A Lean Knowledge-Based Decision Support System for Cost Estimation in the Foundry Industry

Sidra Safdar Khan, Muhammad Sajid, Ahmad Wasim, Essam Shehab, Hassan Abdalla, Salman Hussain

Research output: Contribution to journalArticlepeer-review

Abstract

Precise cost estimation is the first and crucial step for decision-makers at the planning stage of product development. Due to the unavailability of knowledge and the complex nature of manufacturing processes, materials, and product designs, the selection of suitable alternatives based on estimated cost has become more challenging. The purpose of this paper is to present the development of a knowledge-based system (KBS) for assisting decision-makers in identifying suitable alternatives for precise cost estimation in a mistake-proof environment. The system architecture is comprised of three layers namely the database layer, application layer, and end-user layer, and developed using knowledge-based engineering, set-based concurrent engineering, and poke-yoke as lean enabling approaches. Based on the captured knowledge through industrial investigation and experimentation, a real-time system application was developed using C# language and validated using a real case study in the foundry industry using sand casting as the manufacturing process. It ensured the validity of the developed system. KBS provides an extensive tool to decision-makers for accurate decision-making at the design stage using cost and other associated values. Further, it facilitates the practitioners of every business sector to capture, save, and use the identified knowledge for problem-solving in the future.

Original languageEnglish
Article numbere23220658
JournalBrazilian Archives of Biology and Technology
Volume66
DOIs
Publication statusPublished - 2023

Keywords

  • decision making
  • knowledge-based system
  • lean enabling approaches
  • Precise cost estimation
  • sand casting

ASJC Scopus subject areas

  • General

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