Process Improvement Work

Problem

A recurring issue in several production workflows was that information and checks were spread across separate Excel files, production records, test data and informal routines. Tasks often depended on manually copying information, comparing records, checking ranges or knowing which file should be reviewed next.

As the amount of production data increased, these manual steps made follow-up slower and created more opportunities for missed information, inconsistent handling and repeated work. Some processes also depended heavily on knowledge held by individual staff rather than a clearly documented and repeatable workflow.

Solution

I usually start by understanding how the existing process is actually carried out, including which files are used, where information comes from, which checks are performed manually and where staff need to make decisions.

I then simplify or restructure the workflow where possible. Depending on the problem, this can involve reorganizing Excel data, combining sources with Power Query, adding formulas and validation, building a Python tool, or creating a clearer overview for follow-up.

I also try to make the improvement easier for someone else to maintain and use. This includes keeping the workflow understandable, adding checks where mistakes are likely, and documenting important procedures so the process does not depend entirely on one person knowing how it works.

Key Features

• Restructuring Excel data and workbooks so information is easier to enter, search and maintain
• Using Power Query to combine data sources and reduce repeated manual preparation
• Adding formulas, validation and control checks to reduce entry and follow-up errors
• Building Python tools when repetitive tasks can be handled more reliably through automation
• Creating dashboards, summaries and clearer outputs for production and quality follow-up
• Connecting production, traceability, testing, planning and shipment data where separate records need to work together
• Standardizing recurring procedures with clearer instructions, guides and repeatable workflows
• Testing improvements in real use and refining them as new problems or requirements are discovered

Practical Examples

The same improvement approach has been used across several production and data-related workflows, with the solution depending on where the main source of manual work, uncertainty or repeated checking was found.

• Production traceability — connected production, component, service and shipment records so related history can be followed without manually searching several workbooks
• Production log validation — developed LogTool to check completed batches for missing device logs and convert raw TXT records into structured Excel output
• FIFO and shelf-life planning — structured stock and order data so suitable batches can be selected using FIFO, expiry and customer shelf-life rules
• Shipment registration — created a controlled entry workflow with validation, workbook backups and checks for previously shipped device serials
• Test-data follow-up — reorganized leakage-test results into clearer calculations and summaries for batch, equipment and quality review
• Production documentation — created step-by-step guides for recurring tasks so important procedures are easier to repeat and less dependent on individual knowledge

Tools Used

Result

The improvements reduced the amount of repeated manual work needed in several production and follow-up tasks while making information easier to find, compare and verify.

Just as importantly, the workflows became more structured and easier to hand over. Checks, data handling and recurring procedures are increasingly built into the tools or documented clearly instead of depending on someone remembering each step.