Manufacturing problem: Smart

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Literature Review: Smart problem for Manufacturing

This “Ten Problems for Manufacturing in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.

Without further ado, these are the ten problems that we are going to introduce in this booklet:

  1. sustainability, 
  2. industry 4.0,
  3. smart,
  4. responsibility,
  5. additive,
  6. digitalization,
  7. supply chain,
  8. optimization,
  9. skills gap,
  10. distributed.

Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.

The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.

3 Smart

THE PROBLEM — Various characteristics, technologies and enabling factors define a manufacturing system as smart. Socio-technical aspects must be considered for interpreting how enterprises benefit from them. Literature still shows limited guidelines on how to build or retrofit a smart factory. With the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — There are five characteristics, namely: context awareness, modularity, heterogeneity, interoperability and compositionality that are required in smart manufacturing. Business dimensions, special characteristics and adequate design are the relevant parameters to consider. Sociological factors also directly affect the maturity level of smart manufacturing. Sensor capabilities, communication capabilities, storing and processing huge amount of data, and better utilization of technology in management are needed to convert ordinary factories into smart factories. The metaphor of epistemic debt refers to the implied long-term costs of rework caused by a lack of understanding. Artificial Intelligence plays an important role in increasing sustainability through the intelligent utilization of materials and energy consumption. The United States approach to information governance issues is not as clear as those of other leading manufacturing nations like China and Germany. Issues in material master data quality decrease significantly, when comparing time before data monitoring to time after monitoring process is implemented. Retrofits can be used with comparatively little effort to gain many advantages of digital technology, so offering the ideal entry point to gradually digitizing production and converting to Industry 4.0. Numerical experiments show that the introduction of the edge computing mechanism in smart manufacturing can significantly improve the processing time, especially with a large number of tasks.

TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …

“Ten Problems for Manufacturing in the 2020s” booklet for Amazon Kindle, 2020; click on the cover to go to the dedicated Amazon listing page

By TenProblems

Literature Reviews for Inquisitive Minds