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Summary The remaining value within end‐of‐use/life hard disk drives (EoU/L HDDs) is often not optimally recovered. The improper collection and recovery of HDDs contribute not only to rising environmental and social concerns worldwide, but also to the transformation of the economy and a significant loss of value. Currently, the most preferred treatment option for used hard drives is to recover the metals with the highest recycling effectiveness, such as steel and aluminum, via a shredding‐based recycling process that results in both value and material leakages. The complexity of retrieving the remaining values within EoU/L HDDs demands a larger view of the global supply of HDDs available for recovery. The aim of this paper is to first identify the geographical patterns of transboundary global shipments of new and used HDDs between developing and developed regions, and then capture and quantify the value leakage by bringing several unique perspectives. Two analyses have been conducted. First, the loss of value due to the insufficient recovery of neodymium (Nd) at the global level is quantified. Second, the value leakage as a result of the delay on on‐time reuse of HDDs is captured. Furthermore, the central challenges toward proper recovery of HDDs, where consumer electronic industry can make significant contributions, have been identified. HDDs are well positioned to contribute important insights to the recovery of other electronic devices, so the findings from HDDs can be adopted for other types of electronics. 

This chapter addresses problems that arise during product design for sustainability 5 and the life cycle. A description of the problem itself is provided from an industrial 6 engineering viewpoint. The first section describes the problem elements, including 7 the need to expand the set of conflicting objectives under consideration, the need to 8 consider the entire product life cycle, the need to employ new data acquisition tools, 9 and the need to investigate the complex role of consumer behavior before, during, 10 and after the point of purchase. Subsequent sections summarize work the authors 11 have done towards solving these problems. A general mathematical programming 12 framework is first presented. This chapter highlights several instances of the benefits 13 of bringing the logic and mathematical rigor of industrial engineering methods 14 to these problems. The authors’ previous contributions to sustainable design are 15 presented and include defining the concept of the product life cycle from a decision- 16 based design point of view, developing different types of decision-making tech- 17 niques for engineering design (both subjective and objective), normative decision 18 analytic methods (e.g., multiattribute utility, constrained optimization), methods 19 for environmentally conscious design to cover new environmental objectives (e.g., 20 connection of design with the end-of-use phase), and immersive computing tech- 21 nologies to address challenges with information-intensive design procedures. The 22 final section presents methods to consider heterogeneous consumer behavior during 23 product selection, use, and disposal. 

With the modern age of using genetically engineered products and growing concerns about food recalls and outbreaks, businesses are looking for ways to secure their brand names and assuring consumers about food safety and quality. Recently, Blockchain has been introduced as a promising approach for increasing the visibility of the supply chain and reducing the sale of contaminated and counterfeit products. Along this line, this study discusses the capabilities of Blockchain for the collection and monitoring of product lifecycle information ranging from production, wholesale, and logistics to standards, business reputation, and certification. The particular focus of the study is to discuss the use of videogrammetry as a data collection mechanism for bringing the product lifecycle data on digital Blockchain platforms and solving the “last mile” problem and data verification issue on Blockchain platforms. A conceptual example of organic meat processing is discussed to describe the proposed procedure and show how videogrammetry in combination with RFID and fingerprints can be used to solve the data verification issue on Blockchain platforms. 

The potential of smart cities in remediating environmental problems in general and waste management, in particular, is an important question that needs to be investigated in academic research. Built on an integrative review of the literature, this study offers insights into the potential of smart cities and connected communities in facilitating waste management efforts. Shortcomings of existing waste management practices are highlighted and a conceptual framework for a centralized waste management system is proposed, where three interconnected elements are discussed: (1) an infrastructure for proper collection of product lifecycle data to facilitate full visibility throughout the entire lifespan of a product, (2) a set of new business models relied on product lifecycle data to prevent waste generation, and (3) an intelligent sensor-based infrastructure for proper upstream waste separation and on-time collection. The proposed framework highlights the value of product lifecycle data in reducing waste and enhancing waste recovery and the need for connecting waste management practices to the whole product lifecycle. An example of the use of tracking and data sharing technologies for investigating the waste management issues has been discussed. Finally, the success factors for implementing the proposed framework and some thoughts on future research directions have been discussed. 

