Flexibility Focused Decision and Information Sharing Model for Product Recovery System

Remanufacturing is a product recovery process that transforms a used product into “like-new” condition. It can extend the useful life of a product and help in reducing waste caused by a huge amount of short life-cycle products. Pricing decisions are an important aspect of successful remanufacturing and can secure the profitability of a firm. Remanufacturing for end-of-use products needs to cope with high uncertainties in terms of the quality and quantity of the acquired product returns. Therefore, after inspection, only a fraction of returns can be recovered through remanufacturing operations. This uncertainty in recovery yield influences the decisions impacting acquisition, wholesale, and retail prices. We propose a pricing model that accommodates the random yield effect of product returns on pricing decisions for short life-cycle products in a closed-loop supply chain. The system consists of a retailer, a manufacturer, and a collector of used-products. We apply a sequential decision approach to determine the optimum pricing decision to maximize supply chain profit, according to a pricing game that places the manufacturer as a Stackelberg leader. We demonstrate the effect of changing parameter values on the wholesale and retail prices as well as on the profitability. The results indicate that the profitability of each player and the supply chain as a whole is affected by the quality of the collected used products, the acquisition price, the shortage penalty, and the remanufacturing costs. Interestingly, reducing variance of random yield results in lower profit for the collector even though the other players and the whole supply chain are better off.

Joint case pack size and unpacking location optimization in a retail supply chain including product returns

Evaluation of design alternatives of End-Of-Life products using internet of things

In recent years, due to growing environmental concern, there has been an increasing attention to product take‐back, product recovery and the re‐distribution of end‐of‐life products. Reverse logistics (RL), which refers to the distribution activities involved in product returns, has recently received much attention because many companies are using it as a strategic tool to serve their customers; and can generate good revenue. The management of return flow usually requires a specialised infrastructure with special information systems for tracking and dedicated equipment for the processing of returns. An efficient reverse distribution structure may lead to a significant return on investment as well as a significantly increased competitiveness in the market. Therefore, the time seems to be right for a systematic overview of various issues that are arising in the context of reverse distribution. The main purpose of this paper is to review the literature on RL and suggest a classification based on reverse distribution issues. The result of this research provides a better understanding of the RL concept and outlines some future directions of research on modelling and analysis.

Product recovery is beset by uncertainty regarding the quality of end-of-life (EOL) products, and in order to ascertain the reusability of these products, they have to undergo expensive tests. This undermines the profitability of the recovery process. The key to improve the effectiveness of product recovery is to improve the quality of information available before testing. Emerging data capture technologies can significantly improve the availability of information. However, in order to maximise the potential of these technologies, appropriate decision-making algorithms that exploit such information must be developed. We model the recovery process using a decision-theoretic approach, and derive strategies to ascertain the reusability of EOL products, and also to decide when tests are beneficial. We show that improving the quality of information leads to increase in effectiveness of the recovery process by reducing the need for tests.

This article investigates the integration of circular economy methodologies and biorefinery concepts for the sustainable recovery of bio‐based products from industrial effluents, addressing the critical need for resource efficiency and environmental sustainability in the face of climate change and resource depletion. Emphasizing the valorization of industrial by‐products, the study explores innovative, eco‐friendly recovery processes within a biorefinery framework to transform waste into valuable resources such as biofuels, biochemicals, and biomaterials. Through a systematic search in various bibliographic databases and a comprehensive literature review, the study critically analyzes existing studies, identifies research gaps, and offers new perspectives on the integration of biorefinery and circular economy principles. The findings reveal that strategic integration of biorefinery processes and circular economy principles can significantly reduce environmental footprints and foster sustainable industrial practices. Key challenges such as feedstock variability, technological barriers, and economic scalability are identified, along with recommendations for overcoming these obstacles through interdisciplinary collaboration, technological innovation, and supportive policy interventions. The main contribution of this research lies in its comprehensive approach, integrating cutting‐edge technologies and circular economy principles to offer viable strategies for sustainable bio‐based product recovery, thus significantly advancing the field of industrial sustainability. The article contributes to the advancement of sustainable technologies, policies, and strategies, advocating for a transition towards a more circular, resource‐efficient industrial sector. Continued innovation and research are emphasized to optimize recovery processes and explore new applications, supporting a sustainable and thriving future for our planet.

