A deep dive into addressing obsolescence in product design: A review

In today's fast-paced world, products are constantly replaced by newer and more advanced versions. While some products become outdated due to natural causes such as wear and tear or technological advancements, others are strategically designed with a predetermined shelf life to encourage rapid product turnover. Obsolescence is an essential issue in product design because of its impact on product life, efficiency, and sustainability. Although there are approaches to map and measure possible product obsolescence scenarios, it remains a challenge to quantify and diagnose a product's or component's obsolescence potential based on its design attributes. Therefore, this article aims to analyze the existing literature on obsolescence from a product design perspective. It covers its application in methodological design strategies, metrics for measuring obsolescence from early design stages, and identifying understudied research topics, challenges, and trends. On August 15, 2023, a total of 221 articles published between 1983 and 2023 on SCOPUS, Web of Science, and Google Scholar were selected and analyzed using a content-based research approach encompassing three main aspects: objectives and methodologies, strategies and design phases, and metrics for obsolescence analysis. As main findings, this literature review identified several methodological design approaches aimed at resisting and postponing obsolescence, mainly divided into designing long-life products and extending product life. Nevertheless, this study found no formal identification of product design attributes related to the different types of obsolescence, and obsolescence forecasting metrics have focused on defining whether the scenario happens but do not consider what type of obsolescence the product may exhibit. Consequently, it can be challenging to determine the most effective design strategy to reduce obsolescence. This study has limitations, including the potential for researcher bias to affect the systematization of the information.

Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence

Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms.

Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm

Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes these limitations in two ways. First, an unsupervised clustering algorithm is applied to group the data based on their similarity and build independent machine-learning models specialized for each group. Second, a hybrid method including several reliable techniques is constructed to improve the prediction accuracy and overcome the limitation of the lack of data. It is empirically confirmed that the prediction accuracy of the obsolescence date for the electrical component data is improved through the proposed clustering-based hybrid method.

Optimizing the Abandonment of a Technological Innovation

The primary objective of this study is to reveal macro-level knowledge to aid the optimization, evaluation, and strategic planning of technological innovation abandonment. This research uses an exploratory data analysis (EDA) approach to extract directional and associative patterns (macro-level knowledge) to assess technological innovation abandonment optimization. Deterministic and stochastic simulations are employed to reveal the impact of three factors on abandonment optimization, namely, a technological innovation’s diffusion rate, a technological innovation’s probability of achieving a given diffusion rate, and the point of abandonment. The patterns and insights revealed through the graphical examination of the simulation provide associative and directional knowledge to assess the abandonment optimization of technological innovation. These revealed patterns and insights enable decision-makers to develop an abandonment assessment framework for optimizing, evaluating, and proactively planning abandonment at the macro level.

Spare Parts Inventory Management: A Literature Review

Spare parts are held as inventory to support product maintenance in order to reduce downtime and extend the lifetime of products. Recently, spare parts inventory management has been attracting more attention due to the “right-to-repair” movement which requires that manufacturers provide sufficient spare parts throughout the life-cyle of their products to reduce waste so as to achieve sustainability. In this review, 148 papers regarding spare parts inventory management published from 2010 to 2020 are examined. The studies are classified based on two groups of perspectives. The first group includes the characteristics of spare parts, products, inventory systems, and supply chains, while the second group focuses on the characteristics of research methodologies and topics in the reviewed studies. The novelty of this literature review is three-fold. Firstly, we focus on analyzing the supply chain structure of different inventory networks for managing spare parts. Secondly, we classify the current literature based on analytics techniques, i.e., descriptive analytics, predictive analytics, and prescriptive analytics. Finally, the research gaps in this field are discussed from the perspective of reverse logistics, consumer durable goods, inventory network structure and policy, spare parts demand pattern modeling, and big data analytics.

Smartphones in mental health: a critical review of background issues, current status and future concerns

There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.

Development and testing of a Boolean obsolescence assessment tool for built environment asset systems

Purpose

The purpose of this paper is to address the need for further development of tools that could be used to mitigate obsolescence within the built environment. Literature reviewed within this paper indicates a distinct gap in research, allowing for rising obsolescence-driven investments within asset systems. In addition to further conceptual development, case study testing is required to validate the use of certain existing methods.

Design/methodology/approach

This paper has developed a Boolean obsolescence assessment tool, which was then tested within a case study environment. This year-long case study provided real world data across three asset systems within an operational building.

Findings

The findings from this preliminary case study indicate that a Boolean tool of this type has the potential to provide significant insight into obsolescence mitigation. Such a tool, implemented in accordance with onsite asset management processes, has the ability to mitigate and avoid obsolescence-driven investments.

Research limitations/implications

This case study is limited because of its length and size. To mitigate the effects that may have been captured, this research project has been developed and continued.

Originality/value

The model featured within this paper originated from an untested obsolescence indexing technique. This model was adapted and extended to improve its accuracy and functionality, which also involved adding weighting mechanisms, resulting in not only an original model but a novel set of results because of the current lack of explicit testing of similar models.

Weinsier P, Khoury S. A dynamic model for hardware/software obsolescence

Purpose

– Since technological lifecycles do not always match hardware/software (HW/SW) lifecycles, obsolescence becomes a major issue in system lifecycle management as it can cause premature and unscheduled replacement of HW/SW subsystems. The purpose of this paper is to report a dynamic model to predict the obsolescence dates for HW/SW subsystems.

Design/methodology/approach

– The dynamic model estimates obsolescence dates for HW/SW subsystems based on graph theory concept. The model depicts the stages of subsystem obsolescence through transmittances composed of probability and time-distribution elements. The model predicts probability and mean time to obsolescence for line replaceable units (LRUs) over the lifetime of the system. An illustrative example in signaling systems used in a train control system was used to demonstrate the application of this model.

Findings

– Generally, the short timespan for HW/SW subsystems, which are periodically replaced with newer technologies, results in the development of new product lines by suppliers while they try to support legacy systems for a reasonable period of time. Obsolescence of HW/SW subsystems increases operation and maintenance costs as legacy systems are typically more expensive to maintain. The costs can be reduced by an optimum time to obsolescence derived from the model.

Practical implications

– This research adds to the body of knowledge on asset management and maintenance strategy. This paper may be of particular interest to reliability, maintainability and availability practitioners and project managers.

Originality/value

– The originality of this paper lies in developing a graph-based model that predicts probability and mean time to obsolescence for LRUs over the lifetime of the system.