# Towards Adaptable Customer Segments and Reference Geometries

Greta Tjaden, Anne Meyer, Alexandru Rinciog

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Greta Tjaden, Anne Meyer, Alexandru Rinciog. Towards Adaptable Customer Segments and Reference Geometries. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.321-328, 10.1007/978-3-031-16407-1_38 hal-05386056

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# Towards Adaptable Customer Segments and Reference Geometries

Greta Tjaden<fnref n="1" />(✉)D, Anne Meyer<fnref n="2" />(D), and Alexandru Rinciog²D

**Abstract.** For all activities along the product lifecycle, a company should know its (possible) customers. The sheet metal tool manufacturer TRUMPF uses static customer segments and representative sheets with part geometries. Dynamic and use case-specific customer segments and geometries are missing. To overcome these shortcomings, we develop a novel framework for adaptable customer segments and reference geometries: We detail the limitations of the current approach and review literature delineating the research gap. We identify use cases covering the product lifecycle and define requirements for a software system to generate adaptable customer segments and reference geometries.

**Keywords:** Product life cycle management · Customer segmentation · Reference geometries · Sheet metal processing

## 1 Introduction

Companies must understand their customers to design products and services according to their needs and preferences. This also applies to TRUMPF SE + Co. KG (TRUMPF), a German manufacturer of machine tools for sheet metal processing. TRUMPF's product portfolio covers the most important production steps in sheet metal processing, including laser cutting, bending, punching, and automation components. Sheet metal products are utilized in engineering, automotive, electrical industry, and many more.

TRUMPF uses customer segments and benchmark sheets to provide detailed knowledge about customers and the products they manufacture. Several departments at TRUMPF use individual, static customer segments that vary in terms of the number of segments, underlying data, and considered parameters. These co-existing customer segments indicate the need for use case-specific customer segments. To compare the performance of 2D laser machines, several benchmark sheets have been developed at TRUMPF. Representative geometries have been placed on these sheets depending on the geometries' complexity and the sheet thickness. The placement of geometries on sheets is called nesting.

<footnote n="1">¹ TRUMPF SE + Co. KG, Ditzingen, Germany
greta.tjaden@trumpf.com</footnote>

<footnote n="2">² TU Dortmund University, Dortmund, Germany
{anne2.meyer,alexandru.rinciog}@tu-dortmund.de</footnote>
As the customers and their production requirements change over time, these static approaches are not sufficient. Both in practice and research, there is a demand for dynamic, data-driven techniques for the identification of customer segments and knowledge about their production. To overcome the limitations of the current approaches, we propose a software system for the data-based generation of adaptable customer segments and reference geometries that cover the relevant production steps of sheet metal processing. The major difference between reference geometries and the previously mentioned benchmark sheets is the flexibility. While the benchmark sheets consist of standard-sized sheets with certain sheet thicknesses, the reference geometries will not be limited by these factors. The aim is to represent the majority of the customers' jobs, consisting of customer geometries.

Our contribution is fourfold: (1) We analyze the limitation of the current static approaches of describing who the customers are and what they do. (2) We discuss relevant work and define the research gap, following the typical phases of product life cycle management (PLM). (3) We identify use cases for three different areas in our cross-functional workshop at TRUMPF. The results of this workshop let us expect improvements along the product life cycle. (4) We derive system requirements for generating adaptable customer segments and reference geometries on an up-to-date database, using TRUMPF use cases as an example.

The paper's structure closely follows these contributions: Sect. 2 describes the problem, Sect. 3 surveys the related work, while Sect. 4 describes the TRUMPF use cases and their requirements. In Sect. 5, we provide our conclusions together with a brief outlook.

## 2 Problem Description

To align activities to customer needs, we need to answer two questions, namely “which customers do we address?”, and “what do the addressed customers do?”.

The addressed customers vary depending on the use case. We identified three issues with TRUMPF's current static, department-specific customer segments.

Firstly, department-specific customer segments are generated in a time-consuming process. As such, segments are not tailored to particular use cases and are static over time. The absence of up-to-date information can hinder the work of the teams. Furthermore, several departments do much of the same work independently, which constitutes a waste of resources.

