks.

Healthcare Example: Theft of Administrator Credentials

A hospital had not enabled MFA on its patient portal administration interface. An attacker obtained a complex password through targeted phishing.

With this access, they deployed malware across multiple internal departments, disrupting the electronic medical records system. The incident was resolved after two days, with operational recovery costs estimated at €300,000.

Proactive IAM and Governance Approach

Anticipating attacks requires a holistic strategy combining identity management, security culture and systems modernization. Data governance is the cornerstone.

Strengthening IAM and Continuous Monitoring

A unified IAM solution enables control over all access to your applications, whether on-premises or in the cloud. Each authentication is logged and segmented by role and policy.

Identity federation solutions and standard protocols (OAuth2, SAML) ensure fine-grained rights management and facilitate mandatory MFA for sensitive accounts.

Implementing a Security Information and Event Management (SIEM) platform provides real-time event monitoring. Anomalies are detected before they become critical.

Regular Awareness and Training Programs

Training staff on current risks (phishing, social engineering, ransomware) should become routine. Short, interactive modules updated quarterly keep attention levels high.

Attack simulation exercises (table-top drills) measure team responsiveness and refine escalation and incident response procedures.

Awareness must also extend to third-party providers, whose practices can impact your supply chain or digital services.

Systems Modernization and Digital Governance

Adopting a modular architecture based on microservices facilitates targeted updates and limits propagation in the event of a breach. A hybrid open-source approach avoids vendor lock-in and ensures controlled scalability.

Data governance clearly defines responsibilities throughout the customer information lifecycle: collection, storage, access, archiving and deletion.

A cross-functional committee comprising IT, business units and cybersecurity oversees priorities, ensures decision traceability and validates action plans at each strategic iteration.

Cybersecurity as a Trust and Competitiveness Lever

Cybersecurity must be viewed as a value-generating investment, not merely a technical expense. By adopting a proactive approach that combines IAM, training, modernization and data governance, retailers strengthen resilience and protect their reputation.

Rapid digital transformation should not come at the expense of security. It’s about building an ecosystem where each new component is evaluated for risk and business impact.

Edana’s experts support companies in defining and implementing tailored, hybrid and scalable cybersecurity strategies.

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