Large-scale chromatography operations continue to occupy a key position in the overall strategy for the downstream processing and purification of protein products for therapeutic use. Increasing product titres from mammalian cell culture has resulted in a trend to identify ways of improving the economics of product recovery and purification processes. In commercial manufacturing, a requirement exists to increase the scale of the chromatography operations, which are typically developed and optimised in small-scale experiments. This short review discusses the key factors in the chromatography process that need to be considered as the scale of the purification step is increased in order to maintain the purity and integrity of the product purified at smaller scale.

Intelligent System Design for Achieving Competitive Advantage Through Enterprise Systems Flexibility

In large-scale manufacture of proteins, filter applications that utilize micro-and ultrafiltration membranes play a key role in many processing stages. The capabil­ ity of membrane filtration such as ultrafiltration has been well recognized and generally secured as a full-scale industrial process for many years [1,2]. The unit operation offers benefits such as low energy requirements, continuous flow, ease of operation, reproducibility, scalability, and gentleness to the product. The filtra­ tion system can be divided, on the basis of membrane geometry, into four com­ monly known modules: parallel plate, spiral, hollow-fiber, and tubular. Much of the methodology on these systems has been evaluated [2] and reduced to practice in the plasma fractionation industry [3]. Previous work using mostly hollow-fiber systems has not only established and refined the technique but has also elucidated factors affecting process recovery and process efficiency. Once the membrane with the proper pore size is determined, differences in product recovery among the different filtration modules are generally not substantial. The selection of one type over another for a specific application may be dependent on other factors. For example, one system could be more favorable than the others in regard to size, cost, process time, and ease of availability. There are a large number of commercial units available; therefore we decided to limit our present investiga­ tion to the parallel-plate module which allows scaleup by adding modules inparallel. This chapter will discuss our experience using parallel plates in the fol­ lowing areas:Tangential flow cell separation of tissue culture fluid Tangential flow filtration and dead end filtration as applied to tissue culturefluid Concentration of cell-free filtrate Ultrafiltration/diafiltration in downstream processing NanofiltrationII. TANGENTIAL FLOW CELL SEPARATION OF TISSUE CULTURE FLUIDClarification of tissue culture fluid removes DNA-containing cells and cell debris. The operation could be achieved with centrifugation which carries the potential for heat generation, aerosol formation, and labor-intensive assembly. In the pres­ ent study, we investigated a plate-and-frame unit for tangential flow cell separa­ tion. Tissue culture fluid derived from large scaled fermentors was used. The system, Sartocon II, was purchased (Sartorius Corp., Bohemia, NY). The mem­ brane was a 0.45-p.m microfilter with an area of 0.6 m2. Recirculation was achieved with a Watson-Marlowe 701 S/R manual control peristaltic pump (Bacon Technical Industries, Concord, MA). The module, pump, and pressure gauges were mounted on a mobile trolley, which was stationed conveniently close to the fermentors. Figure 1 depicts the equipment arrangement in the experiment.

The development of recovery processes using aqueous two‐phase systems (ATPS) has been limited by the extensive experimental work required to establish the optimal system parameters to ensure selective partitioning of the product of interest. Although using full factorial experiments has been demonstrated to be an effective strategy for the characterization of the partitioning behaviour of biological products in ATPS, this approach is characterized by its costly and time consuming nature. As an alternative, the use of a robotic‐aided strategy has been proposed. However, the need for high cost equipment may limit the generic implementation of this strategy. Based on practical experience using ATPS, practical strategies for the predictive design of primary recovery processes using polymer–salt systems were derived. To evaluate the generic application of the proposed strategies, four experimental models (B‐phycoerythrin, C‐phycocyanin, double layered rotavirus‐like particles and lutein) were selected. The application of these strategies resulted in the development of simplified recovery processes for the selected experimental models. The practical review presented is considered a relevant contribution that will provide general rules to facilitate the establishment of ATPS processes, particularly for new researchers in the field. Copyright © 2007 Society of Chemical Industry

