Soroush Avakh Darestani

A literature review investigation on quality control charts based on fuzzy logic

To assure the quality of product and services, organisations need to employ quality control tools. In this context, as precise data are not always available, fuzzy sets theory can adequately model the processes in which observed data are vague. Researchers have focused on fuzzy control charts that accommodate uncertainty due to fuzziness. Owing to the importance of fuzzy control charts, this research reviewed the literature on fuzzy control charts since 1990-2012. Based on the objective of this paper, fuzzy application of control charts were analysed with respect to the publication year, journals title, authors affiliation, data source, fuzzy theory classification, control chart classification and research place. The results demonstrated positive increasing trends of researches occurred in the last decade. To sum up, the results demonstrated that using fuzzy set theory on control chart still needed for further investigation on performance criteria, membership, distributions function, heuristic methods and attribute control chart.

Development of fuzzy U control chart for monitoring defects

Purpose – Shewharts control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart. Design/methodology/approach – If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified. Findings – The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control ...

Statistical process control

Purpose – In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues. Design/methodology/approach – In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages. Findings – The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings. Research limitations/implications – For further research, the PCIs for non-normal data can be conducted based on TFN...

Improving purchasing performance by implementation of QMS process management approach in a manufacturing company

Process oriented approach in quality management system has been introduced with ISO9001:2000. The international standard promotes the adoption of a process approach when developing, implementing and improving the effectiveness of a QMS to enhance customer satisfaction by meeting customer requirement. The advantage of process approach is to link all parties in scope of business of the organization from suppliers, internal departments of organization to gather to make B2B communication and integration. In this paper, the process approach is defined based on QMS requirement. Supply department was proposed for implementation of process. Purchasing process was designed as a linkage between supplier and internal company departments. Performance indicators were developed and measured accordingly. It shows that process management can improve delivery capability, quality and monitor price of supplied parts by suppliers.

Solving multi-objective supplier selection model using a compensatory approach

Supply chain management (SCM) is one of the most important competitive strategies employed by the modern enterprises. The main aim of SCM is to integrate different suppliers to gratify market demand. Meanwhile, supplier selection and evaluation play an important role in establishing an efficient supply chain. Supplier selection as an essential component of SCM is usually a multi-criteria decision problem in which actual business contexts may be solved in the absence of precise information. Three objective functions are minimization of costs, maximization of presented units as well as maximization of on time delivery, respectively. In order to solve the problem, a compensatory fuzzy model was proposed to satisfy the decision-maker’s aspirations for fuzzy goals. The proposed approach could be efficiently applied to obtain non-dominated solutions. A numerical example was given to demonstrate how the approach was employed. So, for solving the problem, Lingo software was used.