Secondly, the existing customer segments do not distinguish between customers with repetitive, long-term orders and those with small lot sizes and almost no repetition. The differing nature of the customers' orders results in different customer requirements.

To prepare the production, geometries are nested on sheets. Nesting can be optimized, for instance, to minimize material waste. Customers with repetitive, long-term orders usually benefit from nesting optimization. In contrast, for customers with little to no repetition, the cost and effort of the nesting optimization usually exceed their benefits.
We hope to solve these issues with a data-driven method for dynamically generating customer segments considering use case-specific requirements.

We can derive relevant requirements for a customer's production from their production organization, their geometries, and how often these geometries repeat. Up to now, TRUMPF has been using benchmark sheets to represent customer geometries. For their development, geometries and nested sheets from customers were analyzed in 2009 to determine the typical concentration of cutting meters depending on the sheet thickness. This work resulted in eight benchmark sheets, providing exemplary geometries with three different degrees of part complexities, nested on standard-sized sheets with sheet thicknesses between 1 and 4 mm, between 4 and 10 mm, and greater than 10 mm.

Since the benchmark sheets are static, the production times of experiments carried out ten years ago can be compared with experiments carried out today. However, the static nature of the benchmark sheets and the focus on the comparability of 2D laser cutting machines lead to the following problems:

1. **Missing Dynamics:** The technical development since their creation has not been captured. An update of the benchmark sheets on current data is time-consuming and costly and would still not cover other technologies.

2. **Limited Scope:** The benchmark sheets only cover one production step (cutting). To allow for a holistic view of customer production, single geometries that cover the most relevant production steps are needed.

3. **Inflexibility:** Related external influences like lot sizes and repetition factors should be incorporated. For instance, questions regarding the optimal combination of machines and automation components for a customer's order spectrum cannot be answered. Instead, numerous sheet sizes and geometries must be considered.

All in all, the benchmark sheets help compare the performance of laser cutting machines. However, they are ill-suited for the assessment of a portfolio, to help customers find the best machine park and hall layout for their orders, or to support development tasks by providing additional information. Adaptable reference geometries could solve the issues and challenges listed above.

# 3 Related Work

In this section, we review related literature. First, we look at data-driven methods for dynamic customer segmentation in product lifecycle management. Then, we scrutinize the B2B sector's use of customer segmentation. Lastly, we check how the sheet metal industry uses reference geometries.

## 3.1 Data-Driven Methods for PLM

PLM covers the whole journey of a product, starting with its creation and closing with its degradation. The cycle is usually divided into Beginning of Life (BOL),
Middle of Life (MOL), and End of Life (EOL), e.g. [4,12]. We found literature for BOL for the three sections *market analysis*, *product development*, and *product manufacturing*. For MOL, the literature covers *sales & distribution*, and *repair*. Our search revealed no data-driven PLM work related to EOL.

**BOL:** The applications of data-driven methods during *market analysis* include customer identification and customer requirement specification [10,11]. Based on the results of these methods, product features and characteristics, e.g. [11,12], as well as product quality requirements, e.g. [10], can be derived. The authors of [10] introduce data-driven approaches as tools for establishing a customer-centered *product development* practice. Many argue that *product manufacturing* can benefit from data-driven methods, e.g. [4, 10, 12]. Real-time-schedulers enable the optimization of the production plan but need representative production plans for the training and test phases, e.g. [10]. Information about the customer orders may fill the data gap for such a real-time scheduler. Further applications include simulation and testing [4] and optimization of the supply chain network [12].

**MOL:** In [11], the authors use customer segmentation as a data-driven method application for *sales & distribution*. Stormi et al. introduce customer segmentation for sales prediction in [9]. The literature dealing with the *repair* section focuses on data-driven applications within the field of predictive maintenance, e.g. [12], and anomaly detection [12]. Grouping customers by common geometry attributes—which implies similar wear of machines—and linking these groups to a maintenance history could help to improve maintenance.

The literature shows that data-driven and adaptable customer segments are important at multiple stages in PLM. Independently from individual phases, the need for an information management system along the whole PLM is stressed, e.g. in [8].