Essays on Vertical Integration, Vertical R&D Spillovers, Research Joint Ventures, and Information Sharing

The accelerated progression of information technology has necessitated the adoption of more intelligent and adaptive Enterprise Systems (ES) to sustain and optimize organizational processes. The ES serve as critical infrastructures for resource management, operational efficiency, and sustaining competitive advantage; however, their deployment frequently encounters persistent challenges. These include discrepancies between modelled and actual business processes, insufficient visibility in process execution, and limited automation in the detection and optimization of workflows. To mitigate these limitations, this study advances an Artificial Intelligence (AI)-enabled Process Mining paradigm. This approach facilitates the systematic extraction, analysis, and visualization of business processes, thereby supporting the identification of deviations, the detection of anomalies, and the provision of data-driven recommendations for continuous improvement. The overarching aim of the research is to conceptualize and evaluate an enterprise system framework that integrates AI-driven Process Mining to reinforce transparency, efficiency, and effectiveness in business process management. The proposed framework aims to provide automated analytical capabilities, predictive insights, and a robust data-centric foundation to enhance the precision of strategic decision-making, thereby contributing to the advancement of adaptive and intelligent enterprise systems.

The purpose of this study was to investigate the stability of aggregate national residential electricity demand coefficients over time. The hypothesis is maintained that the aggregate residential demand is the sum of various end-use demand components. Since the end-use composition changes over time, the demand relationship may change as well. Since the end-use composition differs among regions, the results obtained from this study can be used for making inferences about regional differences in electricity demand relationships. There are two additional sources for a possible structural change. One is that consumers may react differently to declining and rising prices, secondly, the impact of the 1973 oil embargo may have shifted demand preferences. The electricity demand model used for this study is presented. A moving regression method was employed to investigate changes in residential electricity demand over time. The statistical results show a strikingly consistent pattern of change for most of the structural variables. The most important finding of this study is that the estimated structure of residential electricity demand changes systematically over time as a result of changes in the characteristics (both durability and saturation level) of the stock of appliances. Furthermore, there is not strong evidence that the structural changes in demand occurred due to either the reversal of the declining trend of electricity prices or the impact of the 1973 oil embarge. (LCL)

Product recovery with some byte: an overview of management challenges and environmental consequences in reverse manufacturing for the computer industry

Argentinian Patagonia is a 750,000 km2 semi-arid region. Sheep were introduced there in the late 19th century, peaked at about 20 million head in 1960 and fell sharply thereafter reaching 8 million at present. Decline in productive capacity was blamed on overgrazing or climate change alternatively, but maps in 1998 indicated that 34 % of the region was severely desertified. We analysed the regional FPAR in the GIMMS NDVI dataset from NOAA satellite imagery in the last 30 years, a period in which the land has been destocked and under generally improved management. The trend of FPAR does not show a generalized “greening”, or recovery process, even when the rainfall trend was removed. A case study in Los Pozos Farm in the south shows that total stock has fallen monotonically since 1930 under a rainfall regime that remained constant. Variable stocking rates that tracked the productivity introduced in 1990 stabilized grazing capacity but did not induce a clear recovery in forage production. The erosion of unstable soils and damage to perennial vegetation probably explain the farm production losses, and management rather than climate change is the most probable cause. Production analysis shows that stocking rates that maximize short-term income in wool are too high and will probably damage the land. Good management has the potential to stabilize and maybe slowly revert long-term mismanagement effects on these cold desert ecosystems, but under the present market economy and land tenure system it seems unlikely that producers will give up profits to avoid possible long-term degradation.