Leanness assessment in an auto parts manufacturer

This study applies main Lean Manufacturing (LM) techniques to investigate the accordance of an auto parts manufacturer from Iranian automotive industry. Although the concept of LM has been successfully applied in previous studies, concurrent investigation of its requirements is less examined, especially in developing countries. In addition, the majority of previous studies have applied qualitative leanness assessment tools to investigate LM implementation while it is necessary to focus on both qualitative and quantitate techniques. In this regard, this study has applied seven wastes of LM, Value Stream Mapping (VSM) and Overall Equipment Effectiveness (OEE) to investigate the accordance of an auto parts manufacture with LM requirements. According to the obtained results, this manufacturer is moderately in accordance with LM philosophy

Airline Catering Supply Chain Performance during Pandemic Disruption: A Bayesian Network Modelling Approach

The supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the COVID-19 pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic’s effects and late deployment of recovery measures. This paper presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a Bayesian network (BN) model to measure SC performance and risk events and quantify the consequences of pandemic disruptions. The research simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios. This study’s findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance.Airline Catering Supply Chain Performance during Pandemic Disruption: A Bayesian Network Modelling ApproachacceptedVersio

An efficient integrated simulation–Taguchi approach for sales rate evaluation of a petrol station

This study proposed an incorporated simulation–Taguchi model to optimize a petrol station sales rate. In addition, it provided a regression model to forecast the sales rate. Initially, Witness 2014 simulation software© was used to simulate the operating system of a petrol station. Next, the obtained simulation results were used as the input for Taguchi method to optimize the process. Taguchi L4 standard orthogonal array was taken to optimize the petrol station parameters including the number of pumps, number of cashiers and customers’ interarrival times (IATs) to obtain a better sales rate. Three noise factors such as petrol station location, different cashiers and different dispensers considered as potential factors affecting the response. Based on Taguchi methodology, number of pumps and IAT were identified as highly contributing factors on the sales rate. The remaining factor (number of cashier) similarly influences the response, but the effect is not very significant. Therefore, the importance sequence of the sales rate parameter is IATs > number of pumps > number of cashiers. The regression equation was formulated to maximize the sales rate (Liter) and then verified by the confirmation runs

An efficient integrated simulation-Taguchi approach for sales rate evaluation of a petrol station

This study proposed an incorporated simulation–Taguchi model to optimize a petrol station sales rate. In addition, it provided a regression model to forecast the sales rate. Initially, Witness 2014 simulation software© was used to simulate the operating system of a petrol station. Next, the obtained simulation results were used as the input for Taguchi method to optimize the process. Taguchi L4 standard orthogonal array was taken to optimize the petrol station parameters including the number of pumps, number of cashiers and customers’ interarrival times (IATs) to obtain a better sales rate. Three noise factors such as petrol station location, different cashiers and different dispensers considered as potential factors affecting the response. Based on Taguchi methodology, number of pumps and IAT were identified as highly contributing factors on the sales rate. The remaining factor (number of cashier) similarly influences the response, but the effect is not very significant. Therefore, the importance sequence of the sales rate parameter is IATs > number of pumps > number of cashiers. The regression equation was formulated to maximize the sales rate (Liter) and then verified by the confirmation runs