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Why digitization projects in manufacturing fail: 10 typical reasons from small and medium-sized enterprises

Many medium-sized manufacturers are currently under considerable pressure to act. Increasing competition, higher energy prices, and growing regulatory requirements are making it increasingly difficult to manage purely analog manufacturing efficiently and reliably. The switch to digital production is therefore becoming a practical necessity for many. In implementation, an MES (Manufacturing Execution System) often forms the core because it connects operational manufacturing with planning and control and makes data from the shop floor usable.

Despite this necessity, digitization projects often fail in reality. In very rare cases, this is because a solution is fundamentally unsuitable from a technical standpoint. More often than not, the prerequisites for a smooth introduction are lacking. These include a clear target vision, stable processes, reliable data, and clear responsibilities. Equally important is an approach that fits in with the day-to-day business of small and medium-sized enterprises.

This article describes ten typical patterns that cause digitization projects in manufacturing to fall by the wayside. The focus is on topics related to MES, data acquisition, and shop floor transparency. For each point, you will find countermeasures that have proven themselves in practice.

Image: Example illustration “Digitization project”

Classification: What does digitization mean in concrete terms in production?

In many medium-sized companies, digitalization primarily means reliably recording production data and making orders and capacities transparent. Based on this, key figures can be calculated consistently and decisions in shop floor management can be made on the basis of reliable data.

An integrated MES is the central link for this. It is used to monitor, control, and optimize production in real time. At the same time, it connects ERP and the manufacturing level so that planning, feedback, and evaluations are interlinked without media discontinuity.

1. There is no target vision, only a list of desired functions.

Typical pattern: The project starts with the goal of becoming more digital and ends up with a long list of requirements. There is a lack of prioritization, a clear path to benefits, and a robust scope.

Countermeasures:

Formulate a target vision that contributes to a few measurable results. These could be, for example, better traceability or faster feedback.
Prioritize the most important use cases and derive a roadmap from them that fits your day-to-day business.
Before selecting the tool, clarify which management decisions need to be made better in the future and which data is really needed for this.

2. Digitalization is managed as an IT project, not as a production program.

When responsibility lies entirely with IT, operational ownership is often lacking. The technical implementation is then flawless, but the benefits do not reach the shop floor in everyday use.

Countermeasures:

Anchor technical project management in production and give it clear decision-making authority.
Set up IT as an enabler: for architecture, security, operations, and interfaces.
Define lean governance with roles, decision-making paths, escalation, and a fixed cycle for steering and approvals.

3. Processes are not stable, yet they are being digitized

Digitization reflects reality. If reality consists of special cases, the system quickly becomes a collection of exceptions. This is often evident in work schedules and feedback logic. At the latest, it becomes noticeable in everyday life during shift handovers, setup processes, or in quality assurance.

Countermeasures:

First create standards and then map them digitally.
Manage exceptions consciously: Define what is standard, which cases are considered exceptions, and who decides on them.
Use the logic of lean and shop floor management to ensure that transparency leads to root cause analysis and improvement, rather than just more reporting.

4. The scope is too broad and the rollout is happening too soon

In medium-sized companies, projects run parallel to day-to-day business. If several areas are to go live at the same time, this can lead to overload, quality problems, and declining acceptance.

Countermeasures:

Start with a clearly defined area, stabilize the solution there, and only then scale further.
Take a modular, step-by-step approach. This way, benefits become apparent early on and expansions remain manageable.

5. Data quality and master data are underestimated

An MES is only as good as the data that flows in and out of it. Unclear or inconsistent master data leads to incorrect planning, erroneous feedback, and unreliable evaluations. This creates mistrust, and mistrust blocks its use on the shop floor.

Countermeasures:

Define data ownership: Who is responsible for which master data and who is authorized to approve changes?
Establish validation rules and approvals before data is used productively.
Plan data maintenance as an ongoing task rather than a one-time migration.

6. Machine data and shop floor feedback are not reliably received

Without reliable MDE and BDE data, there can be no real-time transparency. Key figures are discussed, but not believed.

Countermeasures:

Define an integration strategy for machines and consider retrofitting where necessary.
First define data points in technical terms and only then link them technically. This includes statuses, cycles, reasons for downtime, and quality information.
Test early and realistically whether data quality and granularity are sufficient before building key figures and shop floor routines on them.

7. ERP integration and interfaces are planned too late

If MES and ERP are not properly integrated, this results in duplicate maintenance, media breaks, and correction loops. This immediately becomes apparent in everyday work due to the additional effort required.

Countermeasures:

Treat integration as a core task and plan for it early in the project.
Harmonize master data, order information, and feedback, including clear booking logic.
Define responsibilities between production, IT, and ERP operations in a binding manner so that interfaces remain stable in everyday use.

8. Key figures are introduced without their definition and purpose being clear.

Key performance indicators are management tools. If definitions are inconsistent, the team will discuss numbers instead of causes. This is particularly critical for production-related KPIs such as OEE, because they combine time, quantity, and quality in a single indicator.

Countermeasures:

Create a KPI glossary that includes definitions, data sources, calculation logic, responsible parties, and purpose.
Select key figures that guide action and can be directly translated into measures.
Ensure that data collection and KPI logic are aligned so that dashboards remain comprehensible and inspire confidence.

9. Change management is reduced to a briefing

A new system changes routines. Suddenly, reports are no longer submitted on paper, downtime must be justified, and quality data must be recorded in a structured manner. Without acceptance, the system is quickly perceived as a disruptive factor.

Countermeasures:

Qualify based on roles, because workers, shift supervisors, work planners, and production managers require different content.
Make the benefits visible in everyday life, for example through fewer queries, better shift handovers, and faster problem detection.
Take leadership on the shop floor seriously: discuss key figures regularly and follow up on measures consistently.

10. Operation and further development are unclear after go-live

Many projects end organizationally with the go-live. However, the benefits only arise during stable operation and continuous development. Without defined support, release processes, and clear responsibilities, teams gradually revert to Excel and paper.

Countermeasures:

Define an operating model with support, monitoring, roles and rights, and clear escalation paths.
Plan release processes and responsibilities in such a way that further development continues to take place after go-live.

Conclusion

Digitalization in manufacturing is not an end in itself. It succeeds when project work and everyday production are brought together. This starts with a clear vision and stable processes. Added to this are reliable data, well-planned interfaces, and clear responsibilities. Those who lay these foundations significantly reduce project risks and establish the basis on which an MES and data-based control can function permanently.

A pragmatic approach that suits small and medium-sized businesses.

Small and medium-sized businesses benefit from clean execution, clear accountability, and a predictable cycle. An approach that first creates clarity and then delivers step by step has proven successful:

Record the system landscape

and processes in a structured manner and identify the most important bottlenecks.

Define and prioritize goals

and requirements together with specialist departments and management.

Assess potential

and create a roadmap with specific work packages and responsibilities.

Implement in stages

and expand the system in a modular fashion as soon as the previous step is running smoothly.

Embed operation

and continuous improvement in the organization so that the solution is maintained and further developed on a daily basis.

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