Intelligent Decision Support System for Updating Control Plans

TL;DR

This paper introduces an intelligent decision support system for updating manufacturing control plans, combining manual multi-criteria decision making and automatic case-based reasoning to adapt to real-time quality data.

Contribution

It presents a novel DSS that integrates multiple models and learning techniques for dynamic quality control planning in manufacturing.

Findings

The system effectively recommends control scenarios in real case studies.

It demonstrates continuous plan updates based on process quality data.

The approach improves decision accuracy and adaptability.

Abstract

In the current competitive environment, it is crucial for manufacturers to make the best decisions in the shortest time, in order to optimize the efficiency and effectiveness of the manufacturing systems. These decisions reach from the strategic level to tactical and operational production planning and control. In this context, elaborating intelligent decisions support systems (DSS) that are capable of integrating a wide variety of models along with data and knowledge resources has become promising. This paper proposes an intelligent DSS for quality control planning. The DSS is a recommender system (RS) that helps the decision maker to select the best control scenario using two different approaches. The first is a manual choice using a multi-criteria decision making method. The second is an automatic recommendation based on case-based reasoning (CBR) technique. Furthermore, the proposed RS makes it possible to continuously update the control plans in order to be adapted to the actual process quality situation. In so doing, CBR is used for learning the required knowledge in order to improve the decision quality. A numerical application is performed in a real case study in order to illustrate the feasibility and practicability of the proposed DSS.