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 5786

Special Issue Editor


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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

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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

Published Papers (1 paper)

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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 6 | Viewed by 5181
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
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