## 3.2 Customer Segmentation

Clustering customers into similar groups is called customer segmentation and enables customer-specific actions like marketing activities or development projects. However, the literature's focus lies with the B2C market fields and not primarily on B2B, to which sheet metal processing belongs. To the best of our knowledge, sheet metal processing is not yet covered in the literature.

The authors of [9] try to map B2C approaches to B2B, focusing on the market basket analysis, classification, and the Recency, Frequency, Monetary (RFM) model. The RFM value of a customer is calculated based on the determined values for recency (date of the last purchase), frequency (amount of purchases), and monetary (e.g. average purchase monetary value). The RFM model has also been applied with K-means clustering [2] and K-Medoids [5]. The authors of [3] use the Length, Recency, Frequency, Monetary (LRFM) model, which additionally considers the length of the relationship with the customer, to derive marketing strategies from customer segments in the B2B context. They note that the quality of the segments can be increased by considering multiple customer characteristics. Rivera et al. criticize the RFM model, saying it can be confusing
[6]. To make the traditional RFM model more suitable for the B2B context, they use a time-series clustering technique and topological data analysis to forecast the customer segments' demand.

The authors of [9] name customer characteristics suitable for the B2B context, such as purchase behavior, product preferences, frequency, and customer firmographics. The customer purchase behavior and customer value are mentioned in [3]. Simkin et al. similarly identify typical traits of the agrichemical industry, namely product group, location, and business sector, but note that these are not sufficient for creating meaningful customer segments. Additional attributes describing the customers, their needs, their buying behavior, and their decision-making reasoning are required [7].

The literature discussed above proves that customer segmentation is feasible for the B2B context, and provides potential characteristics for distinguishing customers. However, these features alone are not sufficient to alleviate the problem presented in Sect. 2.

## 3.3 Reference Geometries

We found [1] it to be the only application of reference geometries in literature. Here, the authors use benchmark sheets similar to those at TRUMPF to optimize the layout of a sheet metal processing plant using simulation. Since the considered plant has a lot of repeating sheets, the selection of five of their sheets of varying thicknesses was deemed sufficient for the simulation study. The work does not elaborate on the exact sheet selection mechanism. The authors' approach suffers from the missing dynamics (1), limited scope (2), and inflexibility (3) caveats listed in Sect. 2. Consequently, the highly volatile production of many different, customer-specific geometries with decreasing lot sizes cannot be covered. Instead, the method's applicability is limited to a production setup characterized by reoccurring geometries and sheets using static nesting configurations.

# 4 Towards Adaptable Customer Segments and Reference Geometries

We want to tackle the problems described in Sect. 2 with a software system that generates adaptable customer segments and reference geometries. The core difference between our proposal and existing approaches is the adaptability to changing use case requirements and databases. We introduce reference geometries as geometries that represent typical orders for customers or customer groups and can be flexibly nested on all types of sheets.

## 4.1 Use Cases at TRUMPF

The different use cases for customer segments and reference geometries identified at TRUMPF share the need to answer questions based on data, raising the
demand for a dynamic approach. We conducted a cross-functional workshop and identified three areas as most relevant for adaptable customer segments and reference geometries: Research & Development, Product & Portfolio Management, and Sales & Consulting.

**Research & Development** works on the development of new products and the improvement of features of existing ones. In PLM, it covers applications from both BOL and MOL. Extensive knowledge about geometries and edge cases is required, where edge cases represent unusual and above-average complex geometries. This expertise would allow distributing development capacity better.

Moreover, fine-grained availability and understanding of data would accelerate the development of machine learning methods for both services and products. Examples include the training of a real-time scheduler and an algorithm-assisted sorting of produced geometries.

**Product & Portfolio Management** concentrates on BOL but stands responsible for the entire PLM. This area aims for an economically successful portfolio of products and services. It also decides on further developments, additions, and deletions from the portfolio. Customer segments that are adaptable to the specific use cases like “customers that have a specific machine”, “customers that do not use any automation components”, or “differentiation of all customers” pave the way for customer segment-centric developments and decisions. Currently, product management is missing a reliable, data-based distinction between regularly used machine functions and those used only on a minority of geometries. This information is required for efficiently using the capacities for further product and service developments. Either the entirety or a focus group of customers could be segmented, and reference geometries with the desired characteristics could be selected, providing more fine-grained information.

**Sales & Consulting** focus on the distribution of products and services as well as the optimization of existing systems and hence, relate to MOL. In customer projects, they try to balance—beyond others—the contradicting key performance indicators (KPIs) cost per part, throughput, lead time, and delivery reliability. To calculate and weigh these KPIs, fine-grained data about the customers and their production is necessary. Adaptable customer segments in combination with reference geometries may accelerate comparing customers with certain characteristics, supporting these Sales & Consulting activities.

Within the last years, the industry's focus shifted from isolated machines to a holistic view of the production. Since the entirety of the production process affects the listed KPIs, customers need consultation on solutions as a whole, covering machines, automation components, services, and software. Such a perspective requires detailed knowledge about the customer. Reference geometries would allow for a fine-grained production process consideration, leading to better recommendations for overall customer productivity improvement. Adaptable customer segments would enable the assessment of the customer development within their segment and between segments.
## 4.2 Requirements

In this section, we define the requirements for a software system for the generation of adaptable customer segments and reference geometries on up-to-date data. We derived these requirements from the identified use cases and the reviewed literature. The most important requirement we derive is the adaptability of the customer segments and reference geometries. We further specify this as: (1) **Use case specificity:** The users must be able to select the relevant characteristics and weigh them for their specific use case. (2) **Freeze points:** To observe the development of products and customers over time, freeze points must be introduced to make certain customer segments and reference geometries static at fixed points in time. (3) **Dynamic updatability:** To have up-to-date decision databases, automatic updates of customer segments and reference geometries must be possible, whenever new data are available.

A second important aspect is the accessibility of customer and geometry data. These data are immensely sensitive, and without a convincing concept, customers will hesitate to share them. The software system should fulfill the following requirements: (4) **Confidentiality:** Legal restrictions and non-disclosure agreements must be considered. (5) **Stable decision base:** Upon request of a customer, the deletion of data must be possible (see Confidentiality), while the decision base or a description of the data must stay available to ensure transparency of past decisions in the future. (6) **Format compatibility:** The system must cover a wide range of data formats and types, especially for describing geometries. (7) **Data transfer:** Of course, a safe and continuous data transfer must be established.

To ensure a high user acceptance of the software system, the following requirements should be met: (8) **Understandability:** The adaptable customer segments and reference geometries must be applicable and understandable irrespective of the technical background of the users. (9) **Verifiable Validity:** System users are to be enabled to assess the trustworthiness of the generated customer segments and reference geometries.

# 5 Conclusion and Outlook

This paper lays down the challenges of describing customers and their activities in the B2B sector in general, and sheet metal processing in particular. We argue that a software system to generate adaptable customer segments and reference geometries can help meet these challenges. Both customer segmentation and reference geometries can be used independently or linked together.

We identified use cases along the product life cycle: Adaptable customer segments and reference geometries help Research & Development to accelerate the development of products and services tailored to the customers and better handle edge cases; Product & Portfolio Management can distribute and use its capacities more efficiently; Sales & Consulting can better cope with the perspective shift to a holistic view of the production processes.
Building upon a literature review and the use cases obtained through the cross-functional workshop at TRUMPF, we derived requirements for such a software system. We specified adaptability as use case specificity, freeze points, and dynamic updatability. To gain access to the data, we must ensure confidentiality, data deletion, format compatibility, and data transfer. The generation approach must be understandable and have a verifiable validity.

Future work needs to focus more on the exact features describing customers and geometries. In our next steps, we will analyze the data necessary to describe the customers and the reference geometries in detail. Using the results of this analysis, we will endeavor to bridge the gap between data already being collected and data required for the adaptable generation of customer segments and reference geometries. Lastly, we will implement minimum viable products to enhance the understanding of the most relevant use cases and their facets.

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