Donato BlandolinoIn one factory, we found eleven Excel files that production depended on. The oldest was over ten years old. Why it is not wages or site costs that hold back productivity, but master data that nobody trusts.
At one factory, we found eleven Excel files that production depended on. The oldest was around 3,869 days old, so more than ten years. If one of them had failed or the only person who understood it had fallen ill, manufacturing would have ground to a halt.
This is not an outlier. It is the norm in many manufacturing companies, and it explains a productivity problem that never features in debates about business locations.
Where skilled staff actually spend their time
Imagine a company hiring a highly qualified industrial engineer. A specialist whose job is to improve processes, eliminate waste and increase productivity.
Instead, his day starts with correcting master data. Reconciling Excel files. Comparing bills of materials. Tracking down missing information.
That is the real productivity problem in industry. Not wages. Not location costs. Not the shortage of skilled staff. But processes that nobody has questioned for years:
- Data is entered multiple times
- Excel files are sent between departments
- Bills of materials are compared manually
- Master data is corrected rather than used
- Knowledge held only in individual employees’ heads has to be replaced time and again
Why Excel is not the problem
Excel is the symptom, not the cause. The spreadsheets emerge because people want to do their jobs and cannot trust the official data. Every shadow Excel file is a vote of no confidence in the primary system.
If you have your master data under control, you have your business under control.
The real problem is a data foundation that nobody trusts. And that distrust is usually justified: anyone who has placed an incorrect order because of a wrong material number will create their own list. From that point onwards, there are two versions of the truth, and more emerge every year.
Because every incorrect material number, every wrong bill of materials and every routing that is not kept up to date generates costs. In purchasing, which orders the wrong quantities. In planning, which works with incorrect lead times. In production, which has to rework products. In logistics, which has to search for them. And ultimately for the customer, who is left waiting.
What this means for Switzerland as a manufacturing location
Switzerland is an expensive place to manufacture. Its greatest advantage is its people: their skills, their experience, their motivation.
Companies squander that advantage when they have highly qualified specialists spend half their day correcting administrative errors. With local wage costs, every hour a specialist spends reconciling data is doubly expensive: once as working time, and once as a missed opportunity for improvement.
And here is the paradox: many companies invest millions in new software, but not in the quality of the processes and master data on which that software is built. A new system running on poor data is an expensive way to make the same mistakes faster. While others achieve significantly more with the same team, they continue to struggle with Excel and the consequences of poor data.
How to tackle the problem in practice
Getting started is less dramatic than most people expect, and requires no software.
1. Map the shadow Excel files
Spend a week recording which lists exist outside the primary system and what they are used for. Each of these files reveals which information in the system is not trusted. This is the most honest process analysis there is, and it costs nothing.
2. Identify the cause for each file
Why does this list exist? There are usually three answers: the field in the system is maintained incorrectly, the field does not exist, or nobody knows it exists. Each answer calls for a different action, and none of them is a software project.
3. Assign responsibility for each data object
Master data decays when everyone uses it and nobody owns it. The material master, bills of materials and routings each need a named person accountable for their quality. A name, not a department.
4. Tackle the most expensive errors first
Not all data is equally important. The question is which incorrect information has cost the most over the past twelve months: the incorrect order, the planning error, the rework. That is where data cleansing begins, not with the letter A.
5. Only then discuss systems
Once the data foundation is sound and responsibilities are clear, it is worth asking whether the system supports the processes. Until then, any discussion about software is premature.
How to tell whether it is working
One simple measure: the number of shadow files. When people voluntarily give up their own lists because the system has become more reliable, the change is working. As long as new lists keep appearing, trust has not yet returned.
That is why, at SMART iNTERIM, we start not with software, but with the causes. The eleven Excel files in the opening example were not an IT problem. They were eleven unanswered questions about the company’s own data foundation.
When did you last permanently eliminate a manual task or an unreliable data source?
Further reading:
About SMART iNTERIM
SMART iNTERIM Management supports manufacturing companies through operational transformation: restructuring, factory set-up, factory relocation, turnaround and process optimisation. We bring hands-on experience, not a remote perspective, and take operational responsibility rather than simply making recommendations. Head office in Switzerland, branch office in Dubai.
