Challenge Instead of a Specification: Sourcing Packaging Machinery

Buying packaging machinery is a high-stakes decision. The investment is substantial, the equipment has to perform for years, and the choice must hold up against changing products, regulation, material requirements and production constraints. Yet many sourcing processes begin by describing a familiar machine in ever greater detail.

In a recent FACHPACK Virtual Talk, Fabian Haferkamp of PAUL HARTMANN AG, Felix Wieduwilt of NOVISIO and Felix Gass of PSL discussed a different route: a technology-open challenge that defines the required outcome before prescribing the technical solution.

The full 55-minute discussion is available in German on the FACHPACK YouTube channel.

Why a detailed specification can close the solution space too early

A conventional specification creates the impression of precision. In practice, it can lock the project into an existing technology before the team has established whether that technology is still the best answer. Detailed requirements such as component brands or machine architecture may be necessary later, but they do not answer the first strategic question: which packaging technology best fits the product, process and future operating environment?

The problem becomes more visible when a company is not simply replacing machine A with machine B. Different product groups may be better served by fundamentally different concepts: a thermoformer, a flow-wrapper, a pouching line or another format entirely. A specification built around the installed technology tends to reproduce the status quo. It also becomes difficult to incorporate what the team learns during sourcing without repeatedly revising the document and extending the project.

The challenge approach turns the sequence around. It describes the target space, the non-negotiable boundaries and the outcomes the solution has to deliver. Suppliers remain free to propose how to get there.

The work begins before procurement

For HARTMANN, the project started with a strategic question inside its transformation programme: are the current packaging technologies still state of the art, and where could a different approach improve cost, performance or future readiness?

This is sourcing before conventional procurement. The operational team contributes its knowledge of products, production, quality and regulatory requirements. An open-innovation partner structures the collaboration and creates access to a wider solution field. Packaging expertise translates internal needs into questions that suppliers can answer, then turns fundamentally different proposals into a decision the business can evaluate.

That translation role is essential. Supplier concepts should not end up as a stack of individually convincing but incomparable offers. They need a shared decision logic.

A staged challenge lowers the entry barrier

The project used a progressive process rather than asking every supplier for a complete concept on day one:

1. Qualify broadly. More than 30 potential suppliers were approached internationally. A short, structured form covered capabilities, relevant technologies, sustainability, standards, product-group fit and an initial cost frame. The effort was intentionally low so that suppliers could decide quickly whether they were a credible fit.

2. Develop concepts. The qualified group responded to increasingly specific questions. The process became more detailed only where the earlier answers justified further work.

3. Validate in depth. A smaller group moved into detailed technical discussion. Confidential product information, volumes and current packaging dimensions were shared only after a mutual non-disclosure agreement was in place.

This progression respects supplier time while keeping the early search wide. It also allows knowledge from one stage to improve the next rather than forcing the team to pretend that every requirement was known at the start.

How to compare fundamentally different machine concepts

Technology openness does not mean evaluation without structure. It requires more structure at the level of outcomes.

The team organised the decision into common blocks and translated each block into specific questions. The criteria included:

• technical feasibility and compatibility with sterile-barrier requirements;

• quality, regulation and process capability;

• throughput, changeover time, reliability and recovery from downtime;

• economics across the investment and operating period;

• material consumption, packaging efficiency and sustainability;

• flexibility for future products and materials; and

• local service and support.

Each proposal could then be rated against the same dimensions. A flow-wrapper does not need to resemble a thermoformer to be comparable. Both need to show how well they deliver the required outcomes. The result is a transparent view of where each concept is stronger, where it carries risk and why one option may deserve deeper investigation.

What the team learned

The supplier response was stronger than expected. A professionally structured, low-effort first step gave many machine builders enough clarity to participate without committing a large engineering team immediately. Some suppliers found the unfamiliar process difficult and wanted to move straight to site visits and detailed discussions. That difference was informative in itself: the challenge also tested how potential partners work with uncertainty and new forms of collaboration.

The slowest element was not the platform or the evaluation model. It was often the human interface: finding information, aligning calendars, obtaining approvals and waiting for decisions. Better tools can help, but complex sourcing still depends on clear ownership and disciplined collaboration.

The role of procurement changes accordingly. Instead of entering primarily for negotiation and contracting, procurement can help facilitate the process from the beginning, connecting R&D, production, quality, regulatory affairs and suppliers around the same decision frame.

Where AI helps, and where it does not

AI can already reduce the administrative weight of a challenge. It can structure information from multiple submissions, transfer responses into a consistent overview, surface gaps and support discussion around evaluation criteria.

It should not be mistaken for the decision-maker. In a regulated context involving sterile barriers and complex production risks, human specialists still need to test assumptions, judge evidence and accept accountability. The useful near-term role for AI is to make the evidence easier to work with, not to replace the people responsible for the investment.

Five principles for a technology-open sourcing challenge

1. Define the outcome before the machine type. State what the system must achieve and which boundaries cannot move.

2. Start broad and low-friction. Ask only for the information needed to qualify a supplier at each stage.

3. Compare outcomes, not architectures. Use common dimensions that apply across technologies.

4. Protect confidential information at the right moment. Keep early questions general, then introduce mutual confidentiality before sharing sensitive operating data.

5. Keep expert judgement and partnership in the loop. A score creates transparency; it does not remove the need to understand technical risk and supplier capability.

The strategic payoff

The most important result is not simply a faster RFQ. It is a better decision. The challenge creates market transparency across technology boundaries, reduces the risk of overlooking an unfamiliar solution and gives development and procurement a defensible basis for the next investment step.

Sometimes the process confirms that the current technology remains the best available option. That is still a useful outcome: the status quo has been tested rather than assumed. Where a stronger concept emerges, the business can see whether the advantage comes from cost, flexibility, material reduction, performance or a combination of these factors.

The approach is transferable beyond packaging machinery. Any sourcing decision with multiple technical routes, high uncertainty and long-term consequences can benefit from defining the target space first and allowing the solution to remain open for longer.

Key takeaways

• A detailed specification can create false precision and narrow the technology search too early.

• A staged challenge combines a wide supplier search with progressively deeper technical work.

• Different machine concepts become comparable when they are evaluated against the same outcome-based dimensions.

• Procurement adds more value as an early facilitator than as a late-stage negotiator alone.

• AI can organise evidence and support evaluation, while accountable technical judgement remains human.

For teams facing a complex machinery or packaging-technology decision, the starting point is not a longer specification. It is a clearer question.

PSL supports packaging machinery decisions and supplier briefs by translating operating needs into comparable requirements. The same development work connects material and format choices to production trials.