... and achieve a real competitive advantage through efficient data maintenance
Product data maintenance is time-consuming and costly. Its optimisation is a tiresome topic that no one is particularly fond of in a company. This is something we want to change! With our “Potential analysis – cost drivers in product data maintenance” we will work together with you to identify the causes and to make concrete recommendations for improvement. In this way, you will be able to avoid unnecessary costs in the future and establish a sustainable basis for your product data maintenance.
The statement “Product data maintenance is time-consuming and costly” was confirmed by 74% of the participants (!) during the “Product Management Study“. Of the many other challenges mentioned, this problem was given the strongest weighting. Of the many other challenges mentioned, this problem was given the strongest weighting.
For us, this was a good reason to get to the bottom of what causes this and to identify possible solutions for companies.
Important but unpopular
High-quality and highly available product data is immensely important, especially in the age of digitisation – you cannot hope to achieve effective digitisation without a good database! Despite this, we have often seen that product data maintenance is regarded as a necessary evil. One product manager at a major appliance manufacturer said:
“Creating an item in our MDM system requires a lot of work; we have to maintain the fields page by page.”
Why is product data maintenance so unpopular – both with the data maintainers themselves and with those who provide the funds for possible improvement projects? The phrase “considerable effort” mainly refers to monotonous manual activities. Most people find repetitive tasks tedious and dislike doing them simply for that reason. One product manager of a medium-sized machinery manufacturer said:
”We have a great deal of redundant data maintenance because the tools are not interlinked with each other.”
And then there is the lack of transparency regarding what happens to the data. If data maintenance knew right at the beginning of the process chain what was needed for the subsequent use of the data and why, the motivation to provide good data would be quite different. A lack of consistency can have disastrous consequences, as the example of another product manager at a large component manufacturer illustrates:
“Now that we know the huge amount of data maintenance that a product modification entails, we often ask ourselves whether we really want to implement it at all. Sometimes we are even willing to live with a lower-quality design in order to avoid the excessive data maintenance.”
Who will pay for it? No budget for better data maintenance
The willingness to invest in improved product data maintenance is relatively low, even at C-level. Often, data maintenance is generally seen as a cost driver. Additional staff are rarely made available if the costs exceed those that are “already there”.
It is indeed true that a return on investment (ROI) for the optimisation of product data maintenance is very difficult to calculate. This is mainly due to the fact that the costs of data maintenance and the benefits it generates are incurred at different points in the value chain – i.e. in different departments within the company. This makes it difficult to explain why investments should be made in one’s own cost centre, where only a “minor” sub-benefit is generated.
If the investment has to be allocated among different cost centres, it will become too complicated for many of those responsible and nothing will happen.
New requirements – old data
New requirements, such as standardised data exchange formats and the introduction of new IT systems, require a suitable data basis:
- The market is increasingly demanding standardised exchange formats such as eCl@ss and ETIM. This demand has to be catered for accordingly in order to retain the customers. For old products, the master data is often missing and needs to be expanded. It is usually difficult to foresee future requirements – but a well-designed data structure as a basis makes it much easier to expand it at a later stage.
- The transfer of obsolete data into new IT systems is often not given the attention it requires. New systems (for example migration to the new SAP version S/4 HANA or replacing a CRM system with cloud-based software) can only unfold their potential if they are built upon a good data basis. If this basis has not been sufficiently enhanced beforehand, a change of system without the necessary revision will not provide the desired added value. Everyone knows the saying: “Put shit in, get shit out”.
At the end of the day, it is a vicious circle: unpopular data maintenance within complicated processes often leads to “working around the system”. A lack of specifications and standards for process flows, for example, leads to a lack of discipline in day-to-day business. The data basis will then deteriorate continuously.
Avoid and reduce manual activities – it really pays off!
We at encoway are convinced that there is also a direct ROI in product data maintenance! Why is product data maintenance so time-consuming and costly? This is primarily due to the high proportion of manual activities. Essentially it is a matter of achieving the same result with less effort. We are convinced that there is significant potential for cost savings here – for both large and small companies, regardless of whether they manufacture small components or large-scale customised machinery!
1. Avoid manual effort with a clear data strategy and supply.
Isolated data supply processes have often evolved around the IT systems of the individual departments (for example PDM, ERP, PIM). Following the lean principle, non-value-adding activities can be identified all along the data maintenance process: redundant maintenance of product data in multiple IT systems, manual transfer of data due to media discontinuities, searching for data that is not available etc.
On the basis of these insights, a clear data strategy and a transparent data supply process are created. This ensures that standards are reached, redundant data maintenance avoided and time and effort spent on the interfaces and in the search for product data reduced.
2. (Further) reduce the manual effort with well thought-out data structures.
The manual effort can be reduced even further by identifying the positions within the data structures that are responsible for a disproportionate number of manual activities: maintenance of the same data records in different places, mapping of the same or similar contexts using different methods, familiarisation with confusing data structures and programmed rule sets etc.
An example: a product manager at a large component manufacturer told us that changing a single attribute in product management leads to several thousand change requests – in the ERP system alone! And that is not including change requests in other IT systems.
When used correctly, variant management based on modular structures not only facilitates design and production, but also significantly reduces the data maintenance effort. Even the introduction of a consolidated classification system with inheritance across the class hierarchy leads to significant savings.
The way forward
Less manual effort in product data maintenance means not only do you save costs, but the entire process is also much faster. Especially when it comes to launching important new products or introducing changes to the market, you benefit from a shorter time-to-market.
Unravel the Gordian Knot with our “Potential analysis – cost drivers in product data maintenance”.
This is how we proceed:
- Finding starting points for organisational and process optimisation: Together with you, we analyse which new products and changes in the product portfolio lead to which types of data maintenance requests in the IT systems.
- Finding a starting point for structural optimisation:We also analyse how these changes are implemented in the IT systems and what influence the underlying product data structures have on the maintenance effort.
- Generating results and providing recommendations for action: You receive a transparent overview of the current data maintenance process and the effort involved. You receive clear recommendations for action with a structured and prioritised list of areas of potential, including a monetary evaluation.
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