Getting started with data-driven variant management

65% of the product managers questioned said that they lack the necessary data to carry out effective portfolio management.

Data is the gold of the 21st century – this insight is by no means new. However, as of today, only a few companies utilise the potential that can be unlocked through the analysis and use of their own data. This is also confirmed by the results of the study “Product Management 2020”, which encoway conducted with product managers from manufacturing companies. For example, 65% of the product managers questioned said that they lack the necessary data to carry out effective portfolio management. However, business intelligence (BI for short) and professional data analysis are ideal for gaining new insights from past data and using them to make the right decisions for the future.

Especially in the environment of variant and complexity management, the systematic evaluation of quotation and order data can be used to determine how to best manage the portfolio. With the help of BI, it is possible, among other things, to analyse which parts of one's own product portfolio are selling well or less well in order to draw conclusions about the profitability of individual product variants. This allows valuable deductions to be made for future portfolio decisions.

Best practice of Teckentrup GmbH & Co. KG

Automated data analysis in practice

Teckentrup GmbH & Co. KG, one of the largest manufacturers of doors and gates in Europe, has discovered the potential of its own data. Patrick Lohmann, Head of SAP LO-VC and PDM at Teckentrup, explains here how the company successfully got started with data-driven variant management thanks to automated data analysis.

Mr Lohmann, why did you decide to automate the analysis of your data?

One of the main reasons why we wanted to introduce automation was the high level of manual effort we required to analyse our order data. For example, the order data for a configurable product contains over 18 million lines over a time period of just two years. Mapping this in Excel often caused us a lot of frustration. Because of the great effort required to evaluate the data, which is so valuable for us, we were only able to use it very occasionally to support portfolio decisions. This is why we wanted to find a way to analyse our order data that was as automated and integrated as possible.

How did you go about making this change?

At Teckentrup, the orders are managed in SAP ERP. This is where all the order data and the corresponding Bills of Materials are stored. However, when we started, we were not sure what data we actually needed to answer questions such as “Which parts of our product portfolio contribute to our company's success?” There was also still no complete list of all the questions that we want to answer today and in the future. We therefore decided to take a step-by-step approach in order to get to grips with the topic without having to invest too much. After all, we didn't even know whether we could actually achieve anything with the results. However, we soon realised that the existing modules in the ERP system and Excel were unsuitable for our exploratory project.

Where do you believe your data has the greatest potential?

In the past, we mapped standardisable products, individual components and special customer requests in the same way in our processes. Here we can see enormous potential for future optimisation. As a first use case, we therefore decided to use the information obtained to define standards for frequently selected feature combinations, and to discontinue variants with features that were rarely selected. We expect significant savings as a result of focusing in this way. But I also see a lot of potential over and above this. For example, we could use order and quotation data to generate a more accurate forecast for future business based on probable contracts.

Have you already had any initial positive results?

Yes, after a very short time we have been able to gain insights from the order data that were previously beyond our reach. To achieve this, we first focused on providing the relevant data. Instead of rummaging through 18 million data lines in Excel, we pre-filtered the data based on the questions to be answered and organised it in an efficient data management system. We then presented the data relevant to specific questions in a clear, web-based form.

Together with encoway we have created a lightweight and at the same time very flexible solution that meets our requirements more than 100%. In particular, the possibilities for flexible evaluation have exceeded our expectations. The results and data are available to us within a few seconds at the touch of a button. We are able to carry out evaluations quickly and with no manual effort and present all the data on easy-to-read dashboards. In this way, we have created a sound decision-making basis for standardising (certain parts of) our product portfolio.

What concrete advantages has your company gained from this?

In particular the compilation and preparation of data now requires significantly less effort. This saves us a great deal of time. And because the evaluation of the data is so much easier and faster, more people are now able to obtain information on their own and use this as a basis for decision making.Before, the large amount of effort involved and the susceptibility to errors were a major obstacle.

The newly gained transparency also benefits the design, supply chain and production departments among others. For example in the quality assurance process, it is much easier for our design team to use the sales frequency to determine which of our safety-relevant products would most benefit from an improvement or inspection. In production, we are able to optimise machine utilisation and stock management using the analysed data, for example when it comes to which products or materials should be kept in stock and which machines can be replaced in future.

Are you planning to further expand the opportunities for data analysis?

From the data analysis, we have identified a wealth of optimisation opportunities and facilitations that we can use throughout the company. Even in the first stage, we gained far more insights than we had expected. This also impressed our management team. We would very much like to expand this topic in future. In addition to analysing order data, we hope to be able to evaluate other data sources from our CRM and CPQ systems, such as quotations. We believe that the data from the TEO configuration solution in particular will provide even more detailed and specific information to make better decisions in our sales, engineering, purchasing, and supply chain departments in future.

In addition, we are currently working on a number of standardisation topics in the field of master data maintenance in order to further improve this as well. In this way, we also want to generate added value for other areas of the company and constantly optimise our range of products. I believe there are still some fascinating questions and tasks to be solved in the near future, and we are glad that we will be able to count on encoway’s expertise and support once again.

Thank you very much for this interesting conversation, Mr Lohmann!


    Business Intelligence & Analytics

    Get more out of your CPQ data: encoway’s Analytics add-on encoway CPQ BI-Connector transforms configuration and quotation data into valuable information.