Using Blockchain Technologies and Automized Digitalization for Data Collection in the Upstream Supply Chain

A special issue of Challenges (ISSN 2078-1547).

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 6268

Special Issue Editor


E-Mail Website
Guest Editor
Huawei Technologies Sweden AB, Skalholtsgatan 9, 16494 Kista, Sweden
Interests: life cycle assessment; forecasting; energy efficiency; material efficiency; electronic devices; communication systems

Special Issue Information

Dear Colleagues,

Sustainability management involves huge amounts of data. At the same time, if we cannot measure something we cannot control it. As pinpointed by numerous authors – actually in almost every Life Cycle Assessment (LCA) case study published so far - the main challenge for the overall credibility of LCA results is the collection of primary data of high quality.

However, soon the time will come wherein we are able to measure supply chains in real time - as accurately as use stage power consumption which is measured by e.g. smart metering - and then, can LCAs of products finally be validated?

So far, I have not seen a deeper discussion about real-time data collection in the LCA community.

A couple of years ago I had the notion that artificial intelligence (pattern recognition, neural networks, scheduling, reasoning, fuzzy logic, rule-based systems, machine learning) would help revolutionize the Life Cycle Inventory (LCI) data collection in the supply chain [1].

However, I had not thought about the potential role of blockchain technology in removing costs, security issues and inefficiencies in LCI data collection. Blockchain is potentially suitable for LCI data collection as these data should be distributed and shared by many users in a secure manner. As the security of distributed ledger technologies is extraordinary high, these technologies are strong candidates for distribution of more or less sensitive manufacturing LCI data.

I welcome submissions presenting practical solutions for supply chain transparency using blockchain systems – and other competitive solutions - for life cycle inventory data collection. Examples could also include theoretical schemes for blockchain systems for LCI data recordkeeping and distribution.  Already existing data collection solutions – using blockchain - could here be presented in a scientific manner, i.e. how they go beyond of the static state-of-the-art LCI data collection practices. Other progressive solutions for LCI data collection - based on e.g. cloud servers - which move things forward compared to the state-of-the-art, are also interesting for this special issue.

An idea is that the manufacturers anonymously report LCI data (e.g. emissions and energy use) manually - or automatically via sensors installed in the factories - to a distributed ledger. In this context, the well-established LCI data provider Ecoinvent in Switzerland is responsible for developing validation, sanity checks and intelligent “polishing” functions within the ledger.  Then each organisation - subscribing to Ecoinvent - will have access to the LCI data they desire in real time. If materialized, this would be a huge improvement of the current practice of LCI data collection and data relevance.

Reference

  1. Andrae, A. S. G. (2016). Life-Cycle Assessment of Consumer Electronics: A review of methodological approaches. IEEE Consumer Electronics Magazine, 5(1), 51-60.

Dr. Anders S. G. Andrae
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Challenges is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Real Time
  • Life Cycle Assessment
  • Blockchain
  • Distributed ledger technology
  • Distributed information shared by many
  • Manufacturing plants
  • Vehicles
  • Information transactions
  • Sensor based automated data collection
  • Artificial intelligence
  • Dynamic life cycle inventory approaches
  • Data bases
  • Cloud
  • Private Permissions
  • Hosts
  • Miners
  • Smart contracts
  • Intelligence
  • Nodes

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

9 pages, 1491 KiB  
Article
How to Seize the Opportunities of New Technologies in Life Cycle Analysis Data Collection: A Case Study of the Dutch Dairy Farming Sector
by Eric Mieras, Anne Gaasbeek and Daniël Kan
Challenges 2019, 10(1), 8; https://doi.org/10.3390/challe10010008 - 17 Jan 2019
Cited by 9 | Viewed by 5588
Abstract
Technologies such as blockchain, big data, and the Internet of Things provide new opportunities for improving and scaling up the collection of life cycle inventory (LCI) data. Unfortunately, not all new technologies are adopted, which means that their potential is not fully exploited. [...] Read more.
Technologies such as blockchain, big data, and the Internet of Things provide new opportunities for improving and scaling up the collection of life cycle inventory (LCI) data. Unfortunately, not all new technologies are adopted, which means that their potential is not fully exploited. The objective of this case study is to show how technological innovations can contribute to the collection of data and the calculation of carbon footprints at a mass scale, but also that technology alone is not sufficient. Social innovation is needed in order to seize the opportunities that these new technologies can provide. The result of the case study is real-life, large-scale data collected from the entire Dutch dairy sector and the calculation of each individual farm’s carbon footprint. To achieve this, it was important to (1) identify how members of a community can contribute, (2) link their activities to the value it brings them, and (3) consider how to balance effort and result. The case study brought forward two key success factors in order to achieve this: (1) make it easy to integrate data collection in farmers’ daily work, and (2) show the benefits so that farmers are motivated to participate. The pragmatic approach described in the case study can also be applied to other situations in order to accelerate the adoption of new technologies, with the goal to improve data collection at scale and the availability of high-quality data. Full article
Show Figures

Figure 1

Back to TopTop