scholarly journals Two-stage optimization model for process/supplier selection, component allocation, and quality improvement

2018 ◽  
Vol 5 (1) ◽  
pp. 1557504
Author(s):  
Cucuk Nur Rosyidi ◽  
Mega Aria Pratama ◽  
Zude Zhou
Keyword(s):  
Quality Improvement ◽  
Optimization Model ◽  
Supplier Selection ◽  
Two Stage ◽  
Component Allocation

scholarly journals Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

2018 ◽  
Vol 11 (4) ◽  
pp. 794 ◽  
Author(s):  
Mega Aria Pratama ◽  
Cucuk Nur Rosyidi ◽  
Eko Pujiyanto
Keyword(s):  
Quality Improvement ◽  
Optimization Model ◽  
System Modeling ◽  
Investment Decision ◽  
Optimal Investment ◽  
Investment Fund ◽  
Stage Model ◽  
Component Allocation ◽  
Quality Investment ◽  
Two Stages

Purpose: The aim of this research is to develop a two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve. The first stage model is developed to determine the optimal selection of process/suppliers and the component allocation to those corresponding process/suppliers. The second stage model deals with quality improvement efforts to determine the optimal investment to maximize Return on Investment (ROI) by taking into consideration the learning and forgetting curve.Design/methodology/approach: The research used system modeling approach by mathematically modeling the system consists of a manufacturer with multi suppliers where the manufacturer tries to determine the best combination of their own processes and suppliers to minimize certain costs and provides funding for quality improvement efforts for their own processes and suppliers sides.Findings: This research provides better decisions in make or buy analysis and to improve the components by quality investment considering learning and forgetting curve.Research limitations/implications: This research has limitations concerning investment fund that assumed to be provided by the manufacturer which in the real system the fund may be provided by the suppliers. In this model we also does not differentiate two types of learning, namely autonomous and induced learning.Practical implications: This model can be used by a manufacturer to gain deeper insight in making decisions concerning process/suppliers selection along with component allocation and how to improve the component by investment allocation to maximize ROI.  Originality/value: This paper combines two models, which in previous research the models are discussed separately. The inclusions of learning and forgetting also gives a new perspective in quality investment decision.

Factorial Two-Stage Irrigation System Optimization Model

2016 ◽  
Vol 142 (2) ◽  
pp. 04015056 ◽  
Author(s):  
Xiaying Xin ◽  
Guohe Huang ◽  
Wei Sun ◽  
Yang Zhou ◽  
Yurui Fan
Keyword(s):  
Optimization Model ◽  
Irrigation System ◽  
System Optimization ◽  
Two Stage

Incorporating data quality into a multi-product procurement planning under risk

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fereshte Shabani-Naeeni ◽  
R. Ghasemy Yaghin
Keyword(s):  
Data Quality ◽  
Optimization Model ◽  
Industry 4.0 ◽  
Supplier Selection ◽  
Raw Material ◽  
Measure Data ◽  
Content Type ◽  
Multi Objective ◽  
Procurement Planning

Purpose In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and uncertainties associated with raw material purchasing. This paper aims to address the issue of supplier selection and purchasing planning considering the quality of data by benefiting from suppliers’ synergistic effects. Design/methodology/approach An approach is proposed to measure data visibility’s total value using a multi-stage algorithm. A multi-objective mathematical optimization model is then developed to determine the optimal integrated purchasing plan in a multi-product setting under risk. The model contemplates three essential objective functions, i.e. maximizing total data quality and quantity level, minimizing purchasing risks and minimizing total costs. Findings With emerging competitive areas, in the presence of industry 4.0, internet of things and big data, high data quality can improve the process of supply chain decision-making. This paper supports the managers for the procurement planning of modern organizations under risk and thus provides an in-depth understanding for the enterprises having the readiness for industry 4.0 transformation. Originality/value Various data quality attributes are comprehensively subjected to deeper analysis. An applicable procedure is proposed to determine the total value of data quality and quantity required for supplier selection. Besides, a novel multi-objective optimization model is developed to determine the purchasing plan under risk.

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