A smart production system with an autonomation technology and dual channel retailing

Biswajit Sarkar, Bikash Koli Dey, Mitali Sarkar, Sung Jun Kim

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Smart products can be sold through online or offline with different price range. For this, the production management has to take care about the price in both channel. A smart single-stage production model with an autonomation inspection technology is considered to develop this prevailing study. Different quality products are sold in different channels with different price (i.e., online and offline). Due to different quality and channel, the price is also varies based on quality or channel. Owing to this situation, the total demand depends on the price of a different quality product. The imperfect products are produced in the out-of-control situation, which causes the backordered. The generation of defective items lead to generation of wastes, and wastes generation create a bad impact for the industry both economically and environmentally. The imperfect or defective items are detected through a smart autonomation inspection technology and sent back for remanufacturing in the same production cycle. The decision-making variables are obtained through a classical optimization technique. A lemma has been proved to finalize the global optimum solution of the decision-making variables. Numerical experiments prove that the manufacturing system provides 0.3% better result, when the defective rate follows a Triangular distribution, whereas the production increases the profit up to 35% due to use of online-to-offline retailing system, and reduces waste up to 50.84% due to use of autonomation inspection. Moreover, the impact of critical parameters on the total system profit is presented through the sensitivity analysis and graphical representations.

Original languageEnglish
Article number108607
JournalComputers and Industrial Engineering
Publication statusPublished - 2022 Nov

Bibliographical note

Funding Information:
The work is supported by the National Research Foundation of Korea (NRF) grant, funded by the Korea Government (MSIT) (NRF-2020R1F1A1064460).

Publisher Copyright:
© 2022 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)


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