Two common mistakes frequently undermine transformation programmes :
Placing technology before organisation
Starting with tools(ERP, WMS, TMS, AI) before clarifying processes, roles, and governance almost inevitably leads to unmanaged complexity, operational inconsistencies, and disappointing ROI.
Technology should act as a lever for a clearly structured organisation: well-defined data models, explicit responsibilities, user-centred design, and harmonised processes across sites.
Underinvesting in change management
Limiting adoption to end-of-project training often results in unstable go-lives and reduced productivity.
Sustainable adoption requires:
- Early involvement of operational teams
- Clear definition of target roles and responsibilities
- Interfaces designed for real-world use
- A structured hypercare phase (typically one to two months) to stabilise operations
Avoiding these pitfalls means restoring the correct sequence: start with business objectives, clarify roles and processes, and then allow the information system to fulfil its enabling role.
Progress should be incremental and measurable, anchored in reliable data and real usage, with active change management from day one.
In this framework, SAP migrations, AI initiatives, and OPEX/SaaS models become coherent enablers of an end-to-end transformation journey.
Context: Lasting Uncertainty and Performance Pressure
Since the pandemic, supply chains have been operating in an environment of ongoing disruption (geopolitical tensions, climate-related events, labour shortages.
High-performing organisations build a backbone of agility: controlled processes, reliable data, and flexible information systems (often subscription-based) capable of absorbing shocks while maintaining industrial performance.
Making decisions under constraint means moving forward in stages. Inaction often proves more costly than incremental decisions. Robust transformation paths favour short-cycle initiatives that deliver quick, measurable value, while preparing the ground for more structural programmes.
- Making decisions under constraint, progressing step by step: Inaction often proves more costly than incremental decisions. Robust transformation paths prioritise quick, value-driven initiatives that remain reversible if necessary, securing day-to-day operations while preparing more structural programmes.
- Process before tool: the information system as an enabler (SAP and beyond): Once the organisation is clearly structured, the information system can fully play its role: a coherent data model, clearly defined access rights and responsibilities, and improved user experience. Migrations to more recent SAP versions primarily provide a stronger technological foundation, enabling better industrialisation of data and AI use cases, as well as harmonisation across sites.
- Pragmatic AI: targeted use cases and controlled data
The value of artificial intelligence relies on:
- Proofs of concept focused on repetitive tasks, bottlenecks, or frequent sources of error
- High-quality data (collection, validation, governance)
- Careful selection of relevant use cases, recognising that not all processes benefit equally from AI
The objective is to deliver tangible short-term gains while defining a medium- to long-term roadmap integrating organisation, processes, systems, and AI.
- Economic model and pace: OPEX/SaaS and continuous improvement: The shift towards OPEX and SaaS models supports incremental progress, facilitates budget allocation, and reduces overall project risk. Organisations move forward “brick by brick”: measuring results, adjusting where necessary, and consolidating improvements before scaling further.
- Adoption: engaging operational teams from the start: Successful transformation requires early involvement of operational teams, clear definition of target responsibilities, user journeys and interfaces designed for real-world use, and a structured hypercare phase (typically one to two months) supported by KPIs, escalation mechanisms, and feedback loops. Adoption is not an afterthought, it is built into the programme from day one.
- Method: co-designed requirements and a dual perspective: Before selecting tools, clarity is essential.
A co-designed requirements document (bringing together business, IT, finance, and site representatives) reduces misunderstandings. It relies on a dual perspective, operational and technical, and evolves iteratively: the initial version gradually converges towards a realistic, shared target state.
- End-to-end vision: breaking down silos to avoid rebound effects: The supply chain operates as a system.
Optimising warehouse operations without aligning production, procurement, or transport simply shifts constraints elsewhere. Sustainable success requires cross-functional orchestration and ongoing dialogue across business domains throughout the transformation journey.
Operational Checklist
Pre-project
- Clear target state (processes, roles, KPIs) and co-designed requirements
- Data readiness: quality, governance, availability
- Incremental roadmap (POC → deployment waves)
- OPEX/SaaS model aligned with the long-term trajectory
During the project
- Process first, tool second (the system as a structuring lever)
- High-impact AI POCs with predefined success criteria
- Adoption plan: user involvement, UX design, continuous enablement
After go-live
- Short hypercare phase (1–2 months), weekly reviews, prioritised backlog
- Measurement of operational ROI (productivity, data quality, compliance)
- Continuous improvement through planned iterations and regular governance
Successfully transforming a supply chain means aligning business objectives, data, and information systems around a guiding principle: process first, technology second.
By progressing through validated, measurable steps, supported by controlled data, agile systems, and structured adoption, organisations simultaneously strengthen resilience, accelerate execution, and build sustainable performance.