The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (IoT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions. In this study, the importance of value recovery from trash bins is highlighted. A stochastic optimization model based on chance-constrained programming is developed to optimize the planning of waste collection operations. The objective of the proposed optimization model is to minimize the total transportation cost while maximizing the recovery of value still embedded in waste bins. The value of collected waste is modeled as an uncertain parameter to reflect the uncertain value that can be recovered from each trash bin due to the uncertain condition and quality of waste. The application of the proposed model is shown by using a numerical example. The study opens new venues for incorporating the value recovery aspect into waste collection planning and development of new data acquisition technologies that enable municipalities to monitor the mix of recyclables embedded in individual trash bins. 

Since its emergence, the cloud manufacturing concept has been transforming the manufacturing and remanufacturing industry into a big data and service-oriented environment. The aggressive push toward data collection in cloud-based and cyber-physical systems provides both challenges and opportunities for predictive analytics. One of the key applications of predictive analytics in such domains is predictive quality management that aims to fully exploit the potentials provided by the enormous data collected via cloud-based systems. As a case study, a data set of hard disk drives’ Self-Monitoring, Analysis and Reporting Technology (SMART) attributes from a cloud-storage service provider has been analyzed to derive some insights about the challenges and opportunities of using product lifecycle data. An analysis of time-to-failure monitoring of hard disk drives in real-time has been carried out and the corresponding challenges have been discussed. 

The aim of this study is to provide a better understanding of the heterogeneities in user-product relationships and their consequences regarding the household energy predictions. Several supervised and unsupervised machine learning algorithms have been applied to a comprehensive data set of residential energy consumptions collected by the US Energy Information Association. The results of the analyses reveal that, while the heterogeneities in the use-phase of consumer electronics could skew their environmental assessment results, they do not possess the same discriminant influences on the household electricity consumption compared to certain socio-demographics or usage of home appliances. Various cross-comparisons among product features and use-phase behaviors have been made and the most important predictors of the residential electricity consumption based on the data have been introduced. Product-level and user-level discussions on the findings have also been provided. 

Mobile phones have become ‘essential-to-have’ devices for information-gathering and social communication. They are being technologically advanced, while they are readily available at affordable prices. This motivates consumers to upgrade their mobile phones more frequently. Given these considerations and the lack of accessible repair services, mobile phones have a relatively short life span. The underuse of mobile phones, despite the fact that they are made durable, may result in losses or value leakage. In this study, a probabilistic approach is proposed to quantify the value leakage that may occur due to consumer’s decision to not repair broken mobile phones and simply replace them with new ones. A group of 208 mobile phone users has been surveyed to capture consumer’s time-dependent willingness-to-pay for repair services. Then, consumer’s repair behavior is combined with manufacturer’s repair service pricing strategies to calculate the probability of repair or replacement decisions over the life span of mobile phones. Finally, the total expected leakage risk is derived for both consumers and manufacturers. For illustrative purposes, it is shown that a manufacturer may lose up to 331 million dollars over a period of five years due to consumers’ decisions to not repair their cracked-screen mobile phones and switch to another brand. 

Smart manufacturing in an Industry 4.0 setting requires developing unique infrastructures for sensing, wired and wireless communications, cyber-space computations and information tracking. While an exponential growth in smart infrastructures may impose drastic burdens on the environment, the conventional Life Cycle Assessment (LCA) techniques are incapable of quantifying such impacts. Therefore, there is a gap between advances in the manufacturing domain and the environmental assessment field. The capabilities offered by smart manufacturing can be applied to LCA with the aim of providing advanced impact assessment, and decision-making mechanisms that match the needs of its manufacturing counterpart.