Framework for the Continuous Increase of Product Performance by Analyzing Product Usage Data

Riesener, Michael; Dölle, Christian; Becker, Ann; Schuh, Günther

doi:10.1109/ieem44572.2019.8978842

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Operational data collected in the text and validation and in-service phase can enhance the health of the product, which will then increase the product reliability (Geiger and Sarakakis, 2016;Shin et al, 2015). As such, an integrated design improvement approach consisting of data modelling and analysis techniques are proposed by a couple of researchers (Ma et al, 2017;Riesener et al, 2019;Shin et al, 2015). Taking another step forward, Khoshkangini et al (2020) introduce the conceptual view of two machine learning pipelines for predicting individual components' failure/claims.…”

Section: Assess Predict and Improve The System Performancementioning

confidence: 99%

Four Patterns of Data-Driven Design Activities in New Product Development

Lee

Ahmed‐Kristensen

2023

Proc. Des. Soc.

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In the midst of Industry 4.0 where digitalisation is stimulated through the Internet of Things (IoT), Big Data, and machine learning technologies, an increasing volume of valuable data has been acquired from sensors and interconnected devices. This data-driven paradigm can enable organisations to create new or improved products and services, build long-term customer relationships in a value co-creation manner, adapt to continuous business reconfiguration or address societal challenges such as sustainability. Scientific research addressing Data-driven design has increased steadily in the last few years. However, despite this, there is still a need for a comprehensive understanding of data-driven design processes. Thus, through a systematic literature review, we review the data-driven design activities observed in the new product and service development and types of data utilised in New Product Development (NPD) process. This paper contributes to design research and through reviewing the current landscape of Data-driven design identifies ten data-driven design activities and four-dimensional aspects in NPD process.

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Section: Assess Predict and Improve The System Performancementioning

confidence: 99%

Four Patterns of Data-Driven Design Activities in New Product Development

Lee

Ahmed‐Kristensen

2023

Proc. Des. Soc.

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“…Other research efforts have utilized product usage information to improve product performance and evaluate customer requirements’ fulfillment [ 54 , 55 , 56 ]. In [ 54 ], the authors utilized product usage from a secure health communication services provider called “Brightsquid” to gain a better understanding of customers’ needs and determine how well the existing product meets their needs.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 56 ], the authors proposed an approach to improve product performance and customer value through utilizing product usage data. In this approach, the customer value is identified and then mapped to the physical structure and the function of the product.…”

Section: Related Workmentioning

confidence: 99%

A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems

Esheiba

Helal

Elgammal

et al. 2022

Sensors

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Nowadays, manufacturers are shifting from a traditional product-centric business paradigm to a service-centric one by offering products that are accompanied by services, which is known as Product-Service Systems (PSSs). PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers. This is combined with service customization, in which configured products are expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage and facilitate the transition to smart connected products. The concept of PSS customization is gaining significant interest; however, there are still numerous challenges that must be addressed when designing and offering customized PSSs, such as choosing the optimum types of sensors to install on products and their adequate locations during the service customization process. In this paper, we propose a data warehouse-based recommender system that collects and analyzes large volumes of product usage data from similar products to the product that the customer needs to customize by adding IoT smart devices. The analysis of these data helps in identifying the most critical parts with the highest number of incidents and the causes of those incidents. As a result, sensor types are determined and recommended to the customer based on the causes of these incidents. The utility and applicability of the proposed RS have been demonstrated through its application in a case study that considers the rotary spindle units of a CNC milling machine.

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