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Bayesian networks as a guide to value stream mapping for lean office implementation: a proposed framework

Author

Listed:
  • Tamie Takeda Yokoyama

    (University Center Educational Society of Santa Catarina)

  • Satie Ledoux Takeda-Berger

    (Federal University of Santa Catarina)

  • Marco Aurélio Oliveira

    (University Center Educational Society of Santa Catarina)

  • Andre Hideto Futami

    (University Center Educational Society of Santa Catarina)

  • Luiz Veriano Oliveira Dalla Valentina

    (University Center Educational Society of Santa Catarina)

  • Enzo Morosini Frazzon

    (Federal University of Santa Catarina)

Abstract

Bayesian networks (BNs) are recognized worldwide for their ability to work with reasoning involving uncertainty. BNs are formed by a directed acyclic graph whose nodes are random variables with different states with associated conditional probabilities. BN allows identifying and prioritizing where to act first and simulating its results to avoid costs with changes without significant results. On the other hand, the lean office (LO) philosophy is recognized for focusing on reducing waste (which does not add value to the product or service) applied to the office environment. One of its main tools is the value stream mapping (VSM), which assists in the lean transformation, aiming at the visualization and elimination of wastes. In this context, this paper aims to propose a framework for using BNs as a guide in the VSM elaboration to implement the LO, with prioritization of lead time (LD) reduction more reliably. To evaluate the validity of the proposed framework, a case study was conducted in a product development department of an electronics industry. The results demonstrated the feasibility and effectiveness of the proposed framework. Accordingly, the contributions of this paper are twofold. In theoretical terms, it promotes increased knowledge by exploring the combination of BNs with LO. In practical terms, the proposed framework is easy to apply and understand, allowing managers and professionals to implement it to reduce lead time in other types of processes in different industries. Thus, it can support decision-makers in eliminating waste in their processes.

Suggested Citation

  • Tamie Takeda Yokoyama & Satie Ledoux Takeda-Berger & Marco Aurélio Oliveira & Andre Hideto Futami & Luiz Veriano Oliveira Dalla Valentina & Enzo Morosini Frazzon, 2023. "Bayesian networks as a guide to value stream mapping for lean office implementation: a proposed framework," Operations Management Research, Springer, vol. 16(1), pages 49-79, March.
    • Handle: RePEc:spr:opmare:v:16:y:2023:i:1:d:10.1007_s12063-022-00274-8
    DOI: 10.1007/s12063-022-00274-8
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    References listed on IDEAS

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    1. Shuwei Jing & Zhanwen Niu & Pei-Chann Chang, 2019. "The application of VIKOR for the tool selection in lean management," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2901-2912, December.
    2. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    3. Vanessa Fitri Sabur & Togar M. Simatupang, 2015. "Improvement of customer response time using Lean Office," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 20(1), pages 59-85.
    4. Yanzhen Li & Rapinder S. Sawhne & Joseph H. Wilck, 2013. "Applying Bayesian Network Techniques to Prioritize Lean Six Sigma Efforts," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 4(2), pages 1-15, April.
    5. Wenchi Shou & Jun Wang & Peng Wu & Xiangyu Wang & Heap-Yih Chong, 2017. "A cross-sector review on the use of value stream mapping," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3906-3928, July.
    6. Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
    7. Yash Daultani & Mohit Goswami & Omkarprasad S. Vaidya & Sushil Kumar, 2019. "Inclusive risk modeling for manufacturing firms: a Bayesian network approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2789-2803, December.
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    1. Pâmella Rodrigues Silva Carrijo & Maria Luiza Bortolini Rader & Mário Otávio Batalha & Moacir Godinho Filho, 2024. "Lean manufacturing in agriculture: adapting the value stream mapping approach for farm management," Operations Management Research, Springer, vol. 17(4), pages 1444-1468, December.

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