System Integration and Functional Priorities to Maximize Profit and Loss for Smart Factory | SpringerLink
Skip to main content
Springer Nature Link
Log in

System Integration and Functional Priorities to Maximize Profit and Loss for Smart Factory

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12253))

Included in the following conference series:

  • 1400 Accesses

Abstract

Smart factories have already become a key theme in the manufacturing industry. Advanced IT technology, industrial engineering, and management know-how will be combined to improve all areas of manufacturing. Improving productivity is the most important goal for a factory. However, this is only part of the overall goal of corporate management. Corporate management pursues a greater goal of optimizing all of the company’s resources to reduce losses, increase profits and achieve sustainable growth. All companies should achieve smart factories to prevent unnecessary waste of time and resources and maximize profits by increasing productivity. This paper deals with the integration effect of the major fundamental system, including the Manufacturing Execution System that operates production, and the method of measuring it. When integrating core systems to maximize successful inventory management and revenue, a method of assessing them is required. Here it would be appropriate to have an evaluation method in which scores are given and calculated differently depending on the results and functional priorities obtained by the system. This approach will help solve the problem of which systems each company will use and integrate when introducing smart factories.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, S., et al.: Implementing smart factory of industrie 4.0: an outlook. Int. J. Distrib. Sensor Networks 12(1), 3159805 (2016)

    Article  Google Scholar 

  2. Meyr, H., Wagner, M., Rohde, J.: Structure of advanced planning systems. In: Supply Chain Management and Advanced Planning, pp. 99–106. Springer, Berlin, Heidelberg (2015). https://doi.org/10.1007/978-3-642-55309-7_5

    Google Scholar 

  3. Rashid, M.A., Riaz, Z., Turan, E., Haskilic, V., Sunje, A., Khan, N.: Smart factory: E-business perspective of enhanced ERP in aircraft manufacturing industry. In: 2012 Proceedings of PICMET 2012: Technology Management for Emerging Technologies, pp. 3262–3275. IEEE, July 2012

    Google Scholar 

  4. Zhong, R.Y., et al.: RFID-enabled real-time manufacturing execution system for mass-customization production. Robot. Comput.-Integr. Manuf. 29(2), 283–292 (2013)

    Article  Google Scholar 

  5. Liboni, L.B., Cezarino, L.O., Jabbour, C.J.C., Oliveira, B.G., Stefanelli, N.O.: Smart industry and the pathways to HRM 4.0: implications for SCM. Supply Chain Manage. Int. J. 24(1), 124–146 (2019)

    Article  Google Scholar 

  6. Stark, J.: Product Lifecycle Management, vol. 1, pp. 1–29. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24436-5_1

    Google Scholar 

  7. Lech, M.M., et al.: Quality management system with human-machine interface for industrial automation. U.S. Patent No. 6,539,271, 25 Mar 2003

    Google Scholar 

  8. Schulze, B.D.: System and method for automated monitoring and assessment of fabrication facility. U.S. Patent No. 6,671,570. 30 Dec 2003

    Google Scholar 

  9. Kerry, J., Butler, P. (eds.): Smart Packaging Technologies for Fast Moving Consumer Goods. John Wiley & Sons, New York (2008)

    Google Scholar 

  10. Kho, J.S., Jeong, J.: On reflecting optimal inventory of profit and loss perspective for production planning. Procedia Comput. Sci. 155, 722–727 (2019)

    Article  Google Scholar 

  11. Unver, H.O.: An ISA-95-based manufacturing intelligence system in support of lean initiatives. Int. J. Adv. Manuf. Technol. 65(5–8), 853–866 (2013)

    Article  Google Scholar 

  12. Hao, Y., et al.: Designing of cloud-based virtual factory information system. In: Advances in Sustainable and Competitive Manufacturing Systems, pp. 415–426. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00557-7_34

    Google Scholar 

  13. Ge, Z.: Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes. J. Process Control 65, 107–117 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea

    Jeong Seog Kho & Jongpil Jeong

Authors
  1. Jeong Seog Kho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jongpil Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jongpil Jeong .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kho, J.S., Jeong, J. (2020). System Integration and Functional Priorities to Maximize Profit and Loss for Smart Factory. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58814-4_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58813-7

  • Online ISBN: 978-3-030-58